Arabic alphabets (according to dots).
\\n\\n
IntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\\n\\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\\n\\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\\n\\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\\n\\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\\n\\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\\n\\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\\n\\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\\n\\nFeel free to share this news on social media and help us mark this memorable moment!
\\n\\n\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/237"}},components:[{type:"htmlEditorComponent",content:'
After years of being acknowledged as the world's leading publisher of Open Access books, today, we are proud to announce we’ve successfully launched a portfolio of Open Science journals covering rapidly expanding areas of interdisciplinary research.
\n\n\n\nIntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\n\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\n\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\n\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\n\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\n\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\n\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\n\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\n\nFeel free to share this news on social media and help us mark this memorable moment!
\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"10384",leadTitle:null,fullTitle:"Practical Applications in Reliability Engineering",title:"Practical Applications in Reliability Engineering",subtitle:null,reviewType:"peer-reviewed",abstract:"This book compiles and examines advanced technologies in the field of reliability and risk analysis. It presents comprehensive methodologies and up-to-date software along with examples of practical case studies from industrial areas to provide a realistic and authentic platform for readers.",isbn:"978-1-83968-400-5",printIsbn:"978-1-83968-399-2",pdfIsbn:"978-1-83968-401-2",doi:"10.5772/intechopen.91570",price:100,priceEur:109,priceUsd:129,slug:"practical-applications-in-reliability-engineering",numberOfPages:94,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"377d3c041a06cfcfc99bd906fdbbbf46",bookSignature:"Muhammad Zubair",publishedDate:"June 16th 2021",coverURL:"https://cdn.intechopen.com/books/images_new/10384.jpg",numberOfDownloads:1247,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:0,numberOfDimensionsCitationsByBook:1,hasAltmetrics:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"July 8th 2020",dateEndSecondStepPublish:"July 29th 2020",dateEndThirdStepPublish:"September 27th 2020",dateEndFourthStepPublish:"December 16th 2020",dateEndFifthStepPublish:"February 14th 2021",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"320007",title:"Associate Prof.",name:"Muhammad",middleName:null,surname:"Zubair",slug:"muhammad-zubair",fullName:"Muhammad Zubair",profilePictureURL:"https://mts.intechopen.com/storage/users/320007/images/system/320007.png",biography:"Dr. Muhammad Zubair is an Associate Professor at the Department of Mechanical and Nuclear Engineering, University of Sharjah, United Arab Emirates. Prior to this role, Dr. Zubair worked as an assistant professor and graduate program coordinator at the University of Engineering and Technology Taxila, Pakistan.\nDr. Zubair’s interests include nuclear reactor safety, accident analysis, reliability and risk analysis, digital instrumentation and control, and radiation detection and measurements. He has a strong research background supported by publications in international journals, conferences, and book chapters. He is engaged in different research projects including one coordinated by the International Atomic Energy Agency (IAEA). He also serves as editor, associate editor, and technical committee member for international journals and conferences.",institutionString:"University of Sharjah",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of Sharjah",institutionURL:null,country:{name:"United Arab Emirates"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"828",title:"Reliability Engineering",slug:"reliability-engineering"}],chapters:[{id:"76775",title:"Introductory Chapter: An Overview of Reliability and Risk Analysis",doi:"10.5772/intechopen.98255",slug:"introductory-chapter-an-overview-of-reliability-and-risk-analysis",totalDownloads:203,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Muhammad Zubair and Eslam Ahmed",downloadPdfUrl:"/chapter/pdf-download/76775",previewPdfUrl:"/chapter/pdf-preview/76775",authors:[{id:"320007",title:"Associate Prof.",name:"Muhammad",surname:"Zubair",slug:"muhammad-zubair",fullName:"Muhammad Zubair"},{id:"417511",title:"Dr.",name:"Eslam",surname:"Ahmed",slug:"eslam-ahmed",fullName:"Eslam Ahmed"}],corrections:null},{id:"75481",title:"The Optimal System for Complex Series-Parallel Systems with Cold Standby Units: A Comparative Analysis Approach",doi:"10.5772/intechopen.95274",slug:"the-optimal-system-for-complex-series-parallel-systems-with-cold-standby-units-a-comparative-analysi",totalDownloads:222,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The purpose of this research is to propose three reliability models (configurations) with standby units and to study the optimum configuration between configurations analytically and numerically. The chapter considered the need for 60 MW generators in three different configurations. Configuration 1 has four 15 MW primary units, two 15 MW cold standby units and one 30 MW cold standby unit; Configuration 2 has three 20 MW primary units, three 20 cold standby units; Configuration 3 has two 30 MW primary units and three 30 MW cold standby units. Some reliability features of series–parallel systems under minor and complete failure were studied and contrasted by the current. Failure and repair time of all units is assumed to be exponentially distributed. Explanatory expressions for system characteristics such as system availability, mean time to failure (MTTF), profit function and cost benefits for all configurations have been obtained and validated by performing numerical experiments. Analysis of the effect of different system parameters on the function of profit and availability has been carried out. Analytical comparisons presented in terms of availability, mean time to failure, profit function and cost benefits have shown that configuration 3 is the optimal configuration. This is supported by numerical examples in contrast to some studies where the optimal configuration of the system is not uniform as it depends on some system parameters. Graphs and sensitivity analysis presented reveal the analytical results and accomplish that Configuration 3 is the optimal in terms of design, reliability physiognomies such as availability of the system, mean time to failure, profit and cost benefit. The study is beneficial to engineers, system designers, reliability personnel, maintenance managers, etc.",signatures:"Ibrahim Yusuf and Ismail Muhammad Musa",downloadPdfUrl:"/chapter/pdf-download/75481",previewPdfUrl:"/chapter/pdf-preview/75481",authors:[{id:"326781",title:"Associate Prof.",name:"Ibrahim",surname:"Yusuf",slug:"ibrahim-yusuf",fullName:"Ibrahim Yusuf"},{id:"326782",title:"MSc.",name:"Ismail Muhammad",surname:"Musa",slug:"ismail-muhammad-musa",fullName:"Ismail Muhammad Musa"}],corrections:null},{id:"74961",title:"Importance Analysis of Containment Spray System in Pressurized Water Reactor (PWR)",doi:"10.5772/intechopen.94412",slug:"importance-analysis-of-containment-spray-system-in-pressurized-water-reactor-pwr-",totalDownloads:318,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The basic purpose of the containment spray system (CSS) is to cool the containment atmosphere when the internal pressure of the containment exceeds a certain limit. Water is transferred by a pump from the storage tank via heat exchangers to the overhead spray nozzles in the roof of the containment. This water cools the atmosphere of the containment. In this research, the reliability analysis of CSS has been investigated using fault tree analysis (FTA). The results of the top event probabilities, minimal cut sets (MCS), risk decrease factor (RDF), risk increase factor (RIF), and sensitivity analysis were obtained for the WASH-1400 data base.",signatures:"Muhammad Zubair and Priyonta Rahman",downloadPdfUrl:"/chapter/pdf-download/74961",previewPdfUrl:"/chapter/pdf-preview/74961",authors:[{id:"320007",title:"Associate Prof.",name:"Muhammad",surname:"Zubair",slug:"muhammad-zubair",fullName:"Muhammad Zubair"},{id:"331065",title:"BSc.",name:"Priyonta",surname:"Rahman",slug:"priyonta-rahman",fullName:"Priyonta Rahman"}],corrections:null},{id:"75574",title:"Optimal Maintenance Policy for Second-Hand Equipments under Uncertainty",doi:"10.5772/intechopen.96230",slug:"optimal-maintenance-policy-for-second-hand-equipments-under-uncertainty",totalDownloads:197,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This chapter addresses a maintenance optimization problem for re-manufactured equipments that will be reintroduced into the market as second-hand equipments. The main difference of this work and the previous literature on the maintenance optimization of second-hand equipments is the influence of the uncertainties due to the indirect obsolescence concept. The uncertainty is herein about the spare parts availability to perform some maintenance actions on equipment due to technology vanishing. The maintenance policy involves in fact a minimal repair at failure and a preventive repair after some operating period. To deal with this shortcoming, the life cycle of technology or spare parts availability is defined and modeled as a random variable whose lifetimes distribution is well known and Weibull distributed. Accordingly, an optimal maintenance policy is discussed and derived for such equipment in order to overcome the uncertainty on reparation action. Moreover, experiments are then conducted and different life cycle of technologies are evaluated according to their obsolescence processes (accidental or progressive vanishing) on the optimal operating condition.",signatures:"Ibrahima dit Bouran Sidibe, Imene Djelloul, Abdou Fane and Amadou Ouane",downloadPdfUrl:"/chapter/pdf-download/75574",previewPdfUrl:"/chapter/pdf-preview/75574",authors:[{id:"220831",title:"Dr.Ing.",name:"Ibrahima dit Bouran",surname:"Sidibe",slug:"ibrahima-dit-bouran-sidibe",fullName:"Ibrahima dit Bouran Sidibe"},{id:"222503",title:"Dr.",name:"Djelloul",surname:"Imene",slug:"djelloul-imene",fullName:"Djelloul Imene"},{id:"335208",title:"Dr.",name:"Abdou",surname:"Fane",slug:"abdou-fane",fullName:"Abdou Fane"},{id:"335209",title:"Dr.",name:"Amadou",surname:"Ouane",slug:"amadou-ouane",fullName:"Amadou Ouane"}],corrections:null},{id:"75609",title:"Digital On-Chip Calibration of Analog Systems towards Enhanced Reliability",doi:"10.5772/intechopen.96609",slug:"digital-on-chip-calibration-of-analog-systems-towards-enhanced-reliability",totalDownloads:307,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This chapter deals with digital method of calibration for analog integrated circuits as a means of extending its lifetime and reliability, which consequently affects the reliability the analog electronic system as a whole. The proposed method can compensate for drift in circuit’s electrical parameters, which occurs either in a long term due to aging and electrical stress or it is rather more acute, being caused by process, voltage and temperature variations. The chapter reveals the implementation of ultra-low voltage on-chip system of digitally calibrated variable-gain amplifier (VGA), fabricated in CMOS 130 nm technology. It operates reliably under supply voltage of 600mV with 10% variation, in temperature range from −20°C to 85°C. Simulations suggest that the system will preserve its parameters for at least 10 years of operation. Experimental verification over 10 packaged integrated circuit (IC) samples shows the input offset voltage of VGA is suppressed in range of 13μV to 167μV. With calibration the VGA closely meets its nominally designed essential specifications as voltage gain or bandwidth. Digital calibration is comprehensively compared to its widely used alternative, Chopper stabilization through its implementation for the same VGA.",signatures:"Michal Sovcik, Lukas Nagy, Viera Stopjakova and Daniel Arbet",downloadPdfUrl:"/chapter/pdf-download/75609",previewPdfUrl:"/chapter/pdf-preview/75609",authors:[{id:"317026",title:"Dr.",name:"Daniel",surname:"Arbet",slug:"daniel-arbet",fullName:"Daniel Arbet"},{id:"317089",title:"Prof.",name:"Viera",surname:"Stopjakova",slug:"viera-stopjakova",fullName:"Viera Stopjakova"},{id:"317114",title:"Dr.",name:"Lukas",surname:"Nagy",slug:"lukas-nagy",fullName:"Lukas Nagy"},{id:"328726",title:"MSc.",name:"Michal",surname:"Sovcik",slug:"michal-sovcik",fullName:"Michal Sovcik"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"6024",title:"System 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Arabic language is one of the top five languages spoken in the world. Arabic is used by more than 422 million native and non-native speakers in the world. Also, the letters of the Arabic alphabets are used in other languages like Urdu (65 million natives and 94 million non-native) and Persian (110 million) languages. In addition, languages like Baluchi, Brahui, Pashto, Central Kurdish, Sindhi, Kashmiri, Punjabi, and Uyghur are using the Arabic letters. Hence, there is a need to develop an algorithm for character recognition for the Arabic language. Yet, there are major challenges that arise: first, Arabic is a cursive language. Unlike other languages written, Arabic alphabets change shape as written; hence, separate letters in Arabic are usually sub-word rather than stand-alone word. Second, Arabic is written from right to left unlike Latin languages. Third, Arabic has 28 alphabets, with some letters changing shapes based on the location of the letter in the word. Also, some letters are very similar in form yet have secondary marks to differentiate. Furthermore, Arabic is written from right to left cursively. Due to all the mentioned reasons, Arabic character recognition systems are under developed and lacking.
