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Digital twin development : an introduction to Simcenter Amesim / / Frank U. Rückert [and three others]
| Digital twin development : an introduction to Simcenter Amesim / / Frank U. Rückert [and three others] |
| Autore | Rückert Frank U. |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023] |
| Descrizione fisica | 1 online resource (129 pages) |
| Disciplina | 381 |
| Soggetto topico | Digital twins (Computer simulation) |
| ISBN | 3-031-25692-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction -- 2. Mathematics, Signals and Control Library -- 3. The Mechanical Twin -- 4. The Thermal Twin -- 5. The Hydraulic Twin -- 6. The Pneumatic Twin -- 7. The Electric Twin -- 8. Analysis of Complex Technical Systems -- 9. Digital Twins and Artificial Intelligence -- 10. Conclusions. |
| Record Nr. | UNINA-9910682591503321 |
Rückert Frank U.
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| Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Digital twins : basics and applications / / Zhihan Lv, Elena Fersman, editors
| Digital twins : basics and applications / / Zhihan Lv, Elena Fersman, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (102 pages) |
| Disciplina | 003.3 |
| Soggetto topico | Digital twins (Computer simulation) |
| ISBN | 3-031-11401-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- Digital Twins Architecture -- 1 Why to Talk About Digital Twins? -- 2 The Main Digital Twin's Components -- 2.1 Physical System (PS) -- 2.2 Virtual System (VS) -- 2.3 Systems Data (SD) -- 2.4 Communication Interface (CI) -- 3 Is This a Digital Twin? -- 4 Practical Case Studies -- 4.1 Case Study I -- 4.2 Case Study II -- References -- Digital Twins for Physical Asset Lifecycle Management -- 1 Introduction -- 2 Digital Twin Asset Lifecycle Management (DTALM) -- 3 Digital Twin Essence -- 4 Digital Twin Systems -- 4.1 Physical Domain -- 4.2 Digital Domain -- 4.3 Physics-Based Generative Models for Digital Twins -- 4.4 Advances in Parameter Identifiability -- 5 Data-Driven Digital Twins -- 5.1 Statistical Learning Models -- 5.2 Machine Learning Models -- 5.3 Deep Learning Models -- 5.4 Industrial Digital Twin Applications for PALM -- References -- Digital Twins and Additive Manufacturing -- 1 Additive Manufacturing -- 2 Digital Twins -- 3 DTs for AM Needs and Challenges -- 3.1 Real Time Monitoring -- 3.2 Database and Models -- 3.3 Machine Learning -- 3.4 Internet of Things -- 4 Conclusions and Outlook -- References -- Agricultural Digital Twins -- 1 The Digital Twins of Agriculture -- 2 Digital Twins Build Smart Farms -- 2.1 Artificial Intelligence Predicts Plant Growth -- 2.2 Virtual Reality Simulation of 3D Digital Farm -- 2.3 Blockchain Technology Realizes Supply Chain Management -- 2.4 Problems that Still Exist in the Application of Digital Twins in the Agricultural Field -- 3 Conclusion -- References -- The Application of Digital Twins in the Field of Fashion -- 1 Digital Twins of Human Bodies -- 1.1 Virtual Human Models in Fashion Industry -- 1.2 Source Information for Generating Virtual Human Model -- 1.3 Tools for Virtual Body Model Digitalization -- 1.4 Virtual Fit Mannequin Generating -- 2 Digital Twins of Garment.
