\r\n\tWith a history of over 50 years since their introduction into therapy and formulation of medicinal products, hydrogels remain a challenge for researchers in the field. \r\n\tVersatile, with high-water content, tunable properties, and mild processing conditions, hydrogels advanced from simple chemically or physically crosslinked networks to complex double network composites or even more sophisticated new developments as shape memory and self-healing hydrogels. \r\n\tIncreasing knowledge in hybrid or composite hydrogel materials, controlled release of sensitive drugs, or several drugs from the same hydrogel matrix could be achieved. Parallel to targeted efforts aimed to maintain drug micro- or nanoparticle’s distinct three-dimensional structure, synergistic hybrid materials with more than one type of polymer was developed.
\r\n
\r\n\tBut one of the most challenging tasks remains further and continues to improve the clinical translation of these innovative hydrogels. That is what this book intends to provide the reader: a comprehensive overview of the current state-of-the-art, recent advances, new perspectives, and applications of the hydrogels as valuable platforms for targeted delivery. Driven by the need to ensure proper patient compliance, ease of administration, along with the possibility to modulate release and degradation profiles after administration, numerous non-topical hydrogel formulations had been reported. Smart and supramolecular hydrogels, stimuli-reactive materials, that quickly respond in mild conditions, represent today an attractive approach for minimally invasive treatments.
\r\n
\r\n\tThe book will also represent an invitation to discover “new” off-the-shelf hydrogels with highly tunable properties, with low complexity of formulation (environmentally friendly processing), but with adequate features to fulfill clinical requirements and provide desired delivery platforms for therapy.
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Professor POPA LĂCRĂMIOARA (h-index 14) received her Ph.D. in Pharmacy (2000). She has over 28 years of experience in physical pharmacy: Quality by Design (QbD) in the development, analysis, and optimization of pharmaceutical systems; characterization of the raw materials surfaces and pharmaceutical systems; polymeric materials with biomedical applications.",coeditorOneBiosketch:"A pioneering researcher in Taguchi's approach for the development and optimization of biomaterial-based drug delivery systems. Professor Mihaela Violeta Ghica (h-index 16) has over 19 years ‘experience in physical pharmacy: modern methods of experimental statistical design in the development of drug delivery systems and technological processes optimization; biomaterials for tissue regeneration: obtaining, physical-chemical, biopharmaceutical, structural and morphological characterization.",coeditorTwoBiosketch:"A pioneering researcher in nanostructures formulation in pharmaceutical sciences. Professor Cristina-Elena Dinu-Pîrvu (h-index 15) is a member of the Romanian National Council for the Attestation of University Titles, Diplomas, and Certificates–Commission Pharmacy. She has over 28 years of experience in physical pharmacy: development of smart, efficient, and safe biocompatible materials; development of bio- and nanostructures for diagnostic and therapy.",coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"228211",title:"Prof.",name:"Lacramioara",middleName:null,surname:"Popa",slug:"lacramioara-popa",fullName:"Lacramioara Popa",profilePictureURL:"https://mts.intechopen.com/storage/users/228211/images/system/228211.jpeg",biography:"Professor POPA LĂCRĂMIOARA received her PhD in Pharmacy (2000). She is head of Physical and Colloidal Chemistry Department, Faculty of Pharmacy, 'Carol Davila” University of Medicine and Pharmacy from Bucharest, Romania and President of the Ethics and Quality Assurance Commission from the same faculty. She is PhD supervisor (Habilitation thesis, 2014). Professor Popa Lăcrămioara graduated several courses in Intellectual Property (WIPO Academy, Geneva). She has over 28 years’ experience in physical pharmacy: Quality by Design (QbD) in the development, analysis, and optimization of pharmaceutical systems; characterization of the raw materials surfaces and pharmaceutical systems; polymeric materials with biomedical applications. 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She holds a Master’s degree in Physical Chemistry and Applied Radiochemistry at the Faculty of Chemistry from University of Bucharest.\nShe has over 17 years of experience in: design, development, physico-chemical and biopharmaceutical evaluation of drug delivery systems with conventional/modified/ controlled/targeted release for topical application; use of statistic experimental design techniques combined with response surface methodology and Taguchi approach for the optimization of the pharmaceutical systems formulation and some technological processes; valorisation of the biopharmacological potential of the medicinal and aromatic plants. Professor Mihaela Violeta Ghica was project director and partner responsible for some national and international projects. 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Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"8353",title:"Hydrogels",subtitle:"Smart Materials for Biomedical Applications",isOpenForSubmission:!1,hash:"4f87a0823e286477e58c1da8b6cd174c",slug:"hydrogels-smart-materials-for-biomedical-applications",bookSignature:"Lăcrămioara Popa, Mihaela Violeta Ghica and Cristina-Elena Dinu-Pîrvu",coverURL:"https://cdn.intechopen.com/books/images_new/8353.jpg",editedByType:"Edited by",editors:[{id:"228211",title:"Prof.",name:"Lacramioara",surname:"Popa",slug:"lacramioara-popa",fullName:"Lacramioara Popa"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6320",title:"Advances in Glass Science and Technology",subtitle:null,isOpenForSubmission:!1,hash:"6d0a32a0cf9806bccd04101a8b6e1b95",slug:"advances-in-glass-science-and-technology",bookSignature:"Vincenzo M. 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\n
1. Introduction
\n
The quality of a product is the result of production process. The desired properties of the product should be revealed at the design stage. Towards the end of the 1950s, Dr. Genichi Taguchi put forward many concepts and methods to improve quality which based on robust design.
\n
Robust Design (RD) means the design of a product that causes no problem under any case. RD signifies designing of a product which can work properly under different circumstances [1].
\n
One of the important developments of the manufacturing industry is related to the application of modern off-line quality control techniques in product or process engineering. Many of these quality techniques were shaped by W. E. Deming. Taguchi built his philosophy on them. Deming’s main success has been to convince businesses that the production process should be controlled statistically in quality improvement. Taguchi went a little further back and said that quality will be achieved at the design stage before production. Taguchi’s main purpose is to reduce the variability around the target value of product properties. To achieve this, the controllable factors that cause this variability must be identified and the product and production process must be designed according to these factors. Taguchi’s strategy is a systematic application of Experimental Design (DOE) and analysis in order to improve or design product and process quality. This strategy includes experimental minimization of an expected loss function to determine the best product design (or process design) [2].
\n
Taguchi observed that the most important reason for a product to be rejected is variability in product specifications. Improving quality is through reducing variability. Efforts for quality should be made for zero deviation and zero distortion. All quality experts, especially Shewart and Deming, have addressed the issue of variability. Taguchi in one of his articles [3] -by using the Figure 1 which has given under the title “Who is the Better Marksman?”- indicated that it is a difficult problem to eliminate variability in the production process. In this example, both gunners fire ten shots. If the average position of Gunner’s A is calculated, it will be seen that the average is very close to the target. On the other hand, marksman B’s average is far from the target. However, his shots are very consistent. When the variability is calculated for both marksmen, it will be seen that the variability of the gunner B is much less. Those who are interested in shooting can easily say that while it is possible to correct B’s shots with a small adjustment, it will take a lot of effort to make A a good shooter. Taguchi argues that production processes are also similar to shooters in this respect. While it is possible to easily adjust the B sniper-like processes, improving the A sniper-like processes will take a lot of time, maybe even huge investments.
\n
Figure 1.
Who is better gunner? (Adapted from Ref. [3]).
\n
Taguchi proposes a two-step process to reduce product variability. These steps are as follows.
To produce the product with the best methods, technology and techniques
To produce all products in the same way
\n\n
In order to fulfill above issues, Taguchi divides the activities into two parts as On-Line Quality Control and Off-Line Quality Control. While on-line quality control covers the quality activities during and after the manufacture of the product, off-line quality control includes market research and quality activities carried out during the development of the product and production process. These activities are design studies carried out before production begins. Taguchi defines three stages such as system design, parameter design, and tolerance design both for product and process improvement.
\n
The most important stage of product or process design in terms of quality improvement is the parameter design stage. At this stage, DOE method is used to determine the factors affecting product performance and their effects on performance. The aim is to minimize the effect of effective factors on the product [4].
\n
\n
\n
2. Literature review
\n
RD is an important technique for product manufacturability and product life. Although the method was known by 1960’s in Japan it has been used in USA by 1980’s. Since its use in the USA industry in the 1980s, it has attracted a great attention from designers, manufacturers, statisticians and quality experts. Due to this success of robust design, a lot of researches such as master and PhD theses, scientific articles and case studies have been done to understand the method. Literature of Taguchi Method (TM) and RD is very large and it is still growing. When the literature is examined, it will be seen that Tagcuhi method is frequently used for the optimization of critical parameters of product and process in manufacturing industry and it gives useful results. Table 1 presents some examples from last ten years publications about the manufacturing industry. It is important to note that TM has been applied to the service industry too. Antony [5] reports the potential applications of DOE in the service environment as follows.
Identifying the key variables which influence the performance
Identifying the service design parameters
Minimizing the time to respond to customer complaints
Minimizing errors on service orders
Reducing the service delivery time to customers
Providing a better understanding of cause–effect relationships between what we do and what we want to achieve
Reducing cost of quality due to rework and misinformation that lead to bad decision-making
The practical use of TM in the optimization of processing parameters for injection molding was reviewed. Also, integration of TM with various approaches including numerical simulation, GRA, PCA, ANN, and genetic algorithm (GA) were discussed.
The factors which were effective in Thermoelectric Generators (TEG) used in the production of electrical energy a research is carried out by using TM to determine the performance effects.
The effects of eight parameters on the value of borehole thermal resistance and internal thermal resistance are investigated. TM is carried out to obtain the optimal scenarios of parameters combination.
A clustering approach based on TM for effective market segmentation is proposed. To select appropriate initial seeds, the use of TM as a tool is suggested.
The objective of the article is to optimize and design nano-biosystem of Isradipine via novel bioenhancer (Rutin) loaded solid-lipid nanobioparticles using Taguchi design methodology.
Taguchi design method for obtaining lower surface roughness values in terms of process parameters in wood machining is presented. Orthogonal arrays of Taguchi and the signal-to-noise (S/N) ratio is employed to find the optimal levels and to analyze the effect of process parameters on surface roughness.
TM was applied to determine the effects of production factors such as adhesive ratio, press pressure, and pressing time on the thermal conductivity of oriented strand board.
Taguchi’s DOE is used to investigate the main effects of four processing parameters in the Fused Deposition Modeling (FDM) process; those are the infill percentage, infill pattern, layer thickness, and extrusion temperature.
TM of robust optimization has been adapted along with DOE methodology and ANOVA to reduce the variability in the Ride comfort of a vehicle with respect to sprung mass of vehicle.
The optimization of the cutting parameters on drill bit temperature in drilling was evaluated by TM. TM was used to determining the settings of cutting parameters.
\n
\n\n
Table 1.
Some articles from the literature of the last ten years.
\n
Recently publishings deal with the integration of TM and other approaches such as multicriteria decision making (MCDM), principal component analysis, numerical simulation, artificial neural network, and genetic algorithm. Sharma et al. [6] used the TM and PROMETHEE (which widely used MCDM tool) technique to obtain an optimal setting of process parameters for single and multi-optimization resulting in an optimal value of the material removal rate and tool wear rate. Kumar and Mondal [7] compared the results of experimental data on the electric discharge machining of AISI M2 steel by different optimization techniques such as TM, TOPSIS and gray relational analysis (GRA). Viswanathan et al. [8] aimed to investigate the effective factors in turning of magnesium alloy with physical vapor deposition coated carbide insert in dry conditions. To identify the optimal parameters setting, a combination of principal component analysis (PCA) and GRA has been conducted. Liu et al. [9] and, Land and Yeh [10] used both TM and ANSYS which widely used numerical simulation software in order to optimize and design injection molded products. Asafa et al. [11] presented integration of TM and artificial neural network (ANN) technique for the prediction of intrinsic stresses induced during plasma enhanced chemical vapor deposition of hydrogenated amorphous silicon thin films. Parinam et al. [12] described integration of TM and Genetic Algorithms to optimize high transmission optical filter.
\n
\n
\n
3. Robust design
\n
Phadke defines the RD as an engineering methodology for improving productivity during research and development. Hence high-quality products can be produced quickly and at low cost [25]. The emphasis of RD is variability in product and process performance. Reducing variability will result in increased quality. The source of variability can be divided into two groups [26].
Controllable factors: Factors determined by the manufacturer that cannot be changed directly by the customer,
Uncontrollable factors (Noise factors): Factors that the producer cannot directly control and that vary according to customer use and environmental conditions.
\n\n
Uncontrollable factors can be divided into three categories.
External noise factors: factors such as environmental conditions, eg; environmental temperature, workers, different raw material piles etc.
Intrinsic noise factors: time-varying factors, eg; deterioration, aging, discoloration, etc.
Product-related factors: the difference in each product
\n\n
Hence, RD means a design that has minimum sensitivity to variabilty of uncontrollable factors. Taguchi says that it is necessary to minimize the variability in the product or process by choosing the values of the controllable factors (parameters) optimally against the factors that create variability. The word robust in the statement of RD refers to uncontrollable factors which insensitive to environmental conditions such as moisture, dust, heat, different applications in customer use and differences in materials [27, 28]. The key to Taguchi Robust Design; instead of trying to control factors that cannot be controlled or that are too expensive to control, it is to determine the best values of controllable factors that will minimize their effects on the product or process [27]. RD provides answers to the following questions [29].
How to reduce variability when the product is in customer use? How does a product consistently perform at the desired property and thus maximize customer satisfaction?
How is the production process optimized?
\n\n
As will be known, there are many factors that need to be determined and optimally adjusted in product and process parameter design stages. Moreover, many of these factors interact with each other. The most effective method to determine the effects of these controllable and uncontrollable factors on product and product performance is statistical experiment design. Through experimental design, it is possible to economically determine the effect of many factors on the product and to take precautions against factors that cause variability at the design stage. Therefore, we can say that the most important quality assurance method in Taguchi’s off-line quality control system is DOE [30].
\n
RD covers the parameter design and tolerance design steps of TM. System design consists of traditional research and development activities [31].
\n
In order to realize RD, it is necessary to follow a systematic path. Implementation of the below steps are beneficial [26, 32, 33].
Determining the problem and organizing the experiment team
Determination of performance characteristics and measurement system
Determining the variables affecting performance characteristics
Establishing the monitoring design
Identifying controllable and uncontrollable variables and their levels
Identification of possible interactions
Selection of suitable orthogonal array and assignment of variables to relevant columns
Determination of loss function and performance statistics
Establishing the experiment and recording the results
Analysis of data and selection of optimum value of controllable variables
\n\n
\n
3.1 Determining the problem and organizing the experiment team
\n
When a new product is to be developed, there is no need for any examination for the work to be done. If an existing product is to be developed, “why was this product chosen?” The question must be answered. Generally as an answer to this question; scrap, rework, warranty and service costs can be given. After the problem is determined, the team that will do the task should be formed. The team generally; It consists of experts of the problem of interest, DOE experts, senior management representative and people who will conduct the experiment. The other steps we try to explain below are carried out by this team.
