The proposed analytical models to estimate AIS operation quality [18, 19].
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
\n'}],latestNews:[{slug:"intechopen-signs-new-contract-with-cepiec-china-for-distribution-of-open-access-books-20210319",title:"IntechOpen Signs New Contract with CEPIEC, China for Distribution of Open Access Books"},{slug:"150-million-downloads-and-counting-20210316",title:"150 Million Downloads and Counting"},{slug:"intechopen-secures-indefinite-content-preservation-with-clockss-20210309",title:"IntechOpen Secures Indefinite Content Preservation with CLOCKSS"},{slug:"intechopen-expands-to-all-global-amazon-channels-with-full-catalog-of-books-20210308",title:"IntechOpen Expands to All Global Amazon Channels with Full Catalog of Books"},{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"}]},book:{item:{type:"book",id:"8542",leadTitle:null,fullTitle:"Fashion Industry - An Itinerary Between Feelings and Technology",title:"Fashion Industry",subtitle:"An Itinerary Between Feelings and Technology",reviewType:"peer-reviewed",abstract:'Fashion is a lot more than providing an answer to primary needs. 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\r\n\tThe fundamental reasons behind the present day's energy crisis in most parts of the world are insufficient growth in exploration of energy sources, a flawed estimate of the mounting energy demand, and energy losses during transmission and consumption. Process industries have a substantial share in energy consumption and equally high energy saving potentials if they adopt some integrated Energy Management System (EnMS). The EnMS in process industries across the globe is mostly based on manual calculation or methods which are not fully robust and integrated. Hence, a substantial amount of energy is lost which causes damage to the economy, wastage of national resources, and adversely affects the environment. With the emergence of the Internet of Things (IoT), in the developed economies, the EnMS is entering into the phase of automation in the paradigm shift from Industry 3.0 to 4.0.
\r\n\r\n\tThis book encompasses the historical evaluation of EnMS, barriers, and drivers to EnMS, sector-wise and region-wise analysis of EnMS, and prospects and challenges to EnMS in the era of the fourth industrial revolution in process industries. The outcome of the study will be helpful to the decision-maker in the industry as well as the government levels.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"2ba8d5488a593f5f263e8e1c8792e2e9",bookSignature:"Dr. Iftikhar Ahmad",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10378.jpg",keywords:"Energy Efficiency, Energy Conservation, Sustainability, Technology and EnMS, Energy and Economy, Environmental Protection, ISO Certification, EnMS Practices Worldwide, Industrial Management System4.0, Internet of Things, Energy Management Digitalization, Process Industries EnMS",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"July 9th 2020",dateEndSecondStepPublish:"July 30th 2020",dateEndThirdStepPublish:"September 28th 2020",dateEndFourthStepPublish:"December 17th 2020",dateEndFifthStepPublish:"February 15th 2021",remainingDaysToSecondStep:"9 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Dr. Iftikhar Ahmad Salarzai is an expert in virtual sensing, uncertainty quantification, reliability analysis, and automation of process energy management systems (EnMS), he received his Ph.D. in Chemical Engineering from Kyoto University Japan, he also received the outstanding research award from the Society of Chemical Engineers, Japan and ORIC Innovation Award from the University of Engineering & Technology, Peshawar, Pakistan.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"261363",title:"Dr.",name:"Iftikhar",middleName:null,surname:"Ahmad",slug:"iftikhar-ahmad",fullName:"Iftikhar Ahmad",profilePictureURL:"https://mts.intechopen.com/storage/users/261363/images/system/261363.jpg",biography:"Dr. Iftikhar Ahmad Salarzai is expert in virtual sensing, uncertainty quantification, reliability analysis, and automation of process energy management systems (EnMS). 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Utkin",authors:[{id:"16132",title:"Dr.",name:"Andrei",middleName:"B.",surname:"Utkin",fullName:"Andrei Utkin",slug:"andrei-utkin"}]},{id:"14176",title:"A Statistical Theory of the Electromagnetic Field Polarization Parameters at the Scattering by Distributed Radar Objects",slug:"a-statistical-theory-of-the-electromagnetic-field-polarization-parameters-at-the-scattering-by-distr",signatures:"Victor Tatarinov and Sergey Tatarinov",authors:[{id:"3928",title:"Prof.",name:"Victor",middleName:null,surname:"Tatarinov",fullName:"Victor Tatarinov",slug:"victor-tatarinov"},{id:"3929",title:"Dr.",name:"Sergey",middleName:null,surname:"Tatarinov",fullName:"Sergey Tatarinov",slug:"sergey-tatarinov"}]},{id:"14177",title:"Radar Meteor Detection: Concept, Data Acquisition and Online Triggering",slug:"radar-meteor-detection-concept-data-acquisition-and-online-triggering",signatures:"Eric V. C. Leite, Gustavo de O. e Alves, José M. de Seixas, Fernando Marroquim, Cristina S. Vianna and Helio Takai",authors:[{id:"16962",title:"Dr.",name:"José M.",middleName:null,surname:"De Seixas",fullName:"José M. De Seixas",slug:"jose-m.-de-seixas"},{id:"19604",title:"Mr.",name:"Eric",middleName:null,surname:"Leite",fullName:"Eric Leite",slug:"eric-leite"},{id:"19605",title:"Mr.",name:"Gustavo O.",middleName:null,surname:"Alves",fullName:"Gustavo O. Alves",slug:"gustavo-o.-alves"},{id:"19606",title:"Prof.",name:"Fernando",middleName:null,surname:"Marroquim",fullName:"Fernando Marroquim",slug:"fernando-marroquim"},{id:"19607",title:"Prof.",name:"Helio",middleName:null,surname:"Takai",fullName:"Helio Takai",slug:"helio-takai"}]},{id:"14178",title:"Electromagnetic Waves Propagating Around Buildings",slug:"electromagnetic-waves-propagating-around-buildings",signatures:"Mayumi Matsunaga and Toshiaki Matsunaga",authors:[{id:"15254",title:"Prof.",name:"Mayumi",middleName:null,surname:"Matsunaga",fullName:"Mayumi Matsunaga",slug:"mayumi-matsunaga"},{id:"18812",title:"Prof.",name:"Toshiaki",middleName:null,surname:"Matsunaga",fullName:"Toshiaki Matsunaga",slug:"toshiaki-matsunaga"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"69286",title:"Probabilistic Methods for Cognitive Solving of Some Problems in Artificial Intelligence Systems",doi:"10.5772/intechopen.89168",slug:"probabilistic-methods-for-cognitive-solving-of-some-problems-in-artificial-intelligence-systems",body:'\nToday, artificial intelligence (AI) has confidently entered our lives. The first mention of it belongs to the mid-50s of the last century. Under AI, we usually understand it as the branch of computer science devoted to develop data processing systems that perform functions normally associated with human intelligence, such as reasoning, learning, and self-improvement (ISO/IEC 2382-1:1993 Information technology–Vocabulary–Part 1). According to this, over the decades, AI has found its application in expert systems supporting decision-making, in heuristic classification, computer vision, pattern recognition, understanding natural language, etc. [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. Here, under AI systems (AIS), we understand systems that include data processing systems that perform functions by AI, in particular by modeling and logic reasoning.