2.1 Structure of Virtual Fitting System -- 2.2 Generating Virtual Garment from Virtual Patterns -- 2.3 Generating Virtual Garment Directly on Virtual Human Model -- 3 Future Development -- References -- Digital Twins Collaboration in Industrial Manufacturing -- 1 Introduction -- 1.1 Contribution -- 1.2 Chapter Organization -- 2 Lightweight Framework of Digital Twins Collaboration for Industrial Manufacturing -- 2.1 Physical Layer -- 2.2 Digital Twins Layer -- 2.3 Industrial Technologies Layer -- 2.4 Application Layer -- 3 Digital Twins Collaboration in Industrial Manufacturing Use Cases -- 3.1 Energy Industry-Fault Diagnosis of Wind Turbines -- 3.2 Railway Industry-Predictive Maintenance -- 3.3 Logistics Industry-Dynamic Routing -- 4 Future Directions -- 4.1 Security and Privacy -- 4.2 Connectivity -- 4.3 Timing, Speed, and Response -- 4.4 Data Modelling -- 5 Conclusion -- References -- Social Media Perspectives on Digital Twins and the Digital Twins Maturity Model -- 1 Defining Digital Twins -- 2 Use of Social Media Analytics in Research -- 2.1 Social Media Analytics Methodology -- 2.2 Time Series Analysis of Tweets About Digital Twins -- 2.3 Unsupervised Clustering of the Digital Twin Tweets -- 2.4 Twitter Analysis by Industry -- 3 Background on Maturity Models -- 4 The Digital Twin Maturity Model -- 5 Conclusion and Future Work -- References. |
| Record Nr. | UNISA-996499855103316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Digital twins : basics and applications / / Zhihan Lv, Elena Fersman, editors
| Digital twins : basics and applications / / Zhihan Lv, Elena Fersman, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (102 pages) |
| Disciplina | 003.3 |
| Soggetto topico | Digital twins (Computer simulation) |
| ISBN | 3-031-11401-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- Digital Twins Architecture -- 1 Why to Talk About Digital Twins? -- 2 The Main Digital Twin's Components -- 2.1 Physical System (PS) -- 2.2 Virtual System (VS) -- 2.3 Systems Data (SD) -- 2.4 Communication Interface (CI) -- 3 Is This a Digital Twin? -- 4 Practical Case Studies -- 4.1 Case Study I -- 4.2 Case Study II -- References -- Digital Twins for Physical Asset Lifecycle Management -- 1 Introduction -- 2 Digital Twin Asset Lifecycle Management (DTALM) -- 3 Digital Twin Essence -- 4 Digital Twin Systems -- 4.1 Physical Domain -- 4.2 Digital Domain -- 4.3 Physics-Based Generative Models for Digital Twins -- 4.4 Advances in Parameter Identifiability -- 5 Data-Driven Digital Twins -- 5.1 Statistical Learning Models -- 5.2 Machine Learning Models -- 5.3 Deep Learning Models -- 5.4 Industrial Digital Twin Applications for PALM -- References -- Digital Twins and Additive Manufacturing -- 1 Additive Manufacturing -- 2 Digital Twins -- 3 DTs for AM Needs and Challenges -- 3.1 Real Time Monitoring -- 3.2 Database and Models -- 3.3 Machine Learning -- 3.4 Internet of Things -- 4 Conclusions and Outlook -- References -- Agricultural Digital Twins -- 1 The Digital Twins of Agriculture -- 2 Digital Twins Build Smart Farms -- 2.1 Artificial Intelligence Predicts Plant Growth -- 2.2 Virtual Reality Simulation of 3D Digital Farm -- 2.3 Blockchain Technology Realizes Supply Chain Management -- 2.4 Problems that Still Exist in the Application of Digital Twins in the Agricultural Field -- 3 Conclusion -- References -- The Application of Digital Twins in the Field of Fashion -- 1 Digital Twins of Human Bodies -- 1.1 Virtual Human Models in Fashion Industry -- 1.2 Source Information for Generating Virtual Human Model -- 1.3 Tools for Virtual Body Model Digitalization -- 1.4 Virtual Fit Mannequin Generating -- 2 Digital Twins of Garment.
