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8th International Conference on Energy system, Electricity, and Power, ESEP 2023

作者： Zhuang, Xin Gao, Qiang Xu, Zhao Chen, Jiaqi Zhang, Zhengyang Liu, Junjie College of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Complex System Control Theory and Applications Tianjin300384 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

The Rural Hybrid Renewable Energy system (RHRES) holds great significance as a prospective energy system development. Ensuring its economic viability and low-carbon operation is crucial in order to fully harness its potential. Consequently, this research paper presents a multi-party cooperation operation model for RHRES, based on the Nash bargaining theory. To effectively differentiate between the various parties involved in carbon trading and guarantee the system's low-carbon operation, a ladder-type multi-party carbon trading model is formulated. The proposed RHRES cooperation operation model, grounded in Nash bargaining, is approached in two steps: determining the transaction volume between parties and resolving the transaction price through the concept of equivalent transformation. To address concerns regarding party privacy, the alternating direction multiplier method is employed for solution. The numerical findings demonstrate that the suggested cooperation operation model substantially reduces RHRES operating costs and carbon emissions, while enhancing the utilization of wind and solar power. Consequently, this model holds significant practical and promotional value. © 2024 SPIE.

关键词： Solar energy

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Efficient temporal alignment network for smoke detection

Efficient temporal alignment network for smoke detection

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2023 Jiangsu Annual Conference on Automation, JACA 2023

作者： Li, Weitai Lu, Xiaobo Cao, Yichao School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9781839539848

Smoke is a distinctive feature of pre-fire combustion and it is critical to prevent fires by detecting smoke. Previous work has demonstrated that deep learning methods have a wide range of applications in the field of smoke detection. However, existing methods still have some problems, i.e., it is difficult to balance the relationship between accuracy and speed. In order to solve the existing problems, we proposed Efficient Temporal Alignment Network (ETAN) for Video Smoke Detection. Feature Aggregation Module (FAM) was designed to extract the dynamic features between frames, and we proposed Recursive Memory Feature Update (RMFU) module to maintain the features of past frames and fuse the current features to boost detection performance. Finally, experiments demonstrated that the proposed method improved about 3.8 mAP compared with base detector with little increase in the number of parameters, and outperformed all the single-frame detection methods with the same amount of parameters. Compared with video smoke detection methods, the detection achieved close to the same accuracy while 6.1 times faster than multi-frame methods. © The Institution of Engineering & Technology 2023.

关键词： Smoke

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Adaptive control for Stochastic Nonlinear systems with Input Saturation Based on Multi-dimensional Taylor Network 20

Adaptive Control for Stochastic Nonlinear Systems with Input...

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

作者： Zheng, Xiao-Yi Yan, Hong-Sen Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Southeast University School of Automation Nanjing210096 China

ISBN: (纸本)9798350320831

For stochastic nonlinear systems with input saturation, few of the existing control methods use a suitable auxiliary system to solve input saturation problem, and most of them are for deterministic systems. In this study, according to the backstepping approach and the Lyapunov stability theorem, utilizing multi-dimensional Taylor network (MTN), an adaptive controller is presented to secure that all closed-loop signals keep bounded in probability, in which MTN is implemented to approach the uncertain function during the controller design. Moreover, the impact raised by input saturation is mitigated by designing an appropriate auxiliary system, in which the order of the auxiliary system is the same as that of the controlled system. Furthermore, unlike the majority of prior findings, the final output signal could be constrained in a mean quartic sense by an explicit expression of the input saturation error (the difference between the control signal and the saturation input) and the parameters used for design. Finally, the efficiency of the suggested control methodology is confirmed by providing an example. © 2023 IEEE.

