دانلود کتاب Artificial Intelligence in China: Proceedings of the International Conference on Artificial Intelligence in China (Lecture Notes in Electrical Engineering, 572)

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Contents\nGlobal Descriptors of Convolution Neural Networks for Remote Scene Images Classification\n 1 Introduction\n 2 Proposed Method\n 2.1 Global Descriptors Extracting\n 2.2 Feature Fusion and the Dimension Reduction\n 3 Experiments and Analyses\n 3.1 UC Merced Land Use Dataset Description\n 3.2 Experimental Introduction\n 3.3 Discuss Different CNN Models with PCA\n 3.4 Analysis of the Proposed Method\n 4 Discussion of Time\n 5 Conclusion\n References\nPlant Diseases Identification Based on Binarized Neural Network\n Abstract\n 1 1 Introduction\n 2 2 Related Work\n 3 3 Dataset\n 4 4 Convolutional Neural Networks\n 5 5 Binarized Convolutional Neural Network\n 6 6 Training and Discussion\n 6.1 Experiment Platform\n 6.2 Parameter Selection in Experiment\n 6.3 Analysis and Discussion\n 6.4 Conclusion\n References\nHand Detection Based on Multi-scale Fully Convolutional Networks\n Abstract\n 1 1 Introduction\n 2 2 Related Works\n 3 3 Methods\n 3.1 Overview of the Approach\n 3.2 Network Structure\n 3.3 Loss Function\n 3.4 Implementations\n 4 4 Experiments and Results\n 4.1 Dataset\n 4.2 Data Augmentation\n 4.3 Label Generation\n 4.4 Evaluation Criteria\n 5 5 Conclusions\n References\nResearch on UAV Cluster’s Operation Strategy Based on Reinforcement Learning Approach\n Abstract\n 1 1 Introduction\n 2 2 UAV Cluster’s Task Allocation and Modeling\n 3 3 Optimization of Operation Strategic Training Modeling\n 4 4 Research on UAV Cluster’s Operation Strategy Algorithm\n 5 5 Conclusion\n References\nAnalysis and Experimental Research on Data Characteristics of BDS Positioning Error\n Abstract\n 1 1 Introduction\n 2 2 The Error Sources of BDS Positioning\n 3 3 Characteristic Analysis and Experimental Study of BDS Positioning Error\n 3.1 Experimental Environment and Positioning Data Processing\n 3.2 The Self-correlation of Positioning Error\n 3.3 The Extremity of Positioning Error\n 3.4 The Thick Tail of Positioning Error\n 4 4 Conclusion\n References\nDesign of Elderly Fall Detection Based on XGBoost\n Abstract\n 1 1 Introduction\n 2 2 Proposed Falling Detection Based on XGBoost\n 2.1 The Process of Establishing the Model\n 2.2 Notation\n 2.3 Falling Detection Model’s Establishment\n 2.4 Data Threshold Training Based on XGBoost\n 3 3 Performance Evaluation\n 4 4 Conclusions\n Acknowledgements\n References\nSegmentation of Aerial Image with Multi-scale Feature and Attention Model\n 1 Introduction\n 2 Related Work\n 3 Attention Model for Scales\n 3.1 Attention Mechanism\n 3.2 Attention Model for Scales\n 4 Experiments\n 4.1 Extra Supervision\n 4.2 Network Architectures\n 4.3 Datasets\n 4.4 Evaluation\n 4.5 Results\n 5 Conclusion\n References\nRecurrent Neural Detection of Time–Frequency Overlapped Interference Signals\n 1 Introduction\n 2 Problem Formulation\n 3 BI-RNN-Based Prediction of Time Series Signal\n 3.1 Prediction Model\n 3.2 Model Training for Noiseless Prediction\n 3.3 Prediction Performance\n 4 Interference Detection\n 4.1 Classifier for Interference Detection\n 4.2 Performance Evaluation\n 5 Conclusion\n References\nRisk Analysis of Lateral Collision of Military and Civil Aviation Aircraft Based on Event Model\n Abstract\n 1 1 Introduction\n 2 2 Event Model\n 3 3 Collision Probability Model of Military and Civil Aviation\n 3.1 Hypothetical Conditions of the Model\n 3.2 Event Collision Model for Military and Civil Aviation\n 4 4 Military and Civil Aircraft Lateral Overlap Probability\n 4.1 Coordinate System\n 4.2 Lateral Position Deviation of Civil Aviation Aircraft\n 4.3 Probability Model of Lateral Position Deviation of Military Aircraft\n 4.3.1 Turning Radius Probability Density Function\n 4.3.2 Center of the Circling Path\n 4.3.3 Omnidirectional Wind\n 5 5 Simulation of Military and Civil Aviation Lateral Deviation\n 