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Data Communications and Network Technologies / / by Huawei Technologies Co., Ltd
| Data Communications and Network Technologies / / by Huawei Technologies Co., Ltd |
| Autore | Huawei Technologies Co Ltd |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore, : Springer Nature, 2023 |
| Descrizione fisica | 1 online resource (XIX, 558 p. 1 illus.) |
| Disciplina | 004.6 |
| Soggetto topico |
Computer networks
Computer Communication Networks |
| Soggetto non controllato |
Computer Networks
Data Communications Routing Switching Huawei Certification HCIA TCP IP |
| ISBN | 981-19-3029-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Network Fundamentals -- Chapter 2. TCP/IP -- Chapter 3. VRP Fundamentals -- Chapter 4. IP Addresses and Subnetting -- Chapter 5. IP Routing Fundamentals -- Chapter 6. Dynamic Routing -- Chapter 7. Ethernet Switching Technologies -- Chapter 8. Advanced Ethernet Switching Technologies -- Chapter 9. ACLs and AAA -- Chapter 10. Network Address Translation Technologies -- Chapter 11. DHCP -- Chapter 12. WLAN technologies -- Chapter 13. IPv6 -- Chapter 14. WAN Fundamentals -- Chapter 15. Typical Networking Architectures for Campus Networks and Case Practice -- Chapter 16. Network Management, Operation and Maintenance -- Chapter 17. SDN and NFV -- Chapter 18. Network Programming and Automation.-. |
| Record Nr. | UNINA-9910623994303321 |
Huawei Technologies Co Ltd
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| Singapore, : Springer Nature, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Database Systems for Advanced Applications : 25th International Conference, DASFAA 2020, Jeju, South Korea, September 24–27, 2020, Proceedings Part 1. / Yunmook Nah ... [et al.] editors
| Database Systems for Advanced Applications : 25th International Conference, DASFAA 2020, Jeju, South Korea, September 24–27, 2020, Proceedings Part 1. / Yunmook Nah ... [et al.] editors |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | xxxiv, 809 p. : ill. ; 24 cm |
| Soggetto topico |
68-XX - Computer science [MSC 2020]
00B25 - Proceedings of conferences of miscellaneous specific interest [MSC 2020] 68Pxx - Theory of data [MSC 2020] |
| Soggetto non controllato |
Artificial Intelligence
Computer Networks Computer Science Computer hardware Computer systems Computer vision Data Mining Databases Education Engineering Information retrieval Internet Learning Machine learning Mathematics Network protocols Neural networks Signal processing Telecommunication networks Telecommunication systems |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0248966 |
| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Deep reinforcement learning for wireless communications and networking : theory, applications and implementation / / Dinh Thai Hoang [and four others]
| Deep reinforcement learning for wireless communications and networking : theory, applications and implementation / / Dinh Thai Hoang [and four others] |
| Autore | Hoang Dinh Thai <1986-> |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2023] |
| Descrizione fisica | 1 online resource (291 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Reinforcement learning
Wireless communication systems |
| Soggetto non controllato |
Artificial Intelligence
Computer Networks Computers |
| ISBN |
1-119-87374-6
1-119-87368-1 1-119-87373-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Notes on Contributors -- Foreword -- Preface -- Acknowledgments -- Acronyms -- Introduction -- Part I Fundamentals of Deep Reinforcement Learning -- Chapter 1 Deep Reinforcement Learning and Its Applications -- 1.1 Wireless Networks and Emerging Challenges -- 1.2 Machine Learning Techniques and Development of DRL -- 1.2.1 Machine Learning -- 1.2.2 Artificial Neural Network -- 1.2.3 Convolutional Neural Network -- 1.2.4 Recurrent Neural Network -- 1.2.5 Development of Deep Reinforcement Learning -- 1.3 Potentials and Applications of DRL -- 1.3.1 Benefits of DRL in Human Lives -- 1.3.2 Features and Advantages of DRL Techniques -- 1.3.3 Academic Research Activities -- 1.3.4 Applications of DRL Techniques -- 1.3.5 Applications of DRL Techniques in Wireless Networks -- 1.4 Structure of this Book and Target Readership -- 1.4.1 Motivations and Structure of this Book -- 1.4.2 Target Readership -- 1.5 Chapter Summary -- References -- Chapter 2 Markov Decision Process and Reinforcement Learning -- 2.1 Markov Decision Process -- 2.2 Partially Observable Markov Decision Process -- 2.3 Policy and Value Functions -- 2.4 Bellman Equations -- 2.5 Solutions of MDP Problems -- 2.5.1 Dynamic Programming -- 2.5.1.1 Policy Evaluation -- 2.5.1.2 Policy Improvement -- 2.5.1.3 Policy Iteration -- 2.5.2 Monte Carlo Sampling -- 2.6 Reinforcement Learning -- 2.7 Chapter Summary -- References -- Chapter 3 Deep Reinforcement Learning Models and Techniques -- 3.1 Value‐Based DRL Methods -- 3.1.1 Deep Q‐Network -- 3.1.2 Double DQN -- 3.1.3 Prioritized Experience Replay -- 3.1.4 Dueling Network -- 3.2 Policy‐Gradient Methods -- 3.2.1 REINFORCE Algorithm -- 3.2.1.1 Policy Gradient Estimation -- 3.2.1.2 Reducing the Variance -- 3.2.1.3 Policy Gradient Theorem -- 3.2.2 Actor‐Critic Methods -- 3.2.3 Advantage of Actor‐Critic Methods.
