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• Introduction and Overview: Structural Control and Tuned Mass Dampers.- Robust design of different tuned mass damper techniques to mitigate wind-induced vibrations under uncertain conditions.- Machine Learning-Based Model for Optimum Design of TMDs by Using Artificial Neural Networks.
• (source: Nielsen Book Data)

This book is a timely book to summarize the latest developments in the optimization of tuned mass dampers covering all classical approaches and new trends including metaheuristic algorithms. Also, artificial intelligence and machine learning methods are included to predict optimum results by skipping long optimization processes. Another difference and advantage of the book are to provide chapters about several types of control types including passive tuned mass dampers, active tuned mass dampers, tuned liquid dampers, tuned liquid column dampers and inerter dampers. Tuned mass dampers (TMDs) are vibration absorber devices used in all types of mechanic systems. The key factor in the design is an effective tuning of TMDs for the desired performance. In practice, several high-rise structures and bridges were designed by including TMDs. Also, TMDs were installed after the construction of the structures after several negative experiences resulting from the disturbing sway of the structures. In optimum design, several closed-form expressions have been proposed for optimum frequency and damping ratio of TMDs, but the exact optimization requires iterative optimization approaches. The current trend is to use evolutionary algorithms and metaheuristic optimization methods to reach the goal. .
(source: Nielsen Book Data)

• Part I Fundamentals
• 1.0 Introduction
• 1.1. Where machine learning can help engineers
• 1.2. Where machine learning cannot help engineers
• 1.3. Machine learning to correct idealized models
• 2.
• The Landscape of machine learning
• 2.1. Supervised learning
• 2.1.1.
• Regression
• 2.1.2.
• Classification
• 2.1.3.
• Time series
• 2.1.4.
• Reinforcement
• 2.2. Unsupervised Learning
• 2.3. Optimization
• 2.4. Bayesian statistics
• 2.5. Cross-validation
• 3.
• Linear Models
• 3.1. Linear regression
• 3.2. Logistic regression
• 3.3. Regularized regression
• 3.4. Case Study: Determining physical laws using regularized regression
• 4.
• Tree-Based Models
• 4.1. Decision Trees
• 4.2. Random Forests
• 4.3. BART
• 4.4. Case Study: Modeling an experiment using random forest models
• 5.
• Clustering data
• 5.1. Singular value decomposition
• 5.2. Case Study: SVD to standardize several time series
• 5.3. K-means
• 5.4. K-nearest neighbors
• 5.5. t-SNE
• 5.6. Case Study: The reflectance spectrum of different foliage
• Part II Deep Neural Networks
• 6.
• Feed-Forward Neural Networks
• 6.1. Neurons
• 6.2. Dropout
• 6.3. Backpropagation
• 6.4. Initialization
• 6.5. Regression
• 6.6. Classification
• 6.7. Case Study: The strength of concrete as a function of age and ingredients
• 7.
• Convolutional Neural Networks
• 7.1. Convolutions
• 7.2. Pooling
• 7.3. Residual networks
• 7.4. Case Study: Finding volcanoes on Venus
• 8.
• Recurrent neural networks for time series data
• 8.1. Basic Recurrent neural networks
• 8.2. Long-term, Short-Term memory
• 8.3. Attention networks
• 8.4. Case Study: Predicting future system performance
• Part III Advanced Topics in Machine Learning
• 9.
• Unsupervised Learning with Neural Networks
• 9.1. Auto-encoders
• 9.2. Boltzmann machines
• 9.3. Case study: Optimization using Inverse models
• 10. Reinforcement learning
• 10.1.
• Case study: controlling a mechanical gantry
• 11. Transfer learning
• 11.1.
• Case study: Transfer learning a simulation emulator for experimental measurements
• Part IV Appendices
• A.
• SciKit-Learn
• B.
• Tensorflow.
• (source: Nielsen Book Data)

All engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally "analog" disciplines-mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates their specific value to engineers by presenting concrete examples based on physical systems. The book proceeds from basic learning models to deep neural networks, gradually increasing readers' ability to apply modern machine learning techniques to their current work and to prepare them for future, as yet unknown, problems. Rather than taking a black box approach, the author teaches a broad range of techniques while conveying the kinds of problems best addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow, demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally benefits undergraduate engineering students who wish to acquire the skills required by future employers, and practicing engineers who wish to expand and update their problem-solving toolkit.
(source: Nielsen Book Data)

• Application of Supervised learning for Voltage Stability Assessment.- Breast DCE-MRI Segmentation for Lesion Detection using Clustering with Fireworks Algorithm.- Swarm Programming Using Moth-Flame Optimization and Whale Optimization Algorithms.- Nonlinear Regression Analysis Using Multi-Verse Optimizer.- An Extended ACO-Based Routing Algorithm for Cognitive Radio Network.- A Review on Image Classification and Object Detection Using Deep Learning.- Kidney Lesion Segmentation in MRI Using Clustering with Salp Swarm Algorithm.- Smart Approach to Optical Character Recognition and Ubiquitous Speech Synthesis using Real-Time Deep Learning Algorithms.- Sine Cosine Algorithm with Centroid Opposition-based Computation.- Data Mining Techniques for Fraud Detection - Credit Card Frauds.
• (source: Nielsen Book Data)

This book presents best selected papers presented at the First Global Conference on Artificial Intelligence and Applications (GCAIA 2020), organized by the University of Engineering & Management, Jaipur, India, during 8-10 September 2020. The proceeding will be targeting the current research works in the domain of intelligent systems and artificial intelligence.
(source: Nielsen Book Data)