This research is composed of five sections. The first section presents 20 related works. Then, the research explains the letter shapes in Arabic language and the four categories used in the proposed algorithm. The four categorization methods will be employed to develop a deterministic algorithm method of categorization. The first categorization method depends on the number of dots used with each letter. The second categorization method depends on the shape of the letter, with classification to the letters. The third categorization is presented with the shape of the letter as used in the beginning, middle, and end of the word. The fourth categorization method relays on the proportion method, which is a method used in Arabic calligraphy that is based on rhombic dot. Then, the research suggested a deterministic algorithm composed of 34 rules that can predict the character based on the use of all of categorizations as attributes assembled in a matrix for this purpose.
Character recognition is an open-ended problem. A computer cannot recognize character or language alphabets. There is a great progress in character recognition that all can be seen in the different smart application used on smart phones and pad as well as notebooks and PCs. Problems that arise with non-Latin languages are well known, and many researchers have conducted research for their respective languages: Hindi language [1]; Chinese language [2, 3, 4]; and Arabic Language [5, 6]. Furthermore, many researchers have conducted research for the Arabic alphabets: Parvez and Mahmoud [7] conducted a survey for text recognition and published their work in a paper titled
Researchers Supriana and Nasution [13] cited nine works including their own research that all are non-deterministic. The research of Sarfraz, Ahmed, and Ghazi [14] developed a license plate recognition system. The research by Izakian, Monadjemi, Ladani, and Zamanifar [15] used chain codes, while Abandah, Khedher, and Mohammed [16] used selected feature extraction techniques. In their research Al-Taani and Al-Haj [17] used structural features, while Kapogiannopoulos and Kalouptsidis [18] used skew angle. The research of Zidouri [19] proposed a general method for Arabic letter segmentation, while Amin [20] used global features and decision tree technique on printed letters not handwritten. Cowell and Hussain [21] used extracting features.
To develop the proposed algorithm, the researchers studied and presented the different categorizations for Arabic letters. Next, each categorization will be explained accordingly. The first categorization method depends on the number of dots used with each letter. The second categorization method depends on the shape of the letter, with classification to the letters. The third categorization is presented with the shape of the letter as used in the beginning, middle, and end of the word. The fourth categorization method relays on the proportion method, which is a method used in Arabic calligraphy that is based on rhombic dot. Each categorization will be explained in Sections 3.1, 3.2, 3.3, and 3.4.
The use of dots to distinguish letters in Latin-based languages is familiar to people. In English, small letters I and J are distinguished by using a dot on top of the letter. In Arabic the use of a dot is used extensively; in fact, only 12 letters out of 28 letters are not doted. Furthermore, some letters use one, two, and three dots. Next, the concept of doted letters will be explained.
The first categorization is according to the number of dots used with each letter. This categorization splits the 28 letters (Table 1) into five branches, and from within it breeds two extra letters. The first branch is composed of 12 letters that has no dots whatsoever. The second branch is composed of ten letters: the eight letters have their dot above the body of the letter, and the other two letters have their dot below the letter body. The third branch is composed of four letters: the three letters have their two dots above the body of the letter, and the other one has two dots below its body. The fourth branch has two letters with three dots above the body.
First branch | No dots | 12 | ح د ر س ص ط ع ل م ه و ا |
Second branch | One dot | 10 | ب ج خ ز ذ ض ظ غ ف ن |
Third branch | Two dots | 3 | ي ت ق ة |
Fourth branch | Three dots | 2 | ث ش |
Fifth branch | With hamza | 3 | وْ أ ك |
Arabic alphabets (according to dots).
The fifth branch deals with hamza: there is one basic letter where the hamza is part of the letter “ك,” and the other hamza is not part of the letter like the “أ” and “وْ.” The categorization is summarized in Table 1.
The second categorization is according to shape of the letter: this categorization splits the 28 letters into 15 branches based on the body of the letter rather than the dots on the letter (see Table 2). However, some increase the number of shapes to 18 shapes [22]. The first branch is made of four letters all very similar in shape: two of them are differentiated by one dot (one above the letter and below the letter), and the other two (one has two dots above it and one has three dots above it). The second branch has two letters very similar to each other: one can differentiate between them by the dot above one, while the other one has no dot; furthermore, the third branch and the fourth branch have the same idea similar in shape, yet one dot makes a difference. The same happens with the fifth and sixth branches. The seventh branch has three letters that are similar in shape: one without dot, one with dot above it, and one with dot below it. The eighth branch has two letters that are very similar in shape: one with one dot and the other with two dots. The ninth branch has two letters similar in shape: one with no dots and the other with three dots. The tenth has two letters: one with no dots and other with two dots. The 11th branch has two letters: one with no hamza and the other with hamza shape above the body of the letter. The 12th, 13th, 14, and 15th branches are not similar to each other nor to the rest of the letters.
Arabic alphabets (according to shape).
One may add here a note about the shape of the letters; there are nine letters that have as part of them enclosed space that resembles a circle. These nine letters are (م و ه ف ق ط ظ ص ض). The enclosed circle property is an important aspect of the nine letters that will be used in the algorithm at a later stage.
The third categorization of the Arabic alphabets is based on the location of the letter in a word. Generally, shapes of the Arabic alphabets change according to position of the letter in the word itself (beginning, middle, end); some letter can be connected (refers to the letter succeeding or preceding), and others cannot be connected. The shapes of the letters can be generated with ligature or character overlaps [23, 24]. When discussing the letters that start a word, these six letters when falling at the beginning of a word must stand alone; those letters are (ا د ذ ر ز و), and the rest of the letters do change form as seen in Table 3. Using the same six letters in the middle or end of the word, these letters are only connected and they do not change form. All letters when used at the end of the word have two states: connected and stand-alone.
Arabic alphabets: stand alone, beginning, middle, and end of a word.
From the previous one can notice that six letters have special characteristics, namely, (ا, و, د, ذ, ر, ز). These characters when used in the beginning of a word must stand alone, and also when they end a group of characters, they must be followed by independent character. Hence, they only connect to the predecessor not the successor.
The matrix, seen in Table 4, represents the different combination between all 28 letters. The first column in the matrix is the letter coming at the beginning of the order, and the first row is all the letters coming second. Each cell in the matrix shows the two letter shapes and how they change as the order differs. The highlighted letters are the previously mentioned six letters, namely, (ا,و, د, ذ, ر, ز), which if appears at the beginning of the word, then they stand alone. When these letters appear consecutively within a word, they will both be written as stand-alone independent letters.
Matrix of the different combinations for all 28 letters.
To keep letters proportional to each other, two ways were used by calligraphers: rhombic dot and circles. Arabic calligraphy was used in mosques and castells as decoration since Islam forbids pictures and statues [22]. Hence, there is a need to decorate with words. Proportion is an essential part of the written word. The circle proportion was suggested by “Ibn Mugla,” a well-known calligrapher from the eleventh century [25]. Three elements are the bases of proportion in Arabic calligraphy [26, 27]:
The height of the
The width of the alif, (the rhombic dot) which is the square impression formed by pressing the tip of the calligrapher’s reed pen to paper (see Figures 1 and 3).
An imaginary circle with alif as its diameter, within which all Arabic letters could fit and be written (see Figure 2).
Example of measuring the letter by using rhomboid dots [
Example of measuring the letter by using circle [
The rhombic dot as a guide to proportions [
The circle is halved vertically and horizontally, with diameter equals the height of the first letter in Arabic alphabets called
Proportions in Arabic calligraphy.
One can conclude by studying the second categorization and the proportion categorization through the following:
First, the second branch and ninth branch both (four letters) take same area of the circle.
Second, the third branch and tenth branch (four letters) both use the first quarter of the circle.
Third, the fourth branch and seventh branch use the left edge of the circle, yet the differentiation between the two is that one letter is written from right to left and one letter is written from left to right as seen in Figure 5.
Fourth, the fifth and sixth branches (four letters) use the fourth quarter of the circle.
Direction of writing with two circle edge letters.
Hence, give an insight to further classify the letters and manage them into groups. The previous sections explained in details the four categorizations used in the proposed algorithm. Each categorization was an essential in the building blocks and rules of the algorithm.
Based on the four categorizations explained above, a tree of rules can be built as seen in Figure 6. The rule tree has five branches: the first branch is for Arabic alphabets that contain
Categorization of the tree drawn based on the four categorizations.
For the first branch including 12 letters and in order to distinguish among the letters, the fourth categorization logic was used. Each letter in this branch was located in the quarters of the circle suggested in the fourth categorization. Two letters used the same quarters (س ص); both fall in the first and third quarters of the imaginary circle, which explain the fourth categorization. Still, letter (ص) has an enclosed space, while letter (س) has no enclosed space. Hence, differentiating between the two letters depends on the enclosed space. The enclosed space property is explained previously in the third categorization. The edge of the circle from the fourth categorization was used to differentiate between the ten letters and the letters (ح ع). Furthermore, to differentiate between the two letters, the direction of writing was used. The direction of writing was explained in Figure 5 previously.
The second branch consisting of all letters with
The third branch consisting of all letters with
The fourth branch included all letters with
The fifth branch is the
The
After studying all the previously mentioned categorizations, one can reach the conclusion that a deterministic algorithm can predict the character being drawn based on the following matrix in Figure 7 and along with the matrix is the suggested algorithm in Figure 8, hence reducing the determination of a letter to 38 rules.
The property rules to define each letter in the Arabic alphabets.
Determine_Character (input:one_character).
The suggested algorithm shown in Figure 7 is composed of five major if-then statements which are based on the first categorization explained above and later summarized in Figure 7. The first if-then statement runs from line 1 to 12 in Figure 8. The if-then statement really deals with all cases of the letters which have no dots, and their location in the circle is mentioned in proportion categorization. The enclosed space property mentioned earlier was very important to distinguish letter “س” and letter “ص”; both letters fall in the same location in the circle Q1 and Q2, yet the latter has an enclosed space. Also, notice that both “ح” and “ع” have the same properties, yet to differentiate them, the direction of writing is used [9].
The second major if-then statement started at line 13 and dealt with letters with one dot. As shown in Figure 6, the dot can be either above or below the body of the letter, i.e., both letters “ن” and “ب” fall in the lower part of the circle quarters 3 and 4. Yet, to make the differentiation, the dot was essential here, the latter had the dot below as seen in lines 23 and 20 in Figure 8. Also, line 21, in the same figure, dealt with two letters that are essential falling in the same location, and both had the dot above them, distinguished by the writing direction left to right or right to left. The algorithm can be improved by eliminating line 24, hence reducing the number of rules to 37, since one can use one statement.
The third major if-then statement starts at line 25 and ends at line 31 in Figure 8. The if-then statement deals with letters that have two dots according to the first categorization and the matrix seen in Figure 7. The nested if-statement deals with the two dots whether above or below the letter. Three letters have two dots above them, yet their location on the circle is very distinguishable; hence, using the attribute “enclosed space” was not necessary. Furthermore, one can eliminate line 31 since this is the only letter in the alphabet that has two dots below it. Still, for the purpose of clarity, line 31 was left in the suggested algorithm. If line 31 was eliminated, the number of rules will be again reduced to 36 rules.
The fourth major if-then statement deals with letters that have three dots; there are only two of them. Both letters can be distinguished based on their respective location according to the proportion categorization. Again, line 34 can be eliminated but was left for the purpose of clarity. If line 34 was eliminated, the number of rules will be again reduced to 35 rules.
The last major if-then statement starts at line 35; the statement deals with case of “hamza.” The hamza is an essential part in letter “ك” and is used with other letters like “أ ؤ.” The three letters are distinguished by their location within the circle according to proportion categorization. Line 38 can be eliminated but was left to clarify the algorithm, hence reducing the number of rules to 34.
The proposed algorithm stems from many needs that are more apparent today. First, there is a rise in the use of handheld devices, which use character recognition methods that serves mainly Latin-based languages. Arabic language is one of the top five languages spoken in the world. Arabic is used by more than 422 million native and non-native speakers in the world. Arabic language is different from other languages: Arabic is a cursive language, written from right to left, and letters change shape according to the position of the word. Hence, there is a dire need to develop an algorithm for character recognition for the Arabic language. However, many algorithms have used artificial intelligent methods to recognize characters that make their algorithms non-deterministic, while the proposed algorithm is deterministic. This research presents four categorization methods that will be employed to develop a deterministic algorithm method of categorization. The first categorization method depends on the number of dots used with each letter. The second categorization method depends on the shape of the letter, with classification to the letters. The third categorization is presented with the shape of the letter as used in the beginning, middle, and end of the word. The fourth categorization method relays on the proportion method, which is a method used in Arabic calligraphy that is based on rhombic dot. Then, the research suggested a deterministic algorithm composed of 34 rules that can predict the character based on the use of all of categorizations as attributes assembled in a matrix for this purpose [29].
The proposed algorithm is only one piece in the whole puzzle. There are many parts that need to be developed. One major part is the input section of the algorithm. Such part needs to exist in order for the puzzle to be complete. The input section needs to parse the word into segments that can detect the shape of the letters, the dots, and the hamza. Furthermore, this research will be a building block for further research and development.
