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International Conference on Systems and Informatics (ICSAI)

作者： Chunxiao Han Jingyan Zhang Han Wu Tianjin Key Laboratory of Information Sensing & Intelligent Control School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

Accidental falls are the major causes of injury among elderly people living alone. However, traditional fall detection algorithms usually have missed or false detections problems. In this paper, we design a fall detection system based on YOLO algorithm and MobileNetV2 model. First, detection frame coordinates and joint coordinates of the human body are fused as feature information, and background information is filtered out. A lightweight deep learning model, MobileNetV2, is then used for classification. Compared with commonly used lightweight classification models such as ShuffleNet and EfficientNet, the MobileNetV2 model performs better in recognizing fall behavior. Our system can effectively distinguish fall behavior from daily activities, meeting the requirements for fall detection.

关键词： YOLO Deep learning Filtering algorithms Information filters Feature extraction Classification algorithms Fall detection Older adults Informatics Injuries

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Neural-Fuzzy-Based Adaptive Sliding Mode Automatic Steering control of Vision-based Unmanned Electric Vehicles

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Chinese Journal of Mechanical engineering 2021年第5期34卷 56-68页

作者： Jinghua Guo Keqiang Li Jingjing Fan Yugong Luo Jingyao Wang School of Aerospace Engineering Xiamen UniversityXiamen 361005China State Key Laboratory of Automotive Safety and Energy Tsinghua UniversityBeijing 100084China School of Electrical and Control Engineering North China University of TechnologyBeijing 100144China

This paper presents a novel neural-fuzzy-based adaptive sliding mode automatic steering control strategy to improve the driving performance of vision-based unmanned electric vehicles with time-varying and uncertain ***,the kinematic and dynamic models which accurately express the steering behaviors of vehicles are constructed,and in which the relationship between the look-ahead time and vehicle velocity is ***,in order to overcome the external disturbances,parametric uncertainties and time-varying features of vehicles,a neural-fuzzy-based adaptive sliding mode automatic steering controller is proposed to supervise the lateral dynamic behavior of unmanned electric vehicles,which includes an equivalent control law and an adaptive variable structure control *** this novel automatic steering control system of vehicles,a neural network system is utilized for approximating the switching control gain of variable structure control law,and a fuzzy inference system is presented to adjust the thickness of boundary layer in *** stability of closed-loop neural-fuzzy-based adaptive sliding mode automatic steering control system is proven using the Lyapunov ***,the results illustrate that the presented control scheme has the excellent properties in term of error convergence and robustness.

关键词： Vision-based unmanned electric vehicles Automatic steering Neural-fuzzy adaptive sliding control Vehicle lateral dynamics

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Improved Frequency Domain Modeling of Dynamic Heat Flow for DHS Energy Flow Calculation

Improved Frequency Domain Modeling of Dynamic Heat Flow for ...

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Green Energy and Power Systems (ICGEPS), International Conference on

作者： Xiaolong Peng Yixun Xue Peinan Fan Ke Wang Xinyue Chang Key Laboratory of Cleaner Intelligent Control on Coal & Electricity Ministry of Education Taiyuan China College of Electrical and Power Engineering Energy Internet Key Laboratory of Shanxi Province Taiyuan University of Technology Taiyuan China

ISBN: (数字)9798331510329

ISBN: (纸本)9798331510336

With the extensive interconnection and deep coupling of electricity and diversified energy carriers, traditional energy networks are increasingly evolving into integrated energy systems. While energy flow modeling and power flow calculation, as foundational aspects of energy system analysis, have established mature computational models and methods across various energy sectors, current approaches relying on time-domain nodal methods and finite difference schemes lack unified modeling strategies. This limitation introduces excessive spatiotemporal discretization elements during practical computations, resulting in compromised computational efficiency and potential inaccuracies in power flow solutions. This paper proposes a frequency-domain modeling framework for district heating systems (DHS) that refines the existing energy circuit model (ECM) theory, adaptable to both constant-flow and variable-flow operational scenarios. Derived from mass conservation principles, the methodology incorporates scaling transformations for fluid element dynamics and thermal loss corrections. The developed model achieves high precision while significantly enhancing computational performance, demonstrating particular advantages in handling complex DHS network analyses.