6 6 Summary\n References\nImbalanced Data Classification with Deep Support Vector Machines\n Abstract\n 1 1 Introduction\n 2 2 The Deep Support Vector Machine Algorithm\n 2.1 The DSVM Algorithm Model\n 2.2 Optimization Objective Function of the DSVM Algorithm\n 2.3 The DSVM Algorithm Solving Process\n 3 3 Imbalanced Data Classification Experiment\n 3.1 Radar Measurement System and Experimental Implementation Details\n 3.2 Human Target Detection Dataset Descriptions\n 3.3 Experimental Analysis of the DSVM Algorithm\n 3.4 Algorithm Results Comparison\n 4 4 Conclusion and Future Work\n Acknowledgements\n References\nAn Incident Identification Method Based on Improved RCNN\n Abstract\n 1 1 Introduction\n 2 2 Related Works\n 3 3 Improved RCNN Incident Identification Method\n 3.1 The Principle of Traditional RCNN\n 3.2 Model Improvement\n 4 4 Experiment and Discussion\n 4.1 Data Set Construction and Pretreatment\n 4.2 Evaluation Criterion\n 4.3 Analysis and Discussion\n 4.4 Comparison with Other Methods\n 5 5 Conclusion and Future Works\n Acknowledgements\n References\nLatency Estimation of Big Data Processing Under the MapReduce Framework with Coupling Effects\n 1 Introduction\n 2 Models of Data Flow\n 2.1 Queueing Models of Data Processing\n 2.2 Steady-State Conditions of Queuing Models\n 3 Estimation of the Latency of Queueing Models\n 3.1 Estimation of the Waiting Time to Be Served on the Mappers\n 3.2 Estimation of the Waiting Time to Be Transferred to Reducers\n 4 Simulation Results\n 4.1 Latency of Various Classification Algorithms\n 4.2 Latency with Different Number of Mappers and Reducers\n 5 Conclusion\n References\nZone-Based Resource Allocation Strategy for Heterogeneous Spark Clusters\n 1 Introduction\n 2 System Model\n 2.1 The Definition of Zone\n 2.2 Zone Division Strategy\n 2.3 Zone-Based Resource Allocation Strategy\n 3 Simulation\n 3.1 Experiment I\n 3.2 Experiment II\n 4 Conclusion\n References\nBattle Prediction System in StarCraft Combined with Topographic Considerations\n Abstract\n 1 1 Overview\n 2 2 Related Technology Introduction\n 2.1 Maximum Posterior Probability Estimate [2, 3]\n 2.2 StarCraft Combat Simulation System SparCraft\n 3 3 Mathematical Modeling\n 3.1 Core Ideas [6]\n 3.2 Ideas to Improve [8]\n 3.3 Training Model Establishment\n 4 4 Experimental Results\n 4.1 Data set Acquisition\n 4.2 Training Result\n Acknowledge\n References\nTask Scheduling Strategy for Heterogeneous Spark Clusters\n Abstract\n 1 1 Introduction\n 2 2 Research Background\n 2.1 Spark Scheduling Strategy\n 2.2 Task Scheduling Process\n 3 3 Spark Load Balancing Task Scheduling Strategy\n 3.1 Spark Load Definition\n 3.2 Load Balancing Task Scheduling\n 4 4 Experiment and Result Analysis\n 4.1 Experimental Design\n 4.2 Experimental Results and Analysis\n 5 5 Conclusion\n Acknowledge\n References\nResearch on Multi-priority Task Scheduling Algorithms for Mobile Edge Computing\n Abstract\n 1 1 Introduction\n 1.1 Related Work\n 1.2 Motivation and Contributions\n 2 2 Establishing Task Migration Model\n 2.1 Task Processing Time Locally\n 2.2 Task Processing Time in MEC Server\n 2.2.1 Transmission Delay of Tasks in Remote Processing\n 2.2.2 Task Stay Time on MEC Server\n 2.2.3 Total Task Delay on MEC Server\n 2.3 Task Migration Decision Algorithm\n 2.3.1 First Filtering Algorithm\n 2.3.2 Second Filtering Algorithm Based on Channel Transport Priority\n 3 3 Simulation Result\n 3.1 Average Execution Time of Different Algorithms with High Priority\n 3.2 Average Execution Time for Different Priorities\n 4 4 Conclusion\n Acknowledgements\n References\nMicroblog Rumor Detection Based on Comment Sentiment and CNN-LSTM\n Abstract\n 1 1 Introduction\n 2 2 Related Works\n 3 3 Rumor Detection Based on Comment Sentiment and CNN-LSTM\n 3.1 CNN-LSTM\n 3.2 Excavating Sentimental Features of Comments\n 3.3 Incorporating Sentimental Polarity into CNN-LSTM\n 4 4 Experiments and Analysis\n 4.1 Data Set and Spam Comment Filtering\n 4.2 Experimental Result\n 4.3 Comparative