The most viable way to achieve clean and efficient transport is to boost the automotive industry to be concerned with developing advanced battery technologies, especially lithium-ion (Li-ion), to increase the number of electric and hybrid electric vehicles (EVs/HEVs) to dominate the vehicle market. An essential internal parameter of the Li-ion battery is the state of charge (SOC), defined as the available capacity of the cell that changes according to the current profile of the driving cycle. Due to its crucial role in keeping the battery safe for various operating conditions and significantly extending battery life, SOC is a topic of great interest, as evidenced by an impressive number of research papers published in the literature. In the absence of a measurement sensor, the SOC must be estimated since its calculated value is not accurate enough. The most used model-based Kalman filters can estimate the battery SOC with a high grade of accuracy [1, 2, 3, 4]. The Li-ion battery is an important component integrated into battery management system (BMS) that performs tasks regarding the safe operation and reliability of the battery, protecting battery cells and battery systems against damage, as well as battery efficiency and service life [2, 3, 4]. The BMS “plays a significant role in fault diagnosis because it houses all diagnostic subsystems and algorithms” [2, 3]; thus it monitors the battery system through sensors and state estimation, such that to detect any abnormalities during the battery system operation” [2, 5]. A signal processing-based method using wavelet transforms proved to be a viable alternative to conventional Kalman filter state estimators, for designing and implementation of real-time FDI strategies. The new FDI approach avoids battery modeling difficulties and is more straightforward with better dynamic performance [7]. The drawback of this method is the difficulty experienced in dealing with the early faults and fault isolation. Its application also requires a large amount of calculations compared to the model-based methods. An intelligent fault detection scheme for microgrid based on wavelet transform and deep neural networks is used in [6] to “provide fast fault type, phase, and location information for microgrid protection and service recovery” [6]. Similar, a wavelet-based transient fault detection and analysis is used successfully in [7] for a microgrid connected power. In this research, our motivation of using 1-D wavelet analysis comes from the preliminary results obtained for similar investigations on the impact of nonlinearities and uncertainties of actuators (electro-pneumatic valves), such as hysteresis, dead zone, dead band, on a healthy pH neutralization plant [8]. An example of multisignal 1-D wavelet analysis is found in [9], and a useful tutorial of using wavelet transforms presented in [10]. In [11] is shown a generic Simscape model of Li-ion Cobalt battery model used to build a SOC AEKF estimator robust to three different driving cycles profile tests, such as UDDS, EPA-UDDS and FTP-75, the last one also used in the case study of this research. For FDI techniques based on 1-D wavelet analysis are used specific MATLAB commands provided by MATLAB Wavelet Toolbox [12]. A strong theoretical background on wavelet transform and their applications is provided by the fundamental work [13]. In [14] is presented an interesting fault isolation technique based on wavelet transform, and a detailed data-based FDI techniques for a nonlinear ship propulsion system are developed in [15]. Several multimedia applications of wavelet transform can be found in [16], and a better understanding of wavelet transform analysis, design and implementation of features extraction methods, for filtering, denoising, decomposition and reconstructing signals is given in [17, 18, 19, 20, 21, 22, 23]. From our most recent preliminary results in Li-ion battery field, modeling and SOC estimators disseminated in [11, 24, 25], an interesting state-of-art analysis of similar SOC AEKF estimators performance reported in the literature is done in terms of statistical performance criteria values, such as root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), standard deviation (std), mean absolute percentage error (MAPE) and R2 (R-squared). Among three SOC Li-ion battery estimators AEKF, adaptive unscented Kalman filter (AUKF) and particle filter SOC estimators the AEKF proved that is the most suitable for HEVs applications.
Let why is used the AEKF SOC estimator of Li-ion battery in the first part of our research for FDI control strategies, excelling by its simplicity, SOC accuracy, real-time implementation capability and robustness. The robustness is tested for four different scenarios, such as to changes in SOC initial values (guess values), from 70–40%, 20%, 90% and 100%, to FTP-75 driving cycle profile test, changes in measurement level noise (from 0.001 to 0.01), to changes in the battery capacity value from 6 Ah to 4.8 Ah due to aging effects, and changes in internal resistance due to temperature effects, and also for simultaneous changes [11, 23]. Based on a rigorous performance analysis of SOC residuals error compared to the similar results reported in the literature with a typically 2% error, in some situations the AEKF estimator SOC residual error reached values smaller than 1%, such as shown in [25]. Since of the lack of data in the literature field for similar situations developed in our research for Li-ion battery, it is not easy to make a state-of-art analysis of the results reported in the literature related to the FDI techniques design and implementation based on 1-D wavelet analysis. The efficiency of 1-D analysis is proved in this paper based on extensive MATLAB simulations to extract the features of input-output signals such as the energy, skewness, kurtosis, and to compute the MSE statistical criteria performance. Finally, the MATLAB simulation results can provide useful information on detection accuracy, computation time, and robustness against measurement uncertainty, thus showing simply the effectiveness of the FDI proposed scheme. The temperature fault is detected without doubt inside the Li-ion battery based on the significant values reached by the details (D1, D2, and D3) and analysis coefficient (A3) of the output terminal battery voltage residual level three decomposition, represented by the following sets of values (4.46, 2.7, 5.349, 87.5) for energy feature, (0.063, −3.92, 13, −1.33) for skewness signal feature, respectively (5.8, 71.4, 389.13, 56) for kurtosis signal feature. Also, the statistic RMSE performance criterion indicates significant values for D1 coefficient in the presence of the of temperature fault for energy feature (4.4654) and skewness and kurtosis features are the same as for current fault. To detect both faults, a multiresolution analysis (MRA) is performed, capable of extracting a smooth trend term, which provides a valuable information to localize transient changes in the fault injection window [500, 1500] seconds [23].
Thus, the presence of the bias current fault and bias temperature fault is detected and localized as a transient significant change in the nonstationary Li-ion output voltage residual signal. For an appropriate choice of the thresholds’ values, both faults can be detected with a high accuracy detection times directly from S8 graph; thus, the presence of the false alarms is completely removed compared to Kalman filter FDI estimation techniques. The fault signature and considering the variation trend in SOC residual and internal resistance of the battery also provides a piece of useful information for fault isolation.
This section briefly presents the Rint equivalent circuit model (Rint ECM) as a case study to investigate the effectiveness of the proposed fault detection and isolation (FDI) strategy, using a conventional EKF SOC estimator, as a support for performance analysis comparison, in the first part [1, 2, 3, 4], and a 1-D wavelet transformation in the second part [8, 9]. For comparison purpose, an improved adaptive extended conventional Kalman (AEKF) filter algorithm [3, 4, 11] is also briefly presented for estimating the state of charge (SOC) of the adopted Li-ion battery, as well as the faults in Appendix A. Residual methodology is useful to detect and isolate faults. Only three failures of the current, voltage and temperature sensors of the HEV battery management system (BMS) used for the case study are analyzed.
The Rint ECM Li-ion battery model is one of the most common models to describe battery dynamics in many real-time implemented HEV applications with an acceptable range of performance. The reason for using these models is their simplicity, low number of parameters to adjust and easy implementation in a friendly MATLAB simulation environment. Therefore, a compromise we need to make between the accuracy of the battery SOC and the complexity of the model related to the choice of Li-ion battery, so that, for simulation purpose and “proof concept”, we adopt a simple Rint ECM Li-ion battery model, as a reasonably simplified version of RC ECM developed in [1], and in [11] for a Li-ion Cobalt battery, as is shown in Figure 1.
ECM Rint Li-ion battery model (see [
The Rint ECM Li-ion battery model is an equivalent Thevenin electrical circuit consisting of an open circuit-controlled voltage (OCV) source and an internal resistance designated by Rin. The OCV source strongly depends on the state of charge (SOC), i.e. a dependency described by an extremely nonlinear function OCV = f (SOC), represented by different combinations of models reported in the literature such as Shepherd, Nernst and Unnewehr universal model [1, 3, 4]. The dynamics complexity and the accuracy of ECM increase by adding an RC polarization cell (first-order RC model), two RC cells (second-order ECM) respectively three RC cells (third ECM order model), as those developed in [1, 2, 3, 11]. The main input-output and intermediate signals in Figure 1 are Ibatt is the input battery instantaneous value of the direct current (DC) flowing through the open circuit controlled-voltage source, and Vbatt denotes the measured output terminal battery instantaneous value DC voltage that are nonlinear dependent of OCV, as intermediate signal. The internal resistance of the battery is affected by several factors. Still, a significant impact has conductor resistance, electrolyte resistance, ion mobility, separator efficiency, reactive electrode rates, polarization, temperature, and aging effects, and SOC changes, as is mentioned in [11]. Since the SOC of the battery is defined as [1, 2, 3, 4, 11]:
with Q denoting the rating battery capacity, in the schematic shown in Figure 1, the controlled voltage source E (open circuit voltage (OCV)) can be modeled by:
The battery terminal voltage Vbatt is related to OCV according to following nonlinear equation:
where
However, for the implementation of the proposed FDI techniques, a high-precision model is not required, because the extraction of ECM parameters is beneficial to monitor the battery SOC, rather than to model the battery performance.
For a discharging current cycle, when
where
where
Because the internal resistance Rin is an essential parameter of the battery that is affected much more by the temperature than other parameters of the cell, it is necessary to attach to the Li-ion battery model a thermal model, described in continuous time by a first order differential equation:
where:
In discrete time the Eq. (7) becomes:
and
The healthy ECM battery model (free faults model) MATLAB simulations to an input driving cycle Federal Test Procedure (FTP-75) for a city, are shown in Figure 2.
The ECM Li-ion battery healthy model: (a) FTP-75 driving cycle current profile; (b) output terminal voltage; ECM battery model SOC; (d) temperature profile for changes in ambient temperature; (e) the effect of temperature profile on battery internal resistance.
In Figure 2(a) is shown the FTP-75 driving cycle test profile, Figure 2(b) depictures the battery terminal voltage, Figure 2(c) reveals the battery SOC, Figure 2(d) discloses the temperature profile of the thermal model initiated by an ambient temperature of 20°C, and Figure 2(e) exposes the effect of the battery temperature on internal resistance Rin.
For Li-ion batteries, the aspects such as accuracy performance of the SOC estimation and the prediction of the terminal voltage are essential to be analyzed, thus ensuring the safe operation of the cell, and thus maintaining a long life. Therefore, a brief presentation of an appropriate estimation technique is of real use. Moreover, for any battery, whether it is a Li-ion battery, SOC cannot be measured accurately, so it is necessary to estimate it. The most popular estimation algorithm reported in the literature is the Kalman filter (KF) with its improved version for models with extremely nonlinear dynamics, such as an extended Kalman filter (EKF) /adaptive extended Kalman filter (AEKF) [1, 2, 3, 4, 11].
Since the preliminary results obtained in [11] convinced us about the efficiency of applying the AEKF SOC estimator for a Simscape model of Li-ion battery, quite well documented in [4], then the same estimator is used in this paper. For the adopted battery model, the SOC estimator adaptation consists in changing the dimensionality of the state space and the values of the adjustment parameters. For good documentation, the reader can see, in Appendix A, a brief presentation of the steps of AEKF estimation algorithm. Furthermore, the choice of using the AEKF for condition monitoring purposes is explained in this subsection. As is mentioned in the first section, the BMS, through its hardware and software components, plays a vital role in an HEV integrated structure for supervision, control and monitoring all the internal battery parameters. In a BMS, time-based monitoring and FDI techniques based on Kalman filter state and parameters estimators are implementing, and the faults in a system are detected only when measured values exceeded their normal limits [5, 26]. Furthermore, since the Li-ion battery SOC is non-measurable and a critical internal parameter of the battery, the use of AEKF SOC estimator for its estimation is wholly justified.
For a healthy Li-ion battery (free faults), the MATLAB simulations result of applying AEKF SOC estimator, whose steps are briefly presenting in Appendix A, is shown in Figure 3. In Figure 3(a) is shown the battery terminal voltage AEKF estimate values versus the Rint ECM Li-ion battery model terminal voltage true values. The MATLAB simulations result reveals an AEKF SOC estimator with an excellent prediction ability for battery terminal voltage. Figure 3(b) depictures the residual battery terminal voltage calculated as a difference between the battery terminal voltage true values and the corresponding estimate values of battery terminal voltage, as in Eq. (12).
AEKF estimator and Li-ion ECM battery model – Healthy system: (a) AEKF output terminal voltage estimate versus ECM terminal voltage true value; output terminal voltage residual; (b) terminal voltage residual (c) AEKF SOC estimate versus ECM SOC true value; (d) SOC residual.