关键词： Adaptation models Analytical models Frequency-domain analysis Computational modeling Power system dynamics DH-HEMTs Dynamic scheduling Mathematical models Numerical models Integrated circuit modeling

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Real-Time Routing Design for LEO Satellite Networks: An Enhanced Multi-Agent DRL Approach

Real-Time Routing Design for LEO Satellite Networks: An Enha...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Hailong Liao Xian Zhang Jiaen Zhou Xuehua Li Key Laboratory of Information and Communication Systems Ministry of Information Industry and the Key Laboratory of Modern Measurement & Control Technology School of Information and Communication Engineering Beijing Information Science and Technology University Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Low Earth Orbit (LEO) satellite networks have been heralded as a promising solution for provisioning space-based Internet services. However, the inherent dynamic topology, characterized by frequent variations in inter-satellite link states, causes pronounced fluctuations in latency and exacerbated packet loss rates. To address the challenge, in this paper, we formulate a routing design and optimization problem to maximize the average throughput with considering the end-to-end latency constraint and the real-time routing demand in LEO satellite networks. To solve this problem, we design an enhanced Multi-Agent Deep Reinforcement Learning (MADRL) algorithm, which encapsulates the intricate interplay among LEO satellite agents and their environs within a Real-Time Markov Decision Process (RTMDP) framework, and introduces the self-attention mech-anism to endow agents with a better grasp of environmental state dynamics. Simulation results demonstrate that our proposed routing design outperforms the benchmark schemes in terms of both end-to-end latency and network average throughput.

关键词： Satellites Network topology Simulation Heuristic algorithms Low earth orbit satellites Routing Throughput Real-time systems Topology Optimization

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Analysis of Bamboo Resource Survey Application Based on Remote Sensing Images 6

Analysis of Bamboo Resource Survey Application Based on Remo...

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6th International Conference on Computer Information Science and Application technology, CISAT 2023

作者： Liu, Xiaohong Yan, Kai Sun, Maosheng College of Big Data and Intelligent Engineering Southwest Forestry University Kunming650000 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Forestry Southwest Forestry University Kunming650224 China

ISBN: (纸本)9781510668546

Understanding the distribution range of bamboo resources in Yiliang county is important for the ecological services and economic benefits of the county. In this paper, we investigated the distribution of bamboo resources by using 3S (GPS, RS, GIS) technology with remote sensing images and field survey. The survey results show that: the total area of bamboo forest in the county is 65863.72×10-2 km2;the origin of bamboo resources is mainly plantation and natural forest;the types of bamboo forest are pure bamboo forest, tree forest and mixed bamboo forest;the applications are mainly nature reserve bamboo forest, protective bamboo forest, timber bamboo forest and bamboo forest for bamboo shoots;There are 9 kinds of bamboo species, including Jinfo Mountain Fang, Xiaochaoba Fang, Yushan Bamboo, Cixi Bamboo, Mao Bamboo, Bambusa Bamboo, Shui Bamboo, Yongsan Fang and Siban Bamboo;the protection level is Ⅰ, Ⅱ, Ⅲ and Ⅳ respectively. © 2023 SPIE.

关键词： Bamboo

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Optimized Complex Convolutional Neural Network for Steady-State Visual Evoked Potentials Signal classification

Optimized Complex Convolutional Neural Network for Steady-St...

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System Simulation technology and its Application (CCSSTA), IEEE China Conference on

作者： Qingbin Li Chunxiao Han Yanli Liu Junxiu Liu Tianjin Key Laboratory of Information Sensing & Intelligent Control School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

The steady-state visual evoked potential (SSVEP) is one of the most commonly used paradigms in the BCI system, which produces a significant response to visual stimuli of a specific frequency. How to better improve the classification performance of SSVEP signals is currently the main task. In this paper, a modified C-CNN-SVM model of EEG signal is proposed, aiming to enhance the precision of SSVEP signal classification. Firstly, spectral features are obtained from the SSVEP signal using fast fourier transform (FFT) as input to the model. FFT obtains 128 data lengths of the SSVEP signal, including 64 real part data and 64 complex part data. Then, CNN is used to further extract features, and finally, support vector machine (SVM) is used for classification. The experimental results show that the modified C-CNN-SVM model achieves an average accuracy of 93.86%, which is better than the traditional CCA, FBCCA and TRCA algorithms.

关键词： Support vector machines Visualization Fast Fourier transforms Pattern classification Brain modeling Feature extraction Electroencephalography Classification algorithms Steady-state Convolutional neural networks

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A DRL-enabled Adaptive Sparsity Promoting Wide-area Damping controller for Power System with Time-delay and Packet Dropout

A DRL-enabled Adaptive Sparsity Promoting Wide-area Damping ...

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2023 Panda Forum on Power and Energy, PandaFPE 2023

作者： Cao, Di Zhang, Guozhou Hu, Jiaxiang Hu, Weihao Huang, Yuehui Chen, Zhe State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Chengdu China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Aalborg University Department of Energy Technology Aalborg Denmark

ISBN: (纸本)9798350321173

This paper proposes a deep reinforcement learning (DRL) based adaptive sparsity promoting wide-area damping controller for power system. Different from traditional fixed wide-area control structure, the proposed method employs DRL algorithm to identify the optimal sparsity promoting control structure and obtain corresponding parameter settings under different operating conditions considering the time-delay and packet drop. Simulation are carried out on 10-machine 39-bus system to demonstrate the effectiveness of the proposed approach. © 2023 IEEE.