3.2.3.1 Advantage of Actor‐Critic (A2C) -- 3.2.3.2 Asynchronous Advantage Actor‐Critic (A3C) -- 3.2.3.3 Generalized Advantage Estimate (GAE) -- 3.3 Deterministic Policy Gradient (DPG) -- 3.3.1 Deterministic Policy Gradient Theorem -- 3.3.2 Deep Deterministic Policy Gradient (DDPG) -- 3.3.3 Distributed Distributional DDPG (D4PG) -- 3.4 Natural Gradients -- 3.4.1 Principle of Natural Gradients -- 3.4.2 Trust Region Policy Optimization (TRPO) -- 3.4.2.1 Trust Region -- 3.4.2.2 Sample‐Based Formulation -- 3.4.2.3 Practical Implementation -- 3.4.3 Proximal Policy Optimization (PPO) -- 3.5 Model‐Based RL -- 3.5.1 Vanilla Model‐Based RL -- 3.5.2 Robust Model‐Based RL: Model‐Ensemble TRPO (ME‐TRPO) -- 3.5.3 Adaptive Model‐Based RL: Model‐Based Meta‐Policy Optimization (MB‐MPO) -- 3.6 Chapter Summary -- References -- Chapter 4 A Case Study and Detailed Implementation -- 4.1 System Model and Problem Formulation -- 4.1.1 System Model and Assumptions -- 4.1.1.1 Jamming Model -- 4.1.1.2 System Operation -- 4.1.2 Problem Formulation -- 4.1.2.1 State Space -- 4.1.2.2 Action Space -- 4.1.2.3 Immediate Reward -- 4.1.2.4 Optimization Formulation -- 4.2 Implementation and Environment Settings -- 4.2.1 Install TensorFlow with Anaconda -- 4.2.2 Q‐Learning -- 4.2.2.1 Codes for the Environment -- 4.2.2.2 Codes for the Agent -- 4.2.3 Deep Q‐Learning -- 4.3 Simulation Results and Performance Analysis -- 4.4 Chapter Summary -- References -- Part II Applications of DRL in Wireless Communications and Networking -- Chapter 5 DRL at the Physical Layer -- 5.1 Beamforming, Signal Detection, and Decoding -- 5.1.1 Beamforming -- 5.1.1.1 Beamforming Optimization Problem -- 5.1.1.2 DRL‐Based Beamforming -- 5.1.2 Signal Detection and Channel Estimation -- 5.1.2.1 Signal Detection and Channel Estimation Problem -- 5.1.2.2 RL‐Based Approaches -- 5.1.3 Channel Decoding. 5.2 Power and Rate Control -- 5.2.1 Power and Rate Control Problem -- 5.2.2 DRL‐Based Power and Rate Control -- 5.3 Physical‐Layer Security -- 5.4 Chapter Summary -- References -- Chapter 6 DRL at the MAC Layer -- 6.1 Resource Management and Optimization -- 6.2 Channel Access Control -- 6.2.1 DRL in the IEEE 802.11 MAC -- 6.2.2 MAC for Massive Access in IoT -- 6.2.3 MAC for 5G and B5G Cellular Systems -- 6.3 Heterogeneous MAC Protocols -- 6.4 Chapter Summary -- References -- Chapter 7 DRL at the Network Layer -- 7.1 Traffic Routing -- 7.2 Network Slicing -- 7.2.1 Network Slicing‐Based Architecture -- 7.2.2 Applications of DRL in Network Slicing -- 7.3 Network Intrusion Detection -- 7.3.1 Host‐Based IDS -- 7.3.2 Network‐Based IDS -- 7.4 Chapter Summary -- References -- Chapter 8 DRL at the Application and Service Layer -- 8.1 Content Caching -- 8.1.1 QoS‐Aware Caching -- 8.1.2 Joint Caching and Transmission Control -- 8.1.3 Joint Caching, Networking, and Computation -- 8.2 Data and Computation Offloading -- 8.3 Data Processing and Analytics -- 8.3.1 Data Organization -- 8.3.1.1 Data Partitioning -- 8.3.1.2 Data Compression -- 8.3.2 Data Scheduling -- 8.3.3 Tuning of Data Processing Systems -- 8.3.4 Data Indexing -- 8.3.4.1 Database Index Selection -- 8.3.4.2 Index Structure Construction -- 8.3.5 Query Optimization -- 8.4 Chapter Summary -- References -- Part III Challenges, Approaches, Open Issues, and Emerging Research Topics -- Chapter 9 DRL Challenges in Wireless Networks -- 9.1 Adversarial Attacks on DRL -- 9.1.1 Attacks Perturbing the State space -- 9.1.1.1 Manipulation of Observations -- 9.1.1.2 Manipulation of Training Data -- 9.1.2 Attacks Perturbing the Reward Function -- 9.1.3 Attacks Perturbing the Action Space -- 9.2 Multiagent DRL in Dynamic Environments -- 9.2.1 Motivations -- 9.2.2 Multiagent Reinforcement Learning Models. 9.2.2.1 Markov/Stochastic Games -- 9.2.2.2 Decentralized Partially Observable Markov Decision Process (DPOMDP) -- 9.2.3 Applications of Multiagent DRL in Wireless Networks -- 9.2.4 Challenges of Using Multiagent DRL in Wireless Networks -- 9.2.4.1 Nonstationarity Issue -- 9.2.4.2 Partial Observability Issue -- 9.3 Other Challenges -- 9.3.1 Inherent Problems of Using RL in Real‐Word Systems -- 9.3.1.1 Limited Learning Samples -- 9.3.1.2 System Delays -- 9.3.1.3 High‐Dimensional State and Action Spaces -- 9.3.1.4 System and Environment Constraints -- 9.3.1.5 Partial Observability and Nonstationarity -- 9.3.1.6 Multiobjective Reward Functions -- 9.3.2 Inherent Problems of DL and Beyond -- 9.3.2.1 Inherent Problems of DL -- 9.3.2.2 Challenges of DRL Beyond Deep Learning -- 9.3.3 Implementation of DL Models in Wireless