The residues of battery SOC and for internal resistance are calculated by using the Eqs. (13) and (14):
For a healthy battery model, the residual is inside the minimum and maximum values of two thresholds, calculated as [5]:
where
In Figure 3(c) is depicted the battery AEKF SOC estimate values versus the battery model SOC true values, and in Figure 3(d) is showing the battery SOC residual calculated in the same manner as the battery terminal voltage. The MATLAB simulations result reveals an excellent SOC accuracy, and for a clean battery model the SOC residual is inside the band delimited by the minimum respectively maximum values of the SOC threshold calculated by using a similar formula as in Eq. (9). In Figure 4(a) is shown the robustness of AEKF SOC estimator to a change in the initial value of SOC from default value 70% to a SOCini = 40%. A level of the noise in measurements is more realistic in HEVs applications since the initial value of SOC must be guessed, and due to contamination of the measurements with noise. The SOC residual that is showing in Figure 4(b) remains inside the band delimited by the same minimum and maximum values of SOC threshold, and in Figure 4(c) the battery terminal voltage residual also remains inside the band.
Robustness of AEKF SOC to changes in SOC initial value, SOCini =70%: (a) robustness to a decrease of 30% from default value SOCini =70% to a SOCini =40%; (b) SOC residual; (c) battery terminal voltage residual.
The fault injection mechanism based on AEKF fault estimation and residual generation consists of injecting additive bias sensors faults in the input-output Li-ion battery Rint ECM model, as following:
where
First scenario - bias sensor fault injection inside the window (500, 1000) seconds.
At the instance 500 seconds is injected a fault in the Voltage measurement sensor of magnitude 1 V, and after 500 seconds the fault is removed, as shown in Figure 5(a).
First scenario of fault injection: (a) bias fault injection of magnitude 1 V in battery cell terminal voltage measurement sensor; (b) AEKF terminal voltage estimate versus EMC battery model terminal voltage true value; (c) battery terminal voltage residual; (d) AEKF SOC estimate versus EMC battery model SOC true value; (e) SOC residual; (f) the battery internal resistance.
In Figure 5(b) is shown the impact of the injected fault on battery terminal voltage, real and estimated values. The MATLAB simulation result reveals an abnormal behavior of terminal voltage estimate inside the same window of fault injection. The detection of the event is faster at the beginning of the window, persisting only 500 seconds, until the fault is removing. The residual battery terminal voltage is showing in Figure 5(c). It exceeds the band of the clean terminal voltage signal inside the fault window; thus, the same fault is detecting. An abnormal behavior of battery SOC is revealed in Figure 5(d) inside the fault window and persists inside the window until the fault is removed at instance 1000. The SOC residual generated by injecting the bias voltage in the Li-ion cell sensor terminal voltage is shown in Figure 5(e) that also detects the occurrence of the fault inside the same window. After the fault is removed the SOC residual enters inside of the band and indicates a normal SOC behavior. In Figure 5(e) the MATLAB simulations result reveals the fact that the injected fault has not a significant impact on the internal resistance Rin.
Second scenario: bias current sensor fault injection
Between samples 500 and 1000 is injected a fault in the current measurement sensor of magnitude 2A, such is showing in the Figure B1(a) from
Third scenario: injection of bias temperature sensor fault
In the temperature sensor, a fault of magnitude 10°C is injected in the same window, similar for first and second scenario, as is shown in Figure B2(a) from
Residual evaluation supposes to define “proper functions for the generated residue evaluation so that fault occurring in the system can be detected correctly”, as is stated in [5]. Roughly, in the ideal case, “if no fault occurs, the residue will be zero and otherwise, it will be non-zero” [5]. More precisely, in a general formulation, the residue evaluation can be defined as:
where
It is possible that in many cases, “the residue might be non-zero even though no fault has occurred; therefore, the evaluation function of Eq. (18) will not be proper. For this purpose, a statistical evaluation function can be defined as [5]:
otherwise:
for which the values of the parameter
p{
The fault signature for AEKF SOC estimator based diagnostic scheme is shown in Table 1.
Res_y | Res_SOC | Res_Rcell | Fault signature |
---|---|---|---|
1 | 1 (>0) | 0 | Voltage sensor fault |
1 | 1 (<0) | 0 | Current sensor fault, False alarm |
1 | 1 | 1 | Temperature fault sensor |
Fault signature for AEKF SOC estimator based diagnostic scheme.
For the second scenario the isolation of the fault can be done based on the tendency of SOC, i.e. for first scenario the SOC increases (Res_SOC >0) after the fault injection, while for second scenario it decreases (Res_SOC < 0) and persists until the end of driving cycle, generating a false alarm.
This section investigates the use, in a new approach, of 1-D wave signal analysis, a valuable tool for determining the essential characteristics of faults that occur in a Li-ion battery, a useful basic principle for developing a simple detection of their defects. These techniques are based on detecting changes that occur abruptly in the variation of the residual signal due to a faulty current sensor or a defective temperature measurement sensor, such as those developed in the previous section. Therefore, a similar method of residual generation and evaluation is useful to provide a valuable information to use the wavelet transformation ability to extract the essential features (patterns) of the faults from the output voltage residual of the battery. These faults visibly affect the performance of the Li-ion battery, such as the output voltage and SOC. The dynamics of the battery model under investigation is shown in Section 2. Note that SOC plays a critical role in locating faults (isolation).
Over time, Fourier transform (FT) has proven to be a useful tool for analyzing signal frequency components in a wide variety of applications. However, it has a significant disadvantage, because when it covers the entire time axis, it is impossible to see when a frequency increase. Instead, the short-term Fourier transform (STFT) uses a sliding window to find the spectrogram, which provides complete information on both time and frequency. A small impediment when using STFT in applications is due to the length of the window that limits the frequency resolution [10]. In these situations, the wavelet transforms (WT) seems to be a feasible solution, since it can be applied on a small wavelet of limited duration. Specifically, the wavelet provides local frequency information compared to FT, which captures the global features such as the harmonic components of the entire signal. Besides, the scaled wavelets allow to analyze the signal on different scales. The essential functions designate the “wavelets,” which are nothing else than scaled and shifted copies of the same “mother wavelet.” With a proper choice of the mother wavelet, the basis wavelets can be orthonormal, or at least linearly independent. Thus, the wavelets form a complete basis, and the wavelet transforms are designed to be reversible.
A wavelet is a waveform of effectively limited duration that has an average value of zero and nonzero norm, as is stated in [12]. The wavelets compared to sine waves, as the basis of Fourier analysis, “tend to be irregular and asymmetric, while sinusoids are smooth, predictable, and their duration is not limited” [12]. Thus, a wavelet is a wave-like oscillation with an amplitude that starts at zero, increases, and then decreases back to zero. Furthermore, the majority of signals and images of interest “exhibit piecewise smooth behavior punctuated by transients”, and the “signals with sharp changes might analyze with an irregular wavelet than with a smooth sinusoid”, thus an excellent idea for applying it to develop the detection techniques of the faults [12]. A fundamental work recommended to readers to obtain an excellent theoretical background on the wavelets is the reference [13]. Let us consider the wavelet analyzing function, also called “mother wavelet,” and a continuous wavelet transform (CWT). The CWT compares the signal under investigation, denoted by y(t), to shifted and scaling (compressed or stretched) versions of the wavelet function [12]. Since the physical signal y(t), which can be the output of the plant or a residual error, is real-valued, then also the CWT is a real-valued as a function of scale and position. For a scale parameter, a > 0, and location, b, a possible representation of a 1-D CWT can be the same as in [12, 13]:
where
where 1/
Each coefficient of the vector
The higher scales correspond to the “most” stretched wavelets, furthermore “the more stretched the wavelet, the longer the portion of the signal with which is compared, and thus the coarser the signal patterns features measured by the wavelet coefficients.”
The coarser features capture the low frequency components (
The low scale components (
The CWT is computationally inefficient, since it requires to calculate the c(a, b) coefficients at every single scale, so computationally expensive.
An alternative to the CWT is the discrete wavelet transform DWT, much more efficient and of high accuracy, defined in a similar way that CWT in Eq. (24) [14]:
For a parameter (a0, b0) setting to the values: a0 = 2, b0 = 1 is obtained a particular dyadic sampling of the time-frequency plane (a set of coefficients per octave), as is mentioned in [14]. Thus, for this particular sampling, it is possible to obtain for the set
Wavelet function samples: (a) Morlet wavelet function with 10 vanishes moments; (b) Haar wavelet function; (c) Daubechies wavelet function with 4 vanishes moments and its corresponding scaled function.
Finally, according to Eqs. (28) and (29) the original signal can be approximated as,
or simpler,
starting from last stage N toward the first stage in decomposition, and recursively, at stage level k, it can be writing:
In [16] is mentioned the “approximations” of the signals under investigation “provide basic trends and characteristics of the original signals, whereas the details provide the flavor signal”. The result of the applying DWT on the original signal y is the so-called wavelet decomposition around both key coefficient vectors, [A] (“approximation” coefficient vector), and [D] (“detail” coefficient vector). The decomposition is repeating on the approximations in each stage. The multiple stage DWT will break down the original signal into many successively lower resolution components, as is described in [15]. According to [15] “at each stage, the approximation coefficients vector [A] represents the basic trends of the original signal characteristics, while the details coefficients vector [D] provides the flavor of the signal”. The inverse process opposite to decomposition is the signal reconstruction by using an inverse discrete wavelet transform (IDWT). More details about sample wavelet definitions known as Haar, Morlet and Daubechies wavelets, the reader can find in [8, 13, 17]. As is shown in Figure 6, in control systems applications is preferred the Morlet wavelet function for continuous analysis using CWT [13, 14], compared to Haar and the Daubechies wavelet family functions that are very useful for DWT [8, 9, 10]. Using the MATLAB/SIMULINK Wavelet and Processing Toolboxes in real-time, the proposed 1-D wavelet analysis strategy is implementing by following the guidelines from [8, 10, 11, 12].
Signal processing is a well-known tool to deal with fault diagnosis. It is useful to analyze directly the signals measured online, avoiding system modeling compared to Kalman filter techniques that are model-based. A wave transformation offers a new approach to the analysis of transient regimes that vary over time. It has a specific ability to analyze signals simultaneously in both time and frequency domains. Besides, it can automatically adjust the analysis windows according to frequency, namely, shorter windows for higher frequencies and vice versa. Therefore, the wavelet transform is very suitable for identifying the characteristics of the faults that occur in the Li-ion battery under investigation. However, the identification of such wavelet-based features in HEV Li-ion BMS applications is a novelty. Signal features, such as discontinuity or singularity, are easily detectable through a 1-D wavelet transform. Sudden signal transitions lead to wave coefficients with high absolute values. The changes in the evolution of the signal provide valuable information when something fundamental has occurred in the evolution of the signal. These features suggest an excellent idea in our case study on how to detect measurement sensor errors that often occur in a Li-ion battery used in HEV applications.
Step 1. Simulink model diagram of Li-ion battery and fault injection mechanism setup.
At this stage is investigated the capability of using 1-D wavelet analysis to detect some anomalies in a BMS of the Li-ion battery caused by two faults injected in a current, respectively temperature sensor. Figure 7 shows the Simulink diagram of a general model of the Li-ion battery, including the thermal model and fault injection mechanism in both healthy and thermal blocks.
Simulink diagram of Li-ion battery including the thermal model and fault injection mechanism setup.
Step 2. Healthy and faulty models of Li-ion battery setup.
The Simulink diagrams of healthy and defective battery cell models are depictured in Figures 8 and 9. In these figures are visible also the fault injection blocks inside the battery (Figure 8) and thermal (Figure 9) models.
Simulink diagram of thermal model and fault injection mechanism setup.
Simulink diagram of Li-ion battery faulty model setup.
Step 1. Wavelet filter bank decomposition – Biorthogonal wavelet description.
Based on a 1-D DWT signal decomposition, the analysis (decomposition) and synthesis (reconstruction) filters are of more interest than the associated scaling function and wavelet for a 1-D CWT. For example, in Figure 10 are implemented in MATLAB two analysis filters and other two synthesis filters for a B spline biorthogonal wavelet that can reproduce polynomials (vanishing moment property) with three vanishing moments in the reconstruction filter and five vanishing moments in the decomposition filter, very useful to be used in fault detection. More precisely, both phases analysis and synthesis require two low pass filters (LPF) to filtrate low frequencies signals, respectively two high pass filters (HPF), to filtrate the high frequencies signals [8, 12, 18, 19, 20, 21].
Analysis and synthesis low pass and high pass decomposition filters, respectively low pass, and high pass reconstruction filters.
