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Chinese control Conference (CCC)

作者： Yilin Yang Qinghua Zhu Qinglei Hu Xin Wen School of Automation Science and Electrical Engineering Beihang University Beijing China Shanghai Aerospace Control Technology Institute China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In order to maintain national maritime security, it is particularly important to master the navigation rules of ships on important maritime military routes. Traditional trajectory prediction methods cannot flexibly implement the function of autonomous re-prediction based on emergencies. This paper considers the large amount of trajectory data of maritime moving targets and the existence of multi-dimensional factors and presents a high-precision track prediction model under multi-dimensional factors. This algorithm uses the Douglas-Peucker algorithm to compress the track data and uses the DBSCAN(Density-Based Spatial Clustering of Applications with Noise) algorithm for trajectory clustering and predicts the trajectory through the LRCN(Long-term Recurrent Convolutional Networks) prediction model. Simulation results show that using this algorithm greatly reduces the amount of calculations and can achieve a high-precision prediction; Under emergency cases, the algorithm can achieve autonomous re-prediction with high accuracy.

关键词： Trajectory Clustering algorithms Marine vehicles Target tracking Image coding Predictive models Data models

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Mid-infrared-induced Ultrafast Heating for Detecting Alcoholic Solutions

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iScience 2025年

作者： Haishun Liu Zhi Zhu Xiaojiao Deng Xiaoping Zheng Lin Liu Department of Automation Tsinghua University Beijing100084 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 China Beijing Aerospace Institute for Metrology and Measurement Technology Beijing 100076 China

The detection of alcoholic solutions is crucial for safety and industrial applications. This work investigates the ultrafast heating effects of methanol and ethanol aqueous solutions at varying mass fractions triggered by mid-infrared (MI) pulses, using molecular dynamics simulations. The results show that the heating effect is negatively correlated with alcohol concentration at frequencies ranging from 0.1 to 30 THz and from 45 to 50 THz. In contrast, at a frequency of approximately 40 THz, the heating effect is positive correlated with alcohol concentration. These heating effects in response to MI occur on a sub-picosecond timescale. The mechanism is attributed to the resonance effect of solutes and solvents with MI pulses observed across different frequency bands. These findings provide a foundation for enhancing the sensitivity and specificity of MI pulse-based detection of alcoholic aqueous solutions.

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Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management

arXiv

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arXiv 2022年

作者： Li, Yuanzheng He, Shangyang Li, Yang Shi, Yang Zeng, Zhigang School of Artificial Intelligence and Automation Key Laboratory on Image Information Processing and Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan430074 China China-Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China The Department of Mechanical Engineering University of Victoria VictoriaBC V8P 5C2 Canada

The utilization of large-scale distributed renewable energy promotes the development of the multi-microgrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy-sufficiency. The multi-agent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this paper tackles this practical yet challenging issue by proposing a federated multi-agent deep reinforcement learning (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy-sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data is not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge national laboratory distributed energy control communication lab microgrid (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Half-bridge Strategy Based Fault-tolerant control for BLDCM under Open Circuit Fault

A Half-bridge Strategy Based Fault-tolerant Control for BLDC...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Shuai Wang Xinhua Zhang Guansong Xiong Jun Wang Zijun Zhang Xingjian Wang Servo Engineering Center Beijing Institute of Automatic Control Equipment Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

For open circuit fault of permanent magnet brushless DC motor(BLDCM) with trapezoidal back EMF, this paper proposes a half-bridge strategy based fault-tolerant control(FTC) method: by utilizing the current sensors to detect and locate the one-phase open circuit fault, adding a half bridge to the traditional full bridge drive circuit, and connecting it to the neutral point of the motor, so that the BLDCM can realize the fault-tolerant function under open circuit fault condition. The concrete content of this paper are as follows: First, the halfbridge based FTC schematic and theoretical analysis is introduced, then the BLDCM control model under normal and fault conditions are established in Simulink module for the analysis and comparison work, finally experiment work are performed to verify the effectiveness of the proposed control method under open circuit fault condition.