Devices -- 9.4 Chapter Summary -- References -- Chapter 10 DRL and Emerging Topics in Wireless Networks -- 10.1 DRL for Emerging Problems in Future Wireless Networks -- 10.1.1 Joint Radar and Data Communications -- 10.1.2 Ambient Backscatter Communications -- 10.1.3 Reconfigurable Intelligent Surface‐Aided Communications -- 10.1.4 Rate Splitting Communications -- 10.2 Advanced DRL Models -- 10.2.1 Deep Reinforcement Transfer Learning -- 10.2.1.1 Reward Shaping -- 10.2.1.2 Intertask Mapping -- 10.2.1.3 Learning from Demonstrations -- 10.2.1.4 Policy Transfer -- 10.2.1.5 Reusing Representations -- 10.2.2 Generative Adversarial Network (GAN) for DRL -- 10.2.3 Meta Reinforcement Learning -- 10.3 Chapter Summary -- References -- Index -- EULA. |
| Record Nr. | UNINA-9910830760503321 |
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Hoang Dinh Thai <1986->
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| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Intelligent robotics : third China annual conference, CCF CIRAC 2022, Xi'an, China, December 16-18, 2022, proceedings / / Zhiwen Yu [and four others], editors
| Intelligent robotics : third China annual conference, CCF CIRAC 2022, Xi'an, China, December 16-18, 2022, proceedings / / Zhiwen Yu [and four others], editors |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (462 pages) |
| Disciplina | 629.892 |
| Collana | Communications in computer and information science |
| Soggetto topico | Autonomous robots |
| Soggetto non controllato |
Computer Networks
Computer-Assisted Instruction Artificial Intelligence Computers Education |
| ISBN | 981-9903-01-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Robot Safety -- Intelligent Robot Sensing -- Autonomous Robot Navigation -- Artificial Intelligence and Cloud Robot -- Unmanned Cluster Collaboration -- Natural Human-Computer Interaction -- Other Robot-Related Technologies. |
| Record Nr. | UNINA-9910672446203321 |
| Singapore : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Intelligent robotics : third China annual conference, CCF CIRAC 2022, Xi'an, China, December 16-18, 2022, proceedings / / Zhiwen Yu [and four others], editors
| Intelligent robotics : third China annual conference, CCF CIRAC 2022, Xi'an, China, December 16-18, 2022, proceedings / / Zhiwen Yu [and four others], editors |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (462 pages) |
| Disciplina | 629.892 |
| Collana | Communications in computer and information science |
| Soggetto topico | Autonomous robots |
| Soggetto non controllato |
Computer Networks
Computer-Assisted Instruction Artificial Intelligence Computers Education |
| ISBN | 981-9903-01-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Robot Safety -- Intelligent Robot Sensing -- Autonomous Robot Navigation -- Artificial Intelligence and Cloud Robot -- Unmanned Cluster Collaboration -- Natural Human-Computer Interaction -- Other Robot-Related Technologies. |
| Record Nr. | UNISA-996547954603316 |
| Singapore : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Learning and Intelligent Optimization : 13th International Conference, LION 13, Chania, Crete, Greece, May 27–31, 2019, Revised Selected Papers / Nikolaos F. Matsatsinis, Yannis Marinakis, Panos Pardalos editors
| Learning and Intelligent Optimization : 13th International Conference, LION 13, Chania, Crete, Greece, May 27–31, 2019, Revised Selected Papers / Nikolaos F. Matsatsinis, Yannis Marinakis, Panos Pardalos editors |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | xiii, 400 p. : ill. ; 24 cm |
| Soggetto topico |
68-XX - Computer science [MSC 2020]
00B25 - Proceedings of conferences of miscellaneous specific interest [MSC 2020] 90-XX - Operations research, mathematical programming [MSC 2020] |
| Soggetto non controllato |
Artificial Intelligence
Combinatorial optimization Communication systems Computer Networks Computer Science Computer systems Correlation analysis Engineering Evolutionary algorithms Genetic Algorithms Graph theory Internet Mathematics Network protocols Optimization Optimization problems Signal processing Telecommunication networks Telecommunication systems Theoretical computer science |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0249386 |
| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Learning and Intelligent Optimization : 14th International Conference, LION 14, Athens, Greece, May 24–28, 2020, Revised Selected Papers / Ilias S. Kotsireas, Panos Pardalos editors