关键词： Time delay

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Design of a Capsule Robot System for Gastric Hemorrhage Detection using Luminol 20

Design of a Capsule Robot System for Gastric Hemorrhage Dete...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Mao, Yuchen Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Application Complicated Systems Intelligent Robot Laboratory School of Electrical Engineering and Automation Binshui Xidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5 Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

Gastric hemorrhage in upper gastrointestinal bleeding is an obvious indicator of deteriorating gastric disease. This paper proposes a capsule robot system for detecting bleeding in the stomach. The patient ingests a capsule robot containing luminol reagent, which detects bleeding in the stomach via chemiluminescence. The MCU receives the optical sensor signal, and the data is transmitted via radio frequency. The external lower computer receives and forwards the data to the upper computer for processing and display. The data is used to determine the presence and amount of stomach bleeding. Chemiluminescence is an excellent sensor, requiring no external energy supply, and demonstrating excellent sensitivity and selectivity. Combining chemical and electronic sensors presents opportunities for further development. Capsule robot detection reduces the discomfort and risk of secondary injury associated with traditional endoscopy. This paper establishes an experimental platform using porcine blood to simulate human blood, demonstrating the feasibility of the system. © 2023 IEEE.

关键词： Chemiluminescence

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Resilience-oriented critical load restoration for railway distribution systems in remote areas by coordination of photovoltaics and energy storage systems 3

Resilience-oriented critical load restoration for railway di...

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2024 3rd International Conference on Green Energy and Power Systems, ICGEPS 2024

作者： Wang, Ying Liu, Weizhi Ma, Yingtao School of Electrical Engineering Beijing Jiaotong University Beijing100044 China Beijing Zongheng Electro-mechanical Technology Co. Ltd. State Key Laboratory for Traction and Control System EMU and Locomotive China Academy of Railway Sciences Corporation Limited Beijing100081 China

The utility power infrastructure is relatively weak and susceptible to extreme conditions in remote areas, so the resilience enhancement of the railway distribution system is essential to ensure the service to critical loads, such as communication and signal devices, to prevent undesirable crash events. After extreme events, distributed resources such as distributed photovoltaics (PVs) and energy storage systems (ESSs) can be coordinated to restore critical loads as soon as possible. In this paper, a critical load restoration method for railway distribution systems in remote areas is proposed. The PVs and ESSs are coordinated to restore the critical loads distributed along the railway line. The problem is formulated as a mixed integer linear program and solved by off-the-shelf solvers. Finally, the effectiveness of the method is validated through a real-world case in China. © Published under licence by IOP Publishing Ltd.

关键词： Energy storage

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Effects of particle size and oil-immersed concentration on the dust layer minimum ignition temperature and combustion characteristics of oil-immersed coal

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Journal of Thermal Analysis and Calorimetry 2024年第18期149卷 10297-10309页

作者： Luo, Zhenmin Yang, Yong Ding, Xuhan Luo, Chuanxu Zhang, Fan Zhang, Man Shu, Chi-Min School of Safety Science and Engineering Xi’an University of Science and Technology 58 Yanta Mid. Rd. Shaanxi Xi’an710054 China Shaanxi Key Laboratory of Prevention and Control of Coal Fire 58 Yanta Mid. Rd. Shaanxi Xi’an710054 China Shaanxi Engineering Research Centre for Industrial Process Safety and Emergency Rescue 58 Yanta Mid. Rd. Shaanxi Xi’an710054 China Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology 123 University Rd. Sec. 3 Yunlin Douliou64002 Taiwan

This paper studied the minimum ignition temperature of the dust layer (MITL) of oil-immersed coal and the heat effect and apparent activation energy (Ea) of raw coal and oil-immersed coal. Based upon the results, the addition of crude oil to coal could reduce the MITL, ameliorate the ignition sensitivity, and increase the heat effect of coal dust. The average Ea of oil-immersed coal was lower than that of raw coal. When there was a 10.8 mass% oil-immersed concentration of oil-immersed coal with the most acceptable particle size (≤ 75 μm), the MITL was 24.24% lower than that of raw coal. Furthermore, the exothermic value of oil-immersed coal was 26.67% higher than that of raw coal. Besides, the average Ea of oil-immersed coal was 26.8% lower than that of raw coal. The research herein is expected to improve people’s understanding of the thermal hazard characteristics of oil-immersed coal dust, reduce the risk of coal dust fires and explosions, and ensure production safety. Graphical Abstract: (Figure presented.) © Akadémiai Kiadó, Budapest, Hungary 2024.

关键词： Coal

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