\n
\n
\n
3.2 Determination of performance characteristics and measurement system
\n
The product may have one or more performance characteristics, so the selection of performance characteristics is important. The important point here is that the customer’s view should not go unnoticed. Performance characteristics are the basis of the study. Determining the measuring system is the second step in this phase. Each of the performance characteristics may require different measuring systems.
\n
\n
\n
3.3 Determination of variables affecting performance characteristics
\n
Independent variables that affect the product performance characteristics should be determined. Previous experience and expertise are very important in this determination. Brainstorming, cause-effect diagrams and flowcharts are important tools to be used. Easily controllable independent variables are put in the group of control variables (CV) and the others into the group of uncontrollable variables (UCV).
\n
\n
\n
3.4 Establishing the screening design
\n
If the number of CV is large, it may not be possible to carry out the experiment in terms of time and cost. In such a case, there may be some variables that are believed to have no effect at the outset. Of course, making such a choice is difficult. Even after some variables are discarded, there is still the question of whether other variables are important. Screening design allows to get more realistic results with predetermined variables. In the sifter design, the level number is kept as low as possible, usually taken as two. The outputs are analyzed and junk CV is discarded. Significant CV is included in the main experimental group.
\n
\n
\n
3.5 Determining the number and levels of CV and UCV
\n
The number of levels of variables is determined by their characteristics. Thus, possible alternatives are obtained. Taguchi recommends selecting three or more test groups for each CV. Three or more test levels allow a nonlinear effect of CV on the performance characteristic to be revealed. Test levels should be chosen over a wide range so that the CV sequence covers a large region of the CV space. The next step is to determine the set of UCV. This cluster includes the values of the UCV that affect the performance variability the most or the product performance is insensitive. Due to physical impossibilities or lack of information, not all UCV can be included in the experiment. Therefore, it is important to represent all possible combinations of UCV in the experiment [34].
\n
\n
\n
3.6 Identifying possible interactions
\n
The definition of interaction can be as follows: If the effect of a factor on the response variable depends on the value of the other factor, it is said that there is an interaction between two factors as seen in Figure 2 [30]. The interactions can have a significant impact on performance characteristics. Taguchi thinks that interaction is not that important. The reason of this; the view is that in order to detect the interaction, the experimenter has to control the two main effects, and the interaction does not contribute anything when one or more of the main factors are under control [33]. Taguchi and Wu [35] suggest that one of the following techniques should be applied to reduce the interaction effects.
Determining the performance characteristics by weight,
Determining the relationship between CV and its levels and making an adjustment accordingly,
Conducting an analysis for classified data, such as cumulative analysis.
\n\n
Figure 2.
Graphical representation of interaction between two factors. (a) No interaction, (b) Weak interaction, (c) Strong interaction.
\n
However, the experimenter must have the necessary attention and knowledge. It is difficult to add all interaction factors to the experiment due to the high cost and time required. On the other hand, including interaction factors believed to be important in the experiment will increase success. The existence of interaction between two factors can be determined by graphical procedure.
\n
\n
\n
3.7 Choosing appropriate orthogonal arrays
\n
Orthogonal Arrays (OA) take us all the way to Euler’s Greco-Latin squares. But in Euler’s time they were not known as OA. At that time they were known as mathematical games, like 36 office workers’ problems. OA is a matrix of numbers arranged in rows and columns. Orthogonal arrays have a balanced property which entails that every factor setting occurs the same number of times for every setting of all other factors considered in the experiment. In an OA, each row represents the levels of the selected factors in a given experiment, and each column represents a specific factor whose effects on the process performance or product quality characteristic can be studied.
\n
The idea of using OA in DOE independently of each other is originated in the USA and Japan after World War II [36]. The first use of OA was in the 1930s by Fisher in England. Taguchi added three OAs in 1956. And in the following years, three OAs were added by the American NIST [31]. Taguchi makes use of OA in performing multivariate experiments with a small number of trials. Using OA significantly reduces the size of the experiment to be studied [37]. The use of OA is not exclusive to Taguchi. However, Taguchi simplified their usage. Taguchi developed tabulated standard OA and corresponding linear graphs. A typical OA table is shown in Table 2.
\n
\n
\n
\n
\n
\n
\n\n
\n
\n
A
\n
B
\n
C
\n
D
\n
\n\n\n
\n
1
\n
1
\n
1
\n
1
\n
1
\n
\n
\n
2
\n
1
\n
2
\n
2
\n
2
\n
\n
\n
3
\n
1
\n
3
\n
3
\n
3
\n
\n
\n
4
\n
2
\n
1
\n
2
\n
3
\n
\n
\n
5
\n
2
\n
2
\n
3
\n
1
\n
\n
\n
6
\n
2
\n
3
\n
1
\n
2
\n
\n
\n
7
\n
3
\n
1
\n
3
\n
2
\n
\n
\n
8
\n
3
\n
2
\n
1
\n
3
\n
\n
\n
9
\n
3
\n
3
\n
2
\n
1
\n
\n\n
Table 2.
L9 orthogonal Array.
\n
In this array the columns are bilateral orthogonal. In each column there are all combinations of factor levels with an equal number. There are 4 factors (A, B, C, D) and three levels of each. This design is called the L9 design. The letter L indicates the orthogonal array, and 9 the row number, in other words the number of trials [4].
\n
One point we should pay attention to that how much the OA reduces the number of trials. Due to the full factorial design (2k or 3k), OA significantly reduces the number of attempts to be made in large numbers. For our example, 34 = 81 trials are required, but only 9 trials will be done to achieve the same results. It is obvious that it will provide more convenience in larger series. Table 3 highlights the convenience that OA provides in terms of the number of trials [37].
\n
\n
\n
\n
\n\n
\n
OA
\n
# of Factors and levels
\n
Full factorial design trial number
\n
\n\n\n
\n
L4
\n
3 factors 2 levels
\n
8
\n
\n
\n
L8
\n
7 factors 2 levels
\n
128
\n
\n
\n
L9
\n
4 factors 3 levels
\n
81
\n
\n
\n
L16
\n
15 factors 2 levels
\n
32,768
\n
\n
\n
L27
\n
13 factors 3 levels
\n
1,594,323
\n
\n
\n
L64
\n
21 factors 4 levels
\n
4.4*1012
\n
\n\n
Table 3.
Frequently used OAs and full factorial design comparison.
\n
OA allows working economically and simultaneously with many variables that are effective in product mean and variance. Two different OAs can be selected for CV and UCV. Using statistical DOE techniques, suitable subsets for CV and CIA can be demonstrated. Taguchi suggests using OA in planning DOE optimization. The multiplicity of CV and the emergence of interaction require very careful attention in the selection of OA and assignment of CV to columns. Target in establishing CV matrix; It should be to setup a design where the most information can be obtained with the least effort. Table 4 presents a brief knowledge about the OAs.
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
OA
\n
# of Row
\n
# of Maximum factor
\n
# of Maximum column
\n
\n
\n
2 Levels
\n
3 Levels
\n
4 Levels
\n
5 Levels
\n
\n\n\n
\n
L4
\n
4
\n
3
\n
3
\n
—
\n
—
\n
—
\n
\n
\n
L8
\n
8
\n
7
\n
7
\n
—
\n
—
\n
—
\n
\n
\n
L9
\n
9
\n
4
\n
—
\n
4
\n
—
\n
—
\n
\n
\n
L12
\n
12
\n
11
\n
11
\n
—
\n
—
\n
—
\n
\n
\n
L16
\n
16
\n
15
\n
15
\n
—
\n
—
\n
—
\n
\n
\n
L16’\n
\n
16
\n
5
\n
—
\n
—
\n
5
\n
—
\n
\n
\n
L18
\n
18
\n
8
\n
1
\n
7
\n
—
\n
—
\n
\n
\n
L25
\n
25
\n
6
\n
—
\n
—
\n
—
\n
6
\n
\n
\n
L27
\n
27
\n
13
\n
—
\n
13
\n
—
\n
—
\n
\n
\n
L32
\n
32
\n
31
\n
31
\n
—
\n
—
\n
—
\n
\n
\n
L32’\n
\n
32
\n
10
\n
1
\n
—
\n
9
\n
—
\n
\n
\n
L36
\n
36
\n
23
\n
11
\n
12
\n
—
\n
—
\n
\n
\n
L36’\n
\n
36
\n
16
\n
3
\n
13
\n
—
\n
—
\n
\n
\n
....
\n
....
\n
...
\n
....
\n
....
\n
.....
\n
....
\n
\n\n
Table 4.
OA information table.
\n
Depending on the levels of CV, an appropriate OA is chosen or some changes are made on the selected OA. The assignment of the CV and interaction variables to the columns is achieved by using standard linear graphs suitable for the selected OA. To determine a suitable OA for the experiment, the following procedure should be followed.
Determination of the number of factors and their levels
Determining the degree of freedom
Selection of OA
Consideration of interaction
\n\n
\n
\n
3.8 Determination performance statistics
\n
Defining the optimal CV requires the determination of some criteria to be optimized such as Signal / Noise (\n\n\nS\nN\n\n)\n\n\nratio. The analysis of the data obtained from the experiment is made according to performance statistics and / or mean. Wrong selection of performance characteristics leads to erroneous determination of UCV levels and results. The \n\n\nS\n\nN\n\n\n\n\nratio is used to measure the best RD performance. Many different \n\n\nS\n\nN\n\n\n\n\n ratios can be used depending on the purpose of the optimization process. Taguchi mentions that over than 60 \n\n\nS\n\nN\n\n\n\n\n\nratios can be used and that he developed most of them himself [2]. However, all \n\n\nS\n\nN\n\n\n\n\n\nratios must meet the criteria listed below [28].
The \n\n\nS\n\nN\n\n\n\n\n\nratio should reflect the variability of the UCV on the response variable.
The \n\n\nS\n\nN\n\n\n\n\n\nratio is independent of setting the mean. This means; the measuring system should be useful in predicting the quality even if the target value changes.
\n\n\n\nS\n\nN\n\n\n\n\n\nratio measures relative quality. Because it is used for comparative purposes.
\n\n\n\nS\n\nN\n\n\n\n\n\nratio should not cause unnecessary complexity.
\n\n
Many S / N ratios are available. The three commonly used are as below.
Largest - Best
Smallest - Best
Nominal - Best
\n\n
\n
\n
3.9 Establishing the experiment and recording the results
\n
The design optimization experiment can be done in two ways.
Physical performance of the experiment,
Computer simulation.
\n\n
In both experiments, any combination of CV is tested for all combinations of UCV and the results are recorded. The order in which the experiments are performed should be random, as the process will not be constantly stationary. In order for the test results to be evaluated completely and precisely, the test conditions must be recorded.
\n
\n
\n
3.10 Analysis of data and selection of the best values of CV
\n
One of the goals of design optimization experiments is to reduce variability. Another goal is to adjust the mean to the target value. To achieve these two objectives, mean and performance statistics are calculated for each combination of CV in the design model. In order to evaluate the effects of CV on performance statistics and / or mean, Analysis of Variance (ANOVA) is made and percentage contributions are determined. Thus CV can be divided into three classes.
CV, which has a significant impact on performance statistics,
Setting variables that have a significant effect on average but have no effect on performance statistics,
Residual variables that do not affect the average or performance statistics at all.
\n\n
Analysis results are plotted according to the levels of CV, so that the effects are displayed visually. The optimization procedure is different. If the performance statistics are Nominal - Best, TM uses the following two-step procedure.
Investigation of CVs and levels for which the analyst expects the least variability, using calculated performance statistics.
Investigation of the setting variables that will bring the sample mean to the target using the calculated sample mean or sample total.
\n\n
With this method, the variability is reduced in the first step and the sensitivity increases in the second step. If the performance statistics are the smallest best, the TM uses a one-step procedure. This procedure aims to reduce the total variance using the calculated performance statistics; CV affecting the total variance is investigated. Levels of CV where the analyst expects the smallest mean square variability are determined. If the performance statistics are the greatest best, the TM uses a two-sided transformation. Performance statistics change from smallest to best, using the one-step method to reduce the total variance. In case of disagreement between different performance characteristics, one may be abandoned and then the best values selected. If the chosen CV combination is not included in the experiment, the performance values and confidence intervals of the best combination are estimated.
\n
\n
3.10.1 Analysis of variance (ANOVA)
\n
As we mentioned earlier, DOE is used to develop or improve products or processes. The data obtained from the experiment should be analyzed. Variance analysis is used to interpret experimental data. Variance analysis was used for the first time by the British statistician Fisher. Experts usually work with samples. Because it is sometimes impossible to work with the whole population and sometimes it is very expensive. It should not be forgotten that; each individual case study forms part of the error. Sample statistics and assumptions allow the testing of hypotheses regarding experimental parameters. In order to analyze variance with sample data, we have four basic assumptions.
Samples are random,
Population is distributed normally,
Population variances are equal,
The choice of samples is independent of the others.
\n\n
Total variance can be divided into two components such as inter-group variability and intra-group variability. The components of the model are tried to be estimated using the least squares method on the sample data. Total squares are used to show piecemeal variability. After calculating the total squares and determining the appropriate degrees of freedom for each component of variability, the hypothesis is tested using the F distribution [38]. A typical ANOVA table is as Table 5.
After the experiment is set up, the ANOVA is completed, and the important factors and/or interactions are determined, some comments have already been made. However, if it will not be too expensive, it will be beneficial for the experimenter to learn the rest of the information. Here, we will talk about determining contribution percentages.
\n
The rate of variability for each important factor and interaction observed in the experiment is reflected by the percentage of contribution. Percentage contribution is a function of the sum of squares of each significant factor. Percentage of contribution indicates the strength of factors and/or interaction in reducing variability. If the factor and/or interaction levels are fully controllable, the total variability can be reduced by the percentage of contribution. We know that the variance for a factor or interaction includes error variance. So we can arrange the variance for each factor to show the error variance as well.
\n
The percentage contribution of the error provides an estimate of the adequacy of the experiment. If the error contribution percentage is 15% or less, it is assumed that no significant factor has been overlooked in the experiment. If the error contribution percentage is 50% or more, it is considered that the experimental conditions in which some important factors are ignored cannot be fully controlled or the measurement error is made [39].
\n
In order to learn percantage contribution of the factors Pareto ANOVA can be used. Pareto ANOVA is a simplified ANOVA technique based on the Pareto principle. The Pareto ANOVA technique is a quick and easy method to analyze results of the parameter design and it does not need F-test. Pareto ANOVA does not use an F-test, but it identifies the important parameters and determines the percent contribution of each parameter [40, 21].
\n
\n
\n
3.10.2 \n\n\nS\n\nN\n\n\n\n\n\nratio
\n
Taguchi uses the statistical performance measure known as the \n\n\nS\n\nN\n\n\n\n\n\nratio used in electrical control theory to analyze the results [25]. \n\n\nS\n\nN\n\n\n\n\n\nratio is a performance criterion developed by Taguchi to select the best levels of CV that minimize the impact of UCV [41]. The \n\n\nS\n\nN\n\n\n\n\n\nratio takes into account both mean and variability. In its simplest form, the \n\n\nS\n\nN\n\n\n\n\n\nratio is the ratio of the mean (signal) to standard deviation (noise) [4]. TM uses \n\n\nS\n\nN\n\n\n\n\n ratios for two main purposes. The first pupose is to use the \n\n\nS\n\nN\n\n\n\n\n\nratio in order to identify CVs that reduce variability and the second purpose is to identify CVs that move the mean to target. Different \n\n\nS\n\nN\n\n\n\n\n\nratios can be choosen depending on the goal of experiment. In all cases, the \n\n\nS\n\nN\n\n\n\n\n\nratio should be maximized. Although Taguchi mentions over than 60 \n\n\nS\n\nN\n\n\n\n\n\nratios three of them such as smaller-best, larger-best and nominal-best are used frequently. The formulas of them are follows.
where \n\n\ny\n¯\n\n\n is the mean of observed data, s\n2 is the variance of y, n is the number of observed data, and yi\n is the ith observed data.
