Computational intelligence in emerging technologies for engineering applications, 1st ed , orestes llanes santiago, carlos cruz corona, antônio josé silva neto, josé luis verdegay, 2020 89

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Studies in Computational Intelligence  872 Orestes Llanes Santiago Carlos Cruz Corona Antônio José Silva Neto José Luis Verdegay   Editors Computational Intelligence in Emerging Technologies for Engineering Applications Studies in Computational Intelligence Volume 872 Series editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland The series “Studies in Computational Intelligence” (SCI) publishes new developments and advances in the various areas of computational intelligence—quickly and with a high quality The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output The books of this series are submitted to indexing to Web of Science, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink More information about this series at http://www.springer.com/series/7092 Orestes Llanes Santiago • Carlos Cruz Corona Antơnio José Silva Neto • José Luis Verdegay Editors Computational Intelligence in Emerging Technologies for Engineering Applications Editors Orestes Llanes Santiago CUJAE Universidad Tecnológica de La Habana José Antonio Echeverría Marianao, La Habana, Cuba Antơnio José Silva Neto Instituto Politécnico-Universidade Estado Rio de Janeiro Nova Friburgo, Brazil Carlos Cruz Corona University of Granada Granada, Spain José Luis Verdegay University of Granada Granada, Spain ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN 978-3-030-34408-5 ISBN 978-3-030-34409-2 (eBook) https://doi.org/10.1007/978-3-030-34409-2 Mathematics Subject Classification: 15A29, 80A20, 93C42, 74P99 © Springer Nature Switzerland AG 2020 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland To our families Preface Although it seems a paradox, the world is already in the next technological revolution and worldwide it has transformative effects on the way we live, work, and develop our economies New opportunities for entrepreneurs and businesses and the society in general are being created that were unthinkable in the recent past Digitalization is transforming jobs across all sectors and economies, new types of jobs and employment are arising, and the nature and conditions of work are also changing Global production of ICT goods and services now amounts to an estimated 6.5% of global gross domestic product (GDP), and some 100 million people are employed in the ICT services sector alone The United Nations Conference on Trade and Development (UNCTAD) Information Economy Report 2017 highlighted that the digital economy is growing rapidly underpinned by the introduction of new and emergent technologies, such as cloud computing, big data analytics, the Internet of Things, robotics, 3D printing, and artificial intelligence The early detection of promising new and emergent technological areas, wherever they come from, is essential in order to identify and seize opportunities of long-term benefit for the society The problems associated with these areas require the integration of new methodological approaches, tools, and techniques capable of modeling some kind of data ignorance, dynamism, noise, etc Then, it is here where Computational Intelligence methodologies could play an important role in addressing these challenges Committed to this idea, the aim of this book is to offer a comprehensive and up-to-date portfolio of solutions based on Computational Intelligence to different problems related to new and emerging technologies Thus, there are fifteen chapters that present fundamental concepts and analysis of different computational techniques solving problems of acoustic levitation, solar panels, automotive batteries, and UAV Autonomous Navigation, to mention but a few Each chapter is briefly introduced below following the order of the index: Fran Sérgio Lobato, Geisa Arruda Zuffi, Aldemir Ap Cavalini Jr., and Valder Steffen Jr present in their paper Uncertainty Analysis of a Near-Field Acoustic vii viii Preface Levitation System (Chap 1) a proposal to evaluate the influence of uncertainties during the design of engineering systems They used two different methodologies to evaluate uncertainties affecting the maximum force necessary to acoustically levitate a given object Specifically, they proposed Robust Design (RD) by using the Effective Mean Concept (EMC) and Reliability-Based Design (RBD) by using the Inverse Reliability Analysis (IRA) Related to this, two strategies are presented: MODE+EMC to solve multi-objective problems in the robustness context by using the EMC strategy, and MODE+IRA to solve multi-objective problems in the reliability context by using the IRA strategy The analysis of the uncertainty and robustness of infinite dimensional objects such as curves and surfaces is presented by Mohamed Bassi, Emmanuel Pagnacco, and Eduardo Souza de Cursi in the chapter Uncertainty Quantification and Statistics of Curves and Surfaces (Chap 2) Two basic approaches were considered: on the one hand, the use of Hilbert basis to reduce the problem to the analysis of probabilities on spaces formed of sequences of real numbers and, on the other hand, approaches based on the variational characterization of the mean The authors have shown that both are effective to calculate and that a mixed approach can produce gains analogous to those of uncertainty quantification of finite-dimensional objects The roof measurement in solar panels installations is usually