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[null,"1.","Record Nr.",null,"UNINA9910878993703321"]
[null,null,"Autore",null,"Huang De-Shuang"]
[null,null,"Titolo",null,"Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part V / / edited by De-Shuang Huang, Xiankun Zhang, Chuanlei Zhang"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"981-9756-75-8"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (504 pages)"]
[null,null,"Collana",null,"Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 14879"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Computers"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Image processing - Digital techniques"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Software engineering"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computing Milieux"]
[{"$nulls":4},"Computer Communication Networks"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Computer Imaging, Vision, Pattern Recognition and Graphics"]
[{"$nulls":4},"Software Engineering"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part V -- Intelligent Control and Automation -- A Parallel Intelligent Search and Rescue System for Swarm Robots Based on Digital Twin -- 1 Introduction -- 2 System Design Details -- 2.1 System Framework Design -- 2.2 Real-Time Twin Model Modeling Module -- 2.3 Parallel Decision Path Planning Module -- 3 System Application Example -- 3.1 Real-Time Twin Model Module Simulation Validation -- 3.2 Real-Scene System Verification -- 4 Conclusion and Discussion -- References -- Formal Verification of Path Planning Safety and Reachability in Unmanned Surface Vehicles -- 1 Introduction -- 2 Background -- 2.1 Model Checking -- 2.2 Linearly Priced Timed Automata -- 2.3 Linear-Time "]
[{"$nulls":4},"Temporal Logic -- 2.4 UPPAAL Model Checking Tool -- 3 Method -- 3.1 Representation of Energy -- 3.2 Reference and Formal Models -- 3.3 Safety and Reachability Properties -- 3.4 Specification of Requirements -- 4 Verification -- 4.1 Results -- 5 Conclusion -- References -- Refine Camera Calibration with Global Geometry Constraints -- 1 Introduction -- 2 Related Work -- 2.1 Parameter Estimation -- 2.2 Calibration Process -- 3 The Proposed Calibration Framework -- 3.1 Global Pose Constraint -- 3.2 Calibration Data Filter Mechanism -- 4 Experiments -- 4.1 Hardware and Software -- 4.2 Calibration Accuracy Exploration -- 4.3 Calibration Consistency Exploration -- 4.4 Different Camera Lens Exploration -- 5 Conclusion -- References -- OCP: An Online Contingent Planning Method for Robot Tasks with Incomplete Knowledge -- 1 Introduction -- 2 Related Works -- 3 The Online Contingent Planning Method -- 3.1 Overview of OCP -- 3.2 ExPDDL: Extensions to PDDL for Modelling Uncertainty -- 3.3 Online Contingent Planning Algorithm -- 4 Experiment -- 4.1 Evaluations on Contingent Benchmarks -- 4.2 Simulation Experiment -- 4.3 Real-Robot Experiment."]
[{"$nulls":4},"5 Conclusions -- References -- YOLO-Underwater: A Real-Time Object Detection Framework for Enhanced Underwater Robotics Operations -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 YOLOv7 -- 3.2 ConvNeXt -- 3.3 SimAM -- 4 Experiment -- 5 Conclusion -- References -- YOLO-Underwater-Tiny: High-Efficiency Object Detection in Underwater Robots -- 1 Introduction -- 2 Method -- 2.1 YOLOv7-Tiny -- 2.2 Reparameterized Lightweight Backbone Layer of MobileOne -- 2.3 Ghost -- 3 Experiment -- 3.1 Experiment on Replacing Convolution with Ghost Convolution -- 3.2 Ablation Experiments and Model Comparisons in Underwater Environments -- 3.3 Comparative Experiments on Underwater Environments of Multiple Types of Models -- 4 Conclusion -- References -- Shared Subnet Synthesis of Object-Oriented Petri Net Based Representation for  Embedded Systems -- 1 Introduction -- 2 Basic Concepts -- 3 Synthesis Operation and Property Analysis -- 4 Applications -- 5 Conclusion -- References -- Deep Reinforcement Learning Based on Graph Neural Network for Flexible Job Shop Scheduling Problem with Lot Streaming -- 1 Introduction -- 2 Problem Description and Model Formula -- 3 Proposed DRL Methods for FJSPLS -- 3.1 Markov Decision Process Formulation -- 3.2 Enhanced Disjunctive Graph and HGFE -- 3.3 Decision-Making by Dual Agents -- 3.4 Network Structure and Training -- 4 Computational Experiments -- 5 Conclusions -- References -- Robust Adaptive Neural Network-Based Funnel Tracking Control of a Class of Perturbed Euler-Lagrange Systems -- 1 Introduction -- 2 Problem Statement -- 2.1 Dynamic of Euler-Lagrange Systems -- 2.2 Formulations of Neural Networks -- 3 Main Results -- 3.1 Controller Design -- 3.2 Stability Analysis -- 4 Simulation Example -- 5 Conclusion -- References."]