\n
\n
\n
\n
\n
4. Experiment
\n
In this section, a summary of Hamzaçebi [21] is given. Hamzaçebi [21] applied the TM to determine the effects of production factors such as adhesive ratio, press pressure, and pressing time on the thermal conductivity (TC) of oriented strand board (OSB). MINITAB 17 statistical software (State College, PA, USA) was used to analyze experiments in the Taguchi design.
\n
\n
4.1 Data
\n
In the article of Ref [21], adhesive ratio, pressing time, and press pressure were considered as controllable factors. Table 6 indicates the process parameters and their levels. As deduced from Table 6, there are 3 factors, which have 3 levels. After the factor definitions, suitable Taguchi orthogonal array was selected as L9. The L9 design sheet and output of each experiment was given in Table 7.
\n
\n
\n
\n
\n
\n\n
\n
Factors
\n
Level 1
\n
Level 2
\n
Level 3
\n
\n\n\n
\n
Adhesive Ratio (%)
\n
3%
\n
4.5%
\n
6%
\n
\n
\n
Pressing Time (minute)
\n
3
\n
5
\n
7
\n
\n
\n
Press Pressure (kg/cm2)
\n
35
\n
40
\n
45
\n
\n\n
Table 6.
The process parameters and their levels.
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Experiment
\n
Factors
\n
Response
\n
\n
\n
Adhesive ratio
\n
Pressing time
\n
Press pressure
\n
\n\n\n\ny\n¯\n\n\n\n
\n
\n\n\ns\n\n\n
\n
\n\n\n
\n
1
\n
1
\n
1
\n
1
\n
0.129
\n
0.010
\n
\n
\n
2
\n
1
\n
2
\n
2
\n
0.153
\n
0.028
\n
\n
\n
3
\n
1
\n
3
\n
3
\n
0.152
\n
0.025
\n
\n
\n
4
\n
2
\n
1
\n
2
\n
0.142
\n
0.023
\n
\n
\n
5
\n
2
\n
2
\n
3
\n
0.143
\n
0.026
\n
\n
\n
6
\n
2
\n
3
\n
1
\n
0.146
\n
0.025
\n
\n
\n
7
\n
3
\n
1
\n
3
\n
0.163
\n
0.027
\n
\n
\n
8
\n
3
\n
2
\n
1
\n
0.154
\n
0.018
\n
\n
\n
9
\n
3
\n
3
\n
2
\n
0.170
\n
0.019
\n
\n\n
Table 7.
The design sheet and output of each experiment.
\n
In Table 7, \n\n\ny\n¯\n\n\n and \n\ns\n\n present the mean and standard deviation of the TC values, respectively.
\n
\n
\n
4.2 Solution and results
\n
Hamzaçebi [21] was used the \n\n\nS\n\nN\n\n\n\n\n\nratio and Pareto ANOVA analysis to evaluate the results of the experiment.
\n
\nFigure 3 is the main effect graph of \n\n\nS\n\nN\n\n\n\n\n\nratios that states the optimal level of the factors. The biggest \n\n\nS\n\nN\n\n\n\n\n\nratio indicated the optimal combination of parameter values. The ranking of the process parameters was obtained from \n\n\nS\n\nN\n\n\n\n\n\nratio table which is given in Table 8. This order was determined by comparison of delta values. The delta value is equal to the difference between maximum and minimum values for levels of each factor. Table 8 shows that the order of importance in minimizing the TC of OSB is adhesive ratio, press pressure, and pressing time. Figure 3 shows the optimal level of the process parameters. As deduced from Figure 3, the second level of adhesive ratio (3%), the first level of pressing time (3 min), and the first level of press pressure (35 kg/cm2) were the optimal values for the minimization of the TC of OSB.
\n
Figure 3.
Main effect plots for \n\n\nS\n\nN\n\n\n\n\n\nratios of process parameters.
\n
\n
\n
\n
\n
\n\n
\n
Level
\n
Adhesive ratio
\n
Pressing time
\n
Press pressure
\n
\n\n\n
\n
1
\n
16.74
\n
16.77
\n
16.86
\n
\n
\n
2
\n
16.74
\n
16.39
\n
16.14
\n
\n
\n
3
\n
15.74
\n
16.07
\n
16.23
\n
\n
\n
Delta
\n
1.00
\n
0.70
\n
0.72
\n
\n
\n
Rank
\n
1
\n
3
\n
2
\n
\n\n
Table 8.
\n\n\n\nS\n\nN\n\n\n\n\n\nratio values of TC.
\n
Hamzaçebi [21] applied the Pareto ANOVA to determine the percent contribution of each parameter on the TC. To obtain the Pareto ANOVA of \n\n\nS\n\nN\n\n\n\n\n\nratio values, the overall mean of \n\n\nS\n\nN\n\n\n\n\n\nratios and the sum of squares due to variation about overall mean were calculated by Eqs. (4) and (5), respectively.
where \n\n\n\nS\n/\nN\n\n¯\n\n\n is the overall mean of \n\n\nS\n\nN\n\n\n\n\n\nratio, \n\n\n\n\nS\n/\nN\n\n\ni\n\n\n is the \n\n\nS\n\nN\n\n\n\n\n\nratio for ith parameter, and m is the number of \n\n\nS\n\nN\n\n\n\n\n\nratios.
where SS\nTotal is the total sum of squares. Secondly, for the ith process parameter, the sum of squares due to variation about overall mean was calculated by Eq. (6).
where SS\ni is the sum of the square for ith parameter, (\n\n\nS\n\nN\n\n\n\n\n)ij is the \n\n\nS\n\nN\n\n\n\n\n\nratio of ith parameter of jth level, and ki\n\n\n\n\nm\ni\n\n\n is the number of levels of ith parameter. Finally, the contribution (Cont) of ith parameter was calculated by Eq. (7). Table 9 presents the contribution results.
\n
\n
\n
\n
\n
\n\n
\n
Process parameter
\n
Sum of squares (SSi)
\n
% Contribution
\n
Rank
\n
\n\n\n
\n
Adhesive ratio
\n
0.6667
\n
54.64
\n
1
\n
\n
\n
Press pressure
\n
0.2456
\n
20.13
\n
3
\n
\n
\n
Pressing time
\n
0.3078
\n
25.23
\n
2
\n
\n
\n
Total
\n
1.2201
\n
100
\n
\n
\n\n
Table 9.
Contribution of process parameters based on Pareto ANOVA.
When \n\n\nS\n\nN\n\n\n\n\n\nratio results are interpreted, as it can be seen by Table 8, the order of importance in minimizing the TC of OSB is adhesive ratio, press pressure, and pressing time. Also, as deduced from Figure 3, the second level of adhesive ratio (3%), the first level of pressing time (3 min), and the first level of press pressure (35 kg/cm2) were the optimal values for the minimization of the TC of OSB. Beside, according to the Pareto ANOVA results, the most effective factor is the adhesive ratio, the second factor is pressing time and the third one is the press pressure. The results show that the adhesive ratio is the most effective factor on the TC of OSB.
\n
On the other hand, for this problem, if the full factorial design was used instead of using the TM, it would be necessary to set up 27 experimental setups. However, 9 experimental setups are sufficient with the TM. 18 more experimental setups are no longer required. Considering that these experiments must be repetitive, the time and cost savings gained will be appreciated. In addition, the \n\n\nS\n\nN\n\n\n\n\n\nratio criterion used to interpret the results facilitates the decision made by the decision maker.
\n
\n
\n
\n
5. Conclusion
\n
The objective of this study is to give a brief knowledge about the TM which is used in both manufacturing and service sectors as an optimization tool for product and process. Literature of the TM applications is very large and it is stil growing. The objective of the TM is to setup a RD, hence reduce the variabilty of performance characteristcs of the product and/or process. The main advantage of the TM is cost reduction in time and budget.
\n
In order to present an example, the summary of Hamzaçebi [21] is given. The theoretical benefits of the TM can be seen as follows from the result of Ref. [21].
TM is a powerful technique to analyze the effects of the process parameters.
Time and cost of experiments can be reduced by using TM. As a result of a selected orthogonal array, 9 experiments were performed instead of 27 experiments, which should be done for full factorial design implementation.
The same results were obtained by both S/N ratio analysis and Pareto ANOVA. Thus, it can be said that the outputs of the analysis is consistent.
\n\n
\n\n',keywords:"Taguchi method, robust design, orthogonal Array, S/N ratio, Pareto ANOVA",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/74437.pdf",chapterXML:"https://mts.intechopen.com/source/xml/74437.xml",downloadPdfUrl:"/chapter/pdf-download/74437",previewPdfUrl:"/chapter/pdf-preview/74437",totalDownloads:509,totalViews:0,totalCrossrefCites:1,totalDimensionsCites:1,totalAltmetricsMentions:0,impactScore:0,impactScorePercentile:48,impactScoreQuartile:2,hasAltmetrics:0,dateSubmitted:"October 21st 2020",dateReviewed:"November 4th 2020",datePrePublished:"December 15th 2020",datePublished:"March 24th 2021",dateFinished:"December 15th 2020",readingETA:"0",abstract:"Taguchi Method is a powerful technique to optimize performance of the products or process. Taguchi’s main purpose is to reduce the variability around the target value of product properties via a systematic application of statistical experimental design which called robust design. Robust Design is an important technique for product manufacturability and product life. Taguchi simplified the usage of orthogonal arrays to setup experimental design. Thanks to this development, researchers and engineers saved both time and money. Furthermore, Taguchi proposed the usage of S/N ratio in order to measure the effects of factors on the performance characteristics. In this study a brief knowledge about the Taguchi Method is given. Orthogonal Arrays and S/N ratios are described. Summary of a case study is given.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/74437",risUrl:"/chapter/ris/74437",book:{id:"9925",slug:"quality-control-intelligent-manufacturing-robust-design-and-charts"},signatures:"Coşkun Hamzaçebi",authors:[{id:"326874",title:"Prof.",name:"Coşkun",middleName:null,surname:"Hamzaçebi",fullName:"Coşkun Hamzaçebi",slug:"coskun-hamzacebi",email:"chcebi@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Literature review",level:"1"},{id:"sec_3",title:"3. Robust design",level:"1"},{id:"sec_3_2",title:"3.1 Determining the problem and organizing the experiment team",level:"2"},{id:"sec_4_2",title:"3.2 Determination of performance characteristics and measurement system",level:"2"},{id:"sec_5_2",title:"3.3 Determination of variables affecting performance characteristics",level:"2"},{id:"sec_6_2",title:"3.4 Establishing the screening design",level:"2"},{id:"sec_7_2",title:"3.5 Determining the number and levels of CV and UCV",level:"2"},{id:"sec_8_2",title:"3.6 Identifying possible interactions",level:"2"},{id:"sec_9_2",title:"3.7 Choosing appropriate orthogonal arrays",level:"2"},{id:"sec_10_2",title:"3.8 Determination performance statistics",level:"2"},{id:"sec_11_2",title:"3.9 Establishing the experiment and recording the results",level:"2"},{id:"sec_12_2",title:"3.10 Analysis of data and selection of the best values of CV",level:"2"},{id:"sec_12_3",title:"Table 5.",level:"3"},{id:"sec_13_3",title:"3.10.2 \n\n\nS\n\nN\n\n\n\n\n\nratio",level:"3"},{id:"sec_16",title:"4. Experiment",level:"1"},{id:"sec_16_2",title:"4.1 Data",level:"2"},{id:"sec_17_2",title:"4.2 Solution and results",level:"2"},{id:"sec_18_2",title:"4.3 Discussion",level:"2"},{id:"sec_20",title:"5. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'\nTaguchiG., Chowdhury S., Wu Y., Taguchi’s Quality Engineering Handbook, John Wiley&Sons, 2005, NJ\n'},{id:"B2",body:'\nPignatiello, J. J., An Overview of The Strategy And Tactics of Taguchi, IIE Transactions, 20, 1988\n'},{id:"B3",body:'\nClausing, D., Taquchi G., Robust Quality, MA: Harvard Business Review, Jenuary-February 1990\n'},{id:"B4",body:'\nUnal, R., Dean, E. B., Taguchi Approach To Design Optimization For Quality And Cost: An Owerview, Annual Conferennce Of The International Society Of Parametric Analysts, 1991\n'},{id:"B5",body:'\nAntony J., Design of Experiments for Engineers and Scientists, Elsevier, 2014, Scotland,UK\n'},{id:"B6",body:'\nSharma V., Misrab J. P., Singhala P., Optimization of process parameters on Combustor Material Using Taguchi & MCDM Method in Electro-Discharge Machining (EDM), Materials Today: Proceedings, 18, 2019\n'},{id:"B7",body:'\nKumar D., Mondal S., Process parameters optimization of AISI M2 steel in EDM using Taguchi based TOPSIS and GRA, Materials Today: Proceedings, 26(2), 2020\n'},{id:"B8",body:'\nViswanathan R.,.Ramesh S. , Maniraj S., Subburam V.,Measurement and multi-response optimization of turning parameters for magnesium alloy using hybrid combination of Taguchi-GRA-PCA, Measurement, 159, 2020\n'},{id:"B9",body:'\nLiu S.J., C.-H. Lin, and Y.-C. Wu, “Minimizing the sinkmarks in injection-molded thermoplastics,” Advances in Polymer Technology, 20(3), 2001\n'},{id:"B10",body:'\nLan T.S., M.-C. Chiu, and L.-J. Yeh, “An approach to rib design of injection molded product using finite element and Taguchi method,” Information Technology Journal, 7(2), 2008.\n'},{id:"B11",body:'\nAsafa T. B., Tabet N., Said S.A.M, Taguchi method–ANN integration for predictive model of intrinsic stress in hydrogenated amorphous silicon film deposited by plasma enhanced chemical vapour deposition, Neurocomputing, 106, April, 2013\n'},{id:"B12",body:'\nParinam S., Kumar M., Kumari N., Karar V., Sharma A.L, An improved optical parameter optimisation approach using Taguchi and genetic algorithm for high transmission optical filter design, Optik, 182, April 2019\n'},{id:"B13",body:'\nSekulic M., Kovac P., Gostimirovic, M., Kramar, D., “Optimization of high-pressure jet assisted turning process by Taguchi method”, Advances in Production Engineering & Management, 8(1), 2013\n'},{id:"B14",body:'\nFei N. C., Mehat N. M., Kamaruddin S., “Practical Applications of Taguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A Retrospective Review”, , ISRN Industrial Engineering, 2013\n'},{id:"B15",body:'\nDave S. V., Bhogayata A., “The strength oriented mix design for geopolymer concrete using Taguchi method and Indian concrete mix design code”, Construction and Building Materials, 262, November, 2020\n'},{id:"B16",body:'\nTerzioğlu H., “Analysis of effect factors on thermoelectric generator using Taguchi method”, Measurement, 149, January 2020\n'},{id:"B17",body:'\nZhou K., Mao J., Li Y, Xiang J., “Parameters optimization of borehole and internal thermal resistance for single U-tube ground heat exchangers using Taguchi method”, Energy Conversion and Management, 201, December 2019\n'},{id:"B18",body:'\nHong Chien-Wen, “Using the Taguchi method for effective market segmentation”, Expert Systems with Applications, 39(5), 2012\n'},{id:"B19",body:'\nKumar V., Kharb R., Chaudhary H., “Optimization & design of isradipine loaded solid lipid nanobioparticles using rutin by Taguchi methodology”, International Journal of Biological Macromolecules, 92, November 2016\n'},{id:"B20",body:'\nTiryaki, S., Hamzaçebi, C., and Malkoçoğlu, A., Evaluation of process parameters for lower surface roughness in wood machining by using Taguchi design methodology, European Journal of Wood and Wood Products, 73, 2015\n'},{id:"B21",body:'\nHamzaçebi C., “Optimization of Process Parameters in Oriented Strand Board Manufacturing by Taguchi Method”, BioResource, 11(3), 2016\n'},{id:"B22",body:'\nAlafaghani A., Qattawi A., Investigating the effect of fused deposition modeling processing parameters using Taguchi design of experiment method, Journal of Manufacturing Processes, 36, December 2018\n'},{id:"B23",body:'\nMitra Anirban C., Jawarkar M, Soni T., Kiranchand G. R., Implementation of Taguchi Method for Robust Suspension Design, Procedia Engineering, 144, 2016\n'},{id:"B24",body:'\nÇakıroğlu R., Acır A., Optimization of cutting parameters on drill bit temperature in drilling by Taguchi method, Measurement, 46(9), 2013\n'},{id:"B25",body:'\nPhadke, S. M., , Introduction To Quality Engineering, Asian Productivity Organizatio, 1989, Dearborn\n'},{id:"B26",body:'\nHamzaçebi