expensive and risky Luis Diago, Junichi Shinoda, and Ichiro Hagiwara try to solve it using a new methodology to automatically create three-dimensional (3D) house model using its elevation views (i.e., north, south, east, and west views) in their proposal Meta-heuristic Approaches for Automatic Roof Measurement in Solar Panels Installations (Chap 3) They used a polygon matching algorithm based on image processing algorithms in combination with Genetic Algorithms with niching methods to search for the best matching After the best match is found, the scale given by the satellite image is used to rectify the proportions of the 3D house model and to estimate the space available for a solar installation The radiative transfer analysis is studied by a lot of authors because of its application to different areas of interest Lucas Correia da Silva Jardim, Diego Campos Knupp, Wagner Figueiredo Sacco, and Antônio José Silva Neto formulated in the chapter Solution of a Coupled Conduction-Radiation Inverse Heat Transfer Problem with the Topographical Global Optimization Method (Chap 4) an inverse problem combining conduction and radiation used for the determination of thermal and radiative properties To minimize residuals between predictions yielded by a mathematical/computational model and experimental measurements, the authors used a clustering optimization technique based on the topographic information of the objective function, Topographical Global Optimization (TGO), combined with the Nelder–Mead method as local optimization method Soumya Banerjee, Valentina E Balas, Abhishek Pandey, and Samia Bouzefrane investigated various levels of Cyber-Physical Systems (CPS) formulation driven by machine learning and evolutionary algorithms with their strategic similarities in their proposal Towards Intelligent Optimization of Design Strategies of CyberPhysical Systems: Measuring Efficacy Through Evolutionary Computations (Chap 5) The work was focused on the analytical aspects of the design paradigm Preface ix of CPS, but it was also observed that there is a significant trend of multi-objective optimization in terms of resource utilization, scheduling, and even learning the dynamic attributes of design Leakage detection and location is a fundamental task that must be performed to guarantee an efficient operation in Water Distribution Networks In this line, Maibeth Sánchez-Rivero, Marcos Quiñones-Grueiro, Alejandro Rosete Suárez, and Orestes Llanes Santiago propose A Novel Approach for Leak Localization in Water Distribution Networks Using Computational Intelligence (Chap 6) The approach does not depend on the sensitivity matrix neither the labeling method for the nodes, and it solves the inverse problem by using metaheuristic optimization algorithms such as differential evolution, particle swarm optimization, and simulated annealing Lucas Camargos Borges, Eduarda Cristina de Matos Camargo, João Jorge Ribeiro Damasceno, Fabio de Oliveira Arouca, and Fran Sérgio Lobato in their chapter Determination of Nano-aerosol Size Distribution Using Differential Evolution (Chap 7) formulated and solved an inverse problem to characterize the relation between the monodispersed and polydispersed aerosol stream measured by electric mobility in a differential mobility analyzer A Differential Evolution algorithm was used as the optimization tool in which the objective function consists of determining the transfer functions that minimize the sum of difference between predicted and experimental concentrations of sodium chloride The inverse heat conduction problems of estimating timewise and/or spacewise varying functions have become an emerging area of research and development, among other things, due to the huge number of innovative applications that they can enable in engineering and medicine Wellington B da Silva, Julio C S Dutra, Diego C Knupp, Luiz A S Abreu, and Antônio José Silva Neto in their chapter Estimation of Timewise Varying Boundary Heat Flux via Bayesian Filters and Markov Chain Monte Carlo Method (Chap 8) formulated the inverse problem through the Bayesian framework and proposed its solution using Markov Chain Monte Carlo (MCMC) methods, the Sampling Importance Resampling (SIR) Also, a combination of these methodologies is used, consisting of employing the SIR filter solution as the initial state for the MCMC method Incremental Capacity Analysis (ICA) relates battery degradations to changes in the derivative of the charge stored in the battery with respect to the voltage at its terminals Related to this, Luciano Sánchez, José Otero, Inés Couso, and David Anseán in their chapter Health Monitoring of Automotive Batteries in FastCharging Conditions Through a Fuzzy Model of the Incremental Capacity (Chap 9) proposed a method for approximating ICA curves in fast-charging conditions It is based on a dynamic fuzzy model of the derivative of the stored charge with respect to the voltage that can be fitted to data from fast charges and discharges The model contains a fuzzy knowledge base, where the antecedents of the rules match the extrema of the ICA curve and the consequents correspond to the heights of the same curve A bioinspired, complex-adaptive modeling methodology that allows modeling single and multiple faults on smart grid devices using Probabilistic Boolean x Preface Networks (PBN) is presented by Pedro J Rivera-Torres and Orestes Llanes Santiago in the chapter Fault Detection and Isolation in Smart Grid Devices Using Probabilistic Boolean Networks (Chap 10) The proposal is based on a PBN