[{"$nulls":4},"SeqAttention-Net: Design of a Deep Neural Network for Bearing Fault Detection Based on Small Sample Datasets -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Transformer -- 3.2 Fast Fourier Transform -- 4 Experiment -- 4.1 Baseline -- 4.2 Datasets -- 4.3 Data Preprocessing -- 4.4 Evaluation Indicators -- 4.5 Experimental Results -- 5 Summary -- References -- Adaptive Fixed-Time Sliding-Mode Trajectory Tracking Control of a Cart-Pendulum Robot Against Actuator Attacks -- 1 Introduction -- 2 Research Model with Preliminaries -- 2.1 Cart-Pendulum Robot Model -- 2.2 False Data Injection Attacks -- 2.3 Control Intents -- 3 Integral Sliding-Mode Adaptive Control Law Generation -- 3.1 Estimated Value of Unknown Attack Weight -- 3.2 Fixed Time Integral Sliding-Mode Control Law -- 4 Simulation Results "]
[{"$nulls":4},"-- 5 Conclusions -- References -- VirtualOffshore: An Interactive 3D Offshore Environment for Embodied Agents -- 1 Introduction -- 2 Related Works -- 2.1 Embodied Agent -- 2.2 Simulation Environment -- 3 Offshore Environment -- 3.1 Unity Simulator -- 3.2 WebSocket Endpoints -- 3.3 User Interface -- 4 Offshore Task Planning Challenge -- 4.1 Atomic Actions -- 4.2 States -- 4.3 Goals -- 5 Benchmark Experiments -- 5.1 Baseline Models -- 5.2 Results -- 6 Conclusion -- References -- Agent Can Say No: Robot Task Planning by Natural Language Feedback Between Planner and Executor -- 1 Introduction -- 2 Related Work -- 2.1 Task Planning -- 2.2 LLMs as the Robot Planner -- 3 Method -- 3.1 Cloud Component -- 3.2 Local Component -- 3.3 Action Decision -- 4 Experiments -- 4.1 Generalization Experiments -- 4.2 Comparative Experiments -- 5 Conclusion -- References -- A Real-Time Exoskeleton Control Strategy for Multiple Gaits Based on Continuous State Variable Driving and MiniRocket Recognition -- 1 Introduction -- 2 System Model."]
[{"$nulls":4},"3 Continuous State Variable Driven Control Strategy -- 4 Gait Recognition Based on MiniRocket -- 5 Experimental Results -- 5.1 Initial Setup and Adjustment -- 5.2 Gait Recognition Accuracy -- 5.3 Gait Phase Estimation for Three Types of Gaits -- 6 Conclusion -- References -- Scheduling Strategy to Minimize Makespan for Energy-Efficient Parallel Applications in Heterogeneous Computing Systems -- 1 Introduction -- 2 Related Works -- 3 Model -- 3.1 Application Model -- 3.2 Energy Model -- 3.3 Problem Definition -- 4 Algorithm -- 4.1 Pre-allocation of Energy Consumption -- 4.2 A Proposed Algorithm to Minimize the Scheduling Length -- 4.3 Example of the Proposed Algorithm -- 5 Experiment -- 5.1 Fast Fourier Transform Application -- 5.2 Gaussian Elimination Application -- 6 Conclusion -- References -- An Enhanced Driving Trajectory Prediction Method Based on Generative Adversarial Imitation Learning -- 1 Introduction -- 2 State of the Art -- 3 Driving Trajectory Prediction -- 3.1 Multimodal Trajectory Model -- 3.2 High-Definition Driving Scene Map Construction -- 3.3 Strategy Learning -- 3.4 Trajectory Scheme Generation -- 4 Simulation and Result Analysis -- 4.1 Evaluation Indicators -- 4.2 Training -- 4.3 Models Comparison and Analysis -- 4.4 Ablation System -- 4.5 Results Analysis -- 5 Conclusions -- References -- Subtask-Aware Energy Allocation Algorithm for Parallel Applications Scheduling on Heterogeneous Computing Systems -- 1 Introduction -- 2 Related Work -- 3 Model and Problem Definition -- 3.1 Application Model -- 3.2 Energy Model -- 3.3 Problem Definition -- 4 Subtask-Aware Scheduling Algorithm -- 4.1 Task Prioritization Phase -- 4.2 Energy Distribution Phase -- 4.3 Task Allocation Phase -- 4.4 Algorithm Description -- 4.5 Example of the SEA-PAS Algorithm -- 5 Experiments -- 5.1 Experimental Results for FFT Applications."]