• Preface xi 1 Analysis of Six-Phase Grid Connected Synchronous Generator in Wind Power Generation 1 Arif Iqbal and Girish Kumar Singh 1.1 Introduction 2 1.2 Analytical Modeling of Six-Phase Synchronous Machine 4 1.2.1 Voltage Equation 5 1.2.2 Equations of Flux Linkage Per Second 5 1.3 Linearization of Machine Equations for Stability Analysis 10 1.4 Dynamic Performance Results 12 1.5 Stability Analysis Results 15 1.5.1 Parametric Variation of Stator 16 1.5.2 Parametric Variation of Field Circuit 19 1.5.3 Parametric Variation of Damper Winding, Kd 22 1.5.4 Parametric Variation of Damper Winding, Kq 24 1.5.5 Magnetizing Reactance Variation Along q-axis 26 1.5.6 Variation in Load 28 1.6 Conclusions 29 References 30
• Appendix 31 Symbols Meaning 32 2 Artificial Intelligence as a Tool for Conservation and Efficient Utilization of Renewable Resource 37 Vinay N., Ajay Sudhir Bale, Subhashish Tiwari and Baby Chithra R. 2.1 Introduction 38 2.2 AI in Water Energy 39 2.2.1 Prediction of Groundwater Level 39 2.2.2 Rainfall Modeling 46 2.3 AI in Solar Energy 47 2.3.1 Solar Power Forecasting 47 2.4 AI in Wind Energy 53 2.4.1 Wind Monitoring 53 2.4.2 Wind Forecasting 54 2.5 AI in Geothermal Energy 55 2.6 Conclusion 60 References 61 3 Artificial Intelligence-Based Energy-Efficient Clustering and Routing in IoT-Assisted Wireless Sensor Network 79 Nitesh Chouhan 3.1 Introduction 80 3.2 Related Study 81 3.3 Clustering in WSN 84 3.4 Research Methodology 85 3.4.1 Creating Wireless Sensor-Based IoT Environment 85 3.4.2 Clustering Approach 86 3.4.3 AI-Based Energy-Aware Routing Protocol 87 3.5 Conclusion 89 References 89 4 Artificial Intelligence for Modeling and Optimization of the Biogas Production 93 Narendra Khatri and Kamal Kishore Khatri 4.1 Introduction 93 4.2 Artificial Neural Network 96 4.2.1 ANN Architecture 96 4.2.2 Training Algorithms 98 4.2.3 Performance Parameters for Analysis of the ANN Model 98 4.2.4 Application of ANN for Biogas Production Modeling 99 4.3 Evolutionary Algorithms 103 4.3.1 Genetic Algorithm 103 4.3.2 Ant Colony Optimization 104 4.3.3 Particle Swarm Optimization 106 4.3.4 Application of Hybrid Models (ANN and Evolutionary Algorithms) for Biogas Production Modeling 106 4.4 Conclusion 107 References 111 5 Battery State-of-Charge Modeling for Solar PV Array Using Polynomial Regression 115 Siddhi Vinayak Pandey, Jeet Patel and Harsh S. Dhiman 5.1 Introduction 115 5.2 Dynamic Battery Modeling 119 5.2.1 Proposed Methodology 120 5.3 Results and Discussion 122 5.4 Conclusion 126 References 127 6 Deep Learning Algorithms for Wind Forecasting: An Overview 129 M. Lydia and G. Edwin Prem Kumar Nomenclature 129 6.1 Introduction 131 6.2 Models for Wind Forecasting 133 6.2.1 Persistence Model 133 6.2.2 Point vs. Probabilistic Forecasting 133 6.2.3 Multi-Objective Forecasting 134 6.2.4 Wind Power Ramp Forecasting 134 6.2.5 Interval Forecasting 134 6.2.6 Multi-Step Forecasting 134 6.3 The Deep Learning Paradigm 135 6.3.1 Batch Learning 136 6.3.2 Sequential Learning 136 6.3.3 Incremental Learning 136 6.3.4 Scene Learning 136 6.3.5 Transfer Learning 136 6.3.6 Neural Structural Learning 136 6.3.7 Multi-Task Learning 137 6.4 Deep Learning Approaches for Wind Forecasting 137 6.4.1 Deep Neural Network 137 6.4.2 Long Short-Term Memory 138 6.4.3 Extreme Learning Machine 138 6.4.4 Gated Recurrent Units 139 6.4.5 Autoencoders 139 6.4.6 Ensemble Models 139 6.4.7 Other Miscellaneous Models 139 6.5 Research Challenges 139 6.6 Conclusion 141 References 142 7 Deep Feature Selection for Wind Forecasting-I 147 C. Ramakrishnan, S. Sridhar, Kusumika Krori Dutta, R. Karthick and C. Janamejaya 7.1 Introduction 148 7.2 Wind Forecasting System Overview 152 7.2.1 Classification of Wind Forecasting 153 7.2.2 Wind Forecasting Methods 153 7.2.2.1 Physical Method 154 7.2.2.2 Statistical Method 154 7.2.2.3 Hybrid Method 155 7.2.3 Prediction Frameworks 155 7.2.3.1 Pre-Processing of Data 155 7.2.3.2 Data Feature Analysis 156 7.2.3.3 Model Formulation 156 7.2.3.4 Optimization of Model Structure 156 7.2.3.5 Performance Evaluation of Model 157 7.2.3.6 Techniques Based on Methods of Forecasting 157 7.3 Current Forecasting and Prediction Methods 158 7.3.1 Time Series Method (TSM) 159 7.3.2 Persistence Method (PM) 159 7.3.3 Artificial Intelligence Method 160 7.3.4 Wavelet Neural Network 161 7.3.5 Adaptive Neuro-Fuzzy Inference System (ANFIS) 162 7.3.6 ANFIS Architecture 163 7.3.7 Support Vector Machine (SVM) 165 7.3.8 Ensemble Forecasting 166 7.4 Deep Learning-Based Wind Forecasting 166 7.4.1 Reducing Dimensionality 168 7.4.2 Deep Learning Techniques and Their Architectures 169 7.4.3 Unsupervised Pre-Trained Networks 169 7.4.4 Convolutional Neural Networks 170 7.4.5 Recurrent Neural Networks 170 7.4.6 Analysis of Support Vector Machine and Decision Tree Analysis (With Computation Time) 170 7.4.7 Tree-Based Techniques 172 7.5 Case Study 173 References 176 8 Deep Feature Selection for Wind Forecasting-II 181 S. Oswalt Manoj, J.P. Ananth, Balan Dhanka and Maharaja Kamatchi 8.1 Introduction 182 8.1.1 Contributions of the Work 184 8.2 Literature Review 185 8.3 Long Short-Term Memory Networks 186 8.4 Gated Recurrent Unit 190 8.5 Bidirectional Long Short-Term Memory Networks 194 8.6 Results and Discussion 196 8.7 Conclusion and Future Work 197 References 198 9 Data Falsification Detection in AMI: A Secure Perspective Analysis 201 Vineeth V.V. and S. Sophia 9.1 Introduction 201 9.2 Advanced Metering Infrastructure 202 9.3 AMI Attack Scenario 204 9.4 Data Falsification Attacks 205 9.5 Data Falsification Detection 206 9.6 Conclusion 207 References 208 10 Forecasting of Electricity Consumption for G20 Members Using Various Machine Learning Techniques 211 Jaymin Suhagiya, Deep Raval, Siddhi Vinayak Pandey, Jeet Patel, Ayushi Gupta and Akshay Srivastava 10.1 Introduction 211 10.1.1 Why Electricity Consumption Forecasting Is Required? 212 10.1.2 History and Advancement in Forecasting of Electricity Consumption 212 10.1.3 Recurrent Neural Networks 213 10.1.3.1 Long Short-Term Memory 214 10.1.3.2 Gated Recurrent Unit 214 10.1.3.3 Convolutional LSTM 215 10.1.3.4 Bidirectional Recurrent Neural Networks 216 10.1.4 Other Regression Techniques 216 10.2 Dataset Preparation 217 10.3 Results and Discussions 218 10.4 Conclusion 225 Acknowledgement 225 References 225 11 Use of Artificial Intelligence (AI) in the Optimization of Production of Biodiesel Energy 229 Manvinder Singh Pahwa, Manish Dadhich, Jaskaran Singh Saini and Dinesh Kumar Saini 11.1 Introduction 230 11.2 Indian Perspective of Renewable Biofuels 230 11.3 Opportunities 232 11.4 Relevance of Biodiesel in India Context 233 11.5 Proposed Model 234 11.6 Conclusion 236 References 237 Index 239.
• (source: Nielsen Book Data)

ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in today's world, this book was designed to enhance the reader's knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.
(source: Nielsen Book Data)

• Preface xi 1 Analysis of Six-Phase Grid Connected Synchronous Generator in Wind Power Generation 1 Arif Iqbal and Girish Kumar Singh 1.1 Introduction 2 1.2 Analytical Modeling of Six-Phase Synchronous Machine 4 1.2.1 Voltage Equation 5 1.2.2 Equations of Flux Linkage Per Second 5 1.3 Linearization of Machine Equations for Stability Analysis 10 1.4 Dynamic Performance Results 12 1.5 Stability Analysis Results 15 1.5.1 Parametric Variation of Stator 16 1.5.2 Parametric Variation of Field Circuit 19 1.5.3 Parametric Variation of Damper Winding, Kd 22 1.5.4 Parametric Variation of Damper Winding, Kq 24 1.5.5 Magnetizing Reactance Variation Along q-axis 26 1.5.6 Variation in Load 28 1.6 Conclusions 29 References 30
• Appendix 31 Symbols Meaning 32 2 Artificial Intelligence as a Tool for Conservation and Efficient Utilization of Renewable Resource 37 Vinay N., Ajay Sudhir Bale, Subhashish Tiwari and Baby Chithra R. 2.1 Introduction 38 2.2 AI in Water Energy 39 2.2.1 Prediction of Groundwater Level 39 2.2.2 Rainfall Modeling 46 2.3 AI in Solar Energy 47 2.3.1 Solar Power Forecasting 47 2.4 AI in Wind Energy 53 2.4.1 Wind Monitoring 53 2.4.2 Wind Forecasting 54 2.5 AI in Geothermal Energy 55 2.6 Conclusion 60 References 61 3 Artificial Intelligence-Based Energy-Efficient Clustering and Routing in IoT-Assisted Wireless Sensor Network 79 Nitesh Chouhan 3.1 Introduction 80 3.2 Related Study 81 3.3 Clustering in WSN 84 3.4 Research Methodology 85 3.4.1 Creating Wireless Sensor-Based IoT Environment 85 3.4.2 Clustering Approach 86 3.4.3 AI-Based Energy-Aware Routing Protocol 87 3.5 Conclusion 89 References 89 4 Artificial Intelligence for Modeling and Optimization of the Biogas Production 93 Narendra Khatri and Kamal Kishore Khatri 4.1 Introduction 93 4.2 Artificial Neural Network 96 4.2.1 ANN Architecture 96 4.2.2 Training Algorithms 98 4.2.3 Performance Parameters for Analysis of the ANN Model 98 4.2.4 Application of ANN for Biogas Production Modeling 99 4.3 Evolutionary Algorithms 103 4.3.1 Genetic Algorithm 103 4.3.2 Ant Colony Optimization 104 4.3.3 Particle Swarm Optimization 106 4.3.4 Application of Hybrid Models (ANN and Evolutionary Algorithms) for Biogas Production Modeling 106 4.4 Conclusion 107 References 111 5 Battery State-of-Charge Modeling for Solar PV Array Using Polynomial Regression 115 Siddhi Vinayak Pandey, Jeet Patel and Harsh S. Dhiman 5.1 Introduction 115 5.2 Dynamic Battery Modeling 119 5.2.1 Proposed Methodology 120 5.3 Results and Discussion 122 5.4 Conclusion 126 References 127 6 Deep Learning Algorithms for Wind Forecasting: An Overview 129 M. Lydia and G. Edwin Prem Kumar Nomenclature 129 6.1 Introduction 131 6.2 Models for Wind Forecasting 133 6.2.1 Persistence Model 133 6.2.2 Point vs. Probabilistic Forecasting 133 6.2.3 Multi-Objective Forecasting 134 6.2.4 Wind Power Ramp Forecasting 134 6.2.5 Interval Forecasting 134 6.2.6 Multi-Step Forecasting 134 6.3 The Deep Learning Paradigm 135 6.3.1 Batch Learning 136 6.3.2 Sequential Learning 136 6.3.3 Incremental Learning 136 6.3.4 Scene Learning 136 6.3.5 Transfer Learning 136 6.3.6 Neural Structural Learning 136 6.3.7 Multi-Task Learning 137 6.4 Deep Learning Approaches for Wind Forecasting 137 6.4.1 Deep Neural Network 137 6.4.2 Long Short-Term Memory 138 6.4.3 Extreme Learning Machine 138 6.4.4 Gated Recurrent Units 139 6.4.5 Autoencoders 139 6.4.6 Ensemble Models 139 6.4.7 Other Miscellaneous Models 139 6.5 Research Challenges 139 6.6 Conclusion 141 References 142 7 Deep Feature Selection for Wind Forecasting-I 147 C. Ramakrishnan, S. Sridhar, Kusumika Krori Dutta, R. Karthick and C. Janamejaya 7.1 Introduction 148 7.2 Wind Forecasting System Overview 152 7.2.1 Classification of Wind Forecasting 153 7.2.2 Wind Forecasting Methods 153 7.2.2.1 Physical Method 154 7.2.2.2 Statistical Method 154 7.2.2.3 Hybrid Method 155 7.2.3 Prediction Frameworks 155 7.2.3.1 Pre-Processing of Data 155 7.2.3.2 Data Feature Analysis 156 7.2.3.3 Model Formulation 156 7.2.3.4 Optimization of Model Structure 156 7.2.3.5 Performance Evaluation