2.1 Structure of Virtual Fitting System -- 2.2 Generating Virtual Garment from Virtual Patterns -- 2.3 Generating Virtual Garment Directly on Virtual Human Model -- 3 Future Development -- References -- Digital Twins Collaboration in Industrial Manufacturing -- 1 Introduction -- 1.1 Contribution -- 1.2 Chapter Organization -- 2 Lightweight Framework of Digital Twins Collaboration for Industrial Manufacturing -- 2.1 Physical Layer -- 2.2 Digital Twins Layer -- 2.3 Industrial Technologies Layer -- 2.4 Application Layer -- 3 Digital Twins Collaboration in Industrial Manufacturing Use Cases -- 3.1 Energy Industry-Fault Diagnosis of Wind Turbines -- 3.2 Railway Industry-Predictive Maintenance -- 3.3 Logistics Industry-Dynamic Routing -- 4 Future Directions -- 4.1 Security and Privacy -- 4.2 Connectivity -- 4.3 Timing, Speed, and Response -- 4.4 Data Modelling -- 5 Conclusion -- References -- Social Media Perspectives on Digital Twins and the Digital Twins Maturity Model -- 1 Defining Digital Twins -- 2 Use of Social Media Analytics in Research -- 2.1 Social Media Analytics Methodology -- 2.2 Time Series Analysis of Tweets About Digital Twins -- 2.3 Unsupervised Clustering of the Digital Twin Tweets -- 2.4 Twitter Analysis by Industry -- 3 Background on Maturity Models -- 4 The Digital Twin Maturity Model -- 5 Conclusion and Future Work -- References. |
| Record Nr. | UNINA-9910631094203321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Parallel services : intelligent systems of digital twins and metaverses for services science / / Lefei Li and Fei-Yue Wang
| Parallel services : intelligent systems of digital twins and metaverses for services science / / Lefei Li and Fei-Yue Wang |
| Autore | Li Lefei |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (88 pages) |
| Disciplina | 381 |
| Collana | SpringerBriefs in Service Science |
| Soggetto topico |
Digital twins (Computer simulation)
Human-computer interaction Metaverse |
| ISBN | 3-031-25333-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- 1 Introduction -- References -- 2 Motivation: Complexity of Service in the Digital Age -- 2.1 Trends of Services in the Digital Age -- 2.1.1 Smart Services with Smart Sensors -- 2.1.2 Retailing, Logistics, and Financial Services Based on Artificial Intelligence Technology -- 2.1.3 Technology Applications in Services for Emergencies -- 2.2 Complexity of Services System -- 2.3 Challenges in the Digital Age -- References -- 3 Opportunity: The Actual-Artificial Duality of Services -- 3.1 Three Worlds and Three Axial Ages -- 3.2 The ``Cognitive Gap'' Between Two Worlds -- 3.3 Parallel Services as a Bridge -- 3.4 From CPS to CPSS -- 3.5 The Future of Parallel Services Based on True DAO -- References -- 4 Framework of Parallel Services -- 4.1 Definition and Vision of Parallel Services -- 4.2 Framework of Parallel Services -- Reference -- 5 Enabling Methodology -- 5.1 ACP Method -- 5.2 Artificial Services System Design -- 5.2.1 The Services Need-Demand Model -- 5.2.2 The Services Network -- 5.2.3 Parallel Learning and Optimization -- 5.3 Design Thinking -- 5.4 Systems Engineering -- References -- 6 Enabling Technology -- 6.1 Decentralized Technology -- 6.2 Multi-Agent Simulation -- 6.3 Data Fusion Techniques -- References -- 7 Research on Parallel Services -- 7.1 Parallel Transportation Management Systems -- 7.1.1 Background -- 7.1.2 Parallel Transportation Management Systems -- 7.1.3 Applications -- 7.2 Parallel Healthcare Services -- 7.2.1 Background -- 7.2.2 Design of Hybrid Services System -- 7.2.3 Computational Experiments -- 7.2.4 Parallel Execution of the Internet Hospitals -- 7.3 Parallel Retailing Services -- 7.3.1 Background -- 7.3.2 Design of the Artificial Services Systems -- 7.3.3 Computational Experiments -- 7.3.4 Extensions -- 7.4 Parallel Logistics Services -- 7.4.1 Background.