model of Intelligent Power Router (IPR), in which each of the IPR’s faults are modeled using reliability analysis to detect and isolate single and multiple faults Automated Machine Learning (AutoML) is one of the most successful approaches to select the most appropriate machine learning (ML) algorithm and its best hyper-parameter setting given the characteristics of the problem at hand (Model Selection Problem) Juan S Angarita-Zapata, Antonio D Masegosa, and Isaac Triguero explored the benefits of AutoML for Traffic Forecasting supervised regression problems in the chapter Evaluating Automated Machine Learning on Supervised Regression Traffic Forecasting Problems (Chap 11) Auto-WEKA results were compared with some state-of-the-art ML algorithms for the prediction of traffic at scales of predictions focused on the point and the road segment levels within freeway and urban environments The formation of groups of robots to handle and execute the tasks simultaneously and efficiently can be modeled by the multi-robot coalition formation (MRCF) problem Amit Rauniyar and Pranab K Muhuri in their chapter titled Multi-Robot Coalition Formation and Task Allocation Using Immigrant-Based Adaptive Genetic Algorithms (Chap 12) developed different variants of genetic algorithms (GA) as a solution technique for this problem They incorporated immigrantsbased schemes into standard GA (SGA) and develop RIGA (random immigrants GA), EIGA (elitism-based immigrants GA), and also integrated adaptive settings of genetic operations Images taken from an active sensor called Light Detection and Ranging (LiDAR) allow the flight of the Unmanned Aerial Vehicle (UAV) over water-covered areas and under low or no light conditions Related to this, José Renato G Braga, Haroldo F de Campos Velho, and Elcio H Shiguemori in their chapter Lidar and Nonextensive Particle Filter for UAV Autonomous Navigation (Chap 13) proposed to estimate the aircraft position by applying data fusion from two positioning techniques: computer vision and visual odometry Computer vision system (CVS) correlates a geo-referenced image, and an image without geographic coordinate information, to incorporate geographic location to each pixel of the second image Visual odometry determines the position of the aircraft by processing two subsequent images of the same scene A novel and interesting approach based on non-extensive particle filter was used for data fusion applied to the UAV positioning The Time-Dependent Traveling Salesman Problem (TD TSP) is one of the most realistic extensions under real traffic conditions of the TSP Ruba Almahasneh, Boldizsar Tuu-Szabo, Peter Foldesi, and Laszlo T Koczy propose a novel Intuitionistic Fuzzy Time-Dependent Traveling Salesman Problem (IFTD TSP) in their chapter Quasi-optimization of the Time-Dependent Traveling Salesman Problem by Intuitionistic Fuzzy Model and Memetic Algorithm (Chap 14) This proposal introduces an even more real-life model of TSP using intuitionistic fuzzy sets for the definition of uncertain costs, time, and space of the rush hour traffic jam region affecting graph sections A memetic version of the bacterial evolutionary 276 T Ceruto et al Table 15.6 Similarity truth value TVb TV/S H1 H2 H3 H10 P1 P2 P3 P4 P5 P6 CM CW CB C a Values b Values H1 0.06 0 0 0 0 0.12 0.09 0.16 0.09 H2 0.86 0 0.1 0 0 0 0.09 0.05 0.25 0.07 H3 0.56 0.56 0.11 0 0 0 0.04 0.01 0.09 0.01 PQ among clusters, predicates and indices in terms of support Sa and H10 0.77 0.82 0.66 0 0 0 0.09 0.01 0.34 0.01 P1 0.53 0.53 0.1 0.44 0.63 0.55 0.55 0.26 0.58 0.03 0.46 P2 0.52 0.51 0.08 0.41 0.97 0.58 0.68 0.31 0.58 0.03 0.44 P3 0.49 0.48 0.11 0.4 0.94 0.97 0.58 0.31 0.9 0.03 0.44 P4 0.5 0.49 0.1 0.4 0.95 0.98 0.99 0.26 0.58 0.03 0.37 P5 0.48 0.47 0.11 0.39 0.93 0.96 0.99 0.98 0.26 0.03 0.44 P6 0.49 0.48 0.11 0.4 0.94 0.97 0.99 0.99 0 0.03 0.44 CM 0.65 0.65 0.6 0.74 0.41 0.39 0.41 0.4 0.42 0.41 0.01 0.06 0.01 CW 0.44 0.42 0.56 0.34 0.44 0.42 0.45 0.44 0.46 0.45 0.47 0.01 0.04 CB 0.7 0.7 0.82 0.77 0.26 0.24 0.22 0.23 0.22 0.23 0.58 0.48 0.01 C 0.62 0.63 0.26 0.57 0.82 0.8 0.78 0.79 0.77 0.78 0.57 0.6 0.44 above the main diagonal correspond to support S below the main diagonal correspond to truth value TV It is remarkable the great oscillations in all series imply that each series defines different conditions Indeed, the only Pearson correlation coefficients that are over 0.7 are among P3 , P4 , P5 , and P6 Table 15.6 shows the similarity P Q among clusters, predicates, and some representative indices We include in the following analysis only four indices (H1 , H2 , H3 , and H10 ) because the other seven indices are very similar to some of the indices in this selection (see Table 15.3) It is remarkable the similarity among the predicates P1 , P2 , P3 , P4 , and P5 (the support of the similarity among them is greater than 0.93, and their truth value is greater than 0.55 with the exception of them with respect to P5 ) This is natural because all they have high values in general In this sense, it worth remarking that the truth value (TV) of the similarity of P5 with respect to the others is not very high (less than 0.32) which means that P5 reflects a different concept than the others predicates for some territories It is worth noting the low similarity between H3 and the predicates which is caused by the fact that most predicates include the negation of H3 It is also important to remark the relatively high similarity (greater than 0.7) between H10 with respect to CM and CB , i.e., it is similar the degree of accomplishment