\nNote. System is a combination of interacting elements organized to achieve one or more stated purposes (according to ISO/IEC/IEEE 15288 “Systems and software engineering–System life cycle processes”).
\nIf the modern human brain already possesses skills of adaptation to conditions of various uncertainties in the world around, artificial intelligence systems require creation of effective methods for cognitive solving actual practical problems. “Cognitive solving” means relating to or involving the processes of thinking and reasoning (Cambridge English Dictionary). The applicable mathematical methods are focused mainly on conditions of actions in the logician “if …, that …” according to the gathered information, and on an estimation of traced situations by a man-operator. At increase and expansion of uncertainty conditions, quite often, there are failures and errors because of complexity. It means that search of new methods for advanced solving of AIS practical problems today is very important.
\nIn the present chapter, various AIS for supporting decision-making in intellectual manufacture and robotics systems are analyzed. According to robotics, it is supposed that AIS may be used for solving multiple aerial, land, underground, underwater, universal, and special problems of creation and operation. At the same time, we would like to emphasize that the main efforts of this chapter are not focused on illustrating the capabilities of AIS, but on demonstrating the applicability of author’s probabilistic models and methods to improve some of the existing capabilities of AIS.
\nFor this goal, the problem of planning the possibilities of functions performance on the base of monitored information and the problem of robot route optimization under uncertainties limitations are chosen. The choice of these problems in AIS applications is caused on the one hand by increase of quantity and a variety of specific uncertainties conditions, and on the other hand by an urgency and width of areas for their practical use. However, some relevant problems (such as the problems of robotics orientation, localization and mapping, information gathering, the perception and analysis of commands, movement and tactile, realizations of manipulations, and also rational control) for which different probabilistic methods are also applicable have been left out of the scope of work.
\nFor cognitive solving and improvements by the use of probabilistic methods, the chosen problems are transformed more specifically to:
problem 1 of planning the possibilities of functions performance on the base of monitored information about events and conditions, and
problem 2 of robot route optimization under limitations on risk of “failure” in conditions of uncertainties.
The proposed methods for cognitive solving AIS problems are based on theoretical and practical author’s researches [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37] and need to be used either in combination or in addition to existing methods. There, where often it is required prognostic analysis or where the used approaches are not effective, the proposed methods can be used as rational basis or alternative.
\nThe proposed and referred author’s methods and models can be used in AIS life cycle to form system requirements, compare different processes, rationale technical decisions, and estimate reliability, quality, and risks. The decisions, scientifically proved by the offered models and software tools, can provide purposeful essential improvement of quality and mitigation of risks and decrease expenses for created and operating systems. The spectrum of the explored systems by these methods includes systems (not only AIS) operated by government agencies, manufacturing structures (including power generation, coal enterprises, oil and gas systems), food storage, space industry, emergency services, municipal economy, etc. [15, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37]. The supporting software tools are original Russian creations registered by Rospatent [38, 39, 40, 41, 42, 43, 44]. They have been presented at seminars, symposiums, conferences, ISO/IEC working groups, and other forums since 2000 in Russia, Australia, Canada, China, Finland, France, Germany, Italy, Kuwait, Luxembourg, Poland, Serbia, the USA, etc. The software tools were awarded by the Golden Medal of the International Innovation and Investment Salon and the International Exhibition “Intellectual Robots,” acknowledged on the World’s fair of information technologies CeBIT in Germany, noted by diplomas of the Hanover Industrial Exhibition and the Russian exhibitions of software.
\nNote. The proposed methods below do not replace existing methods for robots actions (for example, the methods of solving the systems of differential equations, the methods of refreshed linear and geometric algebra, geometry, Lie groups, linearization, solving Jacobians and Hessians, Kalman filters, Lyapunov analysis, the methods of biomechanics, graph theory, Laplas transforming for large-scale dynamic systems, etc.) [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14].
\nThe structure of the chapter research is shown in Figure 1. It provides an explanation of the essence of cognitive solving of problems on the base of probabilistic modeling, selection of some author’s probabilistic models applicable for cognitive solving problems 1 and 2, the practical steps to solve these problems, and five practical examples demonstrating system planning the possibilities of functions performance by using robot-manipulators (in space), by AIS for a coal company and by AIS used for a security service of floating oil and gas platform, example of forming input for probabilistic modeling from monitored data and example of robot route optimization under limitations on risk of “failure” in conditions of uncertainties. Various areas of the examples’ applications have been chosen purposely to demonstrate universality and analytical usefulness of the proposed methods and models. Appendices includes the proof for the proposed model of a quite general technology of periodical diagnostics of system integrity and some short models results to estimate quality of used information.
\nThe structure of the research.
This section explains the definitions and interpretations which can help to understand the proposed models and results of modeling complex systems in different application areas.