Analysis\n 4.3.1 Effect of Rumor Detection Threshold on Experimental Results\n 4.3.2 The Effect of Adding Sentimental Characteristics on the Experiment\n 4.3.3 Experimental Comparison of Sentimental CNN-LSTM with Other Models\n 5 5 Conclusion and Future Works\n Acknowledgements\n References\nA Guideline for Object Detection Using Convolutional Neural Networks\n Abstract\n 1 1 Introduction\n 2 2 Convolutional Implementation of Sliding Windows\n 3 3 Transfer Learning\n 4 4 Generate Training Dataset\n 5 5 Conclusion\n References\nResearch on Prediction Model of Gas Emission Based on Lasso Penalty Regression Algorithm\n Abstract\n 1 1 Introduction\n 2 2 Principle of Lasso Algorithm\n 3 3 Prediction Model Based on Lasso\n 4 4 Application and Analysis of Model\n 4.1 Selection of Factors Affecting Gas Emission in Working Face\n 4.2 Correlation Analysis\n 4.3 High-Impact Factors Screening by Lasso\n 4.4 Comparison of Prediction Results\n 5 5 Conclusion\n Acknowledgements\n References\nWater Meter Reading Area Detection Based on Convolutional Neural Network\n Abstract\n 1 1 Introduction\n 2 2 System Implementation\n 2.1 Predict the Reading Area with Convolutional Neural Network\n 2.2 Remove Highly Overlapping Results with NMS\n 3 3 Experiment Result\n 3.1 Dataset\n 3.2 The Overlap Between Predicted Area and Real Area\n 3.3 Recognition Accuracy with Three-Layer BP Neural Network\n 4 4 Conclusion\n References\nA Dependency-Extended Method of Multi-pattern Matching for RDF Graph\n 1 Introduction\n 2 Dependent Tree and Node Fragment Tables\n 2.1 Problem Definition\n 2.2 Dependent Tree and Node Fragmentation Table\n 3 M-PM Algorithm\n 4 Experimental Evaluation\n References\nMachine Learning for RF Fingerprinting Extraction and Identification of Soft-Defined Radio Devices\n 1 Introduction\n 2 Overview of RF Fingerprinting Model\n 3 Recognition Methods\n 3.1 Fractal Dimension\n 3.2 Phase Noise Spectrum\n 3.3 Constellation Features\n 3.4 SNR Estimation\n 4 Experimental Environment Construction and Data Collection Classification\n 4.1 Experimental Environment Construction and Data Collection\n 4.2 Data Classification\n 5 Experimental Result\n 6 Conclusion\n References\nAnalysis on Suppression of Echo Signal of Target Body and Translation in Micro-Doppler Signal Processing\n Abstract\n 1 1 Introduction\n 2 2 Echo Signal Analysis\n 3 3 Analysis of Interference Suppression\n 4 4 Simulation\n 5 5 Conclusion\n References\nWeighted Least Square Support Vector Regression Method with GGP-Based Sequential Sampling\n Abstract\n 1 1 Introduction\n 2 2 Weighted Least Square Support Vector Machines for Regression (WLSSVR)\n 3 3 Proposal Method\n 3.1 Sequential Sampling Strategies\n 3.2 Procedure\n 4 4 Application Examples\n 5 5 Conclusions\n Acknowledgements\n References\nData Stream Adaptive Partitioning of Sliding Window Based on Gaussian Restricted Boltzmann Machine\n Abstract\n 1 1 Introduction\n 2 2 Data Flow Blocking in Adaptive Window\n 2.1 Gaussian Restricted Boltzmann Machines\n 2.2 Change Monitoring Under Sliding Window\n 2.3 Algorithm Implementation\n 3 3 Experiment Results\n 4 4 Conclusion\n Acknowledgements\n References\nIntrusion Detection Based on Convolutional Neural Network in Complex Network Environment\n 1 Introduction\n 2 Implementation of the Detection\n 2.1 Data Preprocessing\n 2.2 CNN Modeling\n 2.3 Classifier\n 3 Experimental Results and Analysis\n 4 Conclusion\n References\nApplication of Neural Network in Performance Evaluation of Satellite Communication System: Review and Prospect\n Abstract\n 1 1 Introduction\n 2 2 Current Research on Performance Evaluation of Satellite Communication System\n 2.1 Analytical Method\n 2.2 Software Simulation Method\n 2.3 Index System Method\n 3 3 Application Prospect of Neural Network in Performance Evaluation of Satellite Communication System\n 3.1 Identifying Key Parameters\n 3.2 Adjusting Evaluation Model Adaptively\n 3.3 Comparing Different Satellite Constellations’ Performance\n 4 4 Conclusion\n Acknowledgements\n