C., Kalite Yönetiminde Taguchi Felsefesi, 2000, Gazi Üniversitesi, Ankara, Türkiye\n'},{id:"B27",body:'\nTsui, K. L., An Overview of Taguchi Method And Newly Developed Statistical Methods For Robust Design, IIE Transactions, 24, 1992\n'},{id:"B28",body:'\nConnor, A.M., Parameter Sizing For Fluid Power Circuits Using Taguchi Methods, Journal Of Engineering Design, 10(4),1999\n'},{id:"B29",body:'\nSudhakar, P. R., An Introduction To Quality Improvement Thtough Taguchi Methods, Industrial Engineering, January, 1995,\n'},{id:"B30",body:'\nŞirvancı M., Kalite İçin Deney Tasarımı, Literatür, 1997,İstanbul\n'},{id:"B31",body:'\nBesterfield, D. H., Besterfield,C., Besterfield, G. H., Besterfield, M., Total Quality Management, Prentice Hall Inc., 1995,New Jersey\n'},{id:"B32",body:'\nÇelik, C, Burnak, N., Asystematic Approach To The Solution Of The Design Optimization Problem, Total Quality Management, 9,1998,\n'},{id:"B33",body:'\nRoss, P. J., Taguchi Techniques For Quality Engineering, Mc Graw Hill, 1988, Newyork\n'},{id:"B34",body:'\nKackar, R., “Off-Line Quality Control, Parameter Design And The Taguchi Method”, Journal Of Quality Technology, 17, 1985\n'},{id:"B35",body:'\nTaguchi, G., Wu, Y., , Introduction To Off-Line Quality Control,Central Japan Quality Control Association, 1979, Nagaya\n'},{id:"B36",body:'\nTaguchi, G., System of Experimental Design, ASI, 1991, Dearborn\n'},{id:"B37",body:'\nUnal, R., Dean, E. B., ,Design For Cost And Quality: The Robust Design Approach, Journal Of Parametrics, 11(1), 1991\n'},{id:"B38",body:'\nSower, V. E., Savoıe, M. J., Renick, S., An Introduction to Quality Management And Engineering, Prentice Hall Inc., New Jersey, 1999\n'},{id:"B39",body:'\nKrishnaiah K., Shahabudeen P., Applied Design of Experiments and Taguchi Methods, PHI Learning Private Limited, 2012\n'},{id:"B40",body:'\nVenkateswarlu, G., Davidson, M. J., and Tagore, G. R. N. Influence of process parameters on the cup drawing of aluminum 7075 sheet, International Journal of Engineering Science 2, 2010\n'},{id:"B41",body:'\nBryne, D. M. Ve Taguchi, S., The Taguchi Approach To Parameter Design, ASQC Quality Congress Transactions, Anaheim, 1986\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Coşkun Hamzaçebi",address:"chcebi@gmail.com",affiliation:'
, Karadeniz Technical University, Trabzon, Turkey
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1. Introduction
Loudspeakers play an essential role in spatial sound applications, such as conventional multi-channel sound reproduction, beam steering [1], wave-field reconstruction [2], higher-order ambisonics [3], immersive audio [4], and multi-zone contrast control [5]. Those techniques require many loudspeakers arranged in linear, planar, circular, and spherical arrays [6] to satisfy the spatial sampling theorem at higher frequencies and provide desired directivity, sufficient sound power output, and audio quality. Cost, size, weight, and energy consumption are critical factors limiting the practical application.
Sound-field control techniques can use model-based or data-based methods to calculate the individual driving signals for the loudspeakers. Both approaches prefer an idealized loudspeaker model, usually assuming a linear, time-invariant transfer behavior and omnidirectional radiation while ignoring undesired properties (e.g., distortion) and physical limitations of the loudspeaker.
Loudspeakers are not always omnidirectional, especially at high frequencies. Various theories [7, 8, 9] consider and exploit the loudspeaker directivity in sound-field control. There are exciting opportunities for loudspeaker arrays exploiting a higher-order spherical wave model used in reverberant rooms [10].
Standard characteristics describe the loudspeaker directivity in the far-field [11]. Still, this information is less relevant in applications for home, automotive, or public address systems where either the radiating surface is large (e.g., arrays, flat panel) or the distance to the listener is small. Choi et al. [12] showed that active control could cope with those conditions if the near-field properties of the loudspeaker are considered.
Xiaohui et al. [13] showed that loudspeaker nonlinearities degrade the performance of spatial sound control, as nonlinear distortions limit the acoustic contrast between “bright” and “dark” sound zones. Cobianchi et al. [14] proposed a method for measuring the directivity of the nonlinear distortion in the far-field by using sinusoidal and multi-tone stimuli. Such tests performed in the near and far-field generate a significant test effort and a high amount of data that can be difficult to interpret.
Olsen and Møller [15] showed that typical ambient temperature variations in automotive applications change the loudspeaker properties in ways that compromise the sound zone performance significantly. Production variability, heating of the voice coil, fatigue, and aging of the suspension and other soft parts (cone) can change the loudspeaker properties over time and degrade the performance in a non-adaptive control solution.
This chapter presents models and measurement techniques to assess the loudspeaker transfer behavior from the input to the sound pressure at any point in the sound-field. The objective is to generate comprehensive information for selecting loudspeakers for spatial sound applications, simulating the performance, including room interaction, and maintaining sound quality over product life.
Such measurements are intended to provide meaningful characteristics that describe the sound pressure at a local point, over a listening zone, or in all directions, simplifying loudspeaker diagnostics.
2. General loudspeaker modeling
A single loudspeaker system used in spatial audio applications can be modeled by a multiple-input-multiple-output system (MIMO), as shown in Figure 1.
Figure 1.
Modeling a loudspeaker system with multiple channels in spatial sound applications.
The loudspeaker input signals
ui=fDSPw1w2…wNDSPi=1,…,NuE1
are generated by sound-field control or other DSP algorithms fDSP applied to audio signals wm. The input signal ui can be an analog voltage at the loudspeaker terminals or a digital data stream using other electrical, optical, or wireless transmission means. For each input signal ui, the loudspeaker system uses at least one electro-acoustical transducer (woofer, tweeter, full-band driver) that generates a sound pressure pi(r) at an evaluation point r under free-field condition. In modern loudspeaker systems, the transduction block fSP,i(ui,r) also performs amplification, equalization, active speaker protection against mechanical and thermal overload [16], and adaptive nonlinear control to cancel undesired signal distortion [17]. The total sound pressure output pT(r) is a linear superposition of the contributions pi(r) from all transduction blocks described as
pTr=∑i=1Nupir=∑i=1NufSP,iuirE2
while assuming a negligible coupling between the loudspeaker channels in the electrical, mechanical, or acoustical domain. This assumption is valid for transducers radiating sound independently into the free-field but not for multiple transducers mounted in one enclosure and working on the same air volume.
The function fSP,i(ui, r) describes the nonlinear and time-variant relationship between input ui and output signal pi(r).
The following chapter describes a single loudspeaker channel’s modeling, measurement, and quality assessment while omitting the subscript i in the input voltage u (uj = 0 for j ≠ i) and the sound pressure output p(r).
Figure 2 shows a gray box model representing the nonlinear, time-variant function fSP(u, r) under free-field conditions. At small input signal amplitudes, the linear spatial transfer function HL(f,r) describes the loudspeaker behavior, assuming that other signal distortions are negligible. Still, additional noise n(r) generated by electronics or external sources can corrupt the sound pressure output.
Figure 2.
Gray box model of a single loudspeaker channel describing the relationship between the input signal u and sound pressure output p(r) at an evaluation point r in the free-field.
The time-variant transfer function HV(f,t) represents reversible and nonreversible changes in the loudspeaker properties caused by the stimulus, climate [15], heating [18], aging, fatigue [19], and other external influences. The function HV(f,t) is independent of the evaluation point r because the dominant time-variant processes are in the electrical and mechanical domains. For example, the voice coil resistance [18], the natural frequencies, and loss factors of the modal vibrations [20] affect the sound-field in the same way. Variations of the mode shape, box geometry, and other boundaries can change the loudspeaker directivity but are neglected in the modeling. The HV(f,t) variation can be monitored by endurance, environmental or accelerated-life testing defined in various loudspeaker standards [11, 21].
Nonlinear subsystem NI and ND generate harmonics and intermodulation distortions at higher amplitudes. The first nonlinear system NI in the feedback loop in Figure 2 represents the dominant nonlinearities [22] in the transduction and the mechanical suspension such as force factor, voice coil inductance, and stiffness of a moving coil speaker [23]. A network with lumped parameters models the nonlinear dynamics by generating equivalent input distortion uI added to the input signal u and transferred via the linear transfer path to any point r in the sound-field [11].
The second nonlinear subsystem ND(r) in Figure 2 represents nonlinearities in the cone, diaphragm, surround, horn, port, and other acoustic elements and generates distributed distortion pD(r). The distributed distortion pD(r) depends on the point r and cannot be represented by equivalent input distortion.
The nonlinear distortions uI and pD(r) are considered in loudspeaker design because they affect the maximum output, audio quality, size, cost, and reliability. Finally, the distortions accepted as regular properties give the best performance-cost ratio for the end-user.
Imperfections in the design, manufacturing problems, overload, and other malfunction (“rub&buzz”) generate irregular dynamics perceived as abnormal distortion pID(r) that is partly not deterministic and not predictable.
3. Acoustical loudspeaker measurements
The free model parameters and other signal-dependent characteristics introduced in the gray box model presented in Section 2 can be identified by acoustic measurements.
The sound pressure can be modeled as a superposition of desired and undesired signal components in the time domain as
ptr=pLtr+pVtr+pNtr+pIDtr+ntrE3
and in the frequency domain as a corresponding Fourier spectrum:
Pfr=Fptr=PLfr+PVfr+PNfr+PIDfr+NfrE4
The component pL represents the desired linear output separated from signal distortion components pV, pN, pID, and n corresponding to the time-variant properties, regular loudspeaker nonlinearities, and abnormal distortion generated by irregular vibration and measurement noise, respectively.
New output-based measurement techniques compliant with IEC 60268–21 [11] provide accurate data with sufficient spatial resolution in a non-anechoic environment with minimum test effort (time, equipment).
The following sections will discuss those signal components in greater detail.
3.1 Loudspeaker positioning
The positioning of the loudspeaker in the 3D space is clearly defined by IEC 60268–21 [11] using a spherical coordinate system using the polar angle θ, azimuthal angle ϕ, and distance r. The origin O is placed at a convenient reference point rref, usually on the radiator’s surface, grill, or enclosure, close to the supposed acoustical center. A reference axis nref is orthogonal to the radiator’s surface, and the orientation vector oref usually points upwards in a vertical direction.
3.2 Test environment
To ensure the reproducibility of the test result, it is common practice to measure loudspeakers under free-field conditions using a full-space (4π) or half-space (2π) environment. A half-space anechoic room with a solid ground floor is convenient for moving large and heavy loudspeaker systems and measuring loudspeakers mounted in or placed at a short distance from walls. The IEC standard [11] defines various methods of testing and postprocessing to generate simulated free-field conditions in a non-anechoic environment.
3.3 Far-field measurement
The traditional way to assess the loudspeaker directivity is the measurement of the spatial transfer function HL(f,rD,θ,ϕ) between the input u and the sound pressure output p(rD,θ,ϕ) under far-field condition [11]. The distance rD between the loudspeaker and microphone should be much larger than the size of the speaker and acoustic wavelength. The 1/r law valid in the far-field allows extrapolating the complex transfer function to other distances r as
HLfrθϕ=HLfrDθϕrDre−jkr−rDE5
using the wavenumber k = 2πf/c0 and the speed of sound c0. Large loudspeakers such as loudspeaker arrays, soundbars, flat-panel speakers, and horn loudspeakers require a large measurement distance rD and a sizeable anechoic room with good air conditioning to keep the variance of the temperature field sufficiently small.
The choice of measured directions determines the angular resolution of the directional gain [11], the accuracy of coverage angle [11], and other derived far-field characteristics. 2-degree angular resolution, needed for some professional loudspeakers, requires about 16,000 measurement points. Rotating a large and heavy loudspeaker over all combinations of the two angles requires robust and accurate robotics with speed ramps to accelerate and deaccelerate the mass. A microphone array speeds up the test by simultaneously measuring the sound pressure at multiple points without moving the loudspeaker.
Common far-field measurements usually provide no information about the accuracy of the measured data. They cannot indicate errors related to the positioning of loudspeakers or microphones, insufficient sampling of complex directivity patterns, or acoustical disturbances due to wind, air temperature, static sound pressure, or ambient noise [15].
Minor positioning errors and normal variation of the speed of sound, which is usually not critical for the amplitude response, can cause significant errors in the phase response and degrade the performance of 3D sound applications. For example, a deviation of the room temperature by 2 Kelvin during the test changes the speed of sound by 1.2 m/s and the acoustic propagation time by 50 μs at a measurement distance r = 5 m, which is required to ensure far-field condition for large loudspeakers. This time delay corresponds to a positioning error of 17 mm and generates a phase error of 36 degrees at 2 kHz, increasing linearly with frequency and reaching 180 degrees at 10 kHz.