| Learning and Intelligent Optimization : 14th International Conference, LION 14, Athens, Greece, May 24–28, 2020, Revised Selected Papers / Ilias S. Kotsireas, Panos Pardalos editors |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | xiv, 430 p. : ill. ; 24 cm |
| Soggetto topico |
68-XX - Computer science [MSC 2020]
90-XX - Operations research, mathematical programming [MSC 2020] |
| Soggetto non controllato |
Artificial intellegence
Communication systems Computer Networks Computer hardware Computer systems Correlation analysis Education Engineering Genetic Algorithms Internet Learning Machine learning Mathematics Optimization Optimization problems Signal processing Telecommunication systems Theoretical computer science Wireless telecommunication systems |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0249389 |
| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Mathematical Optimization Theory and Operations Research : 19th International Conference, MOTOR 2020, Novosibirsk, Russia, July 6–10, 2020, Proceedings / Alexander Kononov ... [et al.] editors
| Mathematical Optimization Theory and Operations Research : 19th International Conference, MOTOR 2020, Novosibirsk, Russia, July 6–10, 2020, Proceedings / Alexander Kononov ... [et al.] editors |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | xxxv, 471 p. : ill. ; 24 cm |
| Soggetto topico |
00B25 - Proceedings of conferences of miscellaneous specific interest [MSC 2020]
90-XX - Operations research, mathematical programming [MSC 2020] |
| Soggetto non controllato |
Artificial Intelligence
Computer Networks Continuous Optimization Discrete Optimization Evolutionary algorithms Game Theory Genetic Algorithms Global optimization Machine learning Mathematical Economics Mixed discrete-continuous optimization Networks Operations Research Problem Solving Telecommunication networks Telecommunication traffic |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0249444 |
| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Methods and Applications for Modeling and Simulation of Complex Systems : 21st Asia Simulation Conference, AsiaSim 2022, Changsha, China, December 9-11, 2022, Proceedings, Part II
| Methods and Applications for Modeling and Simulation of Complex Systems : 21st Asia Simulation Conference, AsiaSim 2022, Changsha, China, December 9-11, 2022, Proceedings, Part II |
| Autore | Fan Wenhui |
| Pubbl/distr/stampa | Singapore : , : Springer, , 2023 |
| Descrizione fisica | 1 online resource (639 pages) |
| Disciplina | 003.3 |
| Altri autori (Persone) |
ZhangLin
LiNi SongXiao |
| Collana | Communications in Computer and Information Science |
| Soggetto non controllato |
Artificial Intelligence
Computer Networks Computer Simulation Computers |
| ISBN | 981-19-9195-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Application of Modeling/Simulation in Science/Engineering/ Society/Economy/Management/ Energy/Transportation/ Life/Biology/Medicine etc. -- Flux Modelling of Membrane Bioreactor Process Plant Using Optimized-BPNN -- 1 Introduction -- 2 Artificial Neural Network Principle -- 2.1 Data Preprocessing -- 2.2 Performance Evaluation -- 2.3 Back-Propagation Neural Network -- 3 ANN Based MBR Model Development -- 4 Results and Discussion -- 5 Conclusion -- References -- An Efficient Method of Calculating Stress Intensity Factor for Surface Cracks in Holes Under Uni-variant Stressing -- 1 Introduction -- 2 General Weight Function Method -- 3 Construction of the Matrix of Reference Solutions -- 3.1 Sensitivity of Parameters -- 3.2 Influence of Parameters of Hole Shape -- 3.3 Conclusions of Sensitivity Analysis -- 4 Application -- 5 Conclusion -- References -- Modeling and Simulation Based on Concurrent FC-AE-1553 Network -- 1 Introduction -- 2 Topology Modeling of FC-AE-1553 Networks -- 2.1 Bus Topology Model -- 2.2 Switched Topology Model -- 3 Message Modeling of Concurrent FC-AE-1553 Network -- 4 Network Schedule Modeling Based on Priority -- 4.1 Periodic Scheduling Optimization Mechanism -- 4.2 Burst Scheduling Optimization Mechanism -- 5 Simulation and Analysis -- 6 Conclusion -- References -- Research on Modeling and Simulation Method of Laser System for Multi-domain Unified Model -- 1 Introduction -- 2 Modeling and Simulation Method of Laser System for Multi Domain Unified Model -- 2.1 Modeling Method of Laser System Based on Lumped Parameter Method -- 2.2 Co-simulation Method of Opto-Mechanical System Based on Multi-domain Unified Model -- 3 Modeling and Simulation Experiment of Laser System -- 3.1 Optical Component Model Based on Modelica.