Furthermore, the orthogonal and biorthogonal filters banks are an arrangement of low pass, high pass, and bandpass filters that divide the signals data sets into sub-bands [12, 17, 18, 19, 20, 21]. If the sub-bands are not modified, these filters enable perfect reconstruction of the original data. In most of applications, the data are processed differently in the different sub-bands and then reconstruct a modified version of the original data. Orthogonal filter banks do not have linear phase, compared to biorthogonal filter banks that have linear phase [12, 18, 19, 20]. The wavelet and scaling filters are specifying by the number of the vanishing moments, which allows removing or retaining polynomial behavior in the signals data sets.
In addition, lifting allows designing perfect reconstruction filter banks with specific properties. To obtain and use the most common orthogonal and biorthogonal wavelet filters can be used Wavelet Toolbox™ functions [20]. The design of custom perfect reconstruction filter bank is performing through elementary lifting steps. Besides, can also be added own custom wavelet filters. By using the wavelet filter bank architecture depicted in Figure 11, it is possible to obtain residues that change noticeably in order to offer precious information about the timely detection of the faults and its severity [20, 21]. A sub-band model is suggesting in [18, 19] of the form:
Wavelet filter bank. Three level decomposition using low and high pass filters for down sampling by two.
where s is an integer number, and a, b are real numbers. In [18] is used the ‘db8’ wavelet for wavelet filter bank design of level 3 decomposition for a Single-Input Single-Output (SISO) plant extended in [19] for a multiple inputs and multiple outputs (MIMO) plant. Besides, in same reference is developed a wavelet based-frequency sub-band analytical redundancy scheme to calculate the residuals for different faults that uses for wavelet filter bank synthesis and analysis a level three decomposition, as is shown in Figure 11. The same wavelet filter bank is adopted in our case study, even if the decomposition resolution can increase by increasing the number of levels. Nevertheless, in our case study, the focus is only on the “concept of proof” and to demonstrate the effectiveness of the proposed error detection technique, based on the use of the multi 1-D signal waveform analysis tool. In Figure 11, G(z) and H(z) represent the z-transforms of the low pass filter (LPF) and high pass filter (HPF) respectively. A two-channel critically sampled filters bank play an important role to filtrate the input signal, i.e. the output battery voltage residual, by using a pair of low pass filter (LPF) and high pass filter (HPF) [18, 19, 21]. The subband outputs of the filters are downsampling by two to preserve the overall number of samples. To reconstruct the input, upsampling by two and then interpolate the results using the low pass and high pass synthesis filters. If the filters satisfy specific properties, a perfect reconstruction of the input is achieved [18, 19, 20, 21].
In Figure 12 (a) and (b) are presented the schematic of a Wavelet Filter Bank decomposition on two levels (a), respectively a simple interpretation of the DWT coefficients in frequency domain [14].
DWT coefficients interpretation (snapshot from [
The schematic from Figure 12(a) give us the idea of a recursive numerical algorithm for the DWT coefficients computation based on digital filters at all levels j = 1:N, which take advantage of using a digital signal processor (DSP):
Step 2. Fault injection scenarios presentation:
For simulation and “proof-concept” purpose, only two scenarios for error injection are developed in this section, namely a 2A bias fault injected into the current sensor, and a 10°C bias temperature fault injected into the thermal model of Li-ion BMS. The faults are injected separately, in the same window [500,1500] seconds, and their impact on the battery output voltage is analyzed by using the same Li-ion battery residual generation and evaluation method, like in the previous section.
Step 2.1 Scenario 1: Bias current fault MATLAB implementation.
As first scenario is considered a 2A bias fault injected in the current sensor inside the window (500,1500) seconds.
Step 2.1.1 Li-ion output voltage and MATLAB SOC residual generation-original and reconstructed signals.
The MATLAB simulations results are shown in the Figures 13 and 14 for battery SOC (healthy, faulty and residual), respectively for battery voltage residuals, (healthy and faulty) original and reconstructed, using the analysis (approximation) and details wavelets filters (in reconstruction).
The impact of the injected bias current fault on the Li-ion battery SOC.
Li-ion battery terminal output voltage: (a) healthy signal; (b) faulty signal.
Step 2.1.2. Denoising residual signals methods – MATLAB implementation.
In Figure 15 is used the denoising capability of 1-D wavelet synthesis filters ‘sym4’ to reduce as much as possible the noise level in the healthy and faulty signals. In [22, 23] is showing how to use wavelets to denoise signals and images. Because wavelets localize features in measurement dataset to different scales, an important signal or image features can be preserved while removing noise [22]. The “basic idea behind wavelet denoising, or wavelet thresholding, is that the wavelet transform leads to a sparse representation for many real-world signals and images” [22]. Thus, the wavelet transform concentrates the signal and image features in a few large-magnitude wavelet coefficients [22]. Wavelet coefficients which are small in value are typically noise and can be “diminished” those coefficients or much better can be removed without affecting the signal or image quality. Thresholding operation of the coefficients is followed by the reconstruction of the data using the inverse wavelet transform. The denoising operation of the input-output signals can be performed by using an average moving method [23], or decimated (“wdenoise” MATLAB command) and undecimated (“wden” MATLAB command) wavelet transforms [22]. In Figure 15 is shown the residual between the noisy and denoise signals, where wavelet denoising has removed a considerable amount of the noise while preserving the sharp features in the signal, which is also a challenge for Fourier-based denoising or filtering. The Fourier-based denoising, or filtering, is using a low pass filter (LPF) to remove the noise. However, “when the data has high-frequency features such as spikes in a signal or edges in an image, the low pass filter smooths these out”, as is stated in [22]. Moreover, the wavelets can be used to denoise signals in which the noise is nonuniform [22].
Li-ion battery output voltage residual – Noisy and denoised signals.
Step 2.1.3. Fault detection features:
In Figure 13, it easy to see the impact of the injected fault in the windows (500,1500) seconds, where the SOC change by maximum 10%. The information extracted from SOC residual in Figure 13 and output voltage residual in Figure 15, is valuable to detect the incipient moment of the fault, its duration and severity if a threshold value is chosen. The presence of the fault inside the window [500,1500] is visible since sudden changes in the SOC and output voltage of residual levels is easy to visualize. The fault removal at the end of the injected window is noticeable due to a sudden change of the signals’ levels in the opposite direction at the initiating time instant of the fault injection. In Figure 16(a) and (b) is depicted the output voltage residuals noisy and denoised originals and their perfect reconstruction. An impressive result is showing in Figure 17, where the presence of the fault inside the injected window is without doubt detecting by analyzing the wavelet variance in signal by scale before injected fault, inside the window and after removing the fault, in bar representation. For the proposed fault detection strategy design, a discrete wave transformation is useful to apply on the output voltage signal of the Li-ion battery. It is equivalent to the analysis branch (with downsampling) of the two-channel filter bank (decomposition) using LPFs, and HPFs suggested in [17, 18]. They are used for downsampling the input signal up to level 3, as shown in Figure 18 for all three levels the details of the wave coefficient D1, D2 and D3 and the analysis coefficient A3.
The Li-ion battery terminal output voltage residual - original and reconstructed waveforms using analysis wavelets filters (reconstruction): (a) contaminated with noise; (b) denoised signals.
Li-ion terminal output voltage residual – Wavelet variance in signal by scale before injected fault, inside the window and after removing the fault - bar representation.
Li-ion battery output voltage residual decomposition on three levels.
Step 2.1.4. 1-D wavelet transform analysis used for battery voltage residual three levels decomposition – Approximation coefficient A4, and Details coefficients D1, D2 and D3:
In Figure 18 is presented the MATLAB simulation result of the battery voltage residual decomposition on three level based on the wavelet filter banks shown in Figures 11 and 12.
For decomposition is used a Symlet wavelet transform ‘sym4’ with four vanishing moments. The feature extracted from the wavelet coefficients are summarized in Table 2 and interpreted at the end of this section, in comparison with the second fault.
Details coefficients | Analysis coefficients | ||||
---|---|---|---|---|---|
Extracted features | Faults | D1 | D2 | D3 | A3 |
Energy | Current fault | 1.7821 | 0.8357 | 0.3631 | 97 |
Temperature fault | 4.46 | 2.7 | 5.349 | 87.5 | |
Skewness | Current fault | 0.063 | −0.17 | 0.15 | −4.9 |
Temperature Fault | 0.063 | −3.92 | 13 | −1.33 | |
Kurtosis | Current fault | 5.8 | 11.1 | 23.11 | 27.38 |
Temperature fault | 5.8 | 71.4 | 389.13 | 56 | |
RMSE statistic criterion-performance | D1 coefficient | ||||
Energy | Current fault | 1.7821 | Remark: Temperature fault features shows significant values. | ||
Temperature fault | 4.4654 | ||||
Skewness | Current fault | 0.063 | |||
Temperature fault | 0.063 | ||||
Kurtosis | Current fault | 5.7581 | |||
Temperature fault | 5.7581 |
The main features extracted for faults detection.
Step 2.2. Scenario 2 MATLAB implementation:
As a second fault is investigated a 10°C bias fault injected in the temperature sensor inside the window (500,1500) seconds.
Step 2.2.1 Li-ion output voltage and MATLAB SOC residual generation-original and reconstructed denoised signals:
Like for the first scenario, the same information is extracted from the Figures 19 and 20. In Figure 19(a) is shown the battery SOC with almost a zero impact of the injected temperature fault since we assumed in this research that SOC does not change significantly if the temperature inside the battery changes. This assumption is not realistic, since in “real life” the SOC and internal resistance of Li-ion battery are dependent on temperature. This assumption was adopted to simplify the Li-ion battery model substantially, since a battery model of high complexity is beyond the topic developed in this research work. Moreover, the assumption is also justified by the fact that the fault detection analysis by using a 1-D wavelet analysis tool is performing online. A model is not required, that is a significant advantage of the new approach compared with the model based Kalman filter technique approach developed in the previous section, for which the SOC accuracy of the battery model is critical. Besides, the main objective of this paper is to provide a “proof concept” and to demonstrate the effectiveness of the use of 1-D wavelet analysis of finding the essential features in the output voltage residual variance for MATLAB design and implementation of the investigated fault detection technique. In Figure 19 (b), (c) and (d) are visualized the healthy, faulty and the battery temperature residual (b), the healthy, In faulty and the output voltage residual (c), respectively the use of 1-D wavelet ‘Sym4’ for denoising output voltage residual (d). The residual of denoised battery output voltage and its corresponding constructed wave are presenting in Figure 20(a).
Li-ion battery temperature fault injected: (a) SOC and its residual; (b) healthy, faulty, and residual temperatures; (c) healthy, faulty, and residual battery internal resistance; (d) original (noisy), denoised and residual output voltage signals.
Li-ion battery output voltage residual second scenario: (a) original denoised output voltage residual; (b) the details (D1, D2, D3) and approximation (A3) Symlet4 (four vanishing moments) wavelets coefficients decomposition at level 3.
Step 2.2.2. Fault detection features.
The MATLAB simulation result shown in Figure 19 (b), (c) and (d) reveal that the presence of the temperature fault is noticeable by its effect on the output voltage residual at the beginning, inside and at the end of the injected window. and the coefficients D1, D2, D3 and A3 of the ‘Sym4’ wavelet are shown in Figure 20(b). The features extracted from Figure 20(b) are summarized in Table 2 and analyzed at the end of this section.
A rigorous performance analysis of using 1-D wavelet transform tool for fault detection strategy is accomplished based on the information extracted from the details’ coefficients of output voltage residual decomposition for both scenarios.
From the details coefficients values D1, D2, and D3 can be extracted the wavelet energy, skewness, and kurtosis features. These statistics can identify the types of faults based on their distinct value, as are defined in [14], MATLAB Wavelet Toolbox (for wavelet energy), respectively MATLAB Statistics and Machines Learning Toolbox for skewness and kurtosis).
The wavelet energy is an important indicator that gives a valuable information about the presence of the fault inside a window that has a concentrated large value of the wavelet energy, defined as,
The skewness is a measure of the asymmetry of the data around the sample mean. If skewness is negative, the data spreads out more to the left of the mean than to the right. If skewness is positive, the data spreads out more to the right. It is defined as,
The kurtosis is a measure of whether the distribution is too peaked, i.e. a very narrow distribution with most of the responses in the center, and is defined as,
where
The excess kurtosis and skewness of every coefficient A3, D1, D2 and D3 in the dataset, can be interpreted as follows:
For skewness, if the distribution of responses for a variable stretches toward the right or left tail of the distribution, then the distribution is referred to as skewed. A general guideline for skewness is that if the number is greater than +1 or lower than −1, this is an indication of a substantially skewed distribution.
For kurtosis, if the number is greater than +1, the distribution is too peaked. Otherwise, a kurtosis of less than −1 indicates a distribution is too flat.
When both skewness and kurtosis are zero, the pattern of responses is considered a normal distribution.