\nAIS itself can be considered as an interested system (for example, dispatching intellectual center) or as a part of other, more comprehensive interested system (for example, functionally focused robots in safety systems). The current information is processed in real time for performing the set or expected functions of interested system. To meet system requirements, the solutions of considered problems 1 (of planning the possibilities of functions performance) and 2 (of robot route optimization) are initiated along with the solutions of other problems.
\nThe cognitive solving of problems include improvements, accumulation, analysis, and a use of appearing knowledge, see Figure 2. Possible uncertainties for the given period (from initial time point t1 to future moment tx) may be considered by using proposed probabilistic modeling, prediction, and optimization.
\nThe essence of cognitive of solving of problems.
The solutions for problems 1 and 2 are estimated by probability of “success” and/or “failure” (risk of “failure”) during given prognostic time period. Thus, prognostic period should be defined so to be in time to recover capabilities (which can be lost), or to carry out preventive action (with which the initiation of solving the problem is connected). Such behavior means operation in real time.
\nIn each real case of modeling the term “success” should be defined in terms of admissible condition of interested system to operate for the purpose. The term “failure” means “unsuccess.” Generally, a “success” of interested system operation during the given time period means an admissible degree of integrity. Accordingly, “failure” for interested system during given time period means inadmissible degree of integrity at least once within this period. System (or system element) integrity is defined as such system (system element) state when system (system element) purposes are achieved with the required quality and/or safety. The risk of “failure” is understood as a probabilistic measure of “failure” considering consequences (according to ISO Guide 73).
\nNote. For example, an interested system is a dangerous manufacturing object. The object structure includes an AIS, which monitors events and conditions in and/or around its manufacture. Equipment parameters (temperature, pressure, and so forth) which should be in norm limits are traced. The “failure” of interested system operation may mean an incident or accident on object.
\nGenerally, from the point of view of formalization for each estimated variants (for problem 1 or 2), the interested system is logically decomposed to compound subsystems; see Figures 3 and 4. Each subsystem is a set of components (elements and/or other subsystems): for problem 1, this set covers the components participating in functions performance; and for problem 2, the set covers compound parts of a possible route of the robot in space. Complete set of these components formally characterizes a variant of decomposed system for solving problem 1 or 2. The analysis and optimization are carried out on complete set of all compared possible variants.
\nVariant of system decomposition.
Variant of subsystem decomposition.
Interpretation of such decomposition is the following:
\nThe subsystem from serial connected elements provides functions performance with admissible level of integrity (quality and/or safety) at given time, if:
“AND” 1st component, …, “AND” last element provide admissible level of integrity (quality and/or safety) at given time (for problem 1);
“AND” 1st compound part of the route, …, “AND” last compound part of the route are overcame successfully by the robot at given time (for problem 2).
The subsystem from parallel connected elements provides functions performance with admissible level of integrity (quality and/or safety) at given time, if:
“OR” 1st component, …, “OR” last component in the subsystem provide admissible level of integrity (quality and/or safety) at given time (for problem 1);
“OR” 1st compound part of the route, …, “OR” last compound part of the route are overcame successfully by the robot at given time (for problem 2).
Each component after system decomposition is presented as a “black box.” For each “black box,” various probabilistic models can be applied for calculations and for building required probabilistic distribution function (PDF) of time between the next deviations from an established norm. A norm is connected with definitions of “success” and “failure,” it may be connected with the precondition to “failure” (to prevent “failure”—see Example 2). Focus on processes’ description allows to use only time characteristics (mean time or frequency of events), the dimensionless or cost characteristics peculiar for various applications.
\nAppropriate calculated probabilities of “success” and/or “failure” (risk of “failure”) in comparisons to real events during the prediction periods represent the knowledge of admissibility borders for probabilities of “success” and acceptability borders for risks of “failure.” The process of cognitive solving of problems 1 and 2 means not only the formation and use of this knowledge for interested system, but also the estimated quality of monitored and used information (including definition of input for continuous modeling).
\nThe proposed probabilistic methods for cognitive solving of problems 1 and 2 are based on selected probabilistic models which are implemented effectively in wide application areas. The main principle at a selection of models consists that useful knowledge should be result of their application in conditions of various uncertainties. Knowledge is understood as the form of existence and ordering of results of cognitive activity of human. In the applications to solv
Selected models for every system element, presented as “black box,” allow to estimate probabilities of “success” and/or “failure” during given prognostic period. A probabilistic space (
Not considering uncertainty specificities, in general case, intellectual operation of AIS component aims to provide reliable and timely producing complete, valid and/or, if needed, confidential information; see Figure 5. The gathered information is used for its proper specificity. And, the proposed models [18, 19] allow to estimate the intellectual operation processes on a level of used information quality, which is important for every AIS (information may be used by technical devices, “smart” elements, robotics, users, etc.).
\nQuality of used information (abstraction).
The proposed analytical models (“The model of functions performance by a complex system in conditions of unreliability of its components,” “The models complex of calls processing for the different dispatcher technologies,” “The model of entering into system current data concerning new objects of application domain,” “The model of information gathering,” “The model of information analysis,” “The models complex of dangerous influences on a protected system,” and “The models complex of an authorized access to system resources”) allow to estimate the probability of “success” and risks to lose quality of intellectual operation during given prognostic period considering consequences; see Table 1. Required limits on probability measures are recommended as produced knowledge for the best AIS practice (estimated on dozens practical estimations for various application areas).