3.2 Laser System Simulation in Sysplorer -- 4 Conclusion -- References -- Fast Electromagnetic Scattering Modeling of Complex Scenes Based on Multi-scale Coherent Computation -- 1 Introduction -- 2 Integrated Scattering Modeling Principle of Multi-scale Target and Complex Environment -- 3 Bouncing Ray Method Based on Curvature Adaptive Subdivision -- 3.1 Beam Tracking Based on Curvature Adaptive Subdivision -- 3.2 Field Strength Tracking -- 3.3 Far Field Integral -- 4 Scattering Correction of Rough Surface Based on Multi-scale Coherent Superposition -- 5 Simulation Results and Analysis -- 5.1 Typical Target Scattering Calculation -- 5.2 Scattering Calculation of Sea Surface Environment -- 5.3 Application Verification of Coupling Electromagnetic Scattering in Complex Scene -- 6 Summary -- References -- Pixelated Image Abstraction via Power Diagram -- 1 Introduction -- 2 Related Work -- 2.1 Power Diagram -- 2.2 CCCPD -- 2.3 Image Abstraction -- 3 Image Abstraction Based on Power Diagram -- 3.1 Overall Method -- 3.2 Pre-processing and Density Initialization -- 3.3 Image Segmentation Based on the Power Diagram -- 3.4 Color Filling -- 4 Experiment -- 4.1 Results Analysis -- 4.2 Abstract Controlling -- 4.3 Comparison -- 5 Conclusion -- References -- SMT Component Defection Reassessment Based on Siamese Network -- 1 Introduction -- 2 Proposed Method -- 2.1 Siamese Network -- 2.2 Loss Function -- 2.3 Detailed Detection Scheme -- 3 Experiments -- 3.1 Experimental Data -- 3.2 Optimization -- 3.3 Results and Evaluation -- 3.4 Conclusion -- References -- Real Time Traffic Sign Recognition Algorithm Based on SG-YOLO -- 1 Introduction -- 2 Related Work -- 2.1 Traditional Detection Methods -- 2.2 Deep-Learning Based Detection Methods -- 3 Principles of Algorithm -- 4 SG-YOLO Network Model -- 4.1 Ghost-Bottleneck -- 4.2 Channel Attention Mechanism. 4.3 SG-YOLO Network Architecture -- 4.4 DIoU Loss Function -- 5 Experiments and Analysis -- 5.1 Experimental Environment -- 5.2 Evaluation Metrics -- 5.3 Dataset and Processing -- 5.4 Results and Analysis -- 6 Conclusions -- References -- Research on Task Oriented Reliability Simulation Evaluation and Maintenance Strategy Optimization of Degraded System -- 1 Introduction -- 2 Introduction -- 2.1 Component System -- 2.2 Multi-component System -- 3 Reliability Model of Degraded System -- 3.1 Multi-state Transfer Process -- 3.2 System Availability Model -- 4 Research on Reliability Modeling of Degraded System -- 4.1 Multi Component System Interaction -- 4.2 Simulation Model Design -- 4.3 Simulation Algorithm Design -- 5 Case Analysis -- 5.1 Model Assumptions -- 5.2 Result Analysis -- 6 Summary -- References -- A Campus Scene Navigation Scheme Based on MPCC Dynamic Obstacle Avoidance Method -- 1 Introduction -- 2 System Overview -- 2.1 System Hardware -- 2.2 System Modules -- 3 Proposed Method -- 3.1 Static Obstacle Collision Detection Based on Voxel Grid -- 3.2 MPCC-Based Dynamic Obstacle Avoidance Strategy -- 4 Experiment -- 5 Conclusion and Future Work -- References -- Numerical Simulation of Ship Tank Sloshing Based on MPS Method -- 1 Introduction -- 2 MPS Method -- 2.1 Mathematical Formulation -- 2.2 Procedure of Numerical Simulation -- 3 Validation -- 4 Numerical Results -- 4.1 Description of Test Cases -- 4.2 Snapshots of Flow Field -- 5 Conclusions -- References -- Simulation Study of Dynamic Reactive Power Optimization in Distribution Network with DG Based on Improved Lion Swarm Algorithm -- 1 Introduction -- 2 The RPO Model -- 2.1 The Objective Function -- 2.2 Constraints -- 2.3 Reactive Power of the Distributed Power Supply -- 3 The Time Division Method -- 4 An Improved LSO Algorithm -- 4.1 Principles of the LSO algorithm. 4.2 The Random Black Hole Mechanism -- 5 Simulation Analysis -- 6 Conclusion -- References -- Observation Geometry Improvement of BDS by Near-Space Balloons in Regional Navigation Augmentation -- 1 Introduction -- 2 Methodology -- 3 Experiment Design and Results -- 3.1 Experiment Design -- 3.2 Experiment Design -- 4 Conclusion -- References -- Application of FE Simulation Method in the Field of Electrical Contact Performance Analysis -- 1 Introduction -- 2 Analysis Method of Electrical Contact Performance Based on FE Simulation -- 2.1 Mechanical Field Simulation -- 2.2 Thermoelectric Coupling Field Simulation -- 3 FE Simulation Results and Discussion -- 3.1 Mechanical Field -- 3.2 Thermoelectric Field -- 4 Conclusion -- References -- Damage Localization and Imaging of Composite Materials Based on Sparse Reconstruction Using Lamb Wave Anisotropy Propagation Model -- 1 Introduction -- 2 Theory -- 2.1 Damage Imaging Problem as a Sparse Reconstruction Problem -- 2.2 Lamb Wave Scattering Model in Composite Fiber Laminate -- 3 Methodology -- 3.1 Damage Dictionary Construction -- 3.2 Sparse Reconstruction Solution and Damage Localization Imaging -- 4 Experimental Research and Result Analysis -- 5 Conclusion -- References -- 5G Wireless Network Digital Twin System Based on High Precision Simulation -- 1 Introduction -- 2 Communication System Simulation -- 2.1 Fundamentals of Communication System Simulation -- 2.2 Wireless Communication Propagation Model -- 3 5G Wireless Network Digital Twin System Construction and Application Research -- 3.1 Construction of 5G Wireless Network Digital Twin System -- 3.2 Application Research of 5G Wireless Network Digital Twin System -- 4 Conclusion -- References -- A Hardware-in-the-Loop Microgrid Simulation Method Based on TwinCAT3-Take Black Start as An Example -- 1 Introduction. 