Besides, an assessment statistic criterion root mean square error (RMSE) is introduced in Table 2 for a particular analysis of the high frequency detail component D1 dataset.
The MATLAB simulation results analyzed from the perspective of the fault features extracted from Table 2 reveal the fact that the temperature fault shows significant values compared to a possible occurrence of current fault in Li-ion battery.
Figures 21 and 22 show how multiresolution decomposition technique, such as 1-D wavelet analysis, allow us to study signal components in relative isolation on the same time scale as the original data [22]. Multiresolution analysis (MRA) refers to “breaking up a signal into components, which produce the original signal exactly when added back together” [22]. The components ideally decompose the variability of the data into physically meaningful and interpretable parts, as is stated also in [22].
Li-ion output voltage residual signal using a wavelet MRA for scenario 1 of bias current fault on 8 resolutions (levels) decomposition–extracted smooth trend (S8) and localize transient changes.
Synthetic signal using a wavelet MRA on 8 resolutions (levels) decomposition for scenario 2 of bias temperature fault – Extracted smooth trend (S8) and localize transient changes.
The term MRA is often associated with wavelets, and in the “real life” the signals consist of a mixture of different components. Often the interest is focused only in a subset of these components. That is why the MRA allows us to restrict the analysis of the original signal, by separating it into components at different resolutions. Extracting signal components at different resolutions means a decomposition of variations in the data on different time scales, or equivalently in different frequency bands [22]. Consequently, the signal variability at different scales or frequency bands can be seen simultaneously.
In the Figures 21 and 22, using a wavelet MRA, the Li-ion battery output voltage residual signal is analyzed in MATLAB at eight resolutions or levels, following the procedure shown in [22] for both faults isolation.
Both graphs from Figures 21 and 22 starts from the uppermost plot and proceed down until is reached the plot of the original data and is worth noting that the components have become progressively smoother. D2 graph isolates the time-localized high-frequency component, which can be seen and investigated as an essential signal feature practically in isolation. The next two graphs contain the lower frequency oscillation. It is worth to mention that “an important aspect of multiresolution analysis, namely important signal components may not end up isolated in one MRA component, but they are rarely located in more than two” [22]. Finally, from the S8 graph can be extracted a smooth trend term, which provides us a valuable information to localize transient changes, as it can see in the fault injection window [500, 1500] seconds. Thus, the presence of the bias current fault and bias temperature fault is detected and localized as a significant transient change in the nonstationary Li-ion output voltage residual signal. For an appropriate choice of the thresholds’ values, both faults can be detected directly from the S8 graph, removing the presence of the false alarms completely.
Besides, the value of the RMSE statistical criterion of the energy feature extracted from the detail coefficient D1, for both faults, shown in Figures 18 and 20(b), undoubtedly confirms the validation of the results obtained in Table 3, adequate to differentiate between the two faults. However, in Table 3 is shown the Fault signature of 1-D wavelet analysis transform, useful for fault isolation. To distinguish between both faults injected in Li-ion battery, i.e. current sensor bias fault, respectively, temperature bias fault a valuable information is provided by battery SOC and battery internal resistance residuals. It is showing in Table 3, like for based model AEKF FDI strategy developed in Section 2. An exciting piece of information is related to the “border effects of error injection”, clearly visible when the temperature fault is removing, because the healthy signal emerges from the defective one in the window, before the corresponding time tf = 1500 seconds. These “frontier effects” require further investigation in future work.
Res_y | Res_SOC | Res_Rcell | Fault signature |
---|---|---|---|
1 | 1 (<0) | 0 | Current fault, no false alarm |
1 | 0 | 1 | Temperature fault, no false alarm |
Li-ion battery - fault signature 1-D wavelet analysis transform.
In this research paper is opened a new research direction in HEV BMS applications field by performing many investigations on the use of multisignal 1-D wavelet analysis to improve the accuracy, robustness, the design and the implementation in real-time of Fault detection techniques. Among the most relevant contributions of the authors can be highlighted the following:
The selection of a suitable and straightforward Li-ion battery model, accurate enough for data generation, and to design and implement a robust adaptive extended Kalman filter SOC estimator to changes in SOC initial values, in the level of measurement noise that contaminate the input-output dataset, to changes in the battery capacity value due to aging effects, and changes in the internal resistance of the battery due to temperature effects
Representation of the battery model in continuous and discrete time state-space
Develop the most appropriate thermal model of the battery for data generation and to setup the temperature mechanism fault injection
Adaptive Extended Kalman Filter SOC estimator with fading feature and covariance matrices of noises correction—brief presentation and MATLAB design and implementation.
The battery SOC and output voltage residual generation and bias current fault injection mechanism
The fault detection and isolation estimation technique based on AEKF SOC estimator
Wavelets transform analysis of the faults features extraction in a rechargeable Li-ion battery
SOC and output voltage residual generation-original and reconstructed signals
1-D wavelet transform analysis used for battery voltage residual three levels decomposition – Approximation coefficient A4, and Details coefficients D1, D2 and D3
Denoising residual signals methods analysis – MATLAB implementation
Wavelets transform analysis to extract the fault features for their detection. Performance analysis
Extracting signal components at different resolutions by using a multiresolution analysis (MRA) method for fault detection
The use of the fault signature for fault localization (isolation)
These investigations are performed for the case study, principally chosen to evaluate the impact of two bias faults injected in a current and temperature sensor on the output voltage of a BMS Li-ion rechargeable battery used in HEVs applications.
The effectiveness of fault detection strategy is demonstrated through an extensive number of simulations in a MATLAB R2020a software environment. The preliminary simulation results are encouraging, and extensive investigations will be done in future work to extend the applications area. The performance analysis from the last section reveals that 1-D wavelet analysis is a useful tool for signals processing, design and implementation based on wavelet transforms found in a wide range of control systems industrial applications. Compared to AEKF estimation technique described in Section 2, the 1-D wavelet analysis tool has a significant advantage to perform online. Also, it does not require the model of the battery, since it uses directly the input-output signals generated by the battery model. More precisely, it is based only on the measurement input-output dataset collected by a data acquisition (DAQ) system incorporated in BMS of HEVs. Besides, the battery SOC and output voltage signals’ accuracy is not affected by noise as long as is using the signals denoising techniques, such in the case of AEKF fault detection and isolation technique during the noise correction step of the algorithm.
The authors declare no conflict of interest.
Step 1. Rint ECM battery nonlinear model represented in discrete time [3, 4, 11]:
where the process noise
Step 2. Initialization:
Step 3. Model linearization - The Jacobian matrices of the model linearization are given by:
For
Step 4. Prediction phase (forecast or time update from
Step 4.1 Predict the state ahead:
Step 4.2. Predict the covariance error ahead:
Remark. In this phase, the predicted value of the state vector
Step 4.3 Compute the updated value of Kalman filter gain:
Step 5. Correction phase (analysis or measurement update):
In this phase the Li-ion battery SOC estimated state is updated when an output measurement is available in two steps:
Step 5.1 Update the SOC estimated state covariance matrix with a measurement:
Step 5.2 Update the SOC estimated state variable with the measurement:
Step 5.3 Update the estimated output (battery terminal voltage):
Step 6. Adaptive noise covariance matrices correction:
For k > = L, the length of the window’s samples, compute:
Step 6.1. Output variable error and the correction factor:
Step 6.2. Measurement noise correction:
Step 6.3. Process noise correction:
Second scenario of fault injection: (a) bias fault injection of magnitude 2A in a current measurement sensor; (b) battery terminal voltage residual; (c) AEKF SOC estimate versus EMC battery model SOC true value; (d) SOC residual.
Third scenario of fault injection: (a) bias fault injection; (b) temperature profile; (c) temperature effect on battery internal resistance Rin; (d) AEKF SOC estimate versus ECM battery model SOC true value; (e) SOC residual; (f) AEKF terminal voltage estimate versus ECM battery model terminal voltage true value; (g) battery terminal output voltage residual.
EV | electric vehicle |
HEV | hybrid electric vehicle |
BMS | battery management system |
FTP-75 | Federal test procedure at 75 F |
OCV | open-circuit voltage |
SOC | state of charge |
KF | Kalman filter |
EKF | extended Kalman filter |
AEKF | adaptive Kalman filter |
WCT | wavelet continuous transform |
WDT | wavelet discrete transform |
LPF | low pass filter |
HPF | high pass filter |
Sim4 | Simlet wavelet with 4 vanishing moments |
RMSE | root mean square error |
MSE | mean square error |
MAE | mean absolute error |
MAPE | mean absolute percentage error |
std | standard deviation |
R2 | R-squared |
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\\n"}]'},components:[{type:"htmlEditorComponent",content:'Copyright is the term used to describe the rights related to the publication and distribution of original Works. Most importantly from a publisher's perspective, copyright governs how Authors, publishers and the general public can use, publish, and distribute publications.
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The literature source was Web of Science and SSCI, SCI-EXPANDED, A&HCI, CPCI-S, CPCI-SSH, and ESCI indexes. Fifty-two articles were reviewed; however, 14 of them were not been included in the study. As a result, 38 articles were examined. Level of education, field of education, and material types of AR used in education and reported educational advantages of AR have been investigated. All articles are categorized according to target groups, which are early childhood education, primary education, secondary education, high school education, graduate education, and others. AR technology has been mostly carried out in primary and graduate education. “Science education” is the most explored field of education. Mobile applications and marker-based materials on paper have been mostly preferred. 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The more active a lesson, the more students tend to engage intellectually and emotionally in the learning activities. Cooperative learning is the foundation on which many of the active learning procedures are based. Cooperative learning is the instructional use of small groups so that students work together to maximize their own and each other’s learning. Most of the active learning procedures, such as problem-based learning, team-learning, collaborative learning, and PALS, require that students work cooperatively in small groups to achieve joint learning goals. Cooperative learning is based on two theories: Structure-Process-Outcome theory and Social Interdependence theory. Four types of cooperative learning have been derived: formal cooperative learning, informal cooperative learning, cooperative base groups, and constructive controversy. There is considerable research confirming the effectiveness of cooperative learning. 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The importance of VR/AR for the mental health field comes from three main concepts: (1) VR/AR as an imaginal technology, people can feel “as if they are” in a reality that does not exist in external world; (2) VR/AR as an embodied technology, the experience to feel user’s body inside the virtual environment; and (3) VR/AR as connectivity technology, the “end of geography’. In this chapter, we explore the opportunities provided by VR/AR as technologies to improve people’s quality of life and to discuss new frontiers for their application in mental health and psychological well-being promotion.",book:{id:"6543",slug:"state-of-the-art-virtual-reality-and-augmented-reality-knowhow",title:"State of the Art Virtual Reality and Augmented Reality Knowhow",fullTitle:"State of the Art Virtual Reality and Augmented Reality Knowhow"},signatures:"Sara Ventura, Rosa M. Baños and Cristina Botella",authors:[{id:"106036",title:"Dr.",name:"Rosa Maria",middleName:null,surname:"Baños",slug:"rosa-maria-banos",fullName:"Rosa Maria Baños"},{id:"227763",title:"Ph.D.",name:"Sara",middleName:null,surname:"Ventura",slug:"sara-ventura",fullName:"Sara Ventura"},{id:"229056",title:"Dr.",name:"Cristina",middleName:null,surname:"Botella",slug:"cristina-botella",fullName:"Cristina Botella"}]},{id:"58060",doi:"10.5772/intechopen.72341",title:"Pedagogy of the Twenty-First Century: Innovative Teaching Methods",slug:"pedagogy-of-the-twenty-first-century-innovative-teaching-methods",totalDownloads:8743,totalCrossrefCites:15,totalDimensionsCites:21,abstract:"In the twenty-first century, significant changes are occurring related to new scientific discoveries, informatization, globalization, the development of astronautics, robotics, and artificial intelligence. This century is called the age of digital technologies and knowledge. How is the school changing in the new century? How does learning theory change? Currently, you can hear a lot of criticism that the classroom has not changed significantly compared to the last century or even like two centuries ago. Do the teachers succeed in modern changes? The purpose of the chapter is to summarize the current changes in didactics for the use of innovative teaching methods and study the understanding of changes by teachers. In this chapter, we consider four areas: the expansion of the subject of pedagogy, environmental approach to teaching, the digital generation and the changes taking place, and innovation in teaching. The theory of education, figuratively speaking, has two levels. At the macro-level, in the “education-society” relationship, decentralization and