[{"$nulls":4},"5.2 Experimental Results for GE Applications -- 6 Conclusion -- References -- A Dynamic Model of Multi-state LVAD Based on LSTM Neural Network -- 1 Introduction -- 2 Cardiovascular-LVAD Model -- 3 Data of Multi State -- 3.1 Afterload and Preload -- 3.2 Heart Failure -- 3.3 Suction -- 4 Dynamics Model Based on LSTM -- 4.1 LSTM Neural Network -- 4.2 Train Progress of LSTM -- 5 Evaluation -- 5.1 Non-suction State -- 5.2 Suction State -- 6 Conclusion -- References -- Large-Scale Electroplating Scheduling: A Hierarchical Temporal Planning Approach -- 1 Introduction -- 2 Problem Definition -- 3 Our HIT Approach -- 3.1 Generating Skeletons and Sub-goals -- 3.2 Generating Final Plans -- 3.3 Overview of HIT -- 4 Experiment -- 4.1 Results of Problems with Different Numbers of Products -- 4.2 Results of Problems with Unexpected Events -- 4.3 Case Study -- 5 Conclusion -- References -- Intelligent Image/Document Retrievals -- Blockchain-"]
[{"$nulls":4},"Based Equilibrium Anchors Game Pricing Method for Data Element -- 1 Introduction -- 2 Description of Problem -- 3 Game Pricing Approach for Data Elements in Blockchain -- 3.1 Average Value Line (AVL) -- 3.2 Anchor Bidding Game -- 4 Experimentation and Evaluation -- 4.1 Experimental Data Configuration -- 4.2 Analysis of Experimental Results -- 5 Conclusion -- References -- Ethical Challenges and Governance of Smart Algorithms Empowering Financial Technology -- 1 Background -- 2 Ethical Challenges and Technical Governance of Intelligent Algorithms in Financial Technology -- 2.1 Data Privacy and Security -- 2.2 Algorithmic Discrimination -- 2.3 Algorithm Distortion -- 2.4 Algorithm Black Box -- 2.5 Large Model Risks -- 2.6 Disputes Over Responsibility Attribution -- 3 Other Governance Approaches of Intelligent Algorithms in Financial Technology -- 3.1 Organizational Supervision -- 3.2 Formulate Relevant Legal Regulations."]
[{"$nulls":4},"4 Conclusion and Outlook."]
[null,null,null,"Sommario/riassunto","This 6-volume set LNAI 14875-14880 constitutes - in conjunction with the 13-volume set LNCS 14862-14874 and the 2-volume set LNBI 14881-14882 - the refereed proceedings of the 20th International Conference on Intelligent Computing, ICIC 2024, held in Tianjin, China, during August 5-8, 2024. The total of 863 regular papers were carefully reviewed and selected from 2189 submissions. The intelligent computing annual conference primarily aims to promote research, development and application of advanced intelligent computing techniques by providing a vibrant and effective forum across a variety of disciplines. This conference has a further aim of increasing the awareness of industry of advanced intelligent computing techniques and the economic benefits that can be gained by implementing them. The intelligent computing technology includes a range of techniques such as Artificial Intelligence, Pattern Recognition, Evolutionary Computing, Informatics Theories and Applications, Computational Neuroscience & Bioscience, Soft Computing, Human Computer Interface Issues, etc. ."]