关键词： Fault tolerance Brushless DC motors Fault tolerant systems Permanent magnet motors Circuit faults Mathematical model Integrated circuit modeling

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Research on improved speech recognition based on whale algorithm isolated words

Research on improved speech recognition based on whale algor...

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2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020

作者： Chen, Zhikang Liu, Jia Wang, Dandan Zhang, Yunxi School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin300222 China Qingdao Technicians College Qingdao Shandong266229 China

ISBN: (纸本)9781728152240

Speech recognition is an internationally recognized and promising new technology. This paper studies the improved speech recognition based on whale algorithm isolated words. Firstly, the basic whale algorithm is introduced. U sing standard and improved whale algorithms, the BP neural network is optimized. Then the data are trained and learned respectively, and the recognition rate is compared. Finally the more suitable method for speech recognition of isolated words is determined. The self-built database experiments show that compared with the standard algorithm to optimize the BP neural network, the improved algorithm is helpful to improve the accuracy of speech recognition. © 2020 IEEE.

关键词： Genetic algorithms

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Random Forest Algorithm-based Multi-Feature Vector Optimization for Fatigue Driving Vigilance Monitoring

Random Forest Algorithm-based Multi-Feature Vector Optimizat...

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第32届中国控制与决策会议

作者： Fengjuan Guo Chunxiao Han Ziyu Hu Yaru Yang Mihong Yang Tinghang Guo Jingyu Zhang Tianjin Key Laboratory of Information Sensing and Intelligent Control School of Automation and Electrical Engineering Tianjin University of Technology and Education

Accidents caused by fatigue account for a large proportion of traffic,so it has great significance in preventing fatigue driving by monitoring the vigilance levels of *** order to monitor the changes of vigilance,an experiment was designed to collect the electroencephalogram(EEG) signals of drivers at different vigilance levels,and further to find out the major *** commonly used features,EEG sub-bands and EEG channels were used,and the combination of which can not only improve the diversity of vigilance feature vectors,but also improve the classification accuracy of vigilance ***,too many features in the feature vector will result in a large amount of ***,this paper used the decision tree(DT) and random forest(RF) algorithm to optimize feature vector without affecting the classification accuracy of vigilance *** was used to establish the model with all feature vectors,then by comparing the weight of features,EEG sub-bands and EEG channels,the optimized feature vector FP1-FP2/d4-d5-d6/CV was *** was used to calculate the classification accuracy with the optimized multi-feature vector to ensure the high monitoring accuracy of the changes of vigilance.

关键词： Vigilance Decision Tree Random Forest Driving Fatigue EEG

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Random Forest Algorithm-based Multi-Feature Vector Optimization for Fatigue Driving Vigilance Monitoring

Random Forest Algorithm-based Multi-Feature Vector Optimizat...

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Chinese control and Decision Conference, CCDC

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

Accidents caused by fatigue account for a large proportion of traffic, so it has great significance in preventing fatigue driving by monitoring the vigilance levels of drivers. In order to monitor the changes of vigilance, an experiment was designed to collect the electroencephalogram (EEG) signals of drivers at different vigilance levels, and further to find out the major factors. Several commonly used features, EEG sub-bands and EEG channels were used, and the combination of which can not only improve the diversity of vigilance feature vectors, but also improve the classification accuracy of vigilance monitoring. However, too many features in the feature vector will result in a large amount of calculations. Therefore, this paper used the decision tree (DT) and random forest (RF) algorithm to optimize feature vector without affecting the classification accuracy of vigilance monitoring. DT was used to establish the model with all feature vectors, then by comparing the weight of features, EEG sub-bands and EEG channels, the optimized feature vector FP1-FP2/d4-d5-d6/CV was obtained. RF was used to calculate the classification accuracy with the optimized multi-feature vector to ensure the high monitoring accuracy of the changes of vigilance.