of H10 to the degree of membership to the clusters with medium and best values CM is also very similar to H1 , H2 , and H3 It is also interesting the relatively high similarity (greater than 0.77) between C and the predicates, i.e., the conditions expressed in the predicates define similar membership than those defined by the clustering Figure 15.10 shows the support of the predicates and clusters for each region For most regions all predicates are very near to (they are good descriptions of these 15 Analyzing Information and Communications Technology National Indices 277 Fig 15.10 Support of clusters Ci and predicates Pi in the regions regions) but they have low support in NAC It is also noticeable that EUR and SEAO are apart from the other regions It is worth noting the low degree of membership of NAC and EUR to the worst cluster CW , of NAC and SEAO to CM , and of SSF (followed by LCN, CSA, and NAWA) to the best cluster CB In general, it is also interesting to observe that there is no direct correspondence between geographical regions and clusters NAC (and partially EUR and SEAO) not follow the same status of the whole set Indeed, predicates with high support and truth value in general, are not clearly supported by the most territories in these regions They are a step forward with respect to the whole set 15.4 Conclusions This chapter has shown how the fuzzy sets can be used to analyze ICT indices that describe different technological aspects Based on different data mining techniques (fuzzy clusters, fuzzy predicates, graphs, correlations) 11 fuzzy ICT indices are analyzed in general, and according to the different regions Several interesting findings were commented through the paper, including the similarity among different fuzzy sets introduced and elicited by using fuzzy data mining techniques The patterns presented along the chapter may have different implications according to the position of the different actors For example, from the point of view of a country 278 T Ceruto et al it may be useful to revise how it differs from the regional or world tendency From the point of view of the producers of the ICT indices it may be useful to identify their singularity An interesting aspect to be explored in future studies is to consider other forms of obtaining fuzzy indices that take into account regional relative status or alternative membership functions In 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dynamical system, 24–25 multiobjective optimization, 35–36 proposed methodology, 50 variational approach first dynamical system, 28–29 second dynamical system, 30, 31 Automated CPS, 77 Automated machine learning (AutoML) BA, 197 Bayesian optimisation method, 194 CASH, 194 data-sets, 196–198 execution time vs performance, 198 experimental set-up, 196–197 freeway data, 195 Holm post-hoc test, 200 meta-learning approach, 194 ML, 200 non-parametric statistical tests, 198 RF/NN, 198 RMSE, 198, 199 TF, 189, 192–194 transportation area, 189 urban data, 195 Automated verification techniques, 171 Autonomous navigation CVS, 230–232 INS signal, 229 NExt-PF, 235–237 VO, 229–230 Auto-WEKA, 189, 192–195, 197–202 See also Automated machine learning (AutoML) B Bacterial Evolutionary Algorithm (BEA), 243 Baseline algorithms (BAs), 189, 193, 197–200, 202 Battery, 158–160 Bayesian filters Kalman filter, 138 MCMC, 138, 139 online applications, 138 SIR (see Sampling importance resampling (SIR)) Bayesian optimisation method, 194 Best-of-generation (BOG), 219 Binary support (BS), 258, 267 Biomimetic methodologies, 167 Boltzmann distribution, 112 © Springer Nature Switzerland AG 2020 O Llanes Santiago et al (eds.), Computational Intelligence in Emerging Technologies for Engineering Applications, Studies in Computational Intelligence 872, https://doi.org/10.1007/978-3-030-34409-2 281 282 Boltzmann–Gibbs–Shannon formula, 234 Boolean network (BN), 168–170, 178 Boundary heat flux estimation direct problem, 142–143 evolution, Markov chains, 147, 148 inverse problem, 143–144, 147, 150 MCMC, 145, 147 numerical computations, 145 SIR filter solution, 145, 147 temperature measurements, 149–151 Brachistochrone, 25, 26 C CE, see Crowding with elimination (CE) Central pruning average (CPA), 258, 267 CKPS, 90 Classical UQ approach, 21, 23, 32, 36 CMR, see Crowding with mating restrictions (CMR) Coalition structure (CS) centralized framework, 208 central robot, 210 definition, 208 Combined algorithm selection and hyperparameter optimisation (CASH) problem, 194 Computational fluid dynamics (CFD), 125 Computational intelligence, 75, 89, 190 tools, 167 WDN (see Water distribution networks (WDN)) Computation tree logic (CTL), 170, 171 Computer graphics algorithms, 48 Computer vision system (CVS) MLP-NN, 230–232 MPCA, 230 multi-step process, 230, 231 Condensation particle counter (CPC), 124, 128 Confusion matrix, 115 Contiguity index (CI), 110 Continuous stochastic logic (CSL), 171 Convex optimization, 84, 87–89 Cost model optimization, 77 Coupled conduction–radiation heat transfer, 55–56 CPS design applications, 81–82 aspects, 82 challenges, 81–82 classification, 81 with intelligence and measures of efficiency, 82–95 Index resilience and self-organizing pattern, 95–96 See also Intelligent CPS design Crowding (CW), 49, 51 Crowding with elimination (CE), 49 Crowding with mating restrictions (CMR), 49 Cumulative distribution function, 7, 83 Cuthill–McKee algorithm, 114–121 Cuthill–McKee node labeling method, 117–118 Cyber attack, 77 Cyber-physical system (CPS) adaptive and computer intelligence, 75 