\nThreats to AIS operation quality | \nEvaluated measure (required limits as produced knowledge for the best practice) | \nModel tittle | \n
---|---|---|
Information is not produced as a result of system unreliability | \nProbability of providing reliable functions performance during given time (no less than 0.99). Mean time between failures. System availability (no less than 0.9995) | \nThe model of functions performance by a complex system in conditions of unreliability of its components | \n
Delayed information producing (i.e., not in real time) | \nProbability of well-timed processing during the required term (no less than 0.95). Mean response time. Relative portion of all well-timed processed calls. Relative portion of well-timed processed calls of those types for which the customer requirements are met (no less than 95%) | \nThe models complex of calls processing for the different dispatcher technologies | \n
Producing of incomplete information | \nProbability that system contains information about states of all real object and coincides (no less than 0.9) | \nThe model of entering into system current data concerning new objects of application domain | \n
Information validity deterioration caused by: \n
| \nProbability of information actuality on the moment of its use (no less than 0.9). Probability of errors absence after checking (no less than 0.97). Fraction of errors in information after checking. Probability of correct analysis results obtaining (no less than 0.95) | \nThe model of information gathering. The model of information analysis | \n
Violation of information confidentiality | \nProbability of system protection against unauthorized access during objective period (no less than 0.999) | \nThe models complex of an authorized access to system resources | \n
Violation of secure system operation including \n
| \nProbability of faultless (correct) operation during given time (no less than 0.95). Mean time between errors. Probability of system protection against unauthorized access (no less than 0.99) | \nThe models complex of dangerous influences on a protected system. The models complex of an authorized access to system resources | \n
The next probabilistic model is devoted to estimate a probability of “success” and risk of “failure” on high meta level. This is based on studying the general AIS technology of periodical diagnostics of system integrity. Some general technologies were researched for “The models complex of dangerous influences on a protected system,” see Table 1. Here, the general case for AIS is presented.
\nFor system element allowing prediction of risks to lose its integrity during given prognostic period, there is studied the next general AIS technology of providing system integrity.
\nTechnology is based on the periodical diagnostics of system integrity (without the continuous monitoring between diagnostics). Diagnostics are carried out to detect danger sources occurrence from threats into a system or consequences of negative influences (for example, these may be destabilizing factors on dangerous enterprise). The lost system integrity can be detected only as a result of diagnostics, after which system recovery is started. Dangerous influence on system is acted step-by step: at first, a danger source occurs into a system, and then after its activation may be a loss of integrity; see Figure 6. Occurrence time is a random value that can be distributed by PDF of time between neighboring occurrences of danger Ωoccur(t) =
Some random events for technology: left—correct operation to provide system integrity; right—a loss of integrity during prognostic period Tgiven.\n
It is supposed that used diagnostics tools allow to provide system integrity recovery after revealing danger sources occurrence or the consequences of influences. Thus, the probability (
There are possible the next variants:
variant 1—given prognostic period Tgiven is less than the established period between neighboring diagnostics (Tgiven < Tbetw. + Tdiag);
variant 2—given prognostic period Tgiven is more than or equal to the established period between neighboring diagnostics (Tgiven ≥ Tbetw. + Tdiag).
Here, Tbetw. is the time between the end of diagnostics and the beginning of the next diagnostics and Tdiag is the diagnostics time.
\nFor the given period Tgiven, the next statements are proposed for use, see in detail [18, 19, 35, 36, 37].
\nUnder the condition of independence of considered characteristics, the probability of providing system integrity (probability of “success”) is equal to
for variant 1
\n
for variant 2
where N = [Tgiven/(Тbetw. + Тdiag.)] is the integer part, Trmn = Tgiven − N(Tbetw + Tdiag);
\nmeasure (b)
\nThe probability of success within given prognostic period
The modification of this model allows to use different values of diagnostics and recovery time [35, 36, 37]; for formulas (1)–(3), recovery time is equal to diagnostics time.
\nAll these models, supported by various versions of software tools, registered by Rospatent, may be applied and improved for solving quality and safety problems, connected with intellectual system presented as “black box” [18, 19, 38, 39, 40, 41, 42, 43, 44].
\nSummaries for the last model are as follows:
The input for modeling include: frequency of the occurrences of potential threats (or mean time between the moments of the occurrences of potential threats which equals to 1/frequency); mean activation time of threats; mean recovery time; time between the end of diagnostics and the beginning of the next diagnostics; diagnostics time; and given prognostic period.
The calculated results of modeling include: the probability of providing system integrity within given prognostic period (i.e., probability of “success”); and risk to lose integrity (i.e., probability of “failure”) as addition to 1 for probability of “success.”
If probability of providing system integrity within given prognostic period for all points Tgiven from 0 to ∞ are computed, it means a trajectory of the PDF depending on characteristics of threats, periodic diagnostics, and recovery. And, the building of PDF is the real base to predict probabilistic metrics for given time Tgiven. In analogy with reliability, it is important to know a mean time between neighboring losses of integrity (MTBLI) like mean time between failures in reliability (MTBF), but in application to concepts of quality, safety, etc.
\nFor complex systems with serial or parallel structure, new models with known PDF can be developed by the next method [17, 18, 19, 20, 21]. Let us consider the elementary structure from two independent parallel or serial elements (Figures 3 and 4). Let the PDF of time between losses of i-th element integrity be Вi(t), i.e., Вi(t) = Р(τi ≤ t), then:
time between losses of integrity for system combined from serial connected-independent elements is equal to minimum from two times τi: failure of first or second elements (it means the system goes into a state of lost integrity when either first, or second element integrity is lost). For this case, the PDF of time between losses of system integrity is defined by the expression
\n
2. time between losses of integrity for system combined from parallel connected independent elements (hot reservation) is equal to a maximum from two times τi: failure of first and second elements (it means the system goes into a state of lost integrity when both first and second elements have lost integrity). For this case, the PDF of time between losses of system integrity is defined by the expression
By applying recurrently expressions (4) and (5), it is possible to build PDF of time between losses of integrity for any complex system with parallel and/or serial structure.
\nAs summary, the calculated results of modeling are: PDF of time between losses of integrity for system and each compound subsystems and elements; mean time between losses of integrity for system and each compound subsystems and elements (MTBLI as analog of MTBF).
\nFor example, integrated complex system, combined from intellectual structures for modeling interested system including AIS (Figure 7), can be analyzed by formulas (1)–(5) and probabilistic models described above and allowing to form PDF by (4) and (5). The correct operation of this complex system during the given period means: during this period both first and second subsystems (left and right) should operate correctly according their destinations, i.e., integrity of complex system is provided if “AND” integrity of first system left “AND” integrity of second system right are provided.
\nAn integrated complex system of two serial subsystems (abstraction).