diversification, internationalization of education, and the introduction of digital technologies occur. At the micro-level in the “teacher-learner” relationship, there is an active mix of traditional and innovative methods, combination of an activity approach with an energy-informational environment approach, cognition with constructivism and connectivism.",book:{id:"5980",slug:"new-pedagogical-challenges-in-the-21st-century-contributions-of-research-in-education",title:"New Pedagogical Challenges in the 21st Century",fullTitle:"New Pedagogical Challenges in the 21st Century - Contributions of Research in Education"},signatures:"Aigerim Mynbayeva, Zukhra Sadvakassova and Bakhytkul\nAkshalova",authors:[{id:"201997",title:"Dr.",name:"Aigerim",middleName:null,surname:"Mynbayeva",slug:"aigerim-mynbayeva",fullName:"Aigerim Mynbayeva"},{id:"209208",title:"Dr.",name:"Zukhra",middleName:null,surname:"Sadvakassova",slug:"zukhra-sadvakassova",fullName:"Zukhra Sadvakassova"},{id:"209210",title:"Dr.",name:"Bakhytkul",middleName:null,surname:"Akshalova",slug:"bakhytkul-akshalova",fullName:"Bakhytkul Akshalova"}]},{id:"64583",doi:"10.5772/intechopen.81714",title:"Evaluating a Course for Teaching Advanced Programming Concepts with Scratch to Preservice Kindergarten Teachers: A Case Study in Greece",slug:"evaluating-a-course-for-teaching-advanced-programming-concepts-with-scratch-to-preservice-kindergart",totalDownloads:1408,totalCrossrefCites:13,totalDimensionsCites:18,abstract:"Coding is a new literacy for the twenty-first century, and as a literacy, coding enables new ways of thinking and new ways of communicating and expressing ideas, as well as new ways of civic participation. A growing number of countries, in Europe and beyond, have established clear policies and frameworks for introducing computational thinking (CT) and computer programming to young children. In this chapter, we discuss a game-based approach to coding education for preservice kindergarten teachers using Scratch. The aim of using Scratch was to excite students’ interest and familiarize them with the basics of programming in an open-ended, project-based, and personally meaningful environment for a semester course in the Department of Preschool Education in the University of Crete. For 13 weeks, students were introduced to the main Scratch concepts and, afterward, were asked to prepare their projects. For the projects, they were required to design their own interactive stories to teach certain concepts about mathematics or physical science to preschool-age students. The results we obtained were more satisfactory than expected and, in some regards, encouraging if one considers the fact that the research participants had no prior experiences with computational thinking.",book:{id:"6936",slug:"early-childhood-education",title:"Early Childhood Education",fullTitle:"Early Childhood Education"},signatures:"Stamatios Papadakis and Michail Kalogiannakis",authors:null}],mostDownloadedChaptersLast30Days:[{id:"58060",title:"Pedagogy of the Twenty-First Century: Innovative Teaching Methods",slug:"pedagogy-of-the-twenty-first-century-innovative-teaching-methods",totalDownloads:8743,totalCrossrefCites:15,totalDimensionsCites:21,abstract:"In the twenty-first century, significant changes are occurring related to new scientific discoveries, informatization, globalization, the development of astronautics, robotics, and artificial intelligence. This century is called the age of digital technologies and knowledge. How is the school changing in the new century? How does learning theory change? Currently, you can hear a lot of criticism that the classroom has not changed significantly compared to the last century or even like two centuries ago. Do the teachers succeed in modern changes? The purpose of the chapter is to summarize the current changes in didactics for the use of innovative teaching methods and study the understanding of changes by teachers. In this chapter, we consider four areas: the expansion of the subject of pedagogy, environmental approach to teaching, the digital generation and the changes taking place, and innovation in teaching. The theory of education, figuratively speaking, has two levels. At the macro-level, in the “education-society” relationship, decentralization and diversification, internationalization of education, and the introduction of digital technologies occur. At the micro-level in the “teacher-learner” relationship, there is an active mix of traditional and innovative methods, combination of an activity approach with an energy-informational environment approach, cognition with constructivism and connectivism.",book:{id:"5980",slug:"new-pedagogical-challenges-in-the-21st-century-contributions-of-research-in-education",title:"New Pedagogical Challenges in the 21st Century",fullTitle:"New Pedagogical Challenges in the 21st Century - Contributions of Research in Education"},signatures:"Aigerim Mynbayeva, Zukhra Sadvakassova and Bakhytkul\nAkshalova",authors:[{id:"201997",title:"Dr.",name:"Aigerim",middleName:null,surname:"Mynbayeva",slug:"aigerim-mynbayeva",fullName:"Aigerim Mynbayeva"},{id:"209208",title:"Dr.",name:"Zukhra",middleName:null,surname:"Sadvakassova",slug:"zukhra-sadvakassova",fullName:"Zukhra Sadvakassova"},{id:"209210",title:"Dr.",name:"Bakhytkul",middleName:null,surname:"Akshalova",slug:"bakhytkul-akshalova",fullName:"Bakhytkul Akshalova"}]},{id:"61746",title:"Facilitation of Teachers’ Professional Development through Principals’ Instructional Supervision and Teachers’ Knowledge- Management Behaviors",slug:"facilitation-of-teachers-professional-development-through-principals-instructional-supervision-and-t",totalDownloads:3349,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"With the rise of global competition and the focus on teacher quality, teacher professional development is becoming increasingly crucial, and the stress and challenges for principals are more severe than ever. Teachers can improve their professional abilities through principals’ instructional supervision and their own knowledge-management (KM) behaviors to benefit students. Thus, this chapter analyzes the relationship among principals’ instructional supervision, teachers’ KM, and teachers’ professional development. The author believes that principals’ instructional supervision and effective KM can facilitate the professional development of teachers. The author also believes the readers can know the relationships among them, and teachers’ professional development can be improved through principal’s instructional supervision and teachers’ KM behaviors.",book:{id:"6674",slug:"contemporary-pedagogies-in-teacher-education-and-development",title:"Contemporary Pedagogies in Teacher Education and Development",fullTitle:"Contemporary Pedagogies in Teacher Education and Development"},signatures:"Chien-Chin Chen",authors:[{id:"232569",title:"Ph.D.",name:"Chien Chih",middleName:null,surname:"Chen",slug:"chien-chih-chen",fullName:"Chien Chih Chen"}]},{id:"75908",title:"From the Classroom into Virtual Learning Environments: Essential Knowledge, Competences, Skills and Pedagogical Strategies for the 21st Century Teacher Education in Kenya",slug:"from-the-classroom-into-virtual-learning-environments-essential-knowledge-competences-skills-and-ped",totalDownloads:501,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"As teachers in Kenya begin to migrate from the classroom to virtual learning spaces following COVID 19 pandemic, there is pressing need to realign Teacher Education to requisite Knowledge, competences, skills, and attitudes that will support online teaching. This chapter explores these needs using a combination of lived experiences and literature review that captured a meta-analysis of research trends on e-learning. While trends in Teacher Education indicate progression towards adoption of technology, there are disparities between the theory and practice. Evidence from recent research and reports; and the recollected experiences confirmed knowledge, competence, skills and pedagogical gaps in the implementation of online learning, that have been exacerbated by COVID-19. The researcher recommends that teacher education should sensitize and train teacher trainees on how to access, analyze and use new knowledge emerging with technology; they also should be coached on how learners learn with technology and on fundamentals of the communication process. Particularly the course on educational technology, should focus on how to create and manage online courses. The 5-stage E-Moderator Model and Universal Design for Learning (UDL) are recommended as effective pedagogical scaffold for online teaching.",book:{id:"10229",slug:"teacher-education-in-the-21st-century-emerging-skills-for-a-changing-world",title:"Teacher Education in the 21st Century",fullTitle:"Teacher Education in the 21st Century - Emerging Skills for a Changing World"},signatures:"Catherine Adhiambo Amimo",authors:[{id:"333482",title:"Dr.",name:"Catherine Adhiambo",middleName:null,surname:"Amimo",slug:"catherine-adhiambo-amimo",fullName:"Catherine Adhiambo Amimo"}]},{id:"75224",title:"Decoding the Digital Gap in Teacher Education: Three Perspectives across the Globe",slug:"decoding-the-digital-gap-in-teacher-education-three-perspectives-across-the-globe",totalDownloads:552,totalCrossrefCites:0,totalDimensionsCites:4,abstract:"Educational use of technology is regularly assessed, and results often show a gap between educational policies and what is actually practiced. This chapter will help clarify how teacher educators experience the changing educational contexts due to the digital revolution, how their meaning-making shifts, and how outside forces influence those processes. The results are based on comparative international studies. Central for this study is practitioners’ professional digital competence, their attitudes towards digital technology and the use of digital technology in education. We found that the influence and contribution of digital practice is carried out quite differently across the globe. Our research questions were: How do practitioners experience teaching in a rapidly changing context? How do attitudes change due to top-down governing of education? and What motivates teacher educators to implement digital technology?",book:{id:"10229",slug:"teacher-education-in-the-21st-century-emerging-skills-for-a-changing-world",title:"Teacher Education in the 21st Century",fullTitle:"Teacher Education in the 21st Century - Emerging Skills for a Changing World"},signatures:"Steinar Thorvaldsen and Siri Sollied Madsen",authors:[{id:"332624",title:"Associate Prof.",name:"Siri Sollied",middleName:null,surname:"Madsen",slug:"siri-sollied-madsen",fullName:"Siri Sollied Madsen"},{id:"332626",title:"Prof.",name:"Steinar",middleName:null,surname:"Thorvaldsen",slug:"steinar-thorvaldsen",fullName:"Steinar Thorvaldsen"}]},{id:"75416",title:"Self-Study Research: Challenges and Opportunities in Teacher Education",slug:"self-study-research-challenges-and-opportunities-in-teacher-education",totalDownloads:724,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"This article aims to describe what self-study research is, why self-study can be a good approach to teacher educators’ professional development and improvements in practice and highlight some challenges and opportunities in this research approach. In addition, the article will shed light on some methodological aspects related to self-study. Self-study refers to teacher educators who in an intentionally and systematically way examine their practice to improve it, based on a deeper understanding of practice, as well as the context practice takes place. In the article, I argue that engaging in self-study is a learning and development process and an approach to developing personal professionalism, collective professionalism and improvements in practice.",book:{id:"10229",slug:"teacher-education-in-the-21st-century-emerging-skills-for-a-changing-world",title:"Teacher Education in the 21st Century",fullTitle:"Teacher Education in the 21st Century - Emerging Skills for a Changing World"},signatures:"Kåre Hauge",authors:[{id:"332053",title:"Associate Prof.",name:"Kåre",middleName:null,surname:"Hauge",slug:"kare-hauge",fullName:"Kåre Hauge"}]}],onlineFirstChaptersFilter:{topicId:"265",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81937",title:"Socialization Experiences among Undergraduate Students in Higher Learning Institutions (HLI)",slug:"socialization-experiences-among-undergraduate-students-in-higher-learning-institutions-hli-",totalDownloads:19,totalDimensionsCites:0,doi:"10.5772/intechopen.99007",abstract:"This work portrays the problems of socialization among undergraduate students in higher learning institutions. The socialization processes in higher learning institution are significant for the successful navigation of students in the academic programs and university environment in preparing the next generation of professional practitioners and scholars. But the undergraduate student socialization experiences of students at university environment are overlooked. To navigate in the higher learning institutions, students should be socialized effectively to the normative contexts of the higher learning institutions. The normative contexts of the higher learning institutions are generally categorized into social and academic contexts, because these context academic and social context integration have been linked to student retention and success. Social integration involves interpersonal relationships, support, interactions with others, and a sense of belonging at a university, which stems from extracurricular activities, informal dealings with peer groups, and interactions with faculty and staff, whereas academic integration is described through grade performance and intellectual development that reflects an ability to meet the standards of the academic system; intellectual development involves a student valuing their education as a process of development in which they gain knowledge and ideas. Students’ background is also the contributing factor for students’ socialization in the University.",book:{id:"10911",title:"Higher Education - New Approaches to Accreditation, Digitalization, and Globalization in the Age of Covid",coverURL:"https://cdn.intechopen.com/books/images_new/10911.jpg"},signatures:"Mulusew Birhanu Ayalew"},{id:"80280",title:"Adoption of Online Learning during the Covid19 Pandemic Lockdown by Universities in Garowe",slug:"adoption-of-online-learning-during-the-covid19-pandemic-lockdown-by-universities-in-garowe",totalDownloads:87,totalDimensionsCites:0,doi:"10.5772/intechopen.99941",abstract:"In response to the Covid-19 outbreak the world closed and therefore countries like Somalia have not been exceptional. The government of Somalia and all higher education institutions adopted crisis intervention measures on implementation of blended learning approaches like online teaching and learning. In this chapter we explore the process and challenges of adopting online learning in response to the world wide lockdown due to the pandemic. Given that this was an abrupt requirement, the survey was interested in finding out whether universities adopted and adapted easily. Researchers compared findings from previous studies and theoretical inclinations on