References\nConstruction of Marine Target Detection Dataset for Intelligent Radar Application\n Abstract\n 1 1 Introduction\n 2 2 Description of Existing Marine Target Detection Dataset\n 2.1 IPIX Radar Dataset\n 2.2 CSIR Radar Dataset\n 2.3 DSTO Radar Dataset\n 2.4 UCL Radar Dataset\n 3 3 Construction of Marine Target Detection Dataset\n 3.1 Construction Framework\n 3.2 Dataset Management\n 3.3 Dataset Property Analysis\n 4 4 Conclusions\n Acknowledgements\n References\nPedestrian Retrieval Using Valuable Absence Augmentation\n 1 Introduction\n 2 Approach\n 3 Experiments\n 3.1 Market-1501 Database\n 3.2 Implementation Details\n 3.3 Evaluation of Valuable Parts for Network Performance\n 3.4 Evaluation of the Proposed VAA for the Performance of Pedestrian Retrieval\n 4 Conclusion\n References\nAn End-to-End Neural Network Model for Blood Pressure Estimation Using PPG Signal\n Abstract\n 1 1 Introduction\n 2 2 Data Source\n 3 3 Proposed Method\n 3.1 Data Processing\n 3.2 Model Structure\n 4 4 Experimental Results\n 5 5 Discussion\n 6 6 Conclusion\n References\nConcept Drift Detection Based on Kolmogorov–Smirnov Test\n Abstract\n 1 1 Introduction\n 2 2 Concept Drift Detection Based on Kolmogorov–Smirnov Test\n 2.1 Relevant Definitions\n 2.2 Kolmogorov–Smirnov Test\n 2.3 Model Description\n 3 3 Experimental Process and Results\n 3.1 Experimental Process\n 3.2 Experimental Results\n 4 4 Conclusions\n Acknowledgements\n References\nMillimeter-Wave Beamforming of UAV Communications for Small Cell Coverage\n Abstract\n 1 1 Introduction\n 2 2 System Model\n 3 3 Improved Beam Design\n 3.1 Equivalent Spectral Efficiency\n 3.2 Optimal Beam Pattern\n 3.3 Beam Design Based on DDL-OMP Algorithm\n 4 4 Simulation\n 4.1 Average Spectrum Efficiency\n 4.2 Iterative Efficiency and Complexity\n 5 5 Conclusion\n Acknowledgements\n References\nThe Feasibility Analysis of the Application of Ensemble Learning to Operational Assistant Decision-Making\n Abstract\n 1 1 Introduction\n 2 2 Data Set Description\n 3 3 Algorithms Description\n 3.1 Single and Ensemble Classification Algorithms\n 3.2 Confidence Evaluation Based on Non-parametric Estimation of Probability Density\n 4 4 Experiments\n 4.1 Comparison Experiment Under Ideal Conditions\n 4.2 Comparison Experiment Under the Influence of Noise\n 4.3 Comparison Experiment Under Influence of Small Sample Set\n 4.4 Comparison Experiment with Rejection\n 5 5 Conclusions\n References\nA Review of Artificial Intelligence for Games\n Abstract\n 1 1 Introduction\n 2 2 Ad Hoc Behavior Authoring\n 3 3 Tree Search Algorithms\n 4 4 Evolutionary Computation\n 5 5 Machine Learning\n 6 6 Conclusions\n References\nIntelligent Exhaust System of Cross-Linked Workshop\n Abstract\n 1 1 Introduction\n 2 2 Overall Scheme Design of the System\n 3 3 System Hardware Design\n 3.1 Exhaust Fan Control Module\n 3.2 Data Acquisition Module\n 3.3 Transmission Module\n 4 4 System Software Design\n 4.1 Lower Computer Software Design\n 4.2 Monitoring Management Software Design\n 4.2.1 PC Side Management Software\n 4.2.2 Mobile Side Management Software\n 5 5 System Test\n 6 6 Conclusion\n References\nCombined with DCT, SPIHT and ResNet to Identify Ancient Building Cracks from Aerial Remote Sensing Images\n Abstract\n 1 1 Introduction\n 2 2 Block DCT\n 3 3 Block DCT Combined with SPIHT\n 4 4 ResNet\n 5 5 Conclusion\n References\nDesign and Construction of Intelligent Voice Control System\n 1 Introduction\n 2 Project Description\n 3 System Algorithms\n 3.1 Voice Activity Detection Algorithm\n 3.2 End-to-End Speech Recognition Model\n 3.3 Instruction Analysis Algorithm\n 3.4 Backtracking Algorithm\n 4 Comprehensive Evaluation\n 5 Conclusion\n References\nEstimation of Sea Clutter Distribution Parameters Using Deep Neural Network\n Abstract\n 1 1 Introduction\n 2 2 Sea Clutter Parameter Estimation Based on Deep Learning Theory\n 2.1 Deep Neural Network Model\n 2.2 Estimation Method for Simulated Sea Clutter Data\n 2.3 Estimation Method for Real Sea Clutter