7.4.2 Parallel Logistics Systems -- References -- 8 Parallel Services and Digital Twins -- 8.1 Introduction of Digital Twins -- 8.2 Parallel Services and Digital Twins -- References -- 9 Parallel Services Metaverses -- 9.1 Introduction of Metaverses -- 9.1.1 The Basic Concept of Metaverses -- 9.1.2 The Value Proposition Behind Metaverses -- 9.2 CPSS for Metaverses -- 9.2.1 Parallel Intelligence for Metaverses -- 9.2.2 The Essence of Parallel Services Metaverses -- 9.3 DAOs for Parallel Services Metaverses -- 9.3.1 ``TRUE DAO'' Toward Deep Intelligence -- 9.3.2 Enabling Technologies for DAOs -- References. |
| Record Nr. | UNINA-9910686790703321 |
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Li Lefei
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| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Sensing, modeling and optimization of cardiac systems : a new generation of digital twin for heart health informatics / / Hui Yang, Bing Yao
| Sensing, modeling and optimization of cardiac systems : a new generation of digital twin for heart health informatics / / Hui Yang, Bing Yao |
| Autore | Yang Hui (Professor of industrial engineering) |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (x, 88 pages) : illustrations (some color) |
| Disciplina | 611.120113 |
| Altri autori (Persone) | YaoBing (Professor of industrial engineering) |
| Collana | SpringerBriefs in Service Science |
| Soggetto topico |
Digital twins (Computer simulation)
Heart - Computer simulation Heart - Mathematical models Medical informatics |
| ISBN | 3-031-35952-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Intro -- Preface -- Contents -- 1 Introduction -- 1.1 Cardiac Electrical Signaling -- 1.2 Spatiotemporal Heterogeneity of Heart Diseases -- 1.3 Multi-scale Modeling of Cardiac Systems -- 1.4 Summary -- References -- 2 Multi-scale Simulation Modeling of Cardiac Systems -- 2.1 Computer Modeling of Ion Channels and Tissues -- 2.2 Statistical Metamodeling and Experiments in Cardiac Ion Channel Simulation -- 2.3 Whole-Heart Computer Simulation -- 2.4 Calibration of 3D Cardiac Simulation -- References -- 3 Sensor-Based Modeling and Analysis of Cardiac Systems -- 3.1 Electrocardiogram (ECG) Sensing -- 3.2 Modeling Incomplete and Uncertain Data -- 3.2.1 Introduction -- 3.2.2 Modeling Approaches -- 3.2.3 Summary -- 3.3 Computationally Identify Sensory Biomarkers -- 3.3.1 Introduction -- 3.3.2 Modeling Approaches -- 3.3.3 Summary -- 3.4 Spatiotemporal Monitoring and Modeling -- 3.4.1 Introduction -- 3.4.2 Modeling Approaches -- 3.4.3 Summary -- 3.5 Automatic Disease Detection from ECG Signals -- 3.5.1 Introduction -- 3.5.2 Two-level DNN with Generative Adversarial Network -- First-Level Model: MadeGAN for Anomaly Detection -- Second-Level Model: Transfer-Learning- and Multi-Branching-Enhanced Classification -- 3.5.3 Summary -- 3.6 Characterization of Myocardial Infarction Using Inverse ECG Modeling -- 3.6.1 Introduction -- 3.6.2 Robust Inverse ECG Modeling -- 3.6.3 Characterization of MI on the Heart Surface -- 3.6.4 Summary -- References -- 4 Simulation