All these ideas of analytical modeling operation processes are supported by the software tools [18, 19, 21, 23, 38, 39, 40, 41, 42, 43, 44].
\nWhat about new knowledge by using the proposed methods and models for cognitive solving of problems 1 and 2 of the chapter? A use of these methods and models on different stages of AIS life cycle (concept, development, utilization, support stages) allows to produce cognitive answers for the following questions:
What about different risks to lose integrity in operation?
What about the justified norms for values of monitored parameters?
What requirements should be specified to MTBLI and to repair time for different possible scenarios of operation?
Which information operation processes should be duplicated and how?
What processing devices and technologies should be used to achieve the necessary level of system integrity (quality, safety, etc.)?
What is the system tolerance to data flows changing?
What data flows and functional tasks may be the main causes of “bottlenecks”?
What data gathering technologies and engineering solutions can guarantee the completeness and actuality of used information?
What information verification and validation control should be used?
What qualification requirements should be for the users of AIS (from the AIS effectiveness and efficiency points of view)?
How dangerous are scenarios of environment influences and what protective technologies will provide the required security?
How the use of integrity diagnostics and security monitoring will worsen time-probabilistic characteristics of system?
What protection system effectiveness should be to prevent an unauthorized access?
What are the information security risks? etc.\n
The rationale answers allow to improve and accumulate knowledge concerning AIS.
\nThe proposed methods and models provide the next approach for cognitive solving problems 1 and 2.
\nIt is supposed that the terms “success” and accordingly “failure” are defined in terms of admissible condition of interested system to operate for the purpose.
\nNote. For example, for each parameter of equipment, the ranges of possible values of conditions may be estimated as “Working range inside of norm” and “Out of working range, but inside of norm” (“success”) or “Abnormality” (“failure”), interpreted similarly light signals—“green,” “yellow,” and “red.” For this definition, a “failure” of equipment operation characterizes a threat to lose system norm integrity after danger influence (on the logic level this range “Abnormality” may be interpreted analytically as failure, fault, losses of quality, or safety etc.). But the definition may be another: for example, a “failure” may be defined as incident or accident. For this definition, short-time being in the range “Abnormality” is not “failure,” because the incident or accident may not happen.
\nThere are four steps proposed for cognitive solving of problem 1 of planning the possibilities of functions performance on the base of monitored information about events and conditions; see Figure 8.
\nSteps for cognitive solving of problem 1.
Knowledge base (K-base) is defined as a database that contains inference rules and information about human experience and expertise in a domain (ISO/IEC 2382-1:1993).
\nMaximum of gain as a result of the functions performance under the given conditions and limitations on the acceptable risk of failure and/or other limitations
Maximum probability of “success” or minimum risk of “failure” under limitations
Note. For example, there are proposed the next general formal statements of problems for system optimization:
on the stages of system concept, development, production, and support: system parameters, software, technical, and management measures (Q) are the most rationale for the given period if on them the minimum of expenses (Zdev.) for creation of system is reached
at limitations on probability of an admissible level of quality
on utilization stage: system parameters, software, technical, and management measures (Q) are the most rational for the given period of operation if on them the maximum of probability of correct system operation is reached
at limitations on probability of an admissible level of quality
For limitation on
These statements (6), (7) may be transformed into the problems of expenses or risk minimization in different limitations. There may be a combination of these formal statements in system’s life cycle.
\nNote. A solution that meets all conditions may not exist. In this case, there is no optimal variant of planning the possibilities of functions performance on the base of monitored information. Additional systems analysis, adjustment of the criteria, or limitations is required (see, for example, ISO/IEC/IEEE 15288).
\nFor a robot, the concept of “failure” under uncertainty is defined as the “unsuccess” to achieve the goal within a given time. It is assumed that there are several possible routes to achieve the goal, and uncertainties may include both the conditions for robot operation (including random events in orientation, localization, and mapping in cooperation with drone for gathering actual data). The minimum risk of failure under the existing conditions and limitations is used as a criterion of optimization.
\nThe next four steps are proposed for cognitive solving of problem 2 of robot route optimization under limitations on risk of “failure” in conditions of uncertainties, see Figure 9.
\nSteps for cognitive solving of problem 2.
The index i of the first part of the selected route is set to the initial value i = 1.
\nIf the set of possible options is exhausted and the goal is not achieved, it is concluded that the goal is unattainable with the risk of “failure” less than the acceptable risk (i.e., it means an impossibility of solving problem 2 in the defined conditions).
\nThus, for optimizing robot route in space (i.e., for the “successful” solution of problem 2) in real time, information gathering, probabilistic predictions for possible route variants, their comparison, the choice of the best variant, the implementation of further actions, the improvement, accumulation, systematization, and use of knowledge are being, see Figure 9.
\nNote. The proposed methods of solving problems 1 and 2 are essentially identical approaches based on the use of the same probabilistic models (Section 3). The only difference is that for the system planning the possibilities of functions performance (problem 1), the concept of “success” is used; and for the robot route optimization under limitations on risk of “failure” (problem 2), the concept of “failure,” which is defined as the lack of “success,” is used.
\nHere, problem 1 (of planning the possibilities of functions performance) is solved by the proposed approach on the base of information gathered from different similar projects, accumulated and systematized in K-base including history. Applicability of the proposed probabilistic methods and models on development stage is demonstrated to improve some of the existing capabilities of robot-manipulator. It is required to predict the possible period of robot-manipulator use in space. When planning the possibilities of performing the functions of the cosmonaut-operator, two variants were compared: first variant–without a use of AIS; second–by using some AIS for supporting decision-making and monitoring the status of the operator’s console, power units, central controller, and control handle for manipulator means.
\nA robot-manipulator as a system is composed on subsystems: an operator’s console, a power unit, a central controller with a handle of control and manipulator means. There are supposed that a frequency of anomalies is in average 1 times a year, mean activation time from anomaly occurrence to failure is about 3 days. Time between the end of diagnostics and the beginning of the next diagnostics is about 2 months, and the recovery time is about 2 days.