3.4 Near-field measurement
The IEC standard 60268–21 [11] recommends measurements in the near-field, which overcome the restrictions and problems faced in the far-field. However, the 1/r law in Eq. (5) is not applicable, and a holographic measurement technique that scans the sound pressure and fits a spherical wave model to measured data is required.
Figure 3 shows a scanning system used for measuring the sound pressure generated by a loudspeaker placed at a fixed position on a post. The microphone moves in three axes in cylindrical coordinates (r,φ,z) to multiple test points rk ∈ Sr distributed on a double layer grid Sr close to the speaker’s surface [24]. Moving a lighter microphone instead of rotating the heavier loudspeaker simplifies the robotics, allows faster speed ramps, and reduces the positioning error. Those opportunities make it possible to generate redundancy in the collected data and check the measurement’s accuracy.
Figure 3.
Nearfield measurement by placing the loudspeaker at a fixed position and moving a microphone with robotics over the scanning grid close to the speaker surface.
The scanning points are distributed on two concentric layers, as shown in Figure 3, to measure the local derivative of the sound pressure like a sound intensity probe. That is the basis for separating the outgoing wave comprising direct sound radiated by the loudspeaker (e.g., diaphragm) from the incoming wave generated by reflections on the positioning arm of the robotics, ground floor, and room walls. The close distance to the sound source increases the direct sound, which increases the signal-to-noise ratio (SNR) by more than 20 dB and significantly reduces the phase error caused by varying air properties in far-field measurements.
4. Spatial transfer function
The spatial transfer function HL(f,r) describes the linear relationship between input spectrum U(f) and sound pressure spectrum PL(f,r) generated by the loudspeaker at any point r under the free-field condition as a spherical wave expansion in Eq. (6) using general solutions Bout(f, r) of the Helmholtz equation weighted by complex coefficients in vector CL(f) [25]:
The spherical coordinates allow a separation of angular dependency using the spherical harmonics Ynmθϕ from the radial dependency using the Hankel function of the second kind hn(2)(kr). The spherical harmonics have orthonormal properties representing a monopole (n = 0), dipoles (n = 1), quadrupoles (n = 2), and more complex sources with increasing order n.
Figure 4 illustrates the expansion for a woofer operated in a sealed enclosure at 200 Hz. The measured directivity pattern is presented as a target on the lower left-hand side and compared with the wave model for rising maximum order N. The expansion can be truncated at N = 3 because 16 coefficients weighting the spherical harmonics provide sufficient accuracy. Higher-order terms can be ignored at 200 Hz because they are 50 dB below the total sound power. The contribution of the higher-order terms rises with frequency and is required to explain the directivity pattern at 1 kHz, as shown in the upper diagram on the right-hand side.
Figure 4.
Modeling the total sound power frequency response (upper right) and directivity pattern at 200 Hz (below) of a loudspeaker by spherical wave model (upper left).
The Hankel function hn(2)(kr) in Eq. (6) models the decay of the sound pressure with rising distancer r from expansion point re of the spherical wave expansion. In the near-field for r < rfar, the 1/r law is not valid anymore because sound pressure and particle velocity are not in phase, generating an increase in the apparent power at lower distances [24]. In the far-field r> > rfar, the sound pressure decreases inversely with the rising distance r giving 6 dB less output for doubling the distance. Thus, the apparent sound power radiated from the loudspeaker is constant and corresponds to the real power.
Figure 5 shows the power Πn(r) contributed by spherical waves of order n to the total apparent power Πa(r). Only the order n = 0 (monopole) generates a constant power output for all distances while the steepness of the power curve Πn(r) in the near-field increases with the order n of the waves.
Figure 5.
Total apparent sound power Πa(r) (thick line) generated by a loudspeaker versus radial distance r and the contribution Πn(r) of the spherical waves of order n (thin lines).
4.1 Parameters of the linear model
The optimum coefficients CL(f) in the spherical wave model in Eq. (6) can be calculated by minimizing the mean squared error between the response H′L (f,rk) measured at scanning points rk ∈ Sr and the modeled responses as
CLf=argMINC∑k=1KrHL\'frk−CfBOUT(frk)2E7
Normalizing the mean squared error in Eq. (7) with the total output power gives a valuable criterion e for checking the measurement’s spherical wave expansion accuracy [24].
Figure 6 shows the normalized fitting error e in the wave expansion with rising total order N. A single monopole expansion (N = 0) already gives an error reduction of 10 dB at 100 Hz. Considering the monopole and the three dipoles (N = 1) can reduce the error to minus 20 dB at 100 Hz, which means the model can explain 99% of the output power. A wave expansion of order N = 5 requiring at least 36 measurement points describes the sound output of the woofer channel below 1 kHz with sufficient accuracy (e < 1%). The increase of the fitting error at higher frequencies indicates that higher-order terms are required in the expansion to model the directivity at higher frequencies.
Figure 6.
Normalized fitting error e versus frequency f of the spherical wave expansion truncated at maximum order N (above) and corresponding identified directivity pattern shown as a balloon-plot for the corresponding order N compared with the measured target response (left-hand side below).
This example shows that the loudspeaker properties determine the maximum order N of the expansion, the number of measurement points Kr required to identify the coefficients Ci(f), and the total scanning time.
For acoustic, esthetic, or technical reasons, most loudspeakers have a natural symmetry in the diaphragm’s shape, the cone placement on the front side of the cabinet, and the enclosure’s geometry. Symmetry factors [24] calculated from identified coefficients CL(f) during the scanning process reveal the loudspeaker’s left/right or top/bottom single-plane, dual-plane or rotational symmetry. This information can be used to align the loudspeaker position and orientation with spherical harmonics to reduce the number of measurement points required to fit the wave expansion. As illustrated in Figure 7, considering the rotational symmetry can reduce the number of measurement points to 4%, significantly speeding up the scanning process.
Figure 7.
Exploiting symmetry in the loudspeaker geometry to reduce the number of measurement points required for the spherical wave expansion.
4.2 Simulated free-field condition
The measurement of the spatial transfer function requires free-field conditions or at least simulated free-field conditions as defined in IEC standard 60268–21 [11].
The absorption of the lined walls in “anechoic” rooms is usually imperfect at low frequencies where the wavelength of the standing waves exceeds the thickness of the lining. Gating the sound pressure signal and windowing of the impulse response provides good results at higher frequencies but degrade the frequency resolution at low frequencies.
The wave separation technique based on near-field scanning on two surfaces [25] can be used to separate the direct sound from the room reflections at low and middle frequencies and complements the windowing technique at higher frequencies. The measured transfer function H′L (f,rk) with rk ∈ Sr corrupted by room reflections can be modeled by a spherical wave expansion [26]
considering outgoing wave BOUT(f,rk) radiated by the loudspeaker as used in Eq. (6) and reflected waves BSR(f,rk) represented by Bessel functions of the first kind Jn(kr). The optimal coefficients CL and CSR minimizing the mean squared error between measured and modeled response can be estimated by
Cf=CLfCSRf=argMINC∑k=1KrHL\'frk−CfB(frk)2E9
The coefficients CSR(f) provide the SPL response of the sound reflections shown as a dashed curve in Figure 8 that corrupts the measurement and causes a significant error below 1 kHz in the measured SPL response (thin green solid line). The CL(f) represents the SPL direct sound (thick blue solid line) measured under simulated free-field conditions.
Figure 8.
Generating simulated free-field conditions at low frequencies by separating direct sound (solid line) from the room reflections (dashed line) in the measured SPL frequency response (thin line).
4.3 Interpretation of the spatial transfer function
The interpretation of the spatial transfer function HL(f,r) can be simplified by calculating the SPL frequency response at point r in decibel as
LSPfr=20lgHLfru˜prefdBE10
using a fixed RMS value u˜ of the input signal u(t) and the reference sound pressure pref = 20μPa. The SPL frequency response displayed in 2D or 3D plots (polar, balloon, contour) shows the directional dependency versus angles θ and ϕ in the far-field r > rfar as shown in Figure 9 and the local dependence versus Cartesians coordinates x,y,z in the near and far-field in Figure 10.
Figure 9.
Visualization styles for the far-field directional SPL response LSP(f,r,θ, ϕ) in spherical coordinates.
Figure 10.
Visualization of the SPL of the direct sound-field LSP(f,x,y,z) generated by a loudspeaker at 2 kHz outside the scanning surface.
The phase response at point r calculated as
φfr=argHLfr=φMfr+φAfr−2πfτrE11
provides essential information for combining multiple loudspeaker channels in systems and arrays and applying DSP processing to control the sound-field. The total phase response φ(f,r) can be decomposed into three parts: The minimal phase φM(f, r) corresponds to the amplitude response |HL(f,r)| via the Hilbert Transform. The all-pass phase φM(f,r) reveals the polarity and other loudspeaker properties. A critical part is a total time delay
τr=τDSP+r−rec0TArP0E12
comprising the latency τDSP [11] in DSP processing and the acoustical delay depending on the distance |r-re| and the local speed of sound c0, which is a function of the temperature field TA(r) and the static sound pressure P0.
The (real) sound power ΠL(f) radiated by the loudspeaker into the far-field can be calculated by multiplying the wave coefficients CL(f) with its Hermitian transpose:
ΠLf=CLfCLHf2ρ0ck2Uf2E13
This sound power ΠL(f) is a valuable metric for describing the global acoustic output of the loudspeaker by a single value. Still, it is also a convenient basis to estimate the mean sound pressure of the diffuse sound generated in a non-anechoic room if the reverberation time is known [11].
5. Time-variant distortion
The gray box model from Figure 2 describes the time-variant distortion spectrum Pv(f,r|t) at any point r in the sound-field as
PVfrt=HVft−1HLfrUfE14
Using the spatial transfer HL(f,r), and the input spectrum U(f), and the time-variant transfer function H(f|t), which can be identified as the ratio
HVft≈HfrtHfrt0E15
of two spatial transfer functions H(f,r|t0) and H(f,r|t) measured on the same loudspeaker unit under identical measurement conditions (environment, evaluation point r) at a reference time t0 and a later evaluation time t. The reference measurement at t0 assesses the loudspeaker under climatized standard conditions using a small stimulus generating negligible heating and nonlinear distortion. The subsequent measurement at time t can be performed with any stimulus providing sufficient excitation of the loudspeaker. This measurement requires no scanning process, and the calculated time-variant transfer function Hv(f|t) is independent of the choice of the evaluation point r. Placing the microphone in the near-field ensures a good SNR.
This model is able to predict the amplitude compression at any point r in the sound-field defined in agreement with IEC standard 60268–21 [11] in decibel as
CACfrt=−20lgHVftdBE16
and the phase deviation:
Δφfrt=argHVftE17
The voice coil heating in professional stage loudspeakers can cause significant amplitude compression (up to 6 dB) in the output signal. Fatigue and climate changes can also shift the resonance frequencies of modal cone vibrations, causing more than 90-degree phase deviation. Those variations can impair the intended superposition of multiple loudspeakers’ output in spatial sound applications.
6. Nonlinear distortions
The regular nonlinear distortions found in the sound pressure output pN(t,r) are symptoms of loudspeaker nonlinearities modeled by subsystems NI and ND(r) shown in Figure 2. The input signal u strongly influences the generation process and the spectral and temporal properties of the nonlinear distortion [22].
A typical audio signal (e.g., music) has a dense excitation spectrum, as shown in Figure 11, which makes separating the nonlinear distortion pN in the sound pressure output p more difficult. An adaptive linear filter can model the linear and time-variant components pL + pV in the output [27]. The difference signal e(t) between the measured and the modeled signal comprises nonlinear distortion and noise.
Figure 11.
Spectra of reproduced test stimuli used for nonlinear distortion measurement.
As shown in Figure 11, a sparse multi-tone complex is a stimulus able to represent typical program material such as music and speech by having similar properties such as spectral distribution and crest factor. This stimulus has pseudo-random properties generated by a standardized algorithm [11] to ensure reproducible and comparable test results. The excitation tones are not dense but sufficiently activate harmonics, intermodulation, and other nonlinear distortion components, which can easily be detected and separated from the fundamental response in the spectrum.
The prevalent measurement technique uses a single tone stimulus with a constant or varying excitation frequency fe (e.g., sinusoidal chirp [11]). The harmonic components generated at multiple frequencies nfe with n = 2, 3, 4 can be easily separated from the fundamental part at fe. This measurement technique has a long tradition and is simple but has a significant drawback: It does not consider the intermodulation distortion generated by multiple tones and music.
The measurement technique presented in the following section can also be applied to a burst signal, two-tone signal, white or pink noise, and other input signals.
6.1 Nonlinear distortion in 3D space
A comprehensive measurement of the nonlinear distortion in the 3D space requires near-field scanning providing the distortion spectrum PN(f,rk) at the grid points rk ∈ Sr. The small distance between the microphone and loudspeaker ensures sufficient SNR to cope with noise. The measurement performed at high amplitudes can be integrated into the scanning process for spatial transfer function HL(f,r) measured at low amplitude (see Section 4).
Applying the spherical wave expansion to the measured distortion spectrum PN(f,rk) gives the optimal coefficients
CNf=argMINC∑k=1KrPNfrk−CfBout(frk)2E18
The coefficients in vector CN(f) allow extrapolation of the distortion to any point r outside the scanning surface:
PNfr=CNfBoutfrE19
However, there is a significant difference between the nonlinear coefficients CN(f) and the linear coefficients CL(f) discussed in Section 4. The linear coefficients CL(f) are parameters of a linear system. They can be identified with any broad-band stimulus and used to transfer another input signal into the sound-field, including music and speech. The nonlinear coefficients CN(f) describes the results (distortion) of loudspeaker nonlinearities that depend on the particular stimulus [22].
The sound power spectrum calculated as
ΠNf=CNfCNHf2ρ0ck2UfHV(ft)2E20
is a valuable global metric to assess the nonlinear distortion radiated by the loudspeaker in all directions.
6.2 Equivalent input distortion
The standard IEC 60268–21 calculates the equivalent input distortion (EID) for a single point measurement rk by a simple approximation [28]
UIfrk=PNfrkHVftHLfrkE21
using the time-variant transfer functions HV(f|t) and spatial transfer function HL(f|r). This inverse filtering transforms the sound pressure distortion pN(rk) into virtual input signal u’ (rk), as illustrated in Figure 12.
Figure 12.
Block diagram illustrates the calculation of equivalent input distortion (EID) by applying inverse filtering (right) or optimal estimation (left) based on three sound pressure measurements in the near-field (middle).
The lower middle panel in Figure 12 shows the total harmonic distortion as an absolute SPL frequency response LTH,N(fe,r) measured at three different distances rk in an office room (in-situ). The near-field measurement at 2 cm provides a relatively smooth curve, while the 30 and 60 cm measurements have a lower SPL and are affected by room reflections. The filtering of the sound pressure signals p(rk) with the inverse transfer function H(f,rk)−1 generates a voltage signal u’ (rk) with the total harmonics level LTH, I + D(fe,rk) on the lower right-hand side in Figure 12. This filtering removes the peaky curve shape caused by the room reflections, and the three curves become virtually identical between 100 Hz and 1 kHz. However, noise corrupts the measurement at low frequencies, and the distributed distortion pD causes minor deviations above 800 Hz.