Optimization of Medical Decision Making -- 4.1 Introduction to Simulation Optimization -- 4.1.1 Rank and Selection -- 4.1.2 Response Surface Methodology -- 4.1.3 Stochastic Kriging -- 4.1.4 Simulation Optimization in Healthcare -- 4.2 Sequential Medical Decision Making -- 4.2.1 Model-Based Sequential Decision Making -- 4.2.2 Model-Free Sequential Decision Making -- 4.3 Optimal Cardiac Surgical Planning -- 4.3.1 Sequential Decision Making Formulation of Cardiac Surgery Problems -- 4.3.2 Bayesian Learning-Enhanced Tree Search for Optimal Cardiac Surgical Planning -- 4.4 Conclusions -- References -- 5 Outlook and Future Research. |
| Record Nr. | UNINA-9910739412903321 |
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Yang Hui (Professor of industrial engineering)
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Simulation Techniques of Digital Twin in Real-Time Applications : Design Modeling and Implementation
| Simulation Techniques of Digital Twin in Real-Time Applications : Design Modeling and Implementation |
| Autore | Anand Abhineet |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (372 pages) |
| Disciplina | 003/.3 |
| Altri autori (Persone) |
SardanaAnita
KumarAbhishek MohapatraSrikanta Kumar GuptaShikha |
| Soggetto topico | Digital twins (Computer simulation) |
| ISBN |
1-394-25700-7
1-394-25699-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Part 1: A Guide to Simulated Techniques in Digital Twin -- Chapter 1 Introduction to Different Simulation Techniques of Digital Twin Development -- 1.1 Introduction -- 1.2 Literature Review -- 1.3 Digital Twin Simulation Techniques -- 1.3.1 Finite Element Analysis Simulation -- 1.3.2 Computational Fluid Dynamics Simulation -- 1.3.3 Discrete Event Simulation -- 1.3.4 Agent-Based Modeling Simulation -- 1.3.5 Multi-Body Dynamics Simulation -- 1.3.6 Monte Carlo Simulation -- 1.4 Conclusion -- References -- Chapter 2 Comprehensive Analysis of Error Rate and Channel Capacity of Fisher Snedecor Composite Fading Model -- 2.1 Introduction -- 2.2 Fisher Snedecor Composite Fading -- 2.3 Mathematical Analysis -- 2.3.1 Error Rate Analysis -- 2.3.1.1 NCBFSK and BDPSK -- 2.3.1.2 BPSK, BFSK, and QPSK -- 2.3.1.3 MQAM -- 2.3.1.4 MPSK -- 2.3.1.5 MDPSK -- 2.3.1.6 NCMFSK -- 2.3.1.7 DQPSK -- 2.3.2 Channel Capacity Analysis -- 2.3.2.1 ORA -- 2.3.2.2 OPRA -- 2.3.2.3 CIFR -- 2.3.2.4 TIFR -- 2.4 Numerical Results -- 2.5 Conclusion -- References -- Chapter 3 Implementation of Automatic Driving Car Test Approach Based on a Digital Twinning Technology and by Embedding Artificial Intelligence -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Comparative Analysis -- 3.4 Result -- 3.5 Concluding Remarks and Future Scope -- References -- Chapter 4 Intelligent Monitoring of Transformer Equipment in Terms of Earlier Fault Diagnosis Based on Digital Twins -- 4.1 Introduction -- 4.2 Methodology -- 4.2.1 Arduino Uno -- 4.2.2 ESP32 Microcontroller -- 4.2.3 Data Acquisition -- 4.2.4 Blynk App -- 4.3 Machine Learning-Based Predictive Maintenance -- 4.4 Results and Discussion -- 4.5 Conclusion and Future Work -- References.