\nSystem decomposition is presented on Figure 10. We do STEPS 1–4 (Figure 8) and use formulas (1)–(3) for solving the problem for complex structure composed by elementary variants decompositions presented on Figures 3 and 4. Here, probability of “success” (
Probability of reliable operation of robot-manipulator as a system
Probability of reliable operation of every subsystem
Results of probabilistic modeling robot-manipulator operation.
Risks of “failure” (R) means addition to 1 for probability of “success.”
\nResults of modeling the first variant of project have shown the following (Figure 10): for operator’s console (first subsystem), power unit (second subsystem) and central controller with a handle of control (third subsystem) MTBLI = 8766 h, for manipulator means (including a hinge of roving of key, a hinge of shoulder, a hinge of roving of elbow, a hinge of elbow, a hinge of roving of brush, a hinge of brushes, a hinge of brush rotation, a device for grasping, videocamera—united as subsystem 4, which can operate if one of these means is available) MTBLI = 31,293 h, for all complex 1,…,4 MTBLI = 2672 h; probability of reliable operation of complex 1,…,4 during 8 h is equal to 0.979; probability of reliable operation of complex 1,…,4 during 48 h is equal to 0.965.
\nThe maximum probability of “success” and minimum risk of “failure” under limitations on the successful functions performance are used as a criterion.
\nThe results of first variant are used for estimating input for the second variant of modeling: every subsystem for second variant (for subsystems equipped by AIS) is characterized by MTBLI = 31,293 h in analogy to the subsystem 4 of first variant. Owing to AIS, the frequency of anomalies is about 0.28 year−1 (it is equal to 1/MTBLI), but the conditions of anomalies activation time are more strong: the mean time is 30 min. The time between the end of diagnostics and the beginning of the next diagnostics is 1 month, and the recovery time is about 1 day.
\nWhat about the risks of “failure” during period from 0.05 to 2 years?
\nAnalysis of modeling results proves: risks are very high despite the use of AIS with the described characteristics, see Figure 11.
\nRisks of “failures” depending on the prognostic period of use (from 0.05 to 2 years).
For a robot-manipulator used in space, new knowledge for accumulating and improving K-base is as follows:
The input (used for modeling) characterizes inadmissible conditions for functions performance by robot-manipulator.
The probability of “success” on level 0.98 or risk of “failure” on level 0.02 during six sessions of cosmonaut work is inadmissible for reliable robot-manipulator operation more than 1–2 weeks in space.
For a robot-manipulator used in space, the level 31,293 h of MTBLI is inadmissible level for every compound subsystem equipped by considered AIS.
Analyzed project of robot-manipulator operation effectiveness can be added to K-base history as precedent of “unsuccess.”
For analyzed project, new research for decreasing risks with the proof of its efficiency on the basis of modeling is strongly required after improving characteristics for every subsystem of robot-manipulator.
In practice, many devices proper to intelligent manufacturing are sources of data monitored. This example explains how monitored data can be tailored in AIS for probabilistic modeling to solve both problems 1 and 2.
\nThe approach to form specific input for modeling is demonstrated on example of mean time Toccur for PDF Ωoccur(t) and mean time Tactiv for PDF Ωactiv(t) from random values τoccurrence and τactivation (Figures 6 and 12).
\nThe universal elementary ranges for monitored parameters.
The elementary ranges for monitored parameters from quality or safety point of view should be set. For each parameter, the ranges of possible values of conditions are set: “Working range inside of norm,” “Out of working range, but inside of norm,” and “Abnormality,” The condition “Abnormality” characterizes a threat to lose system integrity after danger influence (on the logic level this range “Abnormality” may be interpreted analytically as failure, fault, losses of quality, or safety etc.). The construction on Figure 12 allows to extract data for probabilistic modeling: time between moments of the occurrences of dangers (potential threats), activation time of occurred dangers, and recovery time.
\nFor example, from Figure 12:
Mean time between moments of the occurrences of dangers (potential threats) Toccur = (τoccurrence 1 + τoccurrence 2)/2
Mean activation time Tactiv = (τactivation 1 + τactivation 2 + τactivation 3)/3
Mean recovery time for Trecovery = (τrecovery 1 + τrecovery 2)/2
This example is auxiliary to understand some sources of input for the proposed models (Sections 3–5) used for the next examples.
\nApplicability of the proposed probabilistic methods and models is demonstrated to improve some of the existing capabilities of AIS for a coal company. This subsection contains an explanation how problem 1 (of planning the possibilities of functions performance) may be solved for intelligent manufacturing by the proposed approach on the base of data monitored. This demonstrates AIS possibilities for a coal company on its operation stage.
\nLet a coal company (as system) is decomposed on 9 subsystems for studying efficiency. Of course, every subsystem also may be considered as complex system, for example, see Figure 7. Components from 1 to 6 united by multifunctional safety system of the mine, component 7 is associated with the washing factory, component 8 is associated with transport, and component 9 with port, see Figure 13: 1—the control system of ventilation and local airing equipment; 2—the system of modular decontamination equipment and compressed air control; 3—the system of air and gas control; 4—the system of air dust content control; 5—the system of dynamic phenomena control and forecasting; 6—the system of fire-prevention protection; 7—the safety system of washing factory; 8—the safety system for transport; and 9—the safety system of port. Information is monitored from different sources, accumulated in a database of dispatcher intelligence center, processed, and systematized (including systematization described in Example 2 to get input for modeling).
\nAn example of a coal company with AISs that transformed all system components to the level which is proper to skilled workers.
For planning possibilities of functions performance by AIS in this example, the probabilistic modeling is being to answer the next two questions:
How every responsible worker can know a residual time before the next parameters abnormalities?
What risks to lose system integrity may be for a year, for 10 and 20 years if all subsystems are supported by AISs that transform all system components to the level which is proper to skilled workers (Optimistic view on dangerous coal intelligent manufacturing)?
