Handbook of Intelligent Computing and Optimization for Sustainable Development. Группа авторов

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СКАЧАТЬ 11.1 Computers usage in modern concept of Manufacturing Industry 4.0.Figure 11.2 Computer usage in car designing and manufacturing.Figure 11.3 Optimization methods.Figure 11.4 Traditional methods and non-traditional methods of optimization.Figure 11.5 Injection molding process.Figure 11.6 Three-dimensional printing process.Figure 11.7 Arc welding process.Figure 11.8 Casting process.Figure 11.9 Machining process classification.Figure 11.10 ANN vs. human brain functioning similarities.Figure 11.11 Modular Neural Network.Figure 11.12 Structure of typical neural network 5-k-l-m-1 [1].Figure 11.13 Convoluted neural networks.Figure 11.14 Recurrent neural network.Figure 11.15 Closed-loop control systems.Figure 11.16 Radial neural structure (self-drawn).Figure 11.17 Multilayer perceptron.Figure 11.18 Kohonen self-organizing neural network.Figure 11.19 LSTM ANN structure.Figure 11.20 ANN training [55, 56].Figure 11.21 R or the correlation coefficient = 1.Figure 11.22 CNC machine.

      12 Chapter 12Figure 12.1 Block diagram for training phase.Figure 12.2 Block diagram for testing phase.Figure 12.3 ASR block diagram.Figure 12.4 Data collection modes.Figure 12.5 Block diagram of system.

      13 Chapter 13Figure 13.1 Extractive summarization model using graph-based approach.Figure 13.2 Architecture of GRAPHSUM model.

      14 Chapter 14Figure 14.1 Twitter users’ worldwide data January 2021 [32].Figure 14.2 The Google Cloud console [6].Figure 14.3 Sentiment analysis techniques [35].Figure 14.4 Fetching of data using BigQuery [37].Figure 14.5 The architecture of Google BigQuery [38].Figure 14.6 Architectural view of the proposed system [42].Figure 14.7 Data refining using BigQuery [39].Figure 14.8 Google App Engine launcher [40].Figure 14.9 (a) and (b) Twitter for BigQuery result window [41].

      15 Chapter 15Figure 15.1 Graphical model representation of LDA.Figure 15.2 Process pipeline.Figure 15.3 Work flow of the proposed system.Figure 15.4 Creation of dataset and training of data.

      16 Chapter 16Figure 16.1 Four-fold plot of predicted class vs. actual class and a ROC curve f...Figure 16.2 Four-fold plot of predicted class vs. actual class and a ROC curve f...Figure 16.3 Four-fold plot of predicted class vs. actual class and an ROC curve ...Figure 16.4 Four-fold plot of predicted class vs. actual class and a ROC curve f...

      17 Chapter 17Figure 17.1 Inventory management system.Figure 17.2 Behavior of cost function I with respect to inventory level.Figure 17.3 Behavior of cost function II with respect to inventory level.

      18 Chapter 18Figure 18.1 Supply chain network.Figure 18.2 Mixed integer programming.Figure 18.3 Inventory in supply chain performance.Figure 18.4 Closed-loop supply chain management.Figure 18.5 Supply chain network design under uncertainty.

      19 Chapter 19Figure 19.1 Architecture of the proposed ensemble model.

      20 Chapter 20Figure 20.1 Type-A SPS designed using microstrip TL.Figure 20.2 Simulated result showing PD of Type-A SPS shown in Figure 20.1.Figure 20.3 Input impedance at port 1 (or 2) of the (a) Type-A SPS has shown in ...Figure 20.4 Modified Type-A SPS with single stub tuner.Figure 20.5 Fabricated PS: (a) signal plane and (b) ground plane.Figure 20.6 Simulated and measured (a) magnitude response, (b) phase response, a...Figure 20.7 Equivalent spice model of varactor diode (SMV2019).Figure 20.8 (a) SPS with single varactor diode. (b) Equivalent lumped RLGC model...Figure 20.9 SPS with (a) two varactor diodes and (b) three varactor diodes.Figure 20.10 For single varactor diode: (a) RL plot; (b) IL plot; (c) PD plot.Figure 20.11 For two varactor diodes: (a) RL plot; (b) IL plot; (c) PD plot.Figure 20.12 For three varactor diodes: (a) RL plot; (b) IL plot; (c) PD plot.