Data\n 3 3 Validation Results\n 3.1 Results of Simulated Data\n 3.2 Results of Measured Data\n 4 4 Conclusions\n Acknowledgements\n References\nMaritime Target Trajectory Prediction Model Based on the RNN Network\n Abstract\n 1 1 Introduction\n 2 2 RNN Network Structure\n 3 3 Maritime Target Trajectory Prediction Model Based on the RNN Network\n 3.1 Data Selection\n 3.2 Data Normalization\n 3.3 Model Construction\n 4 4 Model Simulation and Analysis\n 4.1 Model Simulation\n 4.2 Analysis of Simulation Results\n 5 5 Conclusion\n References\nIndustrial Ventilator Monitoring System Based on Android\n Abstract\n 1 1 Introduction\n 2 2 Overall Scheme Design of the System\n 3 3 Hardware Design\n 3.1 Main Control Chip\n 3.2 Power Supply Circuit\n 3.3 Smoke Monitoring Circuit\n 3.4 Relay Control Circuit\n 4 4 Software Design\n 4.1 Software Design of Acquisition Terminal\n 4.2 Software Design of Sever\n 4.3 Software Design of Android Terminal\n 5 5 Test\n 6 6 Conclusions\n References\nAn Overview of Blockchain-Based Swarm Robotics System\n Abstract\n 1 1 Introduction\n 2 2 Blockchain: Concept and Properties\n 3 3 Application Prospect\n 3.1 Information Safety\n 3.2 Distributed Decision-Making\n 3.3 Behaviors Collaboration\n 4 4 Swarm Robotics-Oriented Blockchain Model\n 5 5 Limitation and Challenge\n References\nA Transmission Method for Intra-flight Ad Hoc Networks Based on Business Classification\n Abstract\n 1 1 Introduction\n 2 2 Related Works\n 3 3 Multi Transmission Modes\n 3.1 Frame Structure of the Transmission Protocol\n 3.2 Segment-Reliable Transmission Mode\n 3.3 Half-Reliable Transmission Mode\n 3.4 Efficient Transmission Mode\n 4 4 Comparison Analysis\n 4.1 Validity of M2T\n 4.2 Comparisons Between M2T and TCP\n 5 5 Conclusions\n References\n3D Statistical Resolution Limit of Two Close Spaced Targets for Active Array in the GLRT Context\n Abstract\n 1 1 Introduction\n 1.1 3D SRL Model\n 1.2 3D Resolution Statistical and Its Performance\n 2 2 The Influence Factors on 3D SRL\n 3 3 Simulation\n 4 4 Conclusions\n References\nA Scene Semantic Recognition Method of Remote Sensing Images Based on CSIFT and PLSA\n Abstract\n 1 1 Introduction\n 2 2 Schemes of Image Scene Recognition\n 3 3 Visual Bag of Features of Remote Sensing Images\n 4 4 Semantic Recognition of Scenes Based on PLSA\n 5 5 Experimental Results and Analysis\n 5.1 A Comparison by Feature Extraction Method of Different Low Level\n 5.2 The Improvement of Recognition Result by PLSA\n 5.3 Effect of Different Visual Words on the Recognition Results\n 5.4 Effect of Recognition Results Caused by Different Number of Latent Semantic Topics\n 6 6 Conclusion\n References\nApplications of Brain-Inspired Intelligence in Intelligentization of Command and Control System\n Abstract\n 1 1 Introduction\n 2 2 Development Tendency of Intelligentization of Command and Control System\n 3 3 Application Advantages of Brain-Inspired Intelligence\n 4 4 Evolution Path of the Invocation Pattern of Intelligent Algorithms in Command and Control System\n 5 5 Conclusion\n References\nThe Integrative Technology of Testability Design and Fault Diagnosis for Complex Electronic Information System\n Abstract\n 1 1 Introduction\n 2 2 Digraph Model (Digraph = Directed Graph)\n 2.1 Definition of Digraph\n 2.2 Diagraph Model\n 2.3 Fault Diagnosis Model Based on Digraph\n 3 3 Correlation Model\n 3.1 Definition of Correlation\n 3.2 Correlation Graphic Model\n 4 4 Sample Analysis\n 4.1 System Composition\n 4.2 Result Analysis\n 5 5 Conclusion\n References\nChinese Named Entity Recognition with Changed BiLSTM and CRF\n 1 Introduction\n 2 Mathematic Models\n 2.1 Improved BiLSTM\n 2.2 Conditional Random Filed\n 2.3 Our Model Structure\n 3 Experiment and Results\n 3.1 The Form of Experiment Data\n 3.2 Data Preprocessing and Model Compile\n 3.3 Experiment Result\n 4 Conclusion\n References\nKey Problems and Solutions of the Application of Artificial Intelligence Technology\n Abstract\n 1 1 Introduction\n 2 2 Problem Analysis\n 3 3 