of Model 157 7.2.3.6 Techniques Based on Methods of Forecasting 157 7.3 Current Forecasting and Prediction Methods 158 7.3.1 Time Series Method (TSM) 159 7.3.2 Persistence Method (PM) 159 7.3.3 Artificial Intelligence Method 160 7.3.4 Wavelet Neural Network 161 7.3.5 Adaptive Neuro-Fuzzy Inference System (ANFIS) 162 7.3.6 ANFIS Architecture 163 7.3.7 Support Vector Machine (SVM) 165 7.3.8 Ensemble Forecasting 166 7.4 Deep Learning-Based Wind Forecasting 166 7.4.1 Reducing Dimensionality 168 7.4.2 Deep Learning Techniques and Their Architectures 169 7.4.3 Unsupervised Pre-Trained Networks 169 7.4.4 Convolutional Neural Networks 170 7.4.5 Recurrent Neural Networks 170 7.4.6 Analysis of Support Vector Machine and Decision Tree Analysis (With Computation Time) 170 7.4.7 Tree-Based Techniques 172 7.5 Case Study 173 References 176 8 Deep Feature Selection for Wind Forecasting-II 181 S. Oswalt Manoj, J.P. Ananth, Balan Dhanka and Maharaja Kamatchi 8.1 Introduction 182 8.1.1 Contributions of the Work 184 8.2 Literature Review 185 8.3 Long Short-Term Memory Networks 186 8.4 Gated Recurrent Unit 190 8.5 Bidirectional Long Short-Term Memory Networks 194 8.6 Results and Discussion 196 8.7 Conclusion and Future Work 197 References 198 9 Data Falsification Detection in AMI: A Secure Perspective Analysis 201 Vineeth V.V. and S. Sophia 9.1 Introduction 201 9.2 Advanced Metering Infrastructure 202 9.3 AMI Attack Scenario 204 9.4 Data Falsification Attacks 205 9.5 Data Falsification Detection 206 9.6 Conclusion 207 References 208 10 Forecasting of Electricity Consumption for G20 Members Using Various Machine Learning Techniques 211 Jaymin Suhagiya, Deep Raval, Siddhi Vinayak Pandey, Jeet Patel, Ayushi Gupta and Akshay Srivastava 10.1 Introduction 211 10.1.1 Why Electricity Consumption Forecasting Is Required? 212 10.1.2 History and Advancement in Forecasting of Electricity Consumption 212 10.1.3 Recurrent Neural Networks 213 10.1.3.1 Long Short-Term Memory 214 10.1.3.2 Gated Recurrent Unit 214 10.1.3.3 Convolutional LSTM 215 10.1.3.4 Bidirectional Recurrent Neural Networks 216 10.1.4 Other Regression Techniques 216 10.2 Dataset Preparation 217 10.3 Results and Discussions 218 10.4 Conclusion 225 Acknowledgement 225 References 225 11 Use of Artificial Intelligence (AI) in the Optimization of Production of Biodiesel Energy 229 Manvinder Singh Pahwa, Manish Dadhich, Jaskaran Singh Saini and Dinesh Kumar Saini 11.1 Introduction 230 11.2 Indian Perspective of Renewable Biofuels 230 11.3 Opportunities 232 11.4 Relevance of Biodiesel in India Context 233 11.5 Proposed Model 234 11.6 Conclusion 236 References 237 Index 239.
• (source: Nielsen Book Data)

ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in today's world, this book was designed to enhance the reader's knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.
(source: Nielsen Book Data)

• 1. Application of Alternative Clean Energy
• 2. Optimization of Hybrid energy generation
• 3. IoET-SG: Integrating Internet of Energy Things with Smart Grid
• 4. Evolution of High Efficiency PERC Solar Cells
• 5. Online Based Approach for Frequency Control of Micro- Grid Using Biological Inspired Based Intelligent Controller
• 6. Optimal Allocation of Renewable Energy Sources in Electrical Distribution Systems Based on Technical and Economic Indexes
• 7. Optimization of Renewable Energy Sources Using Emerging Computational Techniques
• 8. Advanced renewable dispatch with machine-learning based hybrid demand-side controller: state-of-the-art and a novel approach
• 9. Machine learning-based robust and reliable design on PCMs-PV systems with multi-level scenario uncertainty
• 10. Agent-based peer-to-peer energy trading between prosumers and consumers with cost-benefit business models
• 11. Machine learning-based hybrid demand-side controller for renewable energy management
• 12. Prediction of Energy Generation Target of Hydropower Plants using Artificial Neural Network
• 13. Response surface methodology based optimization of Parameters for Biodiesel Production
• 14. Reservoir Simulation Model for the Design of Irrigation Project
• 15. Effect of Hydrofoils on the Starting Torque Characteristics of Darrieus Hydrokinetic Turbine.
• (source: Nielsen Book Data)

Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies analyzes the changes in this energy generation shift, including issues of grid stability with variability in renewable energy vs. traditional baseload energy generation. Providing solutions to current critical environmental, economic and social issues, this book comprises various complex nonlinear interactions among different parameters to drive the integration of renewable energy into the grid. It considers how artificial intelligence and machine learning techniques are being developed to produce more reliable energy generation to optimize system performance and provide sustainable development. As the use of artificial intelligence to revolutionize the energy market and harness the potential of renewable energy is essential, this reference provides practical guidance on the application of renewable energy with AI, along with machine learning techniques and capabilities in design, modeling and for forecasting performance predictions for the optimization of renewable energy systems. It is targeted at researchers, academicians and industry professionals working in the field of renewable energy, AI, machine learning, grid Stability and energy generation.
(source: Nielsen Book Data)

• Intro
• About This Book
• Contents
• About the Authors
• 1 An Overview of Field Classifications to Evaluate Tunnel Boring Machine Performance
• 1.1 Introduction
• 1.2 Tunnel Boring Machine
• 1.2.1 Brief History of TBM
• 1.2.2 Types and Basic Principles of TBM
• 1.2.3 TBM Performance Parameters
• 1.2.4 Factors Influencing TBM Performance
• 1.3 TBM Prediction Field Classifications
• 1.4 TBM Performance Prediction Using Field Approach
• 1.5 RMCs Used in TBM Performance Prediction
• 1.6 Discussion and Conclusion
• References

• 2 Empirical, Statistical, and Intelligent Techniques for TBM Performance Prediction
• 2.1 Introduction
• 2.2 Theoretical Models
• 2.2.1 Cutter Load Approach
• 2.2.2 Specific Energy Approach
• 2.3 Empirical Models
• 2.4 Statistical Approach
• 2.5 Computational-Based Techniques
• 2.6 Discussion and Conclusion
• References
• 3 Developing Statistical Models for Solving Tunnel Boring Machine Performance Problem
• 3.1 Introduction
• 3.2 Regression-Based Models
• 3.2.1 Linear Multiple Regression (LMR)
• 3.2.2 Non-linear Multiple Regression (NLMR)
• 3.3 Case Study

• 3.4 Data Measurement and Input Variables
• 3.4.1 Rock Material Properties
• 3.4.2 Rock Mass Properties
• 3.4.3 Machine Characteristics
• 3.4.4 Input Variables
• 3.5 Regression-Based Models
• 3.5.1 Simple Regression
• 3.5.2 Multiple Regression
• 3.6 Discussion and Conclusion
• References
• 4 A Comparative Study of Artificial Intelligence Techniques to Estimate TBM Performance in Various Weathering Zones
• 4.1 Introduction
• 4.2 Methodology
• 4.2.1 Artificial Neural Network (ANN)
• 4.2.2 Group Method of Data Handling (GMDH)
• 4.3 Tunnel Site and Data Collection

• 4.4 GMDH Model Development
• 4.5 Model Assessment and Discussion
• 4.6 Conclusions
• References

This book covers the tunnel boring machine (TBM) performance classifications, empirical models, statistical and intelligent-based techniques which have been applied and introduced by the researchers in this field. In addition, a critical review of the available TBM performance predictive models will be discussed in details. Then, this book introduces several predictive models i.e., statistical and intelligent techniques which are applicable, powerful and easy to implement, in estimating TBM performance parameters. The introduced models are accurate enough and they can be used for prediction of TBM performance in practice before designing TBMs. .
(source: Nielsen Book Data)

• Chapter 1: OK Computer
• Chapter 2: Mission Control
• Chapter 3: The 5G Car
• Chapter 4: On Brand
• Chapter 5: I.AM.Car
• Chapter 6: Second Home
• Chapter 7: Automation
• Chapter 8: Together in Electric Dreams
• Chapter 9: Smart
• Chapter 10: Digital.
• (source: Nielsen Book Data)

Nothing defined the 20th century more than the evolution of the car industry. The 2020 decade will see the automotive industry leap forward beyond simply moving people geographically toward a new purpose: to become a services industry. This book takes readers on a journey where cars will evolve towards becoming "computers on wheels." The automotive industry is one of the sectors most profoundly changed by digitalization and the 21st century energy needs. You'll explore the shifting paradigms and how cars today represent a new interpretation of what driving should be and what cars should offer. This book presents exciting case studies on how artificial intelligence (AI) and data analytics are used to design future cars, predict car efficiency, ensure safety and simulate engineering dynamics for its design, as well as a new arena for IoT and human data. It opens a window into the origins of cars becoming software-run machines, first to run internal diagnostics, and then to become machines connected to other external machines via Bluetooth, to finally the Internet via 5G. From transportation to solving people's problems, The Future of the Automotive Industry is less about the technology itself, but more about the outcomes of technology in the future, and the transformative power it has over a much beloved item: cars. What You'll Learn Explore smart cities and their evolution when it comes to traffic and vehicles Gain a new perspective on the future of cars and transportation based on how digital technologies will transform vehicles Examine how AI and IoT will create new contexts of interactions with drivers and passengers alike Review concepts such as personalizing the driving experience and how this will take form See how self-driving cars impact data mining of personal data Who This Book Is For Anyone with an interest in digital advancements in the automotive industry beyond the connected car.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Smart Electronic Systems: An Overview
• 3. Design domains and abstraction levels for effective smart system simulation
• 4. Energy-Efficient Digital Processing via Approximate Computing
• 5. Discrete Power Devices and Power Modules
• 6. MEMS System-Level Modeling and Simulation in Smart Systems
• 7. Modeling and Simulation of the Power Flow in Smart Systems
• 8. Smart system case studies.

This book-presents new methods and tools for the integration and simulation of smart devices. The design approach described in this book explicitly accounts for integration of Smart Systems components and subsystems as a specific constraint. It includes methodologies and EDA tools to enable multi-disciplinary and multi-scale modeling and design, simulation of multi-domain systems, subsystems and components at all levels of abstraction, system integration and exploration for optimization of functional and non-functional metrics. By covering theoretical and practical aspects of smart device design, this book targets people who are working and studying on hardware/software modelling, component integration and simulation under different positions (system integrators, designers, developers, researchers, teachers, students etc.). In particular, it is a good introduction to people who have interest in managing heterogeneous components in an efficient and effective way on different domains and different abstraction levels. People active in smart device development can understand both the current status of practice and future research directions. Provides a comprehensive overview of smart systems design, focusing on design challenges and cutting-edge solutions; Enables development of a co-simulation and co-design environment that accounts for the peculiarities of the basic subsystems and components to be integrated; Describes development of modeling and design techniques, methods and tools that enable multi-domain simulation and optimization at various levels of abstraction and across different technological domains.