Those artifacts in the equivalent input distortion (EID) can be removed by minimizing the mean squared error between the estimated and the measured nonlinear distortion spectrum at the scanning points rk with k = 1,.., Kr and Kr ≥ 1:
UIf=argMINUEID∑k=1KrHVftHL(frk)UIf−PN(frk)2E22
This fitting provides the voltage level response LTH,I(f) on the left-hand side in Figure 12, representing the EID.
Figure 13 shows the equivalent input distortion spectrum UI(f) generated by multi-tone stimuli with a different spectral shaping to represent typical test signals and selected audio material. All the stimuli have the same RMS value. Cello music provides the highest low-frequency components, generating the highest voice coil displacement and harmonic components at 500 Hz. Pink noise and IEC noise [11], representing typical program material, cause harmonic and intermodulation distortion at the same SPL over a wide frequency band. The nonlinear distortion rise to higher frequencies for voice and white noise stimuli.
Figure 13.
Relative equivalent input distortion LI(f) measured with various broad-band stimuli at the same RMS input voltage.
The EID spectrum UI(f) at the input of the loudspeaker can also be easily transferred to at any point r in the 3D space by applying linear filtering:
PIfr=HVftHLfrUIf=CLfBoutfrHVftUIfE23
The sound power spectrum ΠI(f) of the equivalent input distortion radiated into the far-field can be similarly calculated as the linear power ΠL(f) in Eq. (13) by using the same wave coefficients CL(f) of the linear wave modeling:
ΠIf=CLfCLHf2ρ0ck2UIfHV(ft)2E24
The transfer functions HL(f,r)Hv(f|r) shape the spectral components of equivalent input distortion and the input stimulus in the same way. Thus, the ratio between distortion and linear signal part is identical in the voltage, sound pressure at any point r, and power output:
UIfUf=PIfrPLfr=ΠIfΠLfE25
This fact simplifies the distortion measurement and motivates the definition of relative distortion metrics discussed in Section 6.4. Furthermore, nonlinear control techniques [17] that cancel the EID at the loudspeaker input by synthesized compensation signal can reduce the sound pressure distortion PI(f,r) everywhere in the 3D space.
6.3 Distributed nonlinear distortion
The distributed nonlinear distortion pD(r) introduced in Section 2 is the remaining distortion part in the sound-field that EID cannot represent:
PDfrk=PNfrk−HVftHL(frk)UIf=CDfBoutfrkHVftUfE26
Eq. (26) uses the basic functions BOUT(f, r) from Eq. (6) for the spherical wave expansion but determines the coefficients CD(f) as:
CDf=argMINC∑k=1KrPDfrk−CfBout(frk)2E27
The residual error in Eq. (27) can be used to find the maximum order N of the wave expansion, as discussed in Section 4. The symmetry properties of the particular loudspeaker are also valuable for minimizing the scanning effort.
The coefficients CD(f) provide the sound power spectrum ΠD(f) of the distributed nonlinear distortion radiated into the far-field as:
ΠDf=CDfCDHf2ρ0ck2UfHV(ft)2E28
The distributed distortion can be ignored if the sound power ΠD(f) is smaller than one-tenth of the EID sound power ΠI(f). Then a single test in the near-field of the loudspeaker is sufficient to measure the dominant EID and predict the total distortion pN in the 3D space.
6.4 Relative distortion metrics
This section introduces metrics that simplify the interpretation of the distortion components. These equations use a symbol # as a placeholder for N, I, or D representing the total, equivalent input, or distributed distortion.
Comparing the spectral components at frequency f in the nonlinear distortion P#(f,r) with the linear output signal PL(f,r) from Eq. (6) at the same point r leads to a spectral nonlinear distortion ratio (SNDR) defined in decibel as:
L#fr=20lgP#frPLfrdB#∈NIDE29
The SNDR is usually negative and describes the SPL difference between the distortion and the linear component at the same spectral frequency f.
It is a proper physical metric for broad-band stimuli such as typical audio signals, noise, and other artificial test stimuli. It also applies to sparse multi-tone stimuli with a resolution smaller than one-third octave by using P#(fi,r) in the nominator of Eq. (29) and the fundamental component PL(fj,r) in the denominator with the smallest frequency difference |fi- fj| for each spectral distortion component.
However, SNDR) is less useful for sinusoidal stimuli generating only a single tone with constant or varying excitation frequency (e.g., chirp) because the harmonics have a significant spectral distance to the fundamental.
An alternative approach considers the total energy ratio between the nonlinear distortion P# and the linear output signal PL for a particular stimulus. It leads to the total distortion ratio (TDR) defined in percent as:
R#r=∫P#fr2df∫PLfr2df100%#∈NIDE30
This metric can be applied to all kinds of stimuli but is very popular for the total harmonic distortion THD measured with a single tone and plotted versus the excitation frequency fe. This metric does reveal the spectral distribution of the nonlinear distortion (second, third, and higher-order harmonics).
Referring the nonlinear sound power spectrum Π#(f) to the linear sound power ΠL(f) in Eq. (13) provides a sound power distortion ratio (SPDR):
RΠ,#=∫Π#fdf∫ΠLfdf100%#∈NIDE31
For a multi-tone stimulus representing typical program material (IEC 60268–21), the SPDR becomes an essential, single-value characteristic for the assessment of the audio quality in a global sense.
The spectral equivalent input distortion ratio (SEIDR) defined in decibel as
LIf=20lgUIfUfdB=LIfr≈LNfrE32
compares the spectral components of distortion UI(f) with the input signal U(f). The metric LI(f) is identical with the metric LI(f,r), assessing the EID at any point r in the sound-field. It is a valid approximation for the total distortion metric LN(f,r) if the distributed distortion PD(f,r) is negligible.
7. Abnormal distortion
Loudspeaker defects such as voice coil rubbing, mechanical vibrations of loose parts, air turbulences, and other irregular nonlinear dynamics that are neither intended nor considered in the design can generate particular distortion that can significantly degrade the audio quality. A loudspeaker generating abnormal distortion, usually called “rub & buzz” should not be shipped to a customer!
Modern measurement techniques exploit unique features of abnormal distortion. Time-analysis applied to a distorted single-tone stimulus reveals a complex fine structure comprising spikes, transients, and noise-like patterns [29]. Contrary to the harmonic and intermodulation distortion discussed in Section 6, the abnormal distortions cover the entire audio band. However, they have a low RMS value, are usually close to the noise floor, and thus require a near-field measurement. Spherical wave expansion or averaging over multiple periods removes the random features of the abnormal distortion.
The IEC standard 60268–21 [11] recommends a chirp stimulus at varying excitation frequency fe and a high-pass tracking filter with a cut-off frequency fc > ncofe to separate the abnormal distortion in the measured sound pressure signal p(t). The factor nco for the cut-off frequency fc (typical value nco = 10) depends on the excitation frequency fe, the transducer type, and properties of potential defects. The optimal value for nco can be determined by maximizing the crest factor CID(r) defined according to IEC 60268–21 [11] as the ratio between peak and RMS values of the high-pass filtered signal pID as:
CIDr=10lgMAXtt+TpIDtr21T∫tt+TpIDtr2dtdBE33
The crest factor CID(r) is independent of the spectral energy but describes the impulsiveness of the abnormal distortion considering the phase relationship between the spectral components. A high crest factor is a unique symptom of abnormal distortion, while the crest factor of the fundamental, regular nonlinear distortions or electronic noise is typically below 12 dB.
This fact initiated the measurement of the impulsive distortion (ID) defined in IEC 60268–21 as a peak level in decibel as
LIDferk=20lgMAXtt+TpIDtrkprefdBE34
Using a peak found over a period length T in the nominator in Eq. (33) and normalized by reference sound pressure pref. This peak level LID(fe,rk) is a helpful metric for finding the most critical excitation frequency fID and a scanning point rID ∈ Sr at the nearest position to the source (e.g., rattling), generating impulsive distortion with CID(f) > 12 dB. The maximum value found under the condition
is the basis for calculating the maximum impulsive distortion ratio (IDR) defined according to IEC 60268–21 [11] as
LIDR=LIDfIDrID−LREFE36
using a reference sound pressure level LREF measured at the standard evaluation point (on axis, r = 1 m) or a scanning point rk generating the largest SPL value:
LREF=10lg1Tpref2MAX∀rk∈Sr∫tt+Tptrk2dtdBE37
Those metrics compared with meaningful limits for passing or failure are essential for the quality control of loudspeakers in manufacturing and maintenance.
8. External noise
The SNR in decibel is defined as
RSNR=LREF−LNE38
using reference SPL LREF from Eq. (37) and a noise SPL LN. The stationary noise caused by the microphone and other electronic parts can be measured with a muted stimulus in a single test at any point r. The instantaneous SNR can be used to validate the distortion ratios TDR in Eq. (30) and IDR in Eq. (36) to remove invalid data.
9. Metrics for sound zones
Audio quality assessment, loudspeaker diagnostics, and active sound-field control require metrics that assess the properties of the sound-field at a specific listening point described by a probability fL(r) of the ear position. The mean sound power found in such a listening zone is a less suitable metric because the listener evaluates the local sound pressure. It is more appropriate to assess the mean and the variance of the perceptual attributes (e.g., loudness) or related physical metrics (e.g., SPL) over the listening zone [30] considering the probability of the ear positioning as a weighting function fL(r). This approach is used in IEC 60268–21 [11] for defining a mean SPL over an acoustical zone, but it can easily be applied to the nonlinear distortion metrics in Eqs. (29) and (30). The variance and the maximum deviation from the mean value are also valuable characteristics of the sound zone.
10. Maximum SPL output
The maximum sound pressure output (max SPL) rated according to IEC standard 60268–21 [11] plays a primary role in adjusting the amplitude of the test stimulus in output-based testing. The max SPL can be used to calibrate any input channel (digital, analog) in passive and active systems and provides a maximum input RMS value umax, depending on the selected input channel, gain control, amplification, and applied signal processing. The amplitude compression CAC(f) from Eq. (16), the sound power distortion ratio RΠN from Eq. (31), and the maximum impulsive distortion ratio RIDR from Eq. (36) are essential criteria for rating max SPL considering the particularities of the target applications.
11. Conclusions
Acoustical measurement in the near-field of the loudspeaker can provide much of the relevant information required for designing and assessing spatial sound control applications. The spatial transfer function HL(f,r) expressed as a spherical wave expansion provides accurate sound pressure amplitude and phase information at any point r in the near and far-field. The spatial scanning effort depends on the particular loudspeaker and can be significantly minimized by considering the symmetry of the loudspeaker. In practice, the spatial transfer function HL(f,r) scanned on a prototype can be applied to other units of the same type as long as the loudspeaker geometry does not change much.
The time-variant transfer function Hv(f|t) represents changes in the material caused by heating, aging, fatigue, and production variability. No scanning is required to measure the transfer function Hv(f|t) and the equivalent input distortion UI(f), ignoring the distributed nonlinear distortion pD. Such an approximation is valid for most loudspeakers used in spatial sound applications and can be verified by scanning the nonlinear distortion in the near-field of the loudspeaker. All time-variant and nonlinear signal distortion can be extrapolated to any point in the 3D space using spherical wave expansions.
The multi-tone complex is a valuable artificial stimulus that can simplify the interpretation of the amplitude compression and the nonlinear distortion. The sinusoidal chirp is required to measure the impulsive distortion ratio, a sensitive characteristic for detecting loudspeaker defects and abnormal behavior degrading the audio quality.
An anechoic room is usually not required for performing the essential loudspeaker measurements at superior accuracy.
The methods for measuring loudspeaker characteristics presented in this chapter are compliant with modern international loudspeaker standards. They are the basis for simplifying the numerical simulation of sound-field control and selecting optimal hardware components offering a maximum performance-cost ratio.