Chapter 5 Digital Twin System for Intelligent Construction of Large Span Assembly Type Steel Bridge -- 5.1 Introduction -- 5.1.1 Digital Twin Technology -- 5.1.2 Technologies Used -- 5.1.3 Why Digital Twin? -- 5.1.4 Types of Digital Twins -- 5.2 Deep Learning -- 5.2.1 Types of Deep Neural Networks -- 5.2.2 Learning or Training in Neural Networks -- 5.3 Simulation vs. Digital Twin Technology -- 5.3.1 Integrating Deep Learning in Simulation Models -- 5.3.2 Benefits of Deep Learning Digital Twin -- 5.3.3 Applications of Digital Twin Technology -- 5.4 Literature Review -- 5.5 Conclusion -- References -- Chapter 6 Digital Twin Application on System Identification and Control -- 6.1 Introduction -- 6.2 Digital Twin Technology and Its Application -- 6.2.1 Related Work on Digital Twin -- 6.2.2 DT Application -- 6.2.3 Different Levels of DT Models -- 6.2.3.1 Pre-Digital Twin -- 6.2.3.2 Model Design -- 6.2.3.3 Adaptive Model With DT Technology -- 6.2.3.4 The Process of Intelligent DT -- 6.2.4 Dynamic Model -- 6.2.5 Digital Twin and Machine Learning -- 6.3 Control and Identification: A Survey -- 6.3.1 Hierarchy of System Identification Methods -- 6.3.1.1 Parametric Methods -- 6.3.1.2 Nonparametric Methods -- 6.3.2 Machine Learning Approach -- 6.3.3 Deep Neural Network Approach -- 6.4 Proposed Methodology -- 6.4.1 DT Technology Application in Identification and Control -- 6.5 Result Analysis and Discussion -- 6.5.1 Case Study: Control Application -- 6.6 Conclusion and Future Work -- References -- Part 2: Real Time Applications of Digital Twin -- Chapter 7 Digital Twinning-Based Autonomous Take-Off, Landing, and Cruising for Unmanned Aerial Vehicles -- 7.1 Introduction -- 7.1.1 Problem Statement -- 7.1.2 Research Objectives -- 7.2 Digital Twinning for UAV Autonomy -- 7.3 Challenges and Limitations -- 7.3.1 Manual Control and Pre-Programmed Flight Paths. 7.3.2 Limited Adaptability to Dynamic Environments -- 7.3.3 Lack of Real-Time Decision-Making -- 7.3.4 Limited Perception and Situational Awareness -- 7.3.5 Computational Complexity and Processing Power -- 7.3.6 Calibration and Validation -- 7.4 Proposed Framework -- 7.4.1 Digital Twin Creation -- 7.4.2 Sensor Fusion and Data Acquisition -- 7.4.3 Environmental Analysis -- 7.4.4 Decision-Making and Control -- 7.4.5 Communication and Synchronization -- 7.4.6 Validation and Calibration -- 7.4.7 Iterative Improvement -- 7.5 Benefits and Feasibility -- 7.5.1 Improved Adaptability -- 7.5.2 Real-Time Decision-Making -- 7.5.3 Enhanced Safety -- 7.5.4 Feasibility Considerations -- 7.6 Conclusion and Future Directions -- References -- Chapter 8 Execution of Fully Automated Coal Mining Face With Transparent Digital Twin Self-Adaptive Mining System -- 8.1 Introduction -- 8.2 Simulation Methods in Digital Twins -- 8.2.1 Computational Fluid Dynamics -- 8.2.1.1 Software Tools That are Being Used in Today's Domain for CFD -- 8.2.1.2 Real-World Applications of CFD -- 8.2.2 Multibody Dynamics -- 8.2.3 Kinematics for Multibody Systems -- 8.3 Literature Review -- 8.3.1 Classification of MBD Simulations -- 8.3.2 Finite Element Analysis -- 8.4 Proposed Work -- 8.5 Conclusion -- References -- Chapter 9 MGF-Based BER and Channel Capacity Analysis of Fisher Snedecor Composite Fading Model -- 9.1 Introduction -- 9.2 Fisher Snedecor Composite Fading Model -- 9.3 Performance Analysis Using MGF -- 9.3.1 ABER -- 9.3.1.1 BDPSK and NBFSK -- 9.3.1.2 BPSK and BFSK -- 9.3.1.3 MAM -- 9.3.1.4 Square MQAM -- 9.3.1.5 MPSK -- 9.3.2 NMFSK -- 9.3.3 Adaptive Channel Capacity -- 9.3.3.1 ORA -- 9.3.3.2 CIFR -- 9.4 Numerical Results -- 9.5 Conclusion -- References. Chapter 10 Precision Agriculture: An Augmented Datasets and CNN Model-Based Approach to Diagnose Diseases in Fruits and Vegetable Crops -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Major Fruit