• Optimal Sizing and Siting of Distributed Generation for Losses Minimization in Distribution System using Fractional Levy Flight Bat Algorithm.- Performance Analysis of a Standalone Inverter System under Variable Loading Conditions.- Performance Study of a Wind-Battery based Islanding System.- Fabric Defect Detection Using Computer Vision.- Energy Audit and Advancement of Solar Installation in SIT: A Case Study.- Random Fault Positioning Based Voltage Sag Assessment for a Large Power Transmission Network.- Feasibility Analysis of SEPIC Converter as a PV Balancer for Practical Photovoltaic System.- Market Clearing Mechanism by Optimal Scheduling of Electric Power Suppliers.- Anti Camcorder Piracy Display System.- Performance Evaluation of HAWT and VAWT based WECS with Advanced Hill Climb Search MPPT and Fuzzy Logic Controller for Low Wind Speed Regions.
• (source: Nielsen Book Data)

This book is a collection of high-quality research papers presented at the International Conference on Smart and Intelligent Systems (SIS 2021), which will be held in Velagapudi Ramakrishna Siddhartha Engineering College (VRSEC), Andhra Pradesh, India, during February 25-26, 2021, in virtual mode. It highlights how recent informatics intelligent systems have successfully been used to develop innovative smart techniques and infrastructure in the field of modern engineering and technology. The book will also be of interest to those working in the field of computational intelligence, smart computer network and security analysis, control and automation system, cloud computing, fog computing and IoT, smart grid communication, smart cities, solar cell synthesis and their performance, green technology, and many more. The contents of this book prove useful to researchers and professionals.
(source: Nielsen Book Data)

• Improvement in Side Lobe Reduction in FIR Filter Design Using Proposed Hybrid Blackman Window
• Low Frequency Stochastic Electromagnetic Field Observed in the Ionosphere Possibly Associated with an Earthquake Activity
• A Review on Role of Solar Drying Technology in Sustainable Development
• Role of Advance Solar Desalination Technique for Sustainable Development
• Effect of Reinforced Nano-Composites on AMC Solidification Curve
• An Efficient Performance of Enhanced Bellman-Ford Algorithm in Wireless Sensor Network Using K-Medoid Clustering
• Wavelet Based Compression of Acne Face Images with Automatic Selection and Lossless Compression of Acne Affected Region
• Absolute Ionization Cross Sections of Hydrogen Chloride Gaseous Molecule by Electron Impact. Plus 43 other papers.

This book delves into various solution paradigms such as artificial neural network, support vector machine, wavelet transforms, evolutionary computing, swarm intelligence. During the last decade, novel solution technologies based on human and species intelligence have gained immense popularity due to their flexible and unconventional approach. New analytical tools are also being developed to handle big data processing and smart decision making. The idea behind compiling this work is to familiarize researchers, academicians, industry persons and students with various applications of intelligent techniques for producing sustainable, cost-effective and robust solutions of frequently encountered complex, real-world problems in engineering and science disciplines. The practical problems in smart grids, communication, waste management, elimination of harmful elements from nature, etc., are identified, and smart and optimal solutions are proposed. .
(source: Nielsen Book Data)

• Introduction.- Smart Devices.- Smart Services.- Safety and Security.- E-Health.- E-Health.- E-Commerce.- Logistics 4.0.- Smart Cities and Communities.- E-Governance.
• (source: Nielsen Book Data)

This book sets the modern infrastructure of smart devices and services into the perspective of the future smart cities and communities. In the course of this, it discusses the major technological solutions and steps toward integrated logistics solutions to be used in these environments with their benefits in terms of efficiency, interoperability, and sustainability. By doing so, it paves the logistician's way toward the aspired innovation society.
(source: Nielsen Book Data)

• Prediction of Liver Cancer by Artificial Neural Network.- Remarks on the limit-circle classification of Conformable Fractional Sturm-Liouville Operator.- Improving Search Relevance with Word Embedding Based Clusters.- Improving Search Relevance with Word Embedding Based Clusters.- Diagnosis of Parkinson's Disease with Acoustic Sounds by Rule Based Model.- Development of Face Recognition System by Using Deep Learning and FaceNet Algorithm in the Operations Processes.- Mobile Assisted Travel Planning Software: The Case of Burdur.- Optimal Coordination of Directional Overcurrent Relays Using Artificial Ecosystem-based Optimization.- The Effect of Auscultation Areas on Nonlinear Classifiers in Computerized Analysis of Chronic Obstructive Pulmonary Disease.
• (source: Nielsen Book Data)

This book briefly covers internationally contributed chapters with artificial intelligence and applied mathematics-oriented background-details. Nowadays, the world is under attack of intelligent systems covering all fields to make them practical and meaningful for humans. In this sense, this edited book provides the most recent research on use of engineering capabilities for developing intelligent systems. The chapters are a collection from the works presented at the 2nd International Conference on Artificial Intelligence and Applied Mathematics in Engineering held within 09-10-11 October 2020 at the Antalya, Manavgat (Turkey). The target audience of the book covers scientists, experts, M.Sc. and Ph.D. students, post-docs, and anyone interested in intelligent systems and their usage in different problem domains. The book is suitable to be used as a reference work in the courses associated with artificial intelligence and applied mathematics. .
(source: Nielsen Book Data)

• State and Trends of Machine Learning Approaches in Business: An Empirical Review.- Piecewise Demodulation based on Combined Artificial Neural Network for Quadrate Frequency Shift Keying Communication Signals.- A New Variable Ordering Method for the K2 Algorithm.- A Benefit Optimization Approach to the Evaluation of Classification Algorithms.- Financial Fraud Detection through Artificial Intelligence.- Deep Learning-based Software Energy Consumption Profiling.- Prediction of Potential Bank Customers: Application on Data Mining.- The Model Selection Methods for Sparse Biological Networks.- A Hybrid Approach for the Sentiment Analysis of Turkish Twitter Data.- Text Mining and Statistical Learning for the Analysis of the Voice of the Customer.- Effect the Number of Reservations on Implementation of Operating Room Scheduling with Genetic Algorithm.- Statistical Learning Applied to Malware Detection.- Improved Social Spider Algorithm via Differential Evolution.- On the Prediction of Possibly Forgotten Shopping Basket items.- Selection and Training of School Administrators in Different Countries.- A Walking and Balance Analysis Based on Pedobarography.- On the Notion of Structure Species in the Bourbaki's Sense.
• (source: Nielsen Book Data)