关键词： Stochastic systems

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LSTM-TD3-based control for delayed drone combat strategies 19th

LSTM-TD3-based control for delayed drone combat strategies

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19th Chinese Intelligent systems Conference, CISC 2023

作者： Ji, Bingyu Wang, Jun Zhang, Hailin Zhang, Ya School of Automation Southeast University Nanjing210096 China Key Laboratory Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9789819968817

Reinforcement learning has made great achievements in the field of game confrontation, and military rendition intelligence is also imminent. In this paper, we propose a game confrontation game model based on LSTM and deep reinforcement learning for the red and blue sides of the drones in the delayed *** action buffer pair is added to the original battlefield environment to create a large latency environment, a TD3 algorithm is designed to join LSTM to predict the state trend, and a historical data pool is built using PER. The training results in the simulation platform show that the model has a significant improvement in effectiveness in the large latency environment, with a 0.22 improvement in battle damage ratio and a 34.76% improvement in win rate over the original model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Reinforcement learning

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Non-Smooth Speed control of Switched Reluctance Motor Based on Generalized Proportional Integral Observer 42

Non-Smooth Speed Control of Switched Reluctance Motor Based ...

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42nd Chinese control Conference, CCC 2023

作者： Yin, Qingzhao Tian, Zheng Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9789887581543

This paper investigates the speed regulation control of switched reluctance motor (SRM) systems. To improve the antidisturbance performance of SRM, a composite non-smooth control strategy is proposed. First, the structure of SRM is analyzed, and a simplified nonlinear model is obtained based on a segmented representation of the varying phase inductance. A virtual control function is introduced to represent the nonlinear part of the torque equation, whose inverse function is cascaded to linearized the nonlinear model. Second, a generalized proportional integral observer (GPIO) is constructed to estimate the lumped disturbance of the system, which is used for feedforward compensation design. Finally, a composite speed controller is designed based on a combination of finite time proportional feedback and feed-forward compensation based on GPIO (FTP+GPIO). The speed error closed-loop system can be regarded as a first-order finite time control system with bounded disturbances. Strict analysis shows that the proposed scheme can improve the anti-disturbance performance of the closed-loop system. The effectiveness of the proposed method is verified by simulation results. Moreover, it is compared with proportional feedback combining feed-forward compensation (P+GPIO) method and proportional integral (PI) control method. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Closed loop systems

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EPM-Net: A better way for optical and infrared scene matching via edge point 12

EPM-Net: A better way for optical and infrared scene matchin...

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SPIE 12th International Symposium on Multispectral Image Processing and Pattern Recognition, MIPPR 2023

作者： Huang, Shixiang Chi, Longyun Fu, Wenxing Tian, Jinwen National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

As a crucial auxiliary technique in inertial navigation, scene matching has been widely applied in aircraft navigation and guidance. To enhance the adaptability and efficiency of scene matching algorithms in complex environments, existing approaches have gradually shifted towards heterogeneous scene matching, such as optical/ infrared scene matching based on deep learning features, with a focus on point features. However, due to the complex and diverse nonlinear distortions between optical and infrared images, the matching accuracy often fails to meet the practical requirements of navigation and positioning. In view of this problem, an EPM-Net algorithm based on edge features is proposed to improve matching performance by extracting more stable and discriminative edge point descriptors. Experimental results on infrared datasets demonstrate that this method achieves an average matching accuracy improvement of more than four times compared to traditional and existing deep learning methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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Person Re-identification Based on Dual-channel Attention and Local Feature Fusion

Person Re-identification Based on Dual-channel Attention and...

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2022 International Conference on Electrical, control and Information Technology, ECITech 2022

作者： Ning, Xinkuang Shao, Jiayu Lin, Wenjie Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9783800759170

Aiming at improving low accuracy of existing person re-identification algorithms in dealing with low image resolution, different illumination, diverse poses, and viewing angles, this paper proposes a person re-id algorithm based on dual-channel attention and local feature fusion. After ResNet50, it is followed by the spatial attention and the channel attention to extract the global features. Then the feature map segmentation is performed to extract the local features, and a fusion feature with strong judgment is obtained. In addition, this paper adds the central loss constraint item to the triplet hard loss. The triplet_center loss joint cross-entropy loss function further to improve adaptability of the model in complex scenarios. The proposed method is compared and tested on the CUHK03, DukeMTMC-reID and Market-1501 data sets, and the accuracy of Rank-1 reached 96.1%, 89.8% and 79.8%. This algorithm makes full use of the features of different depths and the key information of interest. The module has a solid discriminative ability. Also the accuracy of mean average precision of the proposed method is better than other advanced algorithms. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Image resolution