online learning. Results indicate that the adoption of online learning among universities in Garowe was as a matter of crisis management whereby administration, lecturers and students were all not ready and had no prior grounding in this pedagogical learning platform. Just like previous studies online learning implementers have continued to encounter several challenges like intermittent internet network, cost of gadgets and facilities, inadequate skills of both the instructors and students, aspects of communication and satisfaction from stakeholders. With the research survey in Garowe, results show that this is still pervading and therefore need for more rigorous contextualised research on this subject.",book:{id:"10911",title:"Higher Education - New Approaches to Accreditation, Digitalization, and Globalization in the Age of Covid",coverURL:"https://cdn.intechopen.com/books/images_new/10911.jpg"},signatures:"Tumwebaze Alicon Auf and Omer Abdi Hamdi"},{id:"78597",title:"Public-Private Participation in Funding University Education in Sub-Saharan Africa: A Nigerian Case-Study for Sustainable Development",slug:"public-private-participation-in-funding-university-education-in-sub-saharan-africa-a-nigerian-case-s",totalDownloads:134,totalDimensionsCites:0,doi:"10.5772/intechopen.99940",abstract:"The developing countries in Africa still cannot withstand the pressure of the highly competitive global education market. Together with the large numbers of people who make a living in various innovative companies, these countries have solved key contemporary issues affecting global education. For this reason, it is necessary to actively respond to current technological innovation and educational challenges and to eliminate new technology graduates who can effectively interact with students through the responsive expansion of education and training. Expansion of education can produce effective expansion that promotes educational development, but due to budget constraints, most African governments cannot successfully and sustainably implement such educational programs. This is difficult. However, public-private partnership efforts provide a way out of this financial dilemma. The Sub-Saharan Africa initiative has achieved important educational objectives, such as: ensuring relevance for quality; secure funding for sustainability and establish resource mobilization partnerships and connections; and promote international cooperation. This discussion is relevant to the basic conditions for a successful public-private partnership with educational institutions and extended education and sheds light on the impact, lessons, and challenges. The public is increasingly concerned about the importance of higher education in the 21st century. This chapter explores some of the key functions of an innovative education system that supports the development of education in Nigeria and enhances people’s ability to use information. Nigeria’s education system re-emphasizes the importance of public and private universities, but the country does not have a sustainable education system and well-equipped educational institutions to support people’s ability to use information, learning, education, and research activities.",book:{id:"10911",title:"Higher Education - New Approaches to Accreditation, Digitalization, and Globalization in the Age of Covid",coverURL:"https://cdn.intechopen.com/books/images_new/10911.jpg"},signatures:"Lawrence Jones-Esan"},{id:"79197",title:"University Teachers’ Conceptions of What University Is: Implications for the Future of Higher Education",slug:"university-teachers-conceptions-of-what-university-is-implications-for-the-future-of-higher-educatio",totalDownloads:107,totalDimensionsCites:0,doi:"10.5772/intechopen.100813",abstract:"This chapter presents the perception of university teachers about the university, the most recent changes and how they have influenced their activity. The phenomenographic study was conducted with 10 university teachers, nine females and one male with more than 15 years of professional activity. The perception of the university emerges, in the teachers’ voice, focused on the description of its mission, namely as a context for the production and diffusion of knowledge to society, as a space for creative and critical thinking about the world, as an interdisciplinary space and as a system focused on teaching and research. It also includes characteristics related to its structure and functioning, such as the level of hierarchization, bureaucratization, competitiveness, dehumanization and bibliometrics overvaluation. Regarding the perceived changes, they are related to the structural reforms resulting from the Bologna Process, diverse student populations, research and internationalization, new technologies, institutional cooperation, bureaucratization and relationship with the community. Teachers also revealed some dissatisfaction in the way they are experiencing university life due to the overwork resulting from the multiple tasks required in the four activity strands (teaching, research, management and extension) with an impact on quality and innovation, but in line with what the institution demands.",book:{id:"10911",title:"Higher Education - New Approaches to Accreditation, Digitalization, and Globalization in the Age of Covid",coverURL:"https://cdn.intechopen.com/books/images_new/10911.jpg"},signatures:"Elisa Chaleta"},{id:"78595",title:"Globalization and Education: Trends towards Sustainability",slug:"globalization-and-education-trends-towards-sustainability",totalDownloads:57,totalDimensionsCites:0,doi:"10.5772/intechopen.99974",abstract:"Higher Education Institutions (IES) have a very relevant role in the path towards sustainability. The problem of the implementation of curricular sustainability is the disparity of solutions that can be adopted depending on the political and economic situation of each country. The study of a practical case in the south of Honduras allows the student to approach key decisions in a real scenario to bring improvements to a very disadvantaged population, lacking basic services, such as water and electricity, under the premise of sustainability, facing aspects as relevant such as sustainable mobility, water resources management, energy and construction models, in a context where globalization and technological innovation play a very important role. It is essential to know in depth the real context where structural changes will be applied to understand that there is no single reality, that actions are built adapting to specific situations and that the effectiveness of the measures that can be adopted to establish models that prioritize that part of sustainability that best weighs the balance between the environment, society and the economy for each case.",book:{id:"10911",title:"Higher Education - New Approaches to Accreditation, Digitalization, and Globalization in the Age of Covid",coverURL:"https://cdn.intechopen.com/books/images_new/10911.jpg"},signatures:"Maria Olga Bernaldo and Gonzalo Fernandez-Sanchez"},{id:"79255",title:"Higher Education Institutions (HEIs) in Africa Embracing the “New Normal” for Knowledge Production and Innovation: Barriers, Realities, and Possibilities",slug:"higher-education-institutions-heis-in-africa-embracing-the-new-normal-for-knowledge-production-and-i",totalDownloads:128,totalDimensionsCites:1,doi:"10.5772/intechopen.101063",abstract:"If Africa is to remain relevant and competitive in today’s knowledge-based economy, it has to rely on higher education institutions (HEIs) as centers of excellence for knowledge production. HEIs nurture and sustain the production of highly-skilled individuals to support Africa’s growing economies. Among all possible ways, this could be achievable through strategic curricula innovation driven by emerging mobile technologies. Consequently, Africa’s HEIs need to embrace the ‘New Normal’ by optimizing online teaching and learning in their pursuit to expand information and communications technology (ICT) literacy as a means to increase students’ opportunities in higher education (HE). However, Africa’s ability to embrace the ‘New Normal’ has been marred by inadequate ICT infrastructures, low connectivity, unreliable power supply, and national budget constraints that may undermine Africa’s HEIs’ potential to augment knowledge production and innovation.",book:{id:"10911",title:"Higher Education - New Approaches to Accreditation, Digitalization, and Globalization in the Age of Covid",coverURL:"https://cdn.intechopen.com/books/images_new/10911.jpg"},signatures:"Christopher B. Mugimu"}],onlineFirstChaptersTotal:17},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. 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Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. 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He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. 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She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:36,paginationItems:[{id:"82195",title:"Endoplasmic Reticulum: A Hub in Lipid Homeostasis",doi:"10.5772/intechopen.105450",signatures:"Raúl Ventura and María Isabel Hernández-Alvarez",slug:"endoplasmic-reticulum-a-hub-in-lipid-homeostasis",totalDownloads:3,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Updates on Endoplasmic Reticulum",coverURL:"https://cdn.intechopen.com/books/images_new/11674.jpg",subseries:{id:"14",title:"Cell and Molecular Biology"}}},{id:"82409",title:"Purinergic Signaling in Covid-19 Disease",doi:"10.5772/intechopen.105008",signatures:"Hailian Shen",slug:"purinergic-signaling-in-covid-19-disease",totalDownloads:4,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Purinergic System",coverURL:"https://cdn.intechopen.com/books/images_new/10801.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"82374",title:"The Potential of the Purinergic System as a Therapeutic Target of Natural Compounds in Cutaneous Melanoma",doi:"10.5772/intechopen.105457",signatures:"Gilnei Bruno da Silva, Daiane Manica, Marcelo Moreno and Margarete Dulce Bagatini",slug:"the-potential-of-the-purinergic-system-as-a-therapeutic-target-of-natural-compounds-in-cutaneous-mel",totalDownloads:10,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Purinergic System",coverURL:"https://cdn.intechopen.com/books/images_new/10801.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"82103",title:"The Role of Endoplasmic Reticulum Stress and Its Regulation in the Progression of Neurological and Infectious Diseases",doi:"10.5772/intechopen.105543",signatures:"Mary Dover, Michael Kishek, Miranda Eddins, Naneeta Desar, Ketema Paul and Milan Fiala",slug:"the-role-of-endoplasmic-reticulum-stress-and-its-regulation-in-the-progression-of-neurological-and-i",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Updates on Endoplasmic Reticulum",coverURL:"https://cdn.intechopen.com/books/images_new/11674.jpg",subseries:{id:"14",title:"Cell and Molecular Biology"}}}]},overviewPagePublishedBooks:{paginationCount:32,paginationItems:[{type:"book",id:"7006",title:"Biochemistry and Health Benefits of Fatty Acids",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7006.jpg",slug:"biochemistry-and-health-benefits-of-fatty-acids",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Viduranga Waisundara",hash:"c93a00abd68b5eba67e5e719f67fd20b",volumeInSeries:1,fullTitle:"Biochemistry and Health Benefits of Fatty Acids",editors:[{id:"194281",title:"Dr.",name:"Viduranga Y.",middleName:null,surname:"Waisundara",slug:"viduranga-y.-waisundara",fullName:"Viduranga Y. Waisundara",profilePictureURL:"https://mts.intechopen.com/storage/users/194281/images/system/194281.jpg",biography:"Dr. Viduranga Waisundara obtained her Ph.D. in Food Science\nand Technology from the Department of Chemistry, National\nUniversity of Singapore, in 2010. She was a lecturer at Temasek Polytechnic, Singapore from July 2009 to March 2013.\nShe relocated to her motherland of Sri Lanka and spearheaded the Functional Food Product Development Project at the\nNational Institute of Fundamental Studies from April 2013 to\nOctober 2016. She was a senior lecturer on a temporary basis at the Department of\nFood Technology, Faculty of Technology, Rajarata University of Sri Lanka. She is\ncurrently Deputy Principal of the Australian College of Business and Technology –\nKandy Campus, Sri Lanka. She is also the Global Harmonization Initiative (GHI)",institutionString:"Australian College of Business & Technology",institution:null}]},{type:"book",id:"6820",title:"Keratin",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/6820.jpg",slug:"keratin",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Miroslav Blumenberg",hash:"6def75cd4b6b5324a02b6dc0359896d0",volumeInSeries:2,fullTitle:"Keratin",editors:[{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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She has more than fifteen years of teaching and research experience. She has published more than 550 scientific publications/communications, including 15 books, 50 book chapters, 100 original research papers, 380 research communications in national and international conferences, and 12 patents. She is a member of the editorial board of five journals and acts as a reviewer for several national and international journals. Her research interests include microalgal biotechnology with an emphasis on microalgae-based products.",institutionString:"Universidade Federal de Santa Maria",institution:{name:"Universidade Federal de Santa Maria",institutionURL:null,country:{name:"Brazil"}}}]},{type:"book",id:"7953",title:"Bioluminescence",subtitle:"Analytical Applications and Basic Biology",coverURL:"https://cdn.intechopen.com/books/images_new/7953.jpg",slug:"bioluminescence-analytical-applications-and-basic-biology",publishedDate:"September 25th 2019",editedByType:"Edited by",bookSignature:"Hirobumi Suzuki",hash:"3a8efa00b71abea11bf01973dc589979",volumeInSeries:4,fullTitle:"Bioluminescence - Analytical Applications and Basic Biology",editors:[{id:"185746",title:"Dr.",name:"Hirobumi",middleName:null,surname:"Suzuki",slug:"hirobumi-suzuki",fullName:"Hirobumi Suzuki",profilePictureURL:"https://mts.intechopen.com/storage/users/185746/images/system/185746.png",biography:"Dr. Hirobumi Suzuki received his Ph.D. in 1997 from Tokyo Metropolitan University, Japan, where he studied firefly phylogeny and the evolution of mating systems. He is especially interested in the genetic differentiation pattern and speciation process that correlate to the flashing pattern and mating behavior of some fireflies in Japan. He then worked for Olympus Corporation, a Japanese manufacturer of optics and imaging products, where he was involved in the development of luminescence technology and produced a bioluminescence microscope that is currently being used for gene expression analysis in chronobiology, neurobiology, and developmental biology. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. 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Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"2",type:"subseries",title:"Prosthodontics and Implant Dentistry",keywords:"Osseointegration, Hard tissue, Peri-implant soft tissue, Restorative materials, Prosthesis design, Prosthesis, Patient satisfaction, Rehabilitation",scope:"