Diseases in the Valley -- 10.4 Methodology -- 10.5 Results and Discussion -- 10.6 Extended Experiment -- 10.7 Concluding Remarks -- References -- Chapter 11 A Simulation-Based Study of a Digital Twin Model of the Air Purifier System in Chandigarh Using LabVIEW -- 11.1 Introduction -- 11.1.1 Background Information on Chandigarh's Air Pollution Problem -- 11.1.2 Digital Twin Technology and Its Relevance to Air Quality Monitoring -- 11.2 Literature Review -- 11.3 Methodology -- 11.4 Results -- 11.5 Discussion -- 11.6 Conclusion -- References -- Chapter 12 Use of Digital Twin in Predicting the Life of Aircraft Main Bearing -- 12.1 Introduction -- 12.1.1 Background -- 12.1.2 Importance of Predictive Maintenance -- 12.1.3 Challenges in Aircraft Main Bearing Life Prediction -- 12.1.4 Digital Twin Technology in Aviation -- 12.2 Fundamentals of Digital Twin Technology -- 12.2.1 Components of a Digital Twin -- 12.2.2 Enabling Technologies for Digital Twin -- 12.3 Benefits of Digital Twin Technology -- 12.3.1 Aircraft Main Bearings: Structure and Failure Modes -- 12.4 Developing a Digital Twin for Aircraft Main Bearings -- 12.5 Predictive Analytics for Main Bearing Life Prediction -- 12.5.1 Machine Learning Algorithms for Predictive Modeling -- 12.5.2 Challenges of Digital Twin for Aircraft Health -- 12.5.3 Security Threats of the Digital Twin in Aircraft Virtualization -- 12.6 Future Prospects and Conclusion of Digital Twin for Aircraft Health -- References -- Chapter 13 Power Energy System Consumption Analysis in Urban Railway by Digital Twin Method -- 13.1 Introduction -- 13.2 Literature Review -- 13.3 Method -- 13.4 Implementation -- 13.5 Conclusion. References -- Chapter 14 Based on Digital Twin Technology, an Early Warning System and Strategy for Predicting Urban Waterlogging -- 14.1 Introduction -- 14.1.1 Definition -- 14.1.2 Application Areas of Digital Twin Technology -- 14.2 Literature Review -- 14.3 Methodology -- 14.4 Discussion and Conclusion -- References -- Chapter 15 Advanced Real-Time Simulation Framework for the Physical Interaction Dynamics of Production Lines Leveraging Digital Twin Paradigms -- 15.1 Introduction -- 15.2 Introduction to Advanced Simulation Frameworks -- 15.2.1 The Evolution of Production Line Simulations -- 15.2.2 The Promise of Real-Time Analysis -- 15.3 Digital Twins: A Comprehensive Analysis -- 15.3.1 What Defines a Digital Twin? -- 15.3.2 The Architecture and Components of Digital Twins -- 15.3.3 Advantages of Integrating Digital Twins in Manufacturing -- 15.4 Physical Interaction Dynamics in Production Lines -- 15.4.1 The Nature of Physical Interactions -- 15.4.2 The Role of Dynamics in Production Efficiency -- 15.4.3 Challenges in Traditional Simulation Methods -- 15.5 Building the Advanced Real-Time Simulation Framework -- 15.5.1 Core Principles and Design Objectives -- 15.5.2 Data Integration and Processing -- 15.5.2.1 Role of Sensors and IoT -- 15.5.2.2 Algorithmic Foundations for Feedback -- 15.6 Types of Algorithms -- 15.6.1 Pseudocode for Real-Time Adjustments -- 15.6.1.1 Initialization -- 15.6.1.2 Data Collection and Pre-Processing -- 15.6.1.3 Analysis Using Bayesian Inference -- 15.6.1.4 Anomaly Detection and Root Cause Analysis -- 15.6.1.5 Corrective Action Using Gradient Boosting -- 15.6.1.6 Update and Implement -- 15.6.1.7 Continuous Monitoring -- 15.7 Practical Implementations and Case Studies -- 15.7.1 Implementing the Framework: A Step-by-Step Guide -- 15.7.2 Measurable Benefits and Outcomes -- 15.8 Overcoming Challenges and Limitations. 15.8.1 Potential Roadblocks in Framework Implementation. |
| Record Nr. | UNINA-9910863272803321 |
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Anand Abhineet
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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