This book features research presented at the 1st International Conference on Artificial Intelligence and Applied Mathematics in Engineering, held on 20-22 April 2019 at Antalya, Manavgat (Turkey). In today's world, various engineering areas are essential components of technological innovations and effective real-world solutions for a better future. In this context, the book focuses on problems in engineering and discusses research using artificial intelligence and applied mathematics. Intended for scientists, experts, M.Sc. and Ph.D. students, postdocs and anyone interested in the subjects covered, the book can also be used as a reference resource for courses related to artificial intelligence and applied mathematics.
(source: Nielsen Book Data)

• Introduction
• The Combustion Engine at the Turn of Industrialization
• Revolution through Simulation for the development of Powertrains
• Big Data for Powertrains- Powertrain development with Artificial Intelligence.

The variety of future powertrain concepts has drastically increased the development cost for automotive manufactures. Profitable investment requires a significantly leaner and efficient powertrain development process. Traditional methods of test and model based development need to be assisted by big data and data analytics. For this purpose, a valuable tool is available at the right time - artificial intelligence (AI). But what does AI really mean in a narrower sense? What concepts lie behind it? And how are the methods and algorithms transferable to powertrain applications? For the first time, this book aims to bridge the gap between automotive engineering and computer science, by illuminating the complexity of current AI concepts and demystifying it for powertrain applications. By elaborating on work processes, it shows how AI could be implemented and how completely novel methods can help us reshape the future of mobility. Target groups: Engineers in the field of powertrain development Engineers & computer scientists in the field of artificial intelligence Development departments of the automotive industry Suppliers, service providers of the automotive industry, IT industry (AI experts, start-ups etc.) Universities and research institutions All interested readers with technical background knowledge The author: Dr. Aras Mirfendreski has developed into an engine-expert throughout the German and international scientific landscape (studied at RWTH Aachen University, doctorate at Audi AG). Through his experience as a powertrain developer at Stuttgart FKFS for various OEMs as well as his activities as a senior program manager at Toyota, his developments always thrive on the transfer of state-of-the-art methods into corporate structures. Today, he serves as Vice President Engine R&D at Aquarius Engines and is also an AI expert, keynote speaker and consultant on artificial intelligence topics.

The book compiles the research works related to smart solutions concept in context to smart energy systems, maintaining electrical grid discipline and resiliency, computational collective intelligence consisted of interaction between smart devices, smart environments and smart interactions, as well as information technology support for such areas. It includes high-quality papers presented in the International Conference on Intelligent Computing Techniques for Smart Energy Systems organized by Manipal University Jaipur. This book will motivate scholars to work in these areas. The book also prophesies their approach to be used for the business and the humanitarian technology development as research proposal to various government organizations for funding approval.
(source: Nielsen Book Data)

• Flower Pollination Algorithm and its Applications in Engineering
• An Evolutionary Discrete Firefly Algorithm with Novel Operators for Solving the Vehicle Routing Problem with TimeWindows
• The Plant Propagation Algorithm for Discrete Optimisation: The Case of the Travelling Salesman Problem
• Enhancing Cooperative Coevolution with Surrogate-Assisted Local Search
• Cuckoo Search: From Cuckoo Reproduction Strategy to Combinatorial Optimization
• Clustering Optimization for WSN based on Nature-Inspired Algorithms
• Discrete Firefly Algorithm for Recruiting Task in a Swarm of Robots
• Nature-Inspired Swarm Intelligence for Data Fitting in Reverse Engineering: Recent Advances and FutureTrends
• A Novel Fast Optimisation Algorithm Using Differential Evolution Algorithm Optimisation and Meta- Modelling Approach
• A Hybridization of Runner-Based and Seed-Based Plant Propagation Algorithm
• Gravitational Search Algorithm Applied to Cell Formation Problem
• Parameterless Bat Algorithm and its Performace Study.

This timely review book summarizes the state-of-the-art developments in nature-inspired optimization algorithms and their applications in engineering. Algorithms and topics include the overview and history of nature-inspired algorithms, discrete firefly algorithm, discrete cuckoo search, plant propagation algorithm, parameter-free bat algorithm, gravitational search, biogeography-based algorithm, differential evolution, particle swarm optimization and others. Applications include vehicle routing, swarming robots, discrete and combinatorial optimization, clustering of wireless sensor networks, cell formation, economic load dispatch, metamodeling, surrogated-assisted cooperative co-evolution, data fitting and reverse engineering as well as other case studies in engineering. This book will be an ideal reference for researchers, lecturers, graduates and engineers who are interested in nature-inspired computation, artificial intelligence and computational intelligence. It can also serve as a reference for relevant courses in computer science, artificial intelligence and machine learning, natural computation, engineering optimization and data mining.

• Intro
• Preface
• Contents
• About the Editors
• 1 Formation of the Energy-Efficient Platform of Hi-Tech Development of Renewable Power
• 1.1 Introduction
• 1.2 Models and Research Methods
• 1.3 Scientific Novelty
• 1.4 Practical Significance
• 1.5 Conclusion
• References
• 2 Energy Saving Methods of the Industrial Power Development According to the Factors of the New Industrial Revolution
• 2.1 Introduction
• 2.2 Models and Methods of the Industrial Energy Development According to the Factors of the Industrial Revolution
• 2.3 Scientific Novelty
• 2.4 Practical Significance

• 2.5 Conclusion
• References
• 3 Design Methods of Organizing High-Tech Processes of Gas Combustion by Power Efficiency Criteria
• 3.1 Introduction
• 3.2 Models and Organization Methods of High-Tech Fuel Combustion Processes Based on the Project Approach
• 3.3 Scientific Novelty
• 3.4 Practical Significance
• 3.5 Conclusion
• References
• 4 Modeling of Processes of Increase in Power and Environmental Efficiency of Power Process Units
• 4.1 Introduction
• 4.2 Models for Assessment and Regulation of the Rate of Increasing Energy and Environmental Efficiency of Plants