Solution Analysis\n 3.1 Using War Games to Simulate, Generate and Collect Data to Solve Data Source Problems\n 3.2 Explore a New Learning Algorithm to Reduce the Dataset Needed for Model Training and Solve the Problem of Data Annotation\n 3.3 Enhancing AI System’s Autonomous Learning and Environmental Adaptability by Bionics\n 3.4 Setting Up Machine Commonsense Research to Enhance Reasoning Ability of AI System\n 3.5 Developing Cyber Resilience to Actively Address the Security Problem of AI System\n 3.6 Revealing the Transparency of AI Decision-Making and Enhancing Credibility by Improving the Interpretability of AI System\n 4 4 Conclusion\n References\nRecent Advances for Smart Air Traffic Management: An Overview\n Abstract\n 1 1 Introduction\n 2 2 Concept and Connotation of Smart ATM\n 3 3 Application and Expansion of Smart ATM\n 3.1 Cloud Computing\n 3.2 Big Data\n 3.3 Artificial Intelligence\n 3.4 Internet of Things (IoT)\n 3.5 Mobile Internet\n 4 4 Suggestions for Follow-up Research\n 5 5 Conclusion\n References\nApplication of GRIB Data for 4D Trajectory Prediction\n Abstract\n 1 1 Introduction\n 2 2 Format and Processing of Wind Data\n 2.1 Wind Data Format\n 2.2 Wind Data Processing\n 3 3 Meteorological Modeling of Trajectory Prediction\n 3.1 Track Prediction\n 3.2 Wind Data Pre-processing\n 3.3 Wind Impact Analysis\n 4 4 Simulation Verification\n 4.1 Radar Data\n 4.2 Wind Vector\n 4.3 Simulation Verification\n 5 5 Conclusion\n References\nWeibo Rumor Detection Method Based on User and Content Relationship\n Abstract\n 1 1 Introduction\n 2 2 Related Work\n 3 3 Rumor Recognition Based on Composite Model of User-Content Relations\n 3.1 Traditional CNN-LSTM\n 3.2 CNN with Attention Mechanism Combined with LSTM\n 4 4 Conclusion\n Acknowledgements\n References\nImproved HyperD Routing Query Algorithm Under Different Strategies\n Abstract\n 1 1 Introduction\n 2 2 Routing Query Algorithm\n 2.1 Maximum Neighbor Priority Routing Algorithm\n 2.2 Multipath Minimum Routing Algorithm\n 2.3 Multipath Maximum Probability Path Algorithm\n 3 3 Experimental Analysis\n 4 4 Conclusion\n References\nComputer-Aided Diagnosis of Mild Cognitive Impairment Based on SVM\n 1 Introduction\n 2 Materials and Methods\n 2.1 Brain Network Construction\n 2.2 Feature Extraction and Selection\n 2.3 Classification\n 3 Results and Discussion\n 3.1 Subjects\n 3.2 The Small-World Property of Funtional Brain Networks\n 3.3 Feature Selection Results\n 3.4 The Classification Results\n 4 Conclusion\n 5 Ethics and Permissions\n References\nDetails for Person Re-identification Baseline\n 1 Introduction\n 2 Approach\n 2.1 ResNet\n 2.2 From ResNet-50 to the Baseline\n 2.3 Loss Function\n 3 Experiments\n 3.1 Experiment Settings\n 3.2 Performance Evaluation\n 4 Conclusion\n References\nAn Ontology for Decision-Making Support in Air Traffic Management\n 1 Introduction\n 2 Related Work\n 2.1 Ontologies\n 2.2 Ontologies for Decision-Making in Air Traffic Management\n 3 Spatio-Temporal Ontology Model\n 3.1 Temporal Ontology\n 3.2 Spatial Ontology\n 4 Ontological Models of 4D Trajectory and Thunderstorm\n 4.1 Ontological Model of 4D Trajectories\n 4.2 Ontological Model of Thunderstorms\n 5 Reasoning with Ontology\n 6 Case Study\n 7 Conclusion\n References\nFlight Conflict Detection and Resolution Based on Digital Grid\n Abstract\n 1 1 Introduction\n 2 2 Mathematical Model\n 2.1 Spatial Grid Model\n 2.2 Aircraft Model\n 3 3 Conflict Detection and Solution Algorithm\n 3.1 Conflict Detection\n 3.2 Conflict Resolution\n 4 4 Simulation Verification\n 5 5 Conclusions\n References\nAbnormality Diagnosis of Musculoskeletal Radiographs Combined with Shallow Texture Features\n Abstract\n 1 1 Introduction\n 2 2 Musculoskeletal Abnormality Diagnosis Based on Densenet\n 2.1 Preprocessing\n 2.2 DenseNet Neural Networks\n 2.3 Merge Texture Features\n 2.3.1 Extract the LBP Texture Features of the Image\n 2.3.2 Merging LBP Features\n 3 3 The Experiment\n 3.1 Experimental Dataset\n 3.2 The Experiment\n 3.2.1 