applications, 74, 75 classification, 74 definition, 76 EC, 76–82 high-dimensional and scanty information, 74 IoT, 74 ML, 76–82 M2M, 74 style cluster, 74 U-model, 75 D Data-driven methods, 156 Data fusion KF, 228 LiDAR, 228 NExt-PF, 228, 235 UAV positioning, 229, 233, 235 Data mining, 256 Data router, 174 DC, see Deterministic crowding (DC) DE, see Differential evolution (DE) Degeneracy phenomenon, 141 Depth-first search (DFS) algorithm, 114 Descriptors, 77, 230 Design uncertainty analysis, 92–95 collective intelligence, 84 control variables, 82 convex optimization, 84, 88–89 design strategy, 83 DMOPs, 84–85 efficiency measurement IGD, 89 population of evolutionary agents, 89–91 learning vs error rate, 92, 94 mapping of uncertainty, 83 Index measuring performance, 83 notations, 82 stochastic measure of design efficiency, 83 transportation, 85–88 transport centric CPS, 91–93 Deterministic crowding (DC), 49 Differential evolution (DE) algorithm, 105, 108–109, 118–119, 131–132 inverse problem, 130–131 MODE, 8–9 multi-objective optimization, 8–9 OF, 130 parameters, 132–133 T-DE, 108–109 Differential methods battery, 156 ICA (see Incremental capacity analysis (ICA)) Differential mobility analyzer (DMA) designs and manufacturers, 124 long, 125 measurement range, 127 medium, 125 miniature cylindrical, 125 nano, 127–128 nanoparticle cross-flow, 125 sheath-air flow-rate, 125 transfer function (see Transfer function) Digital surface model (DSM), 40 Direct observation methods, 104 Discrete bacterial memetic evolutionary algorithm (DBMEA) computational results, 249–252 Helsgaun–Lin–Kernighan method, 243 IFTD TSP, 243, 251 meta-heuristics, 252 NP-hard graph search, 249 search methods, 243 3FTD TSP model, 243 Discrete uniform law, 26 Discrete zones/district meter areas (DMA), 104 Distribution networks, 172 Driving surface, 3–5, 12 DSM, see Digital surface model (DSM) Dynamic evolutionary strategies (DES), 90, 97 Dynamic genetic algorithm(s) genetic operators, 217 immigrants scheme, 216–217 Dynamic multi-attribute based CPS, 79 Dynamic multi-objective optimization problems (DMOPs), 84–85 283 E Effective mean concept (EMC), 3, 5–6, 14, 15 Electrical power distribution systems (EPDSs) GRNs, 168 IPR, 166 power delivery systems, 173 transmission systems, 173 Electrical power networks, 172, 173 Electrostatically enhanced fibrous filter (EEFF), 124 Electrostatic precipitator (ESP), 124 Elitism-based Immigrants Genetic Algorithm (EIGA), 206, 216–223 EMC, see Effective mean concept (EMC) EPANET software package, 114 Euclidean distance, 49, 84, 105, 115, 118, 120, 230, 242, 249, 251 Evolutionary algorithm, 207, 219 Evolutionary computation (EC) and ML in CPS, 76–82 Execution times (ET), 197 F Failure modes, 174–180, 182 Fast-charging conditions, 156, 160, 162 Fault detection and isolation (FDI) description, 174, 175 diagnosing simultaneous faults, 167 model-based methods, 167 Faults and failures, 181 Fault-tolerant approach, 77 Field of vision (FoV), 40 First order reliability method (FORM), Fitness/objective function, 106–107 Fonseca–Fleming problem, 35 FORM, see First order reliability method (FORM) Forward-looking prediction strategies (FPS), 90 FoV, see Field of vision (FoV) Freeway data, 195 Fresnel reflector systems, 54 Friedman test, 115–118, 198 Fuzzy clustering, 258 Fuzzy C-means (FCM) clustering algorithm, 258 clusters, 271–273 predicates, 273–274 relations, 274–277 Fuzzy costs, 243 Fuzzy data mining clustering, 258 fuzzy sets, 256–258 284 Fuzzy data mining (cont.) ICT (see Information and communications technology (ICT)) indices analysis (see Fuzzy ICT indices) predicates, 258–259 Fuzzy ICT indices differences, 268 original index, 264–265 relations, 268–270 sets, 266–267 Fuzzy jam region, 243 Fuzzy knowledge base, 157–158 Fuzzy logic, 256, 257 Fuzzy model-based (FM), 161 Fuzzy predicates, 258–259 Fuzzy rules, 256, 258, 262, 263 Fuzzy rush hour period, 243 G GA, see Genetic algorithm (GA) GA-based PM, 47–48 Gaussian naive model, 77 Gauss–Legendre quadrature, 56, 60 Gene regulatory networks (GRNs), 167–169, 174 Genetic algorithm (GA) characteristics, 45 domain-independent approach, 45 meta-heuristic and evolutionary approach, 206 minimal subsets, 46 objective function, 46 optimization problem, 45 PM, 47–48 population and combines, 45 random immigrants, 224 variants, 206 Geological Survey of Ireland (GSI), 235 Geometric primitives extraction, PM, 43–45 Global Innovation Index (GII), 261–262 Global navigation satellite system (GNSS), 228, 229 Graphic user interface (GUI), 42 GUI, see Graphic user interface (GUI) H Hanoi network, 113–115, 118–120 Hanoi WDN, 105 Hausdorff distance, 27–29 Heat transfer modeling, 54 Hesitation part, 244 Index Hidden Markov model, 97 High pruning average (HPA), 258, 267 Hilbert basis, 19 Hilbertian approach applications first dynamical system, 22–24 infinite dimensional optimization, 25–26 second dynamical system, 24–25 choice of family, 20 choice of parameterization, 20–21 classical UQ approach, 21 data in large samples, 32–35 empirical mean, 31, 32 Hilbert space, 20 inverted UQ approach, 22 linear system, 20 mean and covariances, 21 and envelope, 31 mixed approach, 22 random curves generation, 25–26 Hilbert space, 20 Holm post-hoc test, 200 Hybrid algorithms, 26 Hydraulic model, 104–106 Hydraulic simulations, 114 I ICT Development Index (IDI), 260, 267 Image sequence, 229 Incremental capacity analysis (ICA) battery deterioration, 161–163 data, 156 dynamic behaviour, battery, 157–160 dynamic fuzzy model, 156 experimental setup, 160–161 fast-charging conditions, 