\n',keywords:"loudspeaker directivity, near-field scanning, signal distortion, nonlinear loudspeaker modeling, sound-field control, spatial sound application",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/80482.pdf",chapterXML:"https://mts.intechopen.com/source/xml/80482.xml",downloadPdfUrl:"/chapter/pdf-download/80482",previewPdfUrl:"/chapter/pdf-preview/80482",totalDownloads:163,totalViews:0,totalCrossrefCites:0,dateSubmitted:"February 25th 2021",dateReviewed:"December 15th 2021",datePrePublished:"February 16th 2022",datePublished:null,dateFinished:"February 16th 2022",readingETA:"0",abstract:"This chapter describes the physical modeling and output-based measurement of loudspeakers, essential hardware components in sound-field control. A gray box model represents linear, time-variant, nonlinear, and non-deterministic signal distortions. Each distortion component requires a particular measurement technique that includes test stimulus generation, sound pressure measurement at selected points in 3D space, and signal analysis for generating meaningful metrics. Near-field scanning measures all signal components at a large signal-to-noise ratio with minor errors caused by loudspeaker positioning, air temperature, room reflections, and ambient noise. Holographic postprocessing based on a spherical wave expansion separates the direct sound from room reflections to assess the linear output and signal distortion. New metrics are presented that simplify the interpretation of the loudspeaker properties at single points, sound zones, and over the entire sound-field.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/80482",risUrl:"/chapter/ris/80482",signatures:"Wolfgang Klippel",book:{id:"10370",type:"book",title:"Advances in Fundamental and Applied Research on Spatial Audio",subtitle:null,fullTitle:"Advances in Fundamental and Applied Research on Spatial Audio",slug:null,publishedDate:null,bookSignature:"Dr. Brian FG Katz and Dr. Piotr Majdak",coverURL:"https://cdn.intechopen.com/books/images_new/10370.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-83969-006-8",printIsbn:"978-1-83969-005-1",pdfIsbn:"978-1-83969-007-5",isAvailableForWebshopOrdering:!0,editors:[{id:"278731",title:"Dr.",name:"Brian FG",middleName:null,surname:"Katz",slug:"brian-fg-katz",fullName:"Brian FG Katz"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"41530",title:"Prof.",name:"Wolfgang",middleName:null,surname:"Klippel",fullName:"Wolfgang Klippel",slug:"wolfgang-klippel",email:"wklippel@klippel.de",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. General loudspeaker modeling",level:"1"},{id:"sec_3",title:"3. Acoustical loudspeaker measurements",level:"1"},{id:"sec_3_2",title:"3.1 Loudspeaker positioning",level:"2"},{id:"sec_4_2",title:"3.2 Test environment",level:"2"},{id:"sec_5_2",title:"3.3 Far-field measurement",level:"2"},{id:"sec_6_2",title:"3.4 Near-field measurement",level:"2"},{id:"sec_8",title:"4. Spatial transfer function",level:"1"},{id:"sec_8_2",title:"4.1 Parameters of the linear model",level:"2"},{id:"sec_9_2",title:"4.2 Simulated free-field condition",level:"2"},{id:"sec_10_2",title:"4.3 Interpretation of the spatial transfer function",level:"2"},{id:"sec_12",title:"5. Time-variant distortion",level:"1"},{id:"sec_13",title:"6. Nonlinear distortions",level:"1"},{id:"sec_13_2",title:"6.1 Nonlinear distortion in 3D space",level:"2"},{id:"sec_14_2",title:"6.2 Equivalent input distortion",level:"2"},{id:"sec_15_2",title:"6.3 Distributed nonlinear distortion",level:"2"},{id:"sec_16_2",title:"6.4 Relative distortion metrics",level:"2"},{id:"sec_18",title:"7. Abnormal distortion",level:"1"},{id:"sec_19",title:"8. External noise",level:"1"},{id:"sec_20",title:"9. Metrics for sound zones",level:"1"},{id:"sec_21",title:"10. Maximum SPL output",level:"1"},{id:"sec_22",title:"11. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Van Veen BD, Buckley KM. Beamforming: A versatile approach to spatial filtering. IEEE ASSP Magazine. 1988;5(2):4-24'},{id:"B2",body:'Berkhout AJ, Vries DD, Vogel P. Acoustical control by wave field synthesis. Journal of the Acoustical Society of America. 1993;93:2764-2778'},{id:"B3",body:'Gerzon MA. Ambisonics in multi-channel broadcasting and video. Journal of the Audio Engineering Society. 1985;33(11):859-871'},{id:"B4",body:'Poletti M. Three-dimensional surround sound systems based on spherical harmonics. Journal of the Audio Engineering Society. 2005;53(11):1004-1025'},{id:"B5",body:'Betlehem T, Zhang W, Poletti M, Abhayapala T. Personal sound zones: Delivering Interface-free audio to multiple listeners. IEEE Signal Processing Magazine. 2015;32:81-91'},{id:"B6",body:'Zotter F. Analysis and Synthesis of Sound Radiation with Spherical Arrays [Dissertation]. Austria: University of Music and Performing Arts; 2009'},{id:"B7",body:'Vries DD. Sound reinforcement by wave field synthesis: Adaptation of the synthesis operator to the loudspeaker directivity characteristics. Journal of the Audio Engineering Society. 1996;44(12):1120-1131'},{id:"B8",body:'Ahrens J, Spors S. An analytical approach to 2.5 D sound field reproduction employing linear distributions of non-omnidirectional loudspeakers. In: Proc. IEEE Int. Conf. Acoust. Speech and Signal Process. (ICASSP). 2010. pp. 105-108. DOI: 10.1109/ICASSP15600.2010'},{id:"B9",body:'Koyama S, Furuya K, Hiwasaki Y, Haneda Y. Sound field reproduction method in Spatio-temporal frequency domain considering directivity of loudspeakers. In: 132nd Convention of the Audio Eng. Soc., Budapest, Paper 8664. 2012. Available from: http://www.aes.org/e-lib/browse.cfm?elib=16302'},{id:"B10",body:'Poletti MAA, Betlehem T, Abhayapala THD. Higher-order loudspeakers and active compensation for improved 2D sound field reproduction in rooms. Journal of the Audio Engineering Society. 2015;63(1/2):31-45. DOI: 10.17743/jaes.2015.0003'},{id:"B11",body:'Sound System Equipment – Part 21. Acoustical (Output-Based) Measurements. Standard of International Electrotechnical Commission IEC 60268–21; 2018'},{id:"B12",body:'Choi J, Kim Y, Ko S. Near and far-field control of focused sound radiation using a loudspeaker Array. In: 129th Convention of the Audio Eng. Soc., San Francisco, Paper 8198. 2010. Available from: http://www.aes.org/e-lib/browse.cfm?elib=15620'},{id:"B13",body:'Ma X et al. Nonlinear distortion reduction in sound zones by constraining individual loudspeaker control effort. Journal of the Audio Engineering Society. 2019;57(9):641-654'},{id:"B14",body:'Cobianchi M, Mizzoni F, Uncini A. Polar measurements of harmonic and multitone distortion of direct radiating and horn loaded transducers. In: 134th Convention of the Audio Eng. Soc., Rome, Paper 8915. 2013. Available from: http://www.aes.org/e-lib/browse.cfm?elib=16815'},{id:"B15",body:'Olsen M, Møller MB. Sound zones: On the effect of ambient temperature variations in feed-forward systems. In: 142nd Convention of the Audio Eng. Soc., Berlin, Paper 9806. 2017. Available from: http://www.aes.org/e-lib/browse.cfm?elib=18680'},{id:"B16",body:'Pedersen KM. Thermal overload protection of high-frequency loudspeakers [Rep. of final year dissertation]. UK: Salford University; 2002'},{id:"B17",body:'Klippel W. Loudspeaker and headphone design approaches enabled by adaptive nonlinear control. Journal of the Audio Engineering Society. 2020;68(6):454-464. DOI: 10.17743/jaes.2020.0037'},{id:"B18",body:'Klippel W. Nonlinear Modeling of the heat transfer in loudspeakers. Journal of the Audio Engineering Society. 2004;52(1/2):3-25'},{id:"B19",body:'Klippel W. Mechanical fatigue and load-induced aging of loudspeaker suspension. In: 131st Convention of Audio Eng. Soc., New York, paper 8474. 2011. Available from: http://www.aes.org/e-lib/browse.cfm?elib=16000'},{id:"B20",body:'Klippel W, Schlechter J. Distributed mechanical parameters of loudspeakers, part 1: Measurements. Journal of the Audio Engineering Society. 2009;57(7/8):500-511'},{id:"B21",body:'Sound system equipment - Part 22. Electrical and Mechanical Measurements on Transducers. Standard of International Electrotechnical Commission, IEC 60268–21; 2020'},{id:"B22",body:'Klippel W. Loudspeaker nonlinearities – Causes parameters, symptoms. Journal of the Audio Engineering Society. Oct 2006;54(10):907'},{id:"B23",body:'Sound System Equipment – Electro-acoustical Transducers – Measurement of Large Signal Parameters, Standard of International Electrotechnical Commission, IEC 62458; 2010'},{id:"B24",body:'Klippel W, Bellmann C. Holographic nearfield measurement of loudspeaker directivity. In: 141st Convention of the Audio Eng. Soc., Los Angeles, Paper 9598. 2016. Available from: http://www.aes.org/e-lib/browse.cfm?elib=18402'},{id:"B25",body:'Williams EG. Fourier Acoustics – Sound Radiation and Nearfield Acoustical Holography. London: Academic Press; 1999'},{id:"B26",body:'Melon M et al. Comparison of four subwoofer measurement techniques. Journal of the Audio Engineering Society. 2007;55(12):1077-1091'},{id:"B27",body:'Klippel W, Irrgang S. Audio system evaluation with music signals. In: AES International Conference on Automotive Audio, San Francico, Paper P4–2. 2017. Available from: http://www.aes.org/e-lib/browse.cfm?elib=19196'},{id:"B28",body:'Klippel W. Measurement and application of equivalent input distortion. Journal of the Audio Engineering Society. 2004;52(9):931-947'},{id:"B29",body:'Klippel W, Seidel U. Measurement of impulsive distortion, rub and buzz and other disturbances. In: 114th Convention of Audio Eng. Soc, Paper 5734. 2003. Available from: http://www.aes.org/e-lib/browse.cfm?elib=12550'},{id:"B30",body:'IEC 62777. Quality Evaluation Method for the Sound Field of Directional Loudspeaker Array System. Standard of the International Electrotechnical Commission. 2016'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Wolfgang Klippel",address:"wklippel@klippel.de",affiliation:'
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DOI for every book and every individual chapter;
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Unfortunately, the comorbidities of aging have created a large economic and health burden on society. Osteoporosis is the most prevalent age-related disease. It is characterized by uncoupled bone resorption that leads to low bone mass, compromised microarchitecture and structural deterioration that increases the likelihood of fracture with minimal trauma, known as fragility fractures. These fractures lead to disproportionally high mortality rate and a drastic decline in quality of life for those affected. While estrogen loss is one known trigger of osteoporosis, a number of recent studies have shown that osteoporosis is a multifactorial condition in both humans and rodent models. The presence or absence of certain factors are likely to determine which subset of the population develop osteoporosis. 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Therefore, the objective of this chapter is to describe the use of these parameters as an auxiliary mechanism in the detection of low bone mineral density, as well as to characterize the radiographic findings of patients with osteoporosis.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"Plauto Christopher Aranha Watanabe, Giovani Antonio Rodrigues, Marcelo Rodrigues Azenha, Michel Campos Ribeiro, Enéas de Almeida Souza Filho, Rafael Angelo Soares Vieira and Fabio Santos Bottacin",authors:[{id:"76171",title:"Prof.",name:"Plauto C. A.",middleName:null,surname:"Watanabe",slug:"plauto-c.-a.-watanabe",fullName:"Plauto C. A. 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Anti-inflammatory diet is designed to improve health and prevent the occurrence and development of chronic diseases associated with inadequate diet. Proper nutrition is based on the anti-inflammatory pyramid and changes in poor eating habits are the long-term strategy for preventing inflammation and chronic diseases. Inflammatory factors from food may play a role in the development of osteoporosis and an anti-inflammatory diet may be a way to control and reduce long-term inflammation and prevent bone loss. Pro-inflammatory cytokines from the fat tissue, through activation of the RANKL/RANK/OPG system could intervene with bone metabolism in a way of increased bone loss. Therefore the special attention need to be given to obese patients due to twofold risk, one related to pro-inflammatory cytokines release and the other related to the deprivation of the vitamin D in the fat tissue.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"Olga Cvijanović Peloza, Sandra Pavičić Žeželj, Gordana Kenđel Jovanović, Ivana Pavičić, Ana Terezija Jerbić Radetić, Sanja Zoričić Cvek, Jasna Lulić Drenjak, Gordana Starčević Klasan, Ariana Fužinac Smojver and Juraj Arbanas",authors:[{id:"339281",title:"Associate Prof.",name:"Olga",middleName:null,surname:"Cvijanović Peloza",slug:"olga-cvijanovic-peloza",fullName:"Olga Cvijanović Peloza"},{id:"346420",title:"Prof.",name:"Sandra",middleName:null,surname:"Pavičić Žeželj",slug:"sandra-pavicic-zezelj",fullName:"Sandra Pavičić Žeželj"},{id:"346421",title:"BSc.",name:"Ivana",middleName:null,surname:"Pavičić",slug:"ivana-pavicic",fullName:"Ivana Pavičić"},{id:"346423",title:"Prof.",name:"Ana Terezija",middleName:null,surname:"Jerbić Radetić",slug:"ana-terezija-jerbic-radetic",fullName:"Ana Terezija Jerbić Radetić"},{id:"346424",title:"Prof.",name:"Sanja",middleName:null,surname:"Zoričić Cvek",slug:"sanja-zoricic-cvek",fullName:"Sanja Zoričić Cvek"},{id:"346426",title:"MSc.",name:"Jasna",middleName:null,surname:"Lulić Drenjak",slug:"jasna-lulic-drenjak",fullName:"Jasna Lulić Drenjak"},{id:"346427",title:"Prof.",name:"Gordana",middleName:null,surname:"Starčević Klasan",slug:"gordana-starcevic-klasan",fullName:"Gordana Starčević Klasan"},{id:"346428",title:"MSc.",name:"Ariana",middleName:null,surname:"Fužinac Smojver",slug:"ariana-fuzinac-smojver",fullName:"Ariana Fužinac Smojver"},{id:"346429",title:"Prof.",name:"Juraj",middleName:null,surname:"Arbanas",slug:"juraj-arbanas",fullName:"Juraj Arbanas"},{id:"350011",title:"Dr.",name:"Gordana",middleName:null,surname:"Kenđel Jovanović",slug:"gordana-kendjel-jovanovic",fullName:"Gordana Kenđel Jovanović"}]},{id:"76351",doi:"10.5772/intechopen.97416",title:"Glucocorticoid-Induced Osteoporosis",slug:"glucocorticoid-induced-osteoporosis",totalDownloads:246,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The use of glucocorticoids (GC) in the medium and long term, causes several considerable side effects, being one of the main ones the reduction of bone mineral density (BMD). Prolonged corticosteroid therapy reduces BMD by up to 20% in trabecular bone and approximately 2–3% in cortical bone in the first year of use. This loss rate declines and stabilizes at approximately 2% in subsequent years. Therefore, there is a considerable increase in the incidence of pathological fractures, whether clinically symptomatic or asymptomatic (detected as a radiological finding), which varies between 30 and 50% of patients who use GC for more than three months. In view of the above, it is essential to prevent fractures and treat osteoporosis in patients using glucocorticoids for long periods (in particular, greater than or equal to 3 months), which may or may not be associated with clinical risk factors or previous fractures. The guidelines for the treatment and prevention of this comorbidity are well established for postmenopausal women and men over 50 years of age. 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Unfortunately, the comorbidities of aging have created a large economic and health burden on society. Osteoporosis is the most prevalent age-related disease. It is characterized by uncoupled bone resorption that leads to low bone mass, compromised microarchitecture and structural deterioration that increases the likelihood of fracture with minimal trauma, known as fragility fractures. These fractures lead to disproportionally high mortality rate and a drastic decline in quality of life for those affected. While estrogen loss is one known trigger of osteoporosis, a number of recent studies have shown that osteoporosis is a multifactorial condition in both humans and rodent models. The presence or absence of certain factors are likely to determine which subset of the population develop osteoporosis. In this chapter, we review the factors that contribute to osteoporosis with an emphasis on its multifactorial nature and the therapeutic consequences.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"Di Wu, Anna Cline-Smith, Elena Shashkova and Rajeev Aurora",authors:[{id:"339667",title:"Associate Prof.",name:"Rajeev",middleName:null,surname:"Aurora",slug:"rajeev-aurora",fullName:"Rajeev Aurora"},{id:"347366",title:"Mr.",name:"Di",middleName:null,surname:"Wu",slug:"di-wu",fullName:"Di Wu"},{id:"347367",title:"Ms.",name:"Anna",middleName:null,surname:"Cline-Smith",slug:"anna-cline-smith",fullName:"Anna Cline-Smith"},{id:"347579",title:"Dr.",name:"Elena",middleName:null,surname:"Shashkova",slug:"elena-shashkova",fullName:"Elena Shashkova"}]},{id:"75742",title:"Osteoporosis and Dietary Inflammatory Index",slug:"osteoporosis-and-dietary-inflammatory-index",totalDownloads:230,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Healthy bones are constantly being renewed and proper nutrition is an important factor in this process. Anti-inflammatory diet is designed to improve health and prevent the occurrence and development of chronic diseases associated with inadequate diet. Proper nutrition is based on the anti-inflammatory pyramid and changes in poor eating habits are the long-term strategy for preventing inflammation and chronic diseases. Inflammatory factors from food may play a role in the development of osteoporosis and an anti-inflammatory diet may be a way to control and reduce long-term inflammation and prevent bone loss. Pro-inflammatory cytokines from the fat tissue, through activation of the RANKL/RANK/OPG system could intervene with bone metabolism in a way of increased bone loss. Therefore the special attention need to be given to obese patients due to twofold risk, one related to pro-inflammatory cytokines release and the other related to the deprivation of the vitamin D in the fat tissue.