Parameter Settings\n 3.2.2 The Experimental Setup\n 3.3 The Experimental Results\n 4 4 Conclusion\n Acknowledgements\n References\nAn ATM Knowledge Graph-Based Method of Dynamic Information Fusion and Correlation Analysis\n 1 Introduction\n 2 Problem Definition\n 3 ATM Knowledge Graph-Based Fusion and Correlation Analysis Method\n 3.1 ATM Knowledge Graph-Based Fusion Method\n 3.2 Breadth-First and Depth-First Search-Based Correlation Analysis Method\n 4 Experimental Results\n 5 Conclusion\n References\nResearch of Lung Cancer-Assisted Diagnosis Algorithm Based on Multi-scale Convolution Kernel Network\n Abstract\n 1 1 Introduction\n 2 2 Materials and Methods\n 2.1 LIDC-IDRI\n 2.2 Machine Learning in CAD\n 2.3 Deep Learning in CAD\n 3 3 Results and Discussion\n 3.1 Results\n 3.2 Discussion\n Acknowledgements\n References\nDesign for Interference Suppression Intelligent Controller Based on Minimax Theory\n Abstract\n 1 1 Introduction\n 2 2 Preliminary Knowledge\n 2.1 Pseudo-generalized Hamilton\n 2.2 Interference Suppression Based on Minimax Theory\n 3 3 The Design of Multi-machine Minimax Interference Suppression Controller\n 4 4 Simulation Results\n 5 5 Conclusion\n References\nResearch on System Architecture Based on Deep Learning Convolutional Neural Network\n Abstract\n 1 1 Introduction\n 2 2 The Composition and Principle of Neural Network\n 3 3 CNN Model\n 4 4 Conclusion\n References\nResearch on the Development and Application of Unmanned Aerial Vehicles\n Abstract\n 1 1 Introduction\n 2 2 Development of UAVs\n 3 3 The Advantages of UAVs\n 4 4 UAV Formation\n 5 5 Conclusions\n References\nRecent Development of Commercial Satellite Communications Systems\n Abstract\n 1 1 Introduction\n 2 2 Typical Commercial Communication Satellite Systems\n 2.1 Iridium-NEXT\n 2.2 LeoSat\n 2.3 OneWeb\n 2.4 StarLink\n 2.5 O3b\n 2.6 Kuiper\n 3 3 Development Prospect\n 4 4 Conclusions\n References\nFeature-Aware Adaptive Denoiser-Selection for Compressed Image Reconstruction\n 1 Introduction\n 2 System Model of CS Image Reconstruction\n 3 Denoiser-Based CS Image Reconstruction\n 3.1 The D-AMP Framework\n 3.2 Typical Denoiser and Their Results\n 4 The Proposed Adaptive Denoiser-Selection Strategy\n 5 Results and Discussion\n 6 Conclusion\n References\nAI-Assisted Complex Wireless Network Evaluation Using Dynamic Ranking Scheme\n Abstract\n 1 1 Introduction\n 2 2 System Model\n 3 3 Generation of Deep Learning Network for Ranking\n 4 4 Simulation Analysis\n 5 5 Conclusion\n Acknowledgements\n References\nRe-sculpturing Semantic Web of Things as a Strategy for Internet of Things’ Intrinsic Contradiction\n Abstract\n 1 1 Introduction\n 2 2 Extension of SWoT Model\n 3 3 Conceptual Model of SWoT\n 4 4 Reasoning Model of Dynamic Relationships Between Entities\n 5 5 Calling Mechanism of CO\n 6 6 Calculations of Inter-Agents Dynamic Relationships in SWoT\n 7 7 Conclusions\n Acknowledgements\n References\nThe Precise Location Method for Initial Target Based on Fused Information\n Abstract\n 1 1 Introduction\n 2 2 Object-like Detection-Based Edge Feature\n 3 3 Initial Tracking Target Precise Location Method Based on Multiple Information Fusion Probability Distribution\n 3.1 The Object-Like Probability Distribution Based Edge Feature\n 3.2 The Saliency Probability Distribution Based on Spectral Residual\n 3.3 The Priori Probability Distribution Based on Detection Result\n 4 4 Diagram of Precise Location Method for Initial Target Based on Fused Information\n 5 5 Experimental Results and Analysis\n 5.1 Validity Experiment of Precise Location Method\n 5.2 Robustness Experiment Precise Location Method\n 6 6 Conclusion\n Acknowledgements\n References\nCMnet: A Compact Model for License Plate Detection\n Abstract\n 1 1 Introduction\n 2 2 A Compact Model for LP Detection\n 3 3 Visualization with a DeconvNet\n 4 4 Experiments\n 4.1 Training\n 4.2 Detection\n 4.2.1 Accuracy Evaluation of Comparison Model\n 4.2.2 Speed Evaluation of Comparison Model\n 4.2.3 Accuracy Evaluation of CMnet