156 fuzzy knowledge base, 157–158 transitions, 156 Inertial navigation system (INS), 228, 229 Inference methods, 104 Infinite dimensional objects., 24 Information and communications technology (ICT) approaches and interests, 262–263 data mining, 256 GII, 261–262 IDI, 260 importance, 259 indices, 256 IP, 259 QS rankings, 260–261 Index SCImago rankings, 261 TOP500 List, 260 Inliers, 44 Instrumental model, 160 Integrated control and communications unit (ICCU), 173, 174 INtegrated Mapping FOr The Sustainable of Ireland’s MArine Resources project (INFOMAR), 235 Intelligent CPS design bi-level/tri-level optimization, 80 dynamic multi-attribute based CPS, 79 meta-components, 81 resilience and self-organizing pattern, 95–96 resource scheduling, 80–81 time driven optimal design solution, 93 uncertain source, sink and transition time parameters, 79–80 Intelligent optimization, see Intelligent CPS design Intelligent power router (IPR) breakers, 176 circuit breakers, 174 command and control, 173 communication protocols, 174 electrical power smart-grid, 173 failure modes, 175–177 faults, 175 intelligence, 173 network architecture, 174 P2P, 173 predictors, 177 PRISM, 175 reliability, 168, 174 router, 176 selection probability, 177 software, 176 Intelligent transportation systems (ITSs), 190 International Water Association (IWA), 104 Internet of Things (IoT), 74 Internet penetration (IP), 259 Intuitionistic fuzzy relation (IFR), 244–246, 248 Intuitionistic fuzzy sets (IFSs), 241, 244, 245, 252 Intuitionistic fuzzy time dependent TSP (IFTD TSP) formulation, 246–249 fuzzy sets, 244 hesitation part, 244 IFR, 244 max–min–max composition, 245 non-membership function, 246 285 Intuitionistic jam region cost factors, 248 Inverse heat conduction, 137 Inverse problems Bayesian framework, 139 Bayes’ theorem, 139 boundary heat flux estimation, 143–144 leak detection and localization, 105–107 MCMC, 139–140 normal distribution, 57 OF, 130 optimization algorithms, 105–107 posterior probability density, 139 sensitivity analysis, 59–61 SIR, 139–142 TGO, 57–59 thermal conductivity, 57 triangular transfer function, 131 uncorrelated measurement errors, 57 unknown variables, 130 Inverse reliability analysis (IRA), 6–8 Inverted generational distance (IGD), 89–90, 92 Inverted UQ approach, 22 IRA, see Inverse reliability analysis (IRA) J Journal rankings, 261 K Kalman filter (KF), 138, 228 Karush–Kuhn–Tucker proximity measure (KKTPM), 94, 96 Kripke structure, 171 L LADS Mk II Ground System, 235 Latin hypercube (LH) approach, Latin hypercube design (LHD), Leak detection and localization application, 107–114 classification, 104 inverse problem fitness/objective function, 106–107 hydraulic model, 105 model-based leak localization, 106 pressure differences, 105 variable types, 107 Leak sensitivity matrix (LSM) methods, 120–121 Level of service (LoS), 191 Levitation techniques, 286 Levy-Gnedenko’s central limit theorem, 234 LHD, see Latin hypercube design (LHD) Life of lithium iron phosphate (LFP), 156, 160, 163 Light detection and ranging (LiDAR) active sensor, 228 autonomous navigation, 228, 235 INFOMAR, 235 SDM, 235 Long differential mobility analyzer (LDMA), 125 Loss of lithium inventory (LLI), 163 Low pruning average (LPA), 258, 267 M Machine learning (ML) AutoML (see Automated machine learning (AutoML)) and EC in CPS ACPS, 77 algorithms, 78 automated CPS, 77 cost model optimization, 77 cyber attack, 77 definition, 76 domain knowledge, 78 Gaussian naive model, 77 hybrid intelligent search process, 77 intelligent optimization, 78–81 intelligent techniques, 76 mathematical analysis, 78–81 meta-adaptation strategies, 76 PSO, 77 security, intelligence flavor, 77 self-organization, 76 social network and sensing, 78 systems with descriptors, 77 Takagi–Sugeno model, 77 trans-disciplinary applications, 78 types of problems, 78 MSP, 188 and optimization, 75 principle, 97 TF, 188, 190–191 traffic prediction, 188 Machine-to-machine communication (M2M), 74 Markov chain Monte Carlo methods (MCMC) Bayesian filters, 138 boundary heat flux estimation, 138 computational cost, 138 inverse problem, 139–140 Index Markov chain theory, 98 Markov decision processes (MDPs), 171 Markov Random Fields theory, 138 Maximum Reward Collection Problem (MRCP), 78 MCS, see Monte Carlo simulation (MCS) Mean time between failures (MTBF), 174, 175, 178 Medium differential mobility analyzer (M-DMA), 125 Meta-heuristic (MHs) algorithms GA, 45–48 niching methods, 48–49 Miniature cylindrical differential mobility analyzer (mini-cyDMA), 125 Minkowski distance metrics, 113 Mobility distributions, 125 MODE, see Multi-objective optimization differential evolution (MODE) MODE+EMC strategy, 9–13 MODE+IRA strategy, 9–12, 14 Model-based leakage localization, 106, 113 Model checking, 170 Model selection problem (MSP), 188, 191–193 Monosized nanoparticles, 124, 125, 128, 129, 134 Monte Carlo methods, 124, 138, 232 Monte Carlo simulation (MCS), Multilayer perceptron neural network (MLP-NN), 231–232 Multimodal transportation changing objectives, 87 definition, 85 driving situation, 86 motion equations, 86 multi-objective algorithm, 87 objectives, 87 physical parameters, 85–86 traffic density, 86 Multi-objective algorithm, 87 Multi-objective optimization, 18, 35–36 CPS, 74 MODE, 8–9 Multi-objective optimization differential evolution (MODE), 8–9 Multiple fault detection, 167 Multiple particle collision algorithm (MPCA), 230, 232, 235, 236 Multi-robot coalition formation (MRCF) CS, 208–210 definition, 206 dynamic GAs, 215–217 evolutionary algorithm, 207 Index