关键词： Electroencephalography Monitoring Radio frequency Classification algorithms Fatigue Feature extraction Time factors

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Content in motion: An edge computing based relay scheme for content dissemination in urban vehicular networks

Content in motion: An edge computing based relay scheme for ...

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作者： Hui, Yilong Su, Zhou Luan, Tom H. Cai, Jun School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Cyber Engineering Xidian University Xi'an China Department of Electrical and Computer Engineering University of Manitoba Winnipeg Canada

Content dissemination, in particular, small-volume localized content dissemination, represents a killer application in vehicular networks, such as advertising distribution and road traffic alerts. The dissemination of contents in vehicular networks typically relies on the roadside infrastructure and moving vehicles to relay and propagate contents. Due to instinct challenges posed by the features of vehicles (mobility, selfishness, and routes) and limited communication ability of infrastructures, to efficiently motivate vehicles to join in the content dissemination process and appropriately select the relay vehicles to satisfy different transmission requirements is a challenging task. This paper develops a novel edge-computing-based content dissemination framework to address the issue, composed of two phases. In the first phase, the contents are uploaded to an edge computing device (ECD), which is an edge caching and communication infrastructure deployed by the content provider. By jointly considering the selfishness and the transmission capability of vehicles, a two-stage relay selection algorithm is designed to help the ECD selectively deliver the content through vehicle-To-infrastructure (V2I) communications to satisfy its requirements. In the second phase, the vehicles selected by the ECD relay the content to the vehicles that are interested in the content during the trip to destinations via vehicle-To-vehicle (V2V) communications, where the efficiency of content delivery is analyzed according to the probability that vehicles encounter on the path. Using extensive simulations, we show that our framework disseminates contents to vehicles more efficiently and brings more payoffs to the content provider than the conventional methods. © 2000-2011 IEEE.

关键词： Edge computing

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A Family of Hybrid Step-up DC-DC Converters based on Switched-capacitor Converters

A Family of Hybrid Step-up DC-DC Converters based on Switche...

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International Conference on Power Electronics and Motion control (IPEMC)

作者： Shouxiang Li Zhenning Li Wei Shang Shuhua Zheng Pengyu Jia School of Automation Beijing Institute of Technology Beijing China School of Electrical and Control Engineering North China University of Technology Beijing China

ISBN: (数字)9781728153018

ISBN: (纸本)9781728153025

Five common step-up switched-capacitor converter (SCC) topologies - Ladder, Dickson, Fibonacci, Series-parallel, Voltage Doubler- have limited voltage regulation ranges, and the maximum voltage gains are predetermined by the circuit configurations. In this paper, one active switch in the five common SCCs is replaced by a filter inductor to form a family of hybrid step-up DC-DC converters. Compared with the traditional SCCs, the new hybrid converters feature wide voltage regulation range and high step-up gain, which is determined by both circuit configuration and duty cycle. A hybrid converter based on 3X Ladder SCC was built and tested to verify the analysis.

关键词： Switches DC-DC power converters Hybrid power systems Power electronics Topology Voltage control Switching circuits

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Adaptive control for Stochastic Nonlinear Systems With Quantized States

Adaptive Control for Stochastic Nonlinear Systems With Quant...

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第三十九届中国控制会议

作者： Mingyuan Zhao Shiqi Zheng Feng Liu Xiang Chen Yuan Sun School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems School of Electrical and Electronic Engineering The University of Adelaide

In this paper, adaptive control problem is investigated for stochastic nonlinear systems with quantized states. Compared with the existing works, the considered system is more general which contains a deterministic nonlinear term and a stochastic disturbance in each channel. In order to handle the high order nonlinearities, we present a new technique which makes the partial derivative of virtual control law become a constant, this makes the adaptive control scheme of state quantization feasible. Then, a new adaptive law is proposed to deal with some completely unknown parameters, which includes an interesting third-order term of the parameters. Finally, simulation results demonstrate the efficacy of the proposed schemes.

关键词： adaptive control backstepping state quantization stochastic system

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