2 Construction of Microgrid Equipment Model -- 2.1 Equipment Modelling -- 2.2 From MATLAB/Simulink to TwinCAT3 -- 3 Construction of the Simulation Environment -- 4 Verification of Hardware-in-the-Loop Simulation Method for Microgrid Based on TwinCAT3 -- 5 Conclusion -- References -- Day-Ahead Scheduling of PV Consumption in Distribution Networks Based on Demand Response of Multiple Types of Customer-Side Loads -- 1 Introduction -- 2 User-Side Load Modeling -- 2.1 Distributed PV Output Load Modeling -- 2.2 Residential Customer Load Model -- 2.3 Commercial User Load Model -- 2.4 Electric Vehicle Charging Load Model -- 3 Day-Ahead Dispatch Model of a Distribution Network Based on Customer-Side Demand Response -- 3.1 Demand Response Incentive Costs -- 3.2 Customer Satisfaction Assessment and Demand Response Balance Point Modeling -- 3.3 Demand Response Day Dispatch Model -- 3.4 Binding Conditions -- 3.5 Demand Response Day Dispatch Model -- 3.6 Model Solving Method -- 4 Algorithm Simulation -- 4.1 Algorithm Parameters -- 4.2 The Role of Demand Response in Suppressing Node Voltage Fluctuations -- 4.3 Analysis of Incentive Cost Results for Scenario 2 -- 4.4 Analysis of User Satisfaction Results for Scenario 3 -- 4.5 Demand Response Balance Point Analysis -- 5 Conclusion -- References -- A Simulink-Based Control Method for Energy Storage Assisted Black-Start -- 1 Introduction -- 2 Architecture of Energy Storage Assisted Black-Start -- 2.1 Microgrid System Structure -- 2.2 Analysis of Black Start Capability of Energy Storage Devices -- 2.3 Energy Storage Assisted Black Start Strategy -- 3 Microgrid System Modeling and Control Implementation -- 3.1 Microgrid Simulink Simulation Model Building -- 3.2 Black Start Control Strategy Implementation -- 4 Simulation Results Analysis -- 4.1 Simulink Simulation Results Analysis -- 4.2 Practical Engineering Applications. 5 Conclusion. |
| Record Nr. | UNINA-9910637738703321 |
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Fan Wenhui
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| Singapore : , : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Methods and Applications for Modeling and Simulation of Complex Systems : 21st Asia Simulation Conference, AsiaSim 2022, Changsha, China, December 9-11, 2022, Proceedings, Part II
| Methods and Applications for Modeling and Simulation of Complex Systems : 21st Asia Simulation Conference, AsiaSim 2022, Changsha, China, December 9-11, 2022, Proceedings, Part II |
| Autore | Fan Wenhui |
| Pubbl/distr/stampa | Singapore : , : Springer, , 2023 |
| Descrizione fisica | 1 online resource (639 pages) |
| Disciplina | 003.3 |
| Altri autori (Persone) |
ZhangLin
LiNi SongXiao |
| Collana | Communications in Computer and Information Science |
| Soggetto non controllato |
Artificial Intelligence
Computer Networks Computer Simulation Computers |
| ISBN | 981-19-9195-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Application of Modeling/Simulation in Science/Engineering/ Society/Economy/Management/ Energy/Transportation/ Life/Biology/Medicine etc. -- Flux Modelling of Membrane Bioreactor Process Plant Using Optimized-BPNN -- 1 Introduction -- 2 Artificial Neural Network Principle -- 2.1 Data Preprocessing -- 2.2 Performance Evaluation -- 2.3 Back-Propagation Neural Network -- 3 ANN Based MBR Model Development -- 4 Results and Discussion -- 5 Conclusion -- References -- An Efficient Method of Calculating Stress Intensity Factor for Surface Cracks in Holes Under Uni-variant Stressing -- 1 Introduction -- 2 General Weight Function Method -- 3 Construction of the Matrix of Reference Solutions -- 3.1 Sensitivity of Parameters -- 3.2 Influence of Parameters of Hole Shape -- 3.3 Conclusions of Sensitivity Analysis -- 4 Application -- 5 Conclusion -- References -- Modeling and Simulation Based on Concurrent FC-AE-1553 Network -- 1 Introduction -- 2 Topology Modeling of FC-AE-1553 Networks -- 2.1 Bus Topology Model -- 2.2 Switched Topology Model -- 3 Message Modeling of Concurrent FC-AE-1553 Network -- 4 Network Schedule Modeling Based on Priority -- 4.1 Periodic Scheduling Optimization Mechanism -- 4.2 Burst Scheduling Optimization Mechanism -- 5 Simulation and Analysis -- 6 Conclusion -- References -- Research on Modeling and Simulation Method of Laser System for Multi-domain Unified Model -- 1 Introduction -- 2 Modeling and Simulation Method of Laser System for Multi Domain Unified Model -- 2.1 Modeling Method of Laser System Based on Lumped Parameter Method -- 2.2 Co-simulation Method of Opto-Mechanical System Based on Multi-domain Unified Model -- 3 Modeling and Simulation Experiment of Laser System -- 3.1 Optical Component Model Based on Modelica.