PSO, 207 quantum evolutionary algorithm, 207 robot coalition, 208 task allocation (see SGA implementation) Multi robots task assignment/allocation (MRTA), 206 Multi-robot system (MRS), 206 N Nano-differential mobility analyzer (nanoDMA), 127–128, 131, 132 Nanoparticle cross-flow differential mobility analyzer (NCDMA), 125 Nanoparticles classification, 127 human health, 124 monodisperse, 124 monosized (see Monosized nanoparticles) NCDMA, 125 physical behavior, 124 NDSolve method, 56 Near-field acoustic levitation, air film, driving surface, 3–4 Neumann contour condition, non-dimensional equations, Reynolds equation, squeeze number, Nelder–Mead (NM) method, 54, 58, 60, 62, 69 Nelder–Mead (NM) optimization, 26 Neumann contour condition, Neural networks (NNs), 188–191, 193, 198, 200, 202 Niching methods crowding (CW), 49 and objective functions, 51 R-functions, 48 sharing (SH), 49 SL, 48–49 Non-dimensional equations, Non-extensive particle filter (NExt-PF) Bayes’ theorem, 232 equiprobability condition, 234 implementation algorithm, 233 kernel, 233 Markov property, 232 non-extensivity parameter, 234 PDF, 232 probability density function, 234 N -order transfer functions, 134–135 287 O Objective function (OF), 5, 6, 9, 10, 12–15, 46–49, 51, 52, 54, 57, 58, 64, 65, 69, 70, 88, 89, 95–97, 105, 106, 113, 115, 118–121, 130, 132, 232 Observation model, 141, 144 Open circuit voltage (OCV), 158, 160, 163 Optimization algorithm Hanoi network, 113, 114 inverse problem, 105–107 objective function, 113 pseudo-code, 107 search space, 113–114 T-DE, 108–109 T-PSO, 110–111 T-SA, 112 Optimum design pattern (ODP), 87 Outliers, 44 Overpotential (OVP), 158, 159 P Parallel location (PL), 48–49 Pareto fronts, 18, 35–37, 87–88 Pareto-optimal solutions (POS), 89–90 Pareto’s curve, 12–15 Particle classification slit, 128 Particle collision algorithm (PCA), 232 Particle filter method, 141 Particle swarm optimization (PSO), 77, 105, 110, 118–119, 121, 207 Pearson correlation, 105, 269, 276 Peer-to-peer (P2P), 173 Photovoltaic (PV) systems, 40 PM, see Polygon matching (PM) Poisson equation, 55 Polygonal shape matching, see Polygon matching (PM) Polygon matching (PM) GA, 47–48 geometric primitives extraction, 43–45 Population prediction strategies (PPS), 90 Posterior probability density, 139 Power delivery systems, 173 Power plants, 171, 172 Predictive gradient strategies (PGS), 90 Pre-optimal front (POF), 89–90 Probabilistic Boolean networks (PBN) automated verification techniques, 171 biological networks, 167 BN, 168, 169 CTL, 170 288 Probabilistic Boolean networks (PBN) (cont.) detection, 177–179 diagnosis, 179–182 EPDS, 174 failure modes, 174 FID, 174, 175 GRNs, 167 IPRs, 166, 175 Kripke structure, 171 laws and regulations, 167 manufacturing systems, 168, 169 MDP model, 171 mechanism, intervention, 169 model checking, 170 model construction, 174 modeling systems, 169 PCTL, 171, 175 PRISM, 175 PTAs, 171 and semantics, 174 transition systems, 168 Probabilistic continuous time logic (PCTL), 171, 175, 178 Probabilistic timed automata (PTAs), 171 Probability density function (PDF), 232 Probability distributions, 33–35 characterization, 17 curves and surfaces, 18 definition, 18 Proximity measures, 94–95 Pseudo-transient approach, 56 PSO, see Particle swarm optimization (PSO) Punctual approach, 19 Q Quacquarelli Symonds (QS) rankings, 260 Quasi-linear behavior, 62 R Radiative transfer analysis, 53 Random curves generation, 25–26 Random forest (RF), 188, 189, 191, 193, 198, 200, 202 Random Immigrants Genetic Algorithm (RIGA), 206, 216, 217, 220, 221 RANdom SAmple Consensus (RANSAC), 235 RBD, see Reliability-based design (RBD) RD, see Robust design (RD) Reflector, Reliability circuit breaker, 174, 176 data router, 174 Index operation, 175 routers, 176 software, 174 Reliability-based design (RBD), 2, 6–8, 13–14 Residual-based analysis, 104 Restricted Boltzmann Machine (RBM), 97 Reynolds equation, R-functions, 48 RIGA and EIGA comparative analysis adaptive rate genetic operators, 221–223 efficiency and average efficiency, 219–220 experimental settings, 218–219 fixed rate genetic operators, 220–221 Robust design (RD), 2, 12–13 Robust optimization, 5–6 Roof measurement ALS, 42 climbing, 40, 41 drones, 40 DSM, 40 elevation views, 42–43 geometrical building/city modeling, 42 PM, 43–45 satellite image, 40, 41 2D and 3D rooftop, 40 Root-mean-square error (RMSE), 197–199 Rosenblatt transformation, S SA, see Simulated annealing (SA) Sampling importance resampling (SIR) Bayes’ theorem, 140 evolution, 141 input vector, 141 MCMC, 138, 139 Monte Carlo problem, 140, 141 observation models, 141 particle filter method, 141 resampling, 141, 142 sequential estimation, functions, 140 state vector, 141 Satellite image, 50 Scalable Uncertainty-Aware Truth Discovery (SUTD), 78 Scaling factor, 108 Scanning mobility particle sizer (SMPS), 124 SCImago Institution Rankings (SIR), 261 SCImago Journal & Country Rank (SJCR), 261 SCImago Journal Rank (SJR), 261 SCImago rankings, 261 Search space, 113–114 Index Sensitivity analysis, 11–13, 54, 59–61, 66, 70 Sensitivity coefficients, 59–60, 62–64, 67 Sensitivity matrix, 104–105 Sensor networks, 79 Sequential location (SL), 48–49, 51 Sequential Monte Carlo technique, 141 Service Quality (QoS), 96 SGA implementation fitness function and evaluation capability values, 212 computation, 212–214 genetic encoding, 210–211 genetic operators crossover, 214–215 mutation, 215 population initialization, 211 selection method, 214 Sharing (SH), 49, 51 Simulated annealing (SA), 105, 112, 118–119 Single