To answer the first question, the ranges of possible values of conditions are established: “Working range inside of norm,” “Out of working range, but inside of norm,” and “Abnormality” for each separate critical parameter of equipment. It is interpreted similarly by light signals—“green,” “yellow,” and “red,” as it is reflected on Figure 12. Some examples of parameters may include compression, capacity, air temperature (out, in, at machinery room), voltage, etc. The information from Example 6.2 and additional time data of enterprise procedures are used by AIS as input for using formulas (1) and (3) and Steps 1–4 (from Figure 8) in real time of company operation activity. Here, risks to lose the system integrity during the given period Tgiven means risks to be at least once in state “Abnormality” within Tgiven. The functions of modeling is performed on special servers (centralized or mapped); see details in [27, 36]. If virtual risks are computed by formulas (1) and (3) for all points Tgiven from 0 to ∞, the calculated values form a trajectory of the PDF. The mathematical expectation of this PDF means the mean residual time to the next state “Abnormality.” It defines MTBLI from this PDF. This output of probabilistic modeling can be transmitted to interested workers. Requirements to AIS operation quality are: quality measures of used information by AIS should meet admissible level recommended in Table 1.
\nThus, the answer on the first question “How responsible worker can know a residual time before the next parameters abnormalities?” is: the calculated mean residual time to the next state “Abnormality” (MTBLI for “red” range on Figure 12) can be transmitted in real time to responsible worker immediately after parameter value cross the border from “Working range inside of norm,” “Out of working range, but inside of norm” (from “green” to “yellow” range on Figures 12 and 13). It is possible as a result of implementation of the proposed approach—see example of implementation in [27, 36].
\nTo answer the second question, let the next input be formed from data monitored.
\nLet for every system component, a frequency of occurrence of the latent or obvious threats is equal to once a month and the mean activation time of threats is about 1 day. The system diagnostics are used once for work shift 8 h, a mean duration of the system control is about 10 min, and the mean recovery time of the lost integrity of object equals to 1 day. The workers (they may be robotics, skilled mechanics, technologists, engineers, etc.) are supported by capabilities of an AIS and a remote monitoring systems allowing estimating in real time the mean residual time before the next parameters abnormalities considering the results of probabilistic modeling. Formally they operate as parallel elements with hot reservation (structure on Figure 4, right). Owing to AIS support workers are capable to revealing signs of a critical situation after their occurrence. Workers can commit errors on the average not more often once a year (it is proper to skilled workers).
\nTo answer the question we do Steps 1–4 (from Figure 8) and use formulas (1)–(3) for solving the problem for complex structure, see Figure 13. Here, risks to lose system integrity means risks of “failure” for every subsystem which can be detailed to the level of every separate critical parameter of equipment.
\nThe fragments of built PDF on Figure 13 show: risk of “failure” increases from 0.000003 for a year to 0.0004 for 10 years and to 0.0013 for 20 years. Thus, the mean time between neighboring losses of integrity (MTBLI) equals to 283 years.
\nThese are some estimations for example assumptions.
\nThus, the answer on second question “What risks to lose system integrity may be for a year, for 10 and 20 years if all subsystems are supported by AISs that transform all system components to the level which is proper to skilled workers?” is: risks to lose system integrity may be 0.000003 for a year, 0.0004 for 10 years and 0.0013 for 20 years, herewith (MTBLI) is equal to 283 years. These are the Optimistic estimations for dangerous coal intelligent manufacturing that make sense to take over a desired level of AIS operation effectiveness.
\nNew knowledge for accumulating and improving K-base is as follows:
The input (used for modeling) characterizes admissible conditions for functions performance by AIS for a coal company.
The probability of “success” on levels 0.99997 for a year, 0.9996 for 10 years and 0.9987 for 20 years or risk of “failure” on levels 0.000003 for a year to 0.0004 for 10 years and 0.0013 for 20 years (with predicted risks levels for discovered “bottlenecks”) are admissible.
Expected term in average 283 years and more is admissible systemic aim for providing safe company operation.
Analyzed project of AISs operation effectiveness (that transform all system components to the level which is proper to skilled workers of coal company) can be added to K-base history as a precedent of “success.”
This subsection continues an explanation on how problem 1 (of planning the possibilities of functions performance) may be solved for intelligent manufacturing by the proposed approach on the base of data monitored. This demonstrates the capabilities of AIS used for a security service of floating oil and gas platform on its operation stage. The difference from previous example is in more degree of uncertainties (because of high complexity) that allows to transform all system components to the level which is proper to medium-level workers of floating oil and gas platform. The same approach, structure, and formulas for probabilistic modeling are used.
\nLet a floating oil and gas platform is also decomposed on nine subsystems. Every subsystem is enumerated on Figure 14, and operates as parallel elements with hot reservation.
\nAn example of a floating oil and gas platform with AISs that transform all system components to the level which is proper to medium-level workers.
Components are: 1—a construction of platform; 2—an AIS on platform for robotics monitoring and control; 3—an underwater communication modem; 4—a remote controlled unmanned underwater robotic vehicle; 5—a sonar beacon; 6—an autonomous unmanned underwater robotic vehicle; 7—non-boarding robotic boat, a spray of the sorbent; 8—non-boarding robotic boat, a pollution collector; and 9—an unmanned aerial vehicle.
\nAnd let input for modeling is the same as in Example 6.3. Only one difference is because of complexity characteristics are proper to medium-level workers of floating oil and gas platform. For this example, it means workers and AIS can commit errors more often in comparison with skilled workers, for one element it is equal to 1 time a month instead of once a year.
\nFor planning possibilities of functions performance by AIS in this example, the probabilistic modeling is being to answer the question:
\nWhat risks to lose system integrity may be for a year, for 10 and 20 years if all subsystems are supported by AISs that transform all system components to the level which is proper to medium-level workers (realistic view on dangerous oil and gas intelligent manufacturing)?
\nTo answer the question, we do Steps 1–4 (from Figure 8) and use formulas (1)–(3) for solving the problem for complex structure, see structure on Figure 13. Here, risks to lose the system integrity mean risks of “failure” for every subsystem. The fragments of built PDF on Figure 14 show: from 0.0009 for a year to 0.0844 for 10 years and 0.25 for 20 years. Thus, MTBLI equals to 24 years. It is 11.4 times less often against the results of Example 6.3.