3.2 Laser System Simulation in Sysplorer -- 4 Conclusion -- References -- Fast Electromagnetic Scattering Modeling of Complex Scenes Based on Multi-scale Coherent Computation -- 1 Introduction -- 2 Integrated Scattering Modeling Principle of Multi-scale Target and Complex Environment -- 3 Bouncing Ray Method Based on Curvature Adaptive Subdivision -- 3.1 Beam Tracking Based on Curvature Adaptive Subdivision -- 3.2 Field Strength Tracking -- 3.3 Far Field Integral -- 4 Scattering Correction of Rough Surface Based on Multi-scale Coherent Superposition -- 5 Simulation Results and Analysis -- 5.1 Typical Target Scattering Calculation -- 5.2 Scattering Calculation of Sea Surface Environment -- 5.3 Application Verification of Coupling Electromagnetic Scattering in Complex Scene -- 6 Summary -- References -- Pixelated Image Abstraction via Power Diagram -- 1 Introduction -- 2 Related Work -- 2.1 Power Diagram -- 2.2 CCCPD -- 2.3 Image Abstraction -- 3 Image Abstraction Based on Power Diagram -- 3.1 Overall Method -- 3.2 Pre-processing and Density Initialization -- 3.3 Image Segmentation Based on the Power Diagram -- 3.4 Color Filling -- 4 Experiment -- 4.1 Results Analysis -- 4.2 Abstract Controlling -- 4.3 Comparison -- 5 Conclusion -- References -- SMT Component Defection Reassessment Based on Siamese Network -- 1 Introduction -- 2 Proposed Method -- 2.1 Siamese Network -- 2.2 Loss Function -- 2.3 Detailed Detection Scheme -- 3 Experiments -- 3.1 Experimental Data -- 3.2 Optimization -- 3.3 Results and Evaluation -- 3.4 Conclusion -- References -- Real Time Traffic Sign Recognition Algorithm Based on SG-YOLO -- 1 Introduction -- 2 Related Work -- 2.1 Traditional Detection Methods -- 2.2 Deep-Learning Based Detection Methods -- 3 Principles of Algorithm -- 4 SG-YOLO Network Model -- 4.1 Ghost-Bottleneck -- 4.2 Channel Attention Mechanism. 4.3 SG-YOLO Network Architecture -- 4.4 DIoU Loss Function -- 5 Experiments and Analysis -- 5.1 Experimental Environment -- 5.2 Evaluation Metrics -- 5.3 Dataset and Processing -- 5.4 Results and Analysis -- 6 Conclusions -- References -- Research on Task Oriented Reliability Simulation Evaluation and Maintenance Strategy Optimization of Degraded System -- 1 Introduction -- 2 Introduction -- 2.1 Component System -- 2.2 Multi-component System -- 3 Reliability Model of Degraded System -- 3.1 Multi-state Transfer Process -- 3.2 System Availability Model -- 4 Research on Reliability Modeling of Degraded System -- 4.1 Multi Component System Interaction -- 4.2 Simulation Model Design -- 4.3 Simulation Algorithm Design -- 5 Case Analysis -- 5.1 Model Assumptions -- 5.2 Result Analysis -- 6 Summary -- References -- A Campus Scene Navigation Scheme Based on MPCC Dynamic Obstacle Avoidance Method -- 1 Introduction -- 2 System Overview -- 2.1 System Hardware -- 2.2 System Modules -- 3 Proposed Method -- 3.1 Static Obstacle Collision Detection Based on Voxel Grid -- 3.2 MPCC-Based Dynamic Obstacle Avoidance Strategy -- 4 Experiment -- 5 Conclusion and Future Work -- References -- Numerical Simulation of Ship Tank Sloshing Based on MPS Method -- 1 Introduction -- 2 MPS Method -- 2.1 Mathematical Formulation -- 2.2 Procedure of Numerical Simulation -- 3 Validation -- 4 Numerical Results -- 4.1 Description of Test Cases -- 4.2 Snapshots of Flow Field -- 5 Conclusions -- References -- Simulation Study of Dynamic Reactive Power Optimization in Distribution Network with DG Based on Improved Lion Swarm Algorithm -- 1 Introduction -- 2 The RPO Model -- 2.1 The Objective Function -- 2.2 Constraints -- 2.3 Reactive Power of the Distributed Power Supply -- 3 The Time Division Method -- 4 An Improved LSO Algorithm -- 4.1 Principles of the LSO algorithm. 