      21 Chapter 21Figure 21.1 Tools used in Fuzzy logic toolbox.Figure 21.2 FIS procedure for present study.Figure 21.3 Empirical transfer function.Figure 21.4 Transfer function in fuzzy format of PC.Figure 21.5 Transfer function in fuzzy format of PPC.Figure 21.6 Transfer function in fuzzy format of QC.Figure 21.7 Transfer function in fuzzy format of MGT.Figure 21.8 Transfer function in fuzzy format of result.Figure 21.9 Fuzzy rules.Figure 21.10 Fuzzy rules.Figure 21.11 Rule viewer.Figure 21.12 Rule viewer.Figure 21.13 Transfer function in fuzzy format of result.Figure 21.14 Fuzzy rules.Figure 21.15 Rule viewer.

      22 Chapter 22Figure 22.1 Inventory optimization.Figure 22.2 Multi-product inventory network.Figure 22.3 Three stage processes.

      23 Chapter 23Figure 23.1 3D Euclidean space.Figure 23.2 (a) 3D model of function. (b) Gradient of function.Figure 23.3 Traveling salesman problem.Figure 23.4 Wing design parameters.Figure 23.5 (a) Convex function. (b) Non-convex function.Figure 23.6 Classification of optimization algorithms.Figure 23.7 Genetic algorithm.Figure 23.8 Torquigener albomaculosus [39].Figure 23.9 Circular structure of pufferfish and zones: (a) sideview and (b) upp...Figure 23.10 Illustration of circular structures.Figure 23.11 The modeling of circular structure.Figure 23.12 Beale function.Figure 23.13 The change of objective function value versus number of iterations.Figure 23.14 Change of circular structures for Beale function.

      24 Chapter 24Figure 24.1 Sperm Swam and the global best value (winner).

      25 Chapter 25Figure 25.1 Diagram depicting the flow of optimization process.Figure 25.2 Complete usage of optimization type of problems.Figure 25.3 Complete framework of metaheuristics approach.

      26 Chapter 27Figure 27.1 Representation of a three-diode model for a solar cell.Figure 27.2 Different phases of Harris hawks optimization (HHO) [18].Figure 27.3 Convergence curve for TDM (Kyocera KC200GT).Figure 27.4 Convergence curve for TDM (Canadian Solar CS6K-280M).Figure 27.5 Convergence curve for TDM (Schutten Solar STM6 40-36).Figure 27.6 Convergence curve for TDM (SolarWorld Pro. SW255).Figure 27.7 Kruskal-Wallis test performance for TDM (KC200GT).Figure 27.8 Kruskal-Wallis test results for TDM (CS6K-280M).Figure 27.9 Kruskal-Wallis test diagram for TDM (STM6 40-36).Figure 27.10 Kruskal-Wallis test results for TDM (Pro. SW255 model).

      27 Chapter 28Figure 28.1 System model of the proposed CSS system with M number of secondary u...Figure 28.2 Illustration showing SVM classifier for two set of data points, mark...Figure 28.3 Illustration of decision thresholds and quantization points.Figure 28.4 (a) SU node consisting of an antenna, RTL-SDR, and RPI SBC. (b) Inte...Figure 28.5 Arrangement of SU nodes. LoS path between PU and SUs is obstructed b...Figure 28.6 ROC for different fusion techniques applied on soft energy values. V...Figure 28.7 ROC for different fusion techniques applied on soft energy values. S...Figure 28.8 ROC plots with EGC decision fusion applied on OQ, UQ, and soft data ...Figure 28.9 ROC plots with KMC decision fusion applied on OQ, UQ, and soft data ...Figure 28.10 ROC plots with SVM decision fusion applied on OQ, UQ, and soft data...Figure 28.11 PD variation with SNR for different

with EGC decision fusion. Val...Figure 28.12 PD variation with SNR for different in case of KMC decision fusio...Figure 28.13 PD variation with SNR for different in case of SVM decision fusio...Figure 28.14 PD variation with SNR for different K in case of EGC decision fusio...Figure 28.15 PD variation with SNR for different K in case of KMC decision fusio...Figure 28.16 PD variation with SNR for different K in case of SVM decision fusio...Figure 28.17 PD variation with number of samples for EGC decision fusion.Figure 28.18 PD variation with number of samples for KMC decision fusion.Figure 28.19 PD variation with number of samples for SVM decision fusion.Figure 28.20 PD variation with SNR for different number of SU for EGC decision f...Figure 28.21 PD variation with SNR for different number of SU for KMC decision f...Figure 28.22 PD variation with SNR for different number of SU for SVM decision f...

      28 Chapter 29Figure 29.1 GIoT–based layered architecture for smart irrigation monitoring.Figure 29.2 Monitored parameters in machine learning–based precision agriculture...Figure 29.3 Monitored parameters in edge Computing–based precision agriculture.Figure 29.4 Monitored parameters in GIoT–based precision agriculture.Figure 29.5 Power, costs, and security optimization in GIoT–based precision agri...Figure 29.6 LPWAN technologies’ paper distribution used in GIoT–based СКАЧАТЬ