\nThese are some estimations for example assumptions.
\nThus, the answer on question is: risks to lose system integrity may be 0.0009 for a year, 0.0844 for 10 years and 0.25 for 20 years; herewith, mean time between neighboring losses of integrity is equal to 24 years. These are the realistic estimations for dangerous oil and gas intelligent manufacturing.
\nNew knowledge for accumulating and improving K-base is as follows:
The input (used for modeling) characterize possible complex conditions for functions performance by AIS used for a security service of floating oil and gas platform.
The probability of “success” on levels 0.9991 for a year, 0.9156 for 10 years and 0.75 for 20 years or risk of “failure” on levels 0.0009 for a year, 0.0844 for 10 years and 0.25 for 20 years (with possible consequences) and expected term in average 24 years as estimation of mean time between neighboring losses of integrity are realistic view on dangerous floating oil and gas platform intelligent manufacturing.
For analyzed project new research to improve characteristics for the security service of floating oil and gas platform for decreasing risks with the proof of its efficiency on the basis of modeling is required.
Analyzed project of AISs operation effectiveness (that transform all system components to the level which is proper to medium-level workers of floating oil and gas platform) can be added to K-base history as precedent.
Applicability of the proposed probabilistic methods and models is demonstrated to improve some of the existing capabilities of rescue robot for route optimization. This subsection contains an explanation on how problem 2 may be cognitively solved. Similar problems of specific robot route optimization from point A (Start) to point F (Finish) can arise on water, under water (Figure 15), in burning wood (Figure 16), in the conditions of a city or in mountains (Figure 17), and in other situations in conditions of uncertainties. Specific cases of uncertainties can be connected additionally with complex conditions of environment and necessity of robotics orientation, localization, and mapping that influences on input for the proposed probabilistic models.
\nA system view on situation for robot route from point A (Start) to point F (Finish) on water and under water.
A system view on situation for robot route from point A (Start) to point F (Finish) in burning wood.
A system view on situation for robot route from point A (Start) to point F (Finish) in mountains.
Here, we demonstrate the proposed approach by a simplified example of moving a special rescue robot from point A to the final point F of the route (from where the SOS signals from tourists are following). It is required to optimize the route of the robot in space under uncertainty of weather, complex snow conditions in mountains to achieve the goal in 2 h with an acceptable risk of failure less than 0.1 (i.e., a probability of success should be more than 0.9). Interaction with the drone-informant is supposed, see Figure 17.
\nThe applications to cognitive solving the problem of robot route optimization are demonstrated by the next steps.
\nFor each variant, a set of system compared by modeling is defined: there are ABCF, AGKF, AHLDEF, and possible combinations. Inputs characterizing every part of route for each of the variants are formed by K-base and gathered data from drone-informant:
Frequencies of the occurrences of potential threats are for route ABCF σ = 1 time at 10 h, AGKF σ = 1.5 times at 10 h, AHLDEF σ = 2 times at 10 h (since 8.00 a.m. to 8.00 p.m.)
Mean activation time of threats Tactiv = 30 min
Time between the end of diagnostics and the beginning of the next diagnostics of robot availability Tbetw. = 2 min
Diagnostics time of robot availability Tdiag = 30 s
Recovery time of robot availability = 10 min (for modified model [42, 43, 44])
Given prognostic period Tgiven = 2 h
i = 1.
\nThe risk of “failure” in dependence on prognostic period during the robot route from point A (Start) to point F (Finish).
i = i + 1 = 2.
\nThus, the way ABCBGHLDEF is the result of optimization. The robot purpose was achieved owing to preventive measures which were defined by using risk control on the way (with probability of “success” more than 0.9).
\nNew knowledge for accumulating and improving K-base is as follows:
The input (used for modeling) characterizes possible complex conditions for rescue robot route optimization under limitations on risk of “failure” in conditions of uncertainties. In particular, the information updates every 2 min for robot route optimization under limitations on risk of “failure” less than 0.1 is admissible for considered situation.
The acceptable risk 0.1 is justified; the predicted risks for all variants of the routes did not exceed 0.1.
Analyzed project can be added to K-base history as precedent.
The proposed approach to build and implement the probabilistic methods and models is demonstrated by application to cognitive solving:
The problem of planning the possibilities of functions performance on the base of monitored information about events and conditions
The problem of robot route optimization under limitations on risk of “failure” in conditions of uncertainties
There is proposed to carry out probabilistic prediction of critical processes in time so that not only to act according to the prediction, but also to compare predictions against their coincidence to the subsequent realities.
\nThe described analytical solutions are demonstrated by practical examples such as:
System planning the possibilities of functions performance in space by using robot-manipulators, by AIS for a coal company and for a floating oil and gas platform
Forming input for probabilistic modeling from monitored data
Robot route optimization under limitations on risk of “failure” in conditions of uncertainties
A cognitive solving of the chosen problems consists in improvements, accumulation, analysis, and use of appearing knowledge.
\nProofs for formulas (1)–(3)\n
\nAccording to the proof of formula (1): because between diagnostics system is not protected from threats an influence (a loss of integrity) will take place only after danger occurrence and activation during given time before the next diagnostic (Figure 6). A risk to lose integrity (i.e., probability of “failure”) is equal to Ωpenetr*Ωactiv(Treq) because these PDF are independent. The found probability of providing system integrity (probability of “success”) is equal to addition to 1.
\nThe proof of formula (1) is complete.
\nFor the special case, if Ωoccur(t) = 1 − exp(σt), σ = 1/Toccur, Ωactiv(t) = 1 − exp(t/β), β = Tactiv\n
\nNote. This formula (1) is used also for the estimation of system operation without diagnostics. There is supposed that before the beginning of period
According to the proofs of formulas (2) and (3), we consider independence. Then formula (2) means measure
\nFormula (3) means measure
The proofs for formulas (1)–(3) are complete.
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Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. 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