4.2 The Random Black Hole Mechanism -- 5 Simulation Analysis -- 6 Conclusion -- References -- Observation Geometry Improvement of BDS by Near-Space Balloons in Regional Navigation Augmentation -- 1 Introduction -- 2 Methodology -- 3 Experiment Design and Results -- 3.1 Experiment Design -- 3.2 Experiment Design -- 4 Conclusion -- References -- Application of FE Simulation Method in the Field of Electrical Contact Performance Analysis -- 1 Introduction -- 2 Analysis Method of Electrical Contact Performance Based on FE Simulation -- 2.1 Mechanical Field Simulation -- 2.2 Thermoelectric Coupling Field Simulation -- 3 FE Simulation Results and Discussion -- 3.1 Mechanical Field -- 3.2 Thermoelectric Field -- 4 Conclusion -- References -- Damage Localization and Imaging of Composite Materials Based on Sparse Reconstruction Using Lamb Wave Anisotropy Propagation Model -- 1 Introduction -- 2 Theory -- 2.1 Damage Imaging Problem as a Sparse Reconstruction Problem -- 2.2 Lamb Wave Scattering Model in Composite Fiber Laminate -- 3 Methodology -- 3.1 Damage Dictionary Construction -- 3.2 Sparse Reconstruction Solution and Damage Localization Imaging -- 4 Experimental Research and Result Analysis -- 5 Conclusion -- References -- 5G Wireless Network Digital Twin System Based on High Precision Simulation -- 1 Introduction -- 2 Communication System Simulation -- 2.1 Fundamentals of Communication System Simulation -- 2.2 Wireless Communication Propagation Model -- 3 5G Wireless Network Digital Twin System Construction and Application Research -- 3.1 Construction of 5G Wireless Network Digital Twin System -- 3.2 Application Research of 5G Wireless Network Digital Twin System -- 4 Conclusion -- References -- A Hardware-in-the-Loop Microgrid Simulation Method Based on TwinCAT3-Take Black Start as An Example -- 1 Introduction. 2 Construction of Microgrid Equipment Model -- 2.1 Equipment Modelling -- 2.2 From MATLAB/Simulink to TwinCAT3 -- 3 Construction of the Simulation Environment -- 4 Verification of Hardware-in-the-Loop Simulation Method for Microgrid Based on TwinCAT3 -- 5 Conclusion -- References -- Day-Ahead Scheduling of PV Consumption in Distribution Networks Based on Demand Response of Multiple Types of Customer-Side Loads -- 1 Introduction -- 2 User-Side Load Modeling -- 2.1 Distributed PV Output Load Modeling -- 2.2 Residential Customer Load Model -- 2.3 Commercial User Load Model -- 2.4 Electric Vehicle Charging Load Model -- 3 Day-Ahead Dispatch Model of a Distribution Network Based on Customer-Side Demand Response -- 3.1 Demand Response Incentive Costs -- 3.2 Customer Satisfaction Assessment and Demand Response Balance Point Modeling -- 3.3 Demand Response Day Dispatch Model -- 3.4 Binding Conditions -- 3.5 Demand Response Day Dispatch Model -- 3.6 Model Solving Method -- 4 Algorithm Simulation -- 4.1 Algorithm Parameters -- 4.2 The Role of Demand Response in Suppressing Node Voltage Fluctuations -- 4.3 Analysis of Incentive Cost Results for Scenario 2 -- 4.4 Analysis of User Satisfaction Results for Scenario 3 -- 4.5 Demand Response Balance Point Analysis -- 5 Conclusion -- References -- A Simulink-Based Control Method for Energy Storage Assisted Black-Start -- 1 Introduction -- 2 Architecture of Energy Storage Assisted Black-Start -- 2.1 Microgrid System Structure -- 2.2 Analysis of Black Start Capability of Energy Storage Devices -- 2.3 Energy Storage Assisted Black Start Strategy -- 3 Microgrid System Modeling and Control Implementation -- 3.1 Microgrid Simulink Simulation Model Building -- 3.2 Black Start Control Strategy Implementation -- 4 Simulation Results Analysis -- 4.1 Simulink Simulation Results Analysis -- 4.2 Practical Engineering Applications. 5 Conclusion. |
| Record Nr. | UNISA-996503563903316 |
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Fan Wenhui
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| Singapore : , : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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