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Design of GPR Transmitting Pulse Signal Based on FPGA 41

Design of GPR Transmitting Pulse Signal Based on FPGA

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第41届中国控制会议

作者： Guangjun Wang Yangzheng Zhang Yi Zhao Zhi Wang School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex System

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Aiming at the non-contact detection scene of GPR,it is necessary to adjust the repetition frequency of transmitting pulse waveform flexibly,and a GPR pulse signal design based on FPGA is *** scheme uses FPGA to control the generation of adjustable pulse signals,which are amplified by high-frequency power amplifier circuit,and the signal repetition frequency can be adjusted by *** theory of transmitting pulse waveform design is analyzed,the FPGA logic circuit is designed to generate pulse signal,and the high frequency power amplifier circuit is designed to solve the problem of low power of FPGA *** effectiveness of the scheme is verified by *** results show that the pulse signal generated by the scheme has stable frequency amplitude and the output power meets the requirements of non-contact *** with the traditional pulse signal generation device using switching devices,the structure is simple and the pulse signal can be set flexibly.

关键词： Ground Penetrating Radar FPGA Pulse Signal Power Amplifier

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Transformer-Based Scenario Generation of Multi-segment Monotonic Increasing Bidding Curves of Power Plants

Transformer-Based Scenario Generation of Multi-segment Monot...

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International Conference of Electrical, Electronic and Networked Energy systems, EENES 2024

作者： Xie, Haochuan Lv, Jianhu Wang, Ying Yuan, Kun College of Software Southeast University Suzhou China China Electric Power Research Institute Beijing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9789819618637

With the relaxation of electricity market regulation, power plants are permitted to engage in bidding. The bidding outcomes in the competitive market differ significantly from those in the previously regulated market. These differences in bidding outcomes will further impact power flow and system security. To provide a reference for practical electricity market operations, make market simulation more realistic, it is crucial to analyze and establish a model to simulate the bidding behavior of power generators. This paper applies Transformer networks to predict multi-segment bidding behaviors of power plants, we have improved the classic Transformer network by adopting a one encoder-two decoder format, considering six key factors influencing bidding behavior: unit ID, fuel type, capacity, location, system load, and load ratio. Additionally, monotonicity of the multi-segment bidding curves of power plants is taken into account, and monotonicity loss functions are introduced to achieve this goal. We apply this method to predict bidding behaviors in power plants in the Australian electricity market, validating the accuracy of the model. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： bidding curves generation monotonicity constraint power market Transformer-based

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Modified multi-scale symbolic dynamic entropy and fuzzy broad learning-based fast fault diagnosis of railway point machines

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Transportation Safety and Environment 2023年第4期5卷 1-7页

作者： Junqi Liu Tao Wen Guo Xie Yuan Cao Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an 710048ShaanxiChina School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center of Rail Transportation Operation Control System Beijing Jiaotong UniversityBeijing 100044China

Railway point machine(RPM)condition monitoring has attracted engineers’attention for safe train operation and accident *** realize the fast and accurate fault diagnosis of RPMs,this paper proposes a method based on entropy measurement and broad learning system(BLS).Firstly,the modified multi-scale symbolic dynamic entropy(MMSDE)module extracts dynamic characteristics from the collected acoustic signals as entropy ***,the fuzzy BLS takes the above entropy features as input to complete model *** BLS introduces the Takagi-Sug eno fuzzy system into BLS,which improves the model’s classification performance while considering computational *** results indicate that the proposed method significantly reduces the running time while maintaining high accuracy.

关键词： railway point machine(RPM) fault diagnosis modified multi-scale symbolic dynamic entropy(MMSDE) fuzzy board learning system(BLS)

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