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"Olga Cvijanović Peloza, Sandra Pavičić Žeželj, Gordana Kenđel Jovanović, Ivana Pavičić, Ana Terezija Jerbić Radetić, Sanja Zoričić Cvek, Jasna Lulić Drenjak, Gordana Starčević Klasan, Ariana Fužinac Smojver and Juraj Arbanas",authors:[{id:"339281",title:"Associate Prof.",name:"Olga",middleName:null,surname:"Cvijanović Peloza",slug:"olga-cvijanovic-peloza",fullName:"Olga Cvijanović Peloza"},{id:"346420",title:"Prof.",name:"Sandra",middleName:null,surname:"Pavičić Žeželj",slug:"sandra-pavicic-zezelj",fullName:"Sandra Pavičić Žeželj"},{id:"346421",title:"BSc.",name:"Ivana",middleName:null,surname:"Pavičić",slug:"ivana-pavicic",fullName:"Ivana Pavičić"},{id:"346423",title:"Prof.",name:"Ana Terezija",middleName:null,surname:"Jerbić Radetić",slug:"ana-terezija-jerbic-radetic",fullName:"Ana Terezija Jerbić Radetić"},{id:"346424",title:"Prof.",name:"Sanja",middleName:null,surname:"Zoričić Cvek",slug:"sanja-zoricic-cvek",fullName:"Sanja Zoričić Cvek"},{id:"346426",title:"MSc.",name:"Jasna",middleName:null,surname:"Lulić Drenjak",slug:"jasna-lulic-drenjak",fullName:"Jasna Lulić Drenjak"},{id:"346427",title:"Prof.",name:"Gordana",middleName:null,surname:"Starčević Klasan",slug:"gordana-starcevic-klasan",fullName:"Gordana Starčević Klasan"},{id:"346428",title:"MSc.",name:"Ariana",middleName:null,surname:"Fužinac Smojver",slug:"ariana-fuzinac-smojver",fullName:"Ariana Fužinac Smojver"},{id:"346429",title:"Prof.",name:"Juraj",middleName:null,surname:"Arbanas",slug:"juraj-arbanas",fullName:"Juraj Arbanas"},{id:"350011",title:"Dr.",name:"Gordana",middleName:null,surname:"Kenđel Jovanović",slug:"gordana-kendjel-jovanovic",fullName:"Gordana Kenđel Jovanović"}]},{id:"76351",title:"Glucocorticoid-Induced Osteoporosis",slug:"glucocorticoid-induced-osteoporosis",totalDownloads:248,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The use of glucocorticoids (GC) in the medium and long term, causes several considerable side effects, being one of the main ones the reduction of bone mineral density (BMD). Prolonged corticosteroid therapy reduces BMD by up to 20% in trabecular bone and approximately 2–3% in cortical bone in the first year of use. This loss rate declines and stabilizes at approximately 2% in subsequent years. Therefore, there is a considerable increase in the incidence of pathological fractures, whether clinically symptomatic or asymptomatic (detected as a radiological finding), which varies between 30 and 50% of patients who use GC for more than three months. In view of the above, it is essential to prevent fractures and treat osteoporosis in patients using glucocorticoids for long periods (in particular, greater than or equal to 3 months), which may or may not be associated with clinical risk factors or previous fractures. The guidelines for the treatment and prevention of this comorbidity are well established for postmenopausal women and men over 50 years of age. However, for patients below this range, studies are still lacking.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"José Renan Vieira da Costa Júnior and Sérgio Luchini Batista",authors:[{id:"164388",title:"Prof.",name:"Sergio",middleName:null,surname:"Luchini Batista",slug:"sergio-luchini-batista",fullName:"Sergio Luchini Batista"},{id:"354032",title:"Dr.",name:"José Renan",middleName:null,surname:"Vieira Da Costa Júnior",slug:"jose-renan-vieira-da-costa-junior",fullName:"José Renan Vieira Da Costa Júnior"}]}],onlineFirstChaptersFilter:{topicId:"1414",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},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:8,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:286,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:9,numberOfPublishedChapters:101,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:11,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{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"}}}},{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"}}}}]},series:{item:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:null,scope:"
\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\t
\r\n
\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t
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\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
\r\n
\r\n\t
\r\n
\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
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\r\n\t
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\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
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\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
",coverUrl:"https://cdn.intechopen.com/series/covers/24.jpg",latestPublicationDate:"April 24th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:0,editor:{id:"262440",title:"Prof.",name:"Usha",middleName:null,surname:"Iyer-Raniga",slug:"usha-iyer-raniga",fullName:"Usha Iyer-Raniga",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRYSXQA4/Profile_Picture_2022-02-28T13:55:36.jpeg",biography:"Usha Iyer-Raniga is a professor in the School of Property and Construction Management at RMIT University. Usha co-leads the One Planet Network’s Sustainable Buildings and Construction Programme (SBC), a United Nations 10 Year Framework of Programmes on Sustainable Consumption and Production (UN 10FYP SCP) aligned with Sustainable Development Goal 12. The work also directly impacts SDG 11 on Sustainable Cities and Communities. She completed her undergraduate degree as an architect before obtaining her Masters degree from Canada and her Doctorate in Australia. Usha has been a keynote speaker as well as an invited speaker at national and international conferences, seminars and workshops. Her teaching experience includes teaching in Asian countries. She has advised Austrade, APEC, national, state and local governments. She serves as a reviewer and a member of the scientific committee for national and international refereed journals and refereed conferences. She is on the editorial board for refereed journals and has worked on Special Issues. Usha has served and continues to serve on the Boards of several not-for-profit organisations and she has also served as panel judge for a number of awards including the Premiers Sustainability Award in Victoria and the International Green Gown Awards. Usha has published over 100 publications, including research and consulting reports. Her publications cover a wide range of scientific and technical research publications that include edited books, book chapters, refereed journals, refereed conference papers and reports for local, state and federal government clients. She has also produced podcasts for various organisations and participated in media interviews. She has received state, national and international funding worth over USD $25 million. Usha has been awarded the Quarterly Franklin Membership by London Journals Press (UK). Her biography has been included in the Marquis Who's Who in the World® 2018, 2016 (33rd Edition), along with approximately 55,000 of the most accomplished men and women from around the world, including luminaries as U.N. Secretary-General Ban Ki-moon. In 2017, Usha was awarded the Marquis Who’s Who Lifetime Achiever Award.",institutionString:null,institution:{name:"RMIT University",institutionURL:null,country:{name:"Australia"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:5,paginationItems:[{id:"91",title:"Sustainable Economy and Fair Society",coverUrl:"https://cdn.intechopen.com/series_topics/covers/91.jpg",isOpenForSubmission:!0,annualVolume:11975,editor:{id:"181603",title:"Dr.",name:"Antonella",middleName:null,surname:"Petrillo",slug:"antonella-petrillo",fullName:"Antonella Petrillo",profilePictureURL:"https://mts.intechopen.com/storage/users/181603/images/system/181603.jpg",biography:"Antonella Petrillo is a Professor at the Department of Engineering of the University of Naples “Parthenope”, Italy. She received her Ph.D. in Mechanical Engineering from the University of Cassino. Her research interests include multi-criteria decision analysis, industrial plant, logistics, manufacturing and safety. She serves as an Associate Editor for the International Journal of the Analytic Hierarchy Process. She is a member of AHP Academy and a member of several editorial boards. 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Her focus is on quality, innovation, leadership, and personalised learning. She works primarily at the strategic and policy levels, both nationally and internationally, and with key international organisations. She is committed to promoting and improving OFDL in the context of SDG4 and the future of education. Ossiannilsson has more than 20 years of experience in her current field, but more than 40 years in the education sector. She works as a reviewer and expert for the European Commission and collaborates with the Joint Research Centre for Quality in Open Education. Ossiannilsson also collaborates with ITCILO and ICoBC (International Council on Badges and Credentials). She is a member of the ICDE Board of Directors and has previously served on the boards of EDEN and EUCEN. Ossiannilsson is a quality expert and reviewer for ICDE, EDEN and the EADTU. She chairs the ICDE OER Advocacy Committee and is a member of the ICDE Quality Network. She is regularly invited as a keynote speaker at conferences. She is a guest editor for several special issues and a member of the editorial board of several scientific journals. She has published more than 200 articles and is currently working on book projects in the field of OFDL. Ossiannilsson is a visiting professor at several international universities and was recently appointed Professor and Research Fellow at Victoria University of Wellington, NZ. Ossiannilsson has been awarded the following fellowships: EDEN Fellows, EDEN Council of Fellows, and Open Education Europe. She is a ICDE OER Ambassador, Open Education Europe Ambassador, GIZ Ambassador for Quality in Digital Learning, and part of the Globe-Community of Digital Learning and Champion of SPARC Europe. On a national level, she is a quality developer at the Swedish Institute for Standards (SIS) and for ISO. She is a member of the Digital Skills and Jobs Coalition Sweden and Vice President of the Swedish Association for Distance Education. She is currently working on a government initiative on quality in distance education at the National Council for Higher Education. She holds a Ph.D. from the University of Oulu, Finland.',institutionString:"Swedish Association for Distance Education, Sweden",institution:null},editorTwo:null,editorThree:null},{id:"94",title:"Climate Change and Environmental Sustainability",coverUrl:"https://cdn.intechopen.com/series_topics/covers/94.jpg",isOpenForSubmission:!1,annualVolume:null,editor:null,editorTwo:null,editorThree:null},{id:"95",title:"Urban Planning and Environmental Management",coverUrl:"https://cdn.intechopen.com/series_topics/covers/95.jpg",isOpenForSubmission:!0,annualVolume:11979,editor:{id:"181079",title:"Dr.",name:"Christoph",middleName:null,surname:"Lüthi",slug:"christoph-luthi",fullName:"Christoph Lüthi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRHSqQAO/Profile_Picture_2022-04-12T15:51:33.png",biography:"Dr. Christoph Lüthi is an urban infrastructure planner with over 25 years of experience in planning and design of urban infrastructure in middle and low-income countries. He holds a Master’s Degree in Urban Development Planning from the University College of London (UCL), and a Ph.D. in Urban Planning & Engineering from TU Berlin. He has conducted applied research on urban planning and infrastructure issues in over 20 countries in Africa and Asia. In 2005 he joined Eawag-Sandec as Leader of the Strategic Environmental Sanitation Planning Group. Since 2015 he heads the research department Sanitation, Water and Solid Waste for Development (Sandec) at the Swiss Federal Institute of Aquatic Research and Technology (Eawag).",institutionString:"Swiss Federal Institute of Aquatic Science and Technology, Switzerland",institution:null},editorTwo:{id:"290571",title:"Dr.",name:"Rui Alexandre",middleName:null,surname:"Castanho",slug:"rui-alexandre-castanho",fullName:"Rui Alexandre Castanho",profilePictureURL:"https://mts.intechopen.com/storage/users/290571/images/system/290571.jpg",biography:"Rui Alexandre Castanho has a master\\'s degree in Planning, Audit, and Control in Urban Green Spaces and an international Ph.D. in Sustainable Planning in Borderlands. Currently, he is a professor at WSB University, Poland, and a visiting professor at the University of Johannesburg, South Africa. Dr. Castanho is a post-doc researcher on the GREAT Project, University of Azores, Ponta Delgada, Portugal. He collaborates with the Environmental Resources Analysis Research Group (ARAM), University of Extremadura (UEx), Spain; VALORIZA - Research Center for the Enhancement of Endogenous Resources, Polytechnic Institute of Portalegre (IPP), Portugal; Centre for Tourism Research, Development and Innovation (CITUR), Madeira, Portugal; and AQUAGEO Research Group, University of Campinas (UNICAMP), Brazil.",institutionString:"University of Johannesburg, South Africa and WSB University, Poland",institution:{name:"University of Johannesburg",institutionURL:null,country:{name:"South Africa"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:26,paginationItems:[{id:"81791",title:"Self-Supervised Contrastive Representation Learning in Computer Vision",doi:"10.5772/intechopen.104785",signatures:"Yalin Bastanlar and Semih Orhan",slug:"self-supervised-contrastive-representation-learning-in-computer-vision",totalDownloads:3,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"79345",title:"Application of Jump Diffusion Models in Insurance Claim Estimation",doi:"10.5772/intechopen.99853",signatures:"Leonard Mushunje, Chiedza Elvina Mashiri, Edina Chandiwana and Maxwell Mashasha",slug:"application-of-jump-diffusion-models-in-insurance-claim-estimation-1",totalDownloads:2,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Data Clustering",coverURL:"https://cdn.intechopen.com/books/images_new/10820.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"81557",title:"Object Tracking Using Adapted Optical Flow",doi:"10.5772/intechopen.102863",signatures:"Ronaldo Ferreira, Joaquim José de Castro Ferreira and António José Ribeiro Neves",slug:"object-tracking-using-adapted-optical-flow",totalDownloads:10,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Information Extraction and Object Tracking in Digital Video",coverURL:"https://cdn.intechopen.com/books/images_new/10652.jpg",subseries:{id:"24",title:"Computer Vision"}}},{id:"81558",title:"Thresholding Image Techniques for Plant Segmentation",doi:"10.5772/intechopen.104587",signatures:"Miguel Ángel Castillo-Martínez, Francisco Javier Gallegos-Funes, Blanca E. 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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:"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:"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:"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. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",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:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",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:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. 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. 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\r\n\tIn general, the harsher the environmental conditions in an ecosystem, the lower the biodiversity. Changes in the environment caused by human activity accelerate the impoverishment of biodiversity.
\r\n
\r\n\tBiodiversity refers to “the variability of living organisms from any source, including terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; it includes diversity within each species, between species, and that of ecosystems”.
\r\n
\r\n\tBiodiversity provides food security and constitutes a gene pool for biotechnology, especially in the field of agriculture and medicine, and promotes the development of ecotourism.
\r\n
\r\n\tCurrently, biologists admit that we are witnessing the first phases of the seventh mass extinction caused by human intervention. It is estimated that the current rate of extinction is between a hundred and a thousand times faster than it was when man first appeared. The disappearance of species is caused not only by an accelerated rate of extinction, but also by a decrease in the rate of emergence of new species as human activities degrade the natural environment. The conservation of biological diversity is "a common concern of humanity" and an integral part of the development process. Its objectives are “the conservation of biological diversity, the sustainable use of its components, and the fair and equitable sharing of the benefits resulting from the use of genetic resources”.
\r\n
\r\n\tThe following are the main causes of biodiversity loss:
\r\n
\r\n\t• The destruction of natural habitats to expand urban and agricultural areas and to obtain timber, minerals and other natural resources.
\r\n
\r\n\t• The introduction of alien species into a habitat, whether intentionally or unintentionally which has an impact on the fauna and flora of the area, and as a result, they are reduced or become extinct.
\r\n
\r\n\t• Pollution from industrial and agricultural products, which devastate the fauna and flora, especially those in fresh water.
\r\n
\r\n\t• Global warming, which is seen as a threat to biological diversity, and will become increasingly important in the future.
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This series will focus on various crucial factors related to emerging viral infectious diseases, including epidemiology, pathogenesis, host immune response, clinical manifestations, diagnosis, treatment, and clinical recommendations for managing viral infectious diseases, highlighting the recent issues with future directions for effective therapeutic strategies.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",keywords:"Novel Viruses, Virus Transmission, Virus Evolution, Molecular Virology, Control and Prevention, Virus-host Interaction"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:{id:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343",scope:"Biomedical Engineering is one of the fastest-growing interdisciplinary branches of science and industry. The combination of electronics and computer science with biology and medicine has improved patient diagnosis, reduced rehabilitation time, and helped to facilitate a better quality of life. Nowadays, all medical imaging devices, medical instruments, or new laboratory techniques result from the cooperation of specialists in various fields. The series of Biomedical Engineering books covers such areas of knowledge as chemistry, physics, electronics, medicine, and biology. 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Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},subseries:[{id:"7",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:null,institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda",middleName:"R.",surname:"Gharieb",fullName:"Reda Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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