After Model Optimization\n 5 5 Conclusions\n Acknowledgements\n References\nParallelization of Preprocessing for Automatic Detection of Pulmonary Nodules in 3D CT Images\n Abstract\n 1 1 Introduction\n 2 2 Parallelization Scheme Design\n 2.1 Static Grouping-Based Parallelization Scheme\n 2.2 Pipeline-Based Parallelization Scheme\n 2.3 Bus-Based Parallelization Scheme\n 3 3 Experiments\n 4 4 Conclusion\n References\nPredicting the Trajectory Tracking Control of Unmanned Surface Vehicle Based on Deep Learning\n Abstract\n 1 1 Introduction\n 2 2 Background\n 2.1 The MOOS-IvP Architecture\n 2.2 Waypoint Behavior\n 3 3 Deep Learning for Waypoint Behavior of USV\n 3.1 Feature Selection\n 3.2 Dataset Generation\n 3.3 Fully Connected Network Architecture\n 3.4 Training\n 4 4 Results\n 5 5 Conclusions\n References\nCoordinated Learning for Lane Changing Based on Coordination Graph and Reinforcement Learning\n Abstract\n 1 1 Introduction\n 2 2 Related Work\n 3 3 Related Theories and Approaches\n 3.1 MDP Formalization for Autonomous Driving\n 3.2 Dynamic Coordination Graph for Multi-vehicle\n 3.3 Pretraining of Local Cooperative Control Model\n 3.4 The Coordinated Learning Mechanisms\n 4 4 Experimental Evaluation\n 5 5 Conclusions\n Acknowledgements\n References\nPretreatment Method of Deep Learning Method for Target Detection in Sea Clutter\n Abstract\n 1 1 Introduction\n 2 2 Pretreatment Mode\n 2.1 One-Dimensional Form\n 2.1.1 Direct Input\n 2.1.2 Input After Pretreatment\n 2.2 Two-Dimensional Form\n 2.2.1 Input After Direct Interception\n 2.2.2 Input After Transform to Two-Dimensional\n 3 3 Summary\n References\nA Novel Endmember Extraction Method Based on Manifold Dimensionality Reduction\n Abstract\n 1 1 Introduction\n 2 2 Local Tangent Space Arrangement Algorithm\n 3 3 Models and Methods\n 4 4 Experimental Results\n 4.1 AVIRIS Cuprite Data\n 4.2 AVIRIS Indian Pine Data\n 5 5 Conclusion\n References\nResearch on Chinese Short Text Clustering Ensemble via Convolutional Neural Networks\n Abstract\n 1 1 Introduction\n 2 2 Vectorization of Short Text\n 2.1 Pre-processing Short Texts\n 2.2 Generating Word Vectors\n 2.3 Extracting Feature Vectors\n 3 3 Clustering Ensemble of Short Text\n 3.1 Selecting Base Clustering\n 3.2 Clustering Ensemble\n 3.2.1 Gini Coefficient\n 3.2.2 Formulation of the Ensemble Clustering Problem\n 3.2.3 Reliability of Clusters\n 3.2.4 Consensus Functions\n 4 4 Conclusion\n References\nA Survey of Moving Objects kNN Query in Road Network Environment\n Abstract\n 1 1 Introduction\n 2 2 Problem Description\n 3 3 Existing Solutions and Existing Problems\n 4 4 Outlook\n Acknowledgements\n References\nThe Importance of Researching and Developing the Semantic Web of Things\n 1 The Inherent Contradiction of the Internet of Things and Its Countermeasures\n 2 Research Trends of Intelligent Internet of Things\n 3 Various Branches of the Intelligent Internet of Things\n 4 Semantic Web of Things—Intelligent Reasoning and Smart Service Networking\n 5 Conclusion\n References\nFacial Expression Recognition Based on Strengthened Deep Belief Network with Eye Movements Information\n 1 Introduction\n 2 Methodology\n 2.1 Brief in SDBN Framework\n 2.2 The Construction of the SDBN Framework\n 2.3 Eye Movements Data Labels\n 2.4 Top-Down Supervised Fine-Tuning Process\n 3 Experiments\n 4 Conclusion\n References\nReal Image Reproduction Algorithm Based on Sigmoid-iCAM Color Model\n Abstract\n 1 1 Introduction\n 2 2 Models and Methods\n 3 3 Experimental Results\n 4 4 Conclusion\n References\nGame Traffic Classification Based on DNS Domain Name Resolution\n 1 Introduction\n 2 Related Work\n 2.1 Network Traffic Classification\n 2.2 Network Game Traffic\n 3 Game Traffic Classification Based on DNS Domain Name Resolution\n 3.1 Network Game Traffic Data Set Construction Based on DNS Domain Name Resolution\n 3.2 Classifier\n 3.3 Classifier Training\n 4 Experimental Results and Analysis\n 4.1 Experimental Results\n 4.2 Analysis of Results\n 5 Conclusion\n References