• 4.3 Scientific Novelty
• 4.4 Modeling of Processes of Increase in Power and Environmental Efficiency
• 4.5 Practical Significance
• 4.6 Conclusion
• References
• 5 Vessel's Maneurability Assessment Using Linguistic Numbers
• 5.1 Introduction
• 5.2 Model Representation and Problem Statement
• 5.3 The Method of Solving the Problem
• 5.4 Results of Numerical Simulation
• References
• 6 Simulation of Thickness-Dependent Polarization Switching in Ferroelectric Thin Films Using COMSOL Multiphysics
• 6.1 Introduction
• 6.2 Mathematical Model

• 6.3 The Use of COMSOL Multiphysics Platform for Model Implementation
• 6.4 Simulation Results and Discussion
• 6.5 Conclusion
• References
• 7 Assessment of the Economic Risk of Projects for the Export of Electricity from Russia to Northeast Asia
• 7.1 Introduction
• 7.2 Problem Statement (Assessment of the Projects Effectiveness)
• 7.3 Research Method (Economic Risk Assessment)
• 7.4 Assumed Economic Conditions
• 7.5 The Calculation Results and Their Analysis
• 7.6 Conclusions
• References
• 8 Development of an Eco-Friendly Wave-Type Engine for Small River Vessels
• 8.1 Introduction

• 8.2 Wave Type Driver
• 8.3 Conclusions
• References
• 9 Vessels Traffic Data Capturing and Analysis
• 9.1 Introduction
• 9.2 Overview of Ways to Collect Vessels Traffic Data
• 9.2.1 Radar
• 9.2.2 Video Camera
• 9.2.3 AIS
• 9.3 Collecting AIS Traffic Data
• 9.4 Collecting AIS Traffic Data
• 9.5 Conclusion
• References
• 10 Economic Feasibility Assessment of Using Ammonia for Hydrogen Transportation
• 10.1 Introduction
• 10.2 Choosing a Method for Separating a Nitrogen-Hydrogen Mixture and Removing Undecomposed Ammonia

This book gathers selected papers presented at the International Conference on SMART Automatics and Energy (SMART-ICAE 2021), held in Far Eastern Federal University, Vladivostok, Russian Federation during 7-8 October 2021. The book will be useful for wide range of specialists in the field of designing innovative solutions and organizational measures that increase the efficiency of the use of industry technologies in their various manifestations. The issue is also of interest to scientific and engineering personnel engaged in the achievements and farsighted researches in the area of intellectual technology use for solving of real, applied tasks in various areas of industries and policies of nations and systems and for students and undergraduates studying "Power systems engineering and electrotechnics", "Automatized systems", "Managerial systems in power technologies", etc., and postgraduate students in the corresponding branches of study.
(source: Nielsen Book Data)

• Hybrid health-aware supervisory control framework with a prognostic decision-making.- Reconfiguration of nonlinear faulty systems via linear methods.- Tri-valued evaluation of residuals as a method of addressing the problem of fault compensation effect.- Leader-following formation control for networked multi-agent systems under communication.- Regular approach to additive fault detection in discrete-time linear descriptor systems.- Descriptor principle in residual filter design for strictly Metzler linear systems.- Hierarchical model for testing a distributed computer system.- Diagnostics of rotary vane vacuum pumps using signal processing, analysis and clustering methods.- Neural modelling of steam turbine control stage.- Diagnostic of calfs body temperature by using thermal imaging camera and correction of camera errors.- Intruder detection on mobile phones using keystroke dynamic and application usage patterns.- . Application of deep learning to seizure classification.- Patient managed patient health record based on blockchain technology.
• (source: Nielsen Book Data)

This book contains a collection of 13 carefully selected papers contributed by researches in technical and partial medical diagnostics as well as fault-tolerant control and constitutes a comprehensive study of the field. Nowadays technical diagnostics and fault-tolerant control are a field of intensive scientific research that covers well-established topics along with emerging developments in control engineering, artificial intelligence, applied mathematics and statistics. At the same time, a growing number of applications of different fault diagnosis methods, especially in the electrical, mechanical, chemical and medical areas, are being observed. The aim of the book is to show the bridge between technical and medical diagnosis based on analytical and artificial intelligence methods and techniques. The book is divided into three parts: I. Fault-Tolerant Control and Reconfiguration, II. Fault Diagnosis of Processes and Systems, III. Medical Applications. The book is of interest to scientists, engineers and academics dealing with the problems of designing technical diagnosis and fault-tolerant control systems. Its target readers are also junior researchers and students of control, artificial intelligence and computer engineering.
(source: Nielsen Book Data)

• Motivation and approach.- The big picture - Information technology in enterprises.- Virtual Product Creation (VPC)- what is it?- The technology history of Virtual Product Creation.- The role of Virtual Product Creation for Engineering and PLM- evolving from IT.- The traditional approach of Virtual Product Creation in industry and its flaws.- The major technologies of Virtual Product Creation (CAID, CAD, CAM, CAE, PDM/BOM, DMU, VR, AR, Digital Factory).- The hidden demands of the engineering community.- Best practices of integrating Virtual Product Creation into mainstream engineering.- The challenge of modifying Management Leadership behavior towards Virtual Product Creation in enterprises.- The role and future of IT/PLM vendors.- Outlook to future Virtual Product Creation solutions.
• (source: Nielsen Book Data)

Today, digital technologies represent an absolute must when it comes to creating new products and factories. However, day-to-day product development and manufacturing engineering operations have still only unlocked roughly fifty percent of the "digital potential". The question is why? This book provides compelling answers and remedies to that question. Its goal is to identify the main strengths and weaknesses of today's set-up for digital engineering working solutions, and to outline important trends and developments for the future. The book concentrates on explaining the critical basics of the individual technologies, before going into deeper analysis of the virtual solution interdependencies and guidelines on how to best align them for productive deployment in industrial and collaborative networks. Moreover, it addresses the changes needed in both, technical and management skills, in order to avoid fundamental breakdowns in running information technologies for virtual product creation in the future.
(source: Nielsen Book Data)