scattering albedo, 54, 55, 57, 67 Singular value decomposition (SVD), 230 Sinusoidal behavior, SL, see Sequential location (SL) Smart power systems, 167 Solar energy, 40 Solar panels installations, 40–42 Speeded up robust features (SURF), 230, 235 Squeeze number, Standard GA (SGA), 206 Standing waves acoustic levitation, State of health (SoH), 155, 157 State space model, 140 Stefan–Boltzmann constant, 56 Stochastic process, 18 Stolzenburg’s transfer function, 124–125 Supervised TF regression problem, 188 Supply chain management (SCM), 240 Support vector machines (SVMs), 188, 189, 191, 193, 199, 200, 202 Surface digital model (SDM), 235, 237 T Takagi–Sugeno model, 77 T-DE, see Topological-differential evolution (T-DE) TGO, see Topographical global optimization (TGO) Thermal conductivity, 54, 56, 57 Thermal parameters estimation, 54, 57, 69 Three-dimensional (3D) building modeling elevation views, 41 polygon, 41 structure information, 41 289 Three-dimensional (3D) rooftop, 40 Time dependent traveling salesman problem (TD TSP), 241, 243, 249, 252 TOP500 List, 260, 270 Topographical global optimization (TGO) average of executions, 64–69 in coupled conduction and radiation heat transfer, 55–56 experimental error, 67–69 generation and distribution, 61 inverse problem, 56–61 minimization, 58, 59 NM, 54, 58, 60 optimal design, 54 quasi-linear behavior, 62 radiation intensities detectors, 67, 69 sensitivity coefficients, 62–64, 67 standard deviation, 64–66 temperature detectors, 67 test cases, 61 Topological-differential evolution (T-DE), 108–109, 114 Friedman test, 115–116 global performance, 115, 116 parameters, 114 performance comparison, 118 pseudo-code, 109 Topological-particle swarm optimization (T-PSO), 110–111 Friedman tests, 117 parameters, 115 Pearson’s correlation coefficient, 117 performance comparison, 118 pseudo-code, 111 Topological-simulated annealing (T-SA), 115 Friedman tests, 117, 118 performance comparison, 118 pseudo-code, 112 T-PSO, see Topological-particle swarm optimization (T-PSO) Traffic data freeway data-sets, 197 target location, 196 telecommunications technologies, 188, 190 temporal-spatial component, 193 TF, 189, 190 urban, 195, 197 Traffic forecasting (TF) AutoML, 192–193 computational intelligence, 190 definition, 188 ITSs, 190 ML, 190–191 MSP, 188 290 Traffic forecasting (TF) (cont.) sensing and telecommunications technologies, 188, 190 supervised regression approach, 188 traffic data, 190 Trans-disciplinary applications, 78 Transfer functions definition, 129 N -order, 134–135 Stolzenburg’s, 124–125 triangular, 129–132, 134–135 Transmission systems, 173 Transmission/transport networks, 172 Transport centric CPS, 91–93 Traveling salesman problem (TSP) BEA, 243 classes, 242 cost calculation, 241–243 definition, 241 heuristic methods, 240 IFSs, 241 IFTD TSP (see Intuitionistic fuzzy time dependent TSP (IFTD TSP)) quasi-optimal solutions, 240 real-life applications, 240 Triangular transfer functions, 124, 129–132, 134–135 Triple fuzzy time dependent traveling salesman problem (3FTD TSP), 243 T-SA, see Topological-simulated annealing (T-SA) TSP fuzzification, 242–243 Two-dimensional (2D) rooftop, 40 U Ultrasonic actuators, Ultrasonic transducer, Uncertainty analysis acoustic levitation, 2–3 advantages, characterization, driving surface, EMC, 5–6 IRA, 6–8 LHD, MCS, MODE, 8–9 MODE+EMC strategy, 9–12 MODE+IRA strategy, 9–12 near-field acoustic levitation, 2–5 non-dimensional parameters, operational environment, RBD, 2, 6–8, 13–14 Index RD, 2, 12–13 reflector, robust optimization, 5–6 sensitivity analysis, 12, 13 standing waves acoustic levitation, ultrasonic actuators, ultrasonic transducer, Uncertainty model (U-model), 75 Uncertainty quantification (UQ) finite number of objects, 30 framework, 32 Hilbert basis, 19 Hilbertian approach, 20–26 multiobjective optimization, 18, 35–36 punctual approach, 19 punctual mean, 30–31 variational approach, 26–31 Unmanned aerial vehicle (UAV) advantages, 228 application areas, 227 autonomous navigation (see Autonomous navigation) data fusion, 228 GNSS, 228 image processing system, 228 INS, 228 KF, 228 LiDAR, 228 V Variational approach applications first dynamical system, 28–29 second dynamical system, 30, 31 definition, 26 discrete universe, 26 Hausdorff distance, 27–29 mean, 27 median and confidence interval, 31 median estimation, 31, 32 optimization problem, 27 optimization procedure, 27 Vector function, 18 Vehicle detection stations (VDS), 190, 191 Visual odometry (VO), 229–230, 235, 237 W Water distribution networks (WDN) confusion matrix, 115 DMA, 104 Hanoi network, 115 hydraulic simulations, 114 Index IWA, 104 leak detection and localization (see Leak detection and localization) proactive strategies, 104 residual-based analysis, 104 291 sensitivity matrix, 104–105 water to customers, 104 WDN, see Water distribution networks (WDN) Wilcoxon test, 115 Wireless sensors, 78 ... Springer Nature Switzerland AG 2020 O Llanes Santiago et al (eds. ), Computational Intelligence in Emerging Technologies for Engineering Applications, Studies in Computational Intelligence 87 2,. .. http://www.springer.com/series/7092 Orestes Llanes Santiago • Carlos Cruz Corona Antơnio José Silva Neto • José Luis Verdegay Editors Computational Intelligence in Emerging Technologies for Engineering. .. School of Chemical Engineering, Federal University of Uberlândia, Uberlândia, Brazil Wellington B da Silva Chemical Engineering Program, CCAE-UFES, Alegre, ES, Brazil Lucas Correia da Silva Jardim
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