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9th International Conference on Applying New Technology in Green Buildings, ATiGB 2024

作者： Cao, Trung Thanh Van, Cuong Trinh Van, Hung Pham Nguyen, Minh Quang Hoang, Anh Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi Viet Nam Ha Noi University of Industry Department of Automation and Control Hanoi Viet Nam

ISBN: (纸本)9798331505042

DC-DC converters are essential components in power supply applications, portable electronics, renewable energy systems, and various other industries. An accurate system model is crucial for the development of the controller for a DC-DC converter. This research study focused on the development of an iterative learning control that operates without requiring the system model. The study employs PSCAD simulation software to simulate and analyze the effectiveness of an iterative learning control and a sliding mode controller for flyback converter. Detailed simulation results and comparisons between the two methodologies will be presented. © 2024 IEEE.

关键词： Sliding mode control

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Review of Islanding Detection Methods for New Energy Grid Integration Systems 3rd

Review of Islanding Detection Methods for New Energy Grid In...

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3rd International Joint Conference on Energy, Electrical and Power engineering, CoEEPE 2023

作者： He, Xianqiang Zhao, Weixing Cao, Fei Ma, Qinfeng Liu, Mingshun Qin, Hai Pu, Qingxin Zhang, Wanting Yuan, Jiaxin Guizhou Power Grid Co. Ltd. Electric Dispatching and Control Center Guiyang China Beijing Sifang Protection and Automation Engineering Technology Co. Ltd. Beijing China School of Electrical Engineering and Automation Wuhan University Wuhan China

ISBN: (纸本)9789819739394

With the continuous development and growth of new energy grid integration, the phenomenon of islanding occurs frequently in the new energy grid integration system, which poses a significant threat to the safety and stable operation of the power grid. Islanding detection is an essential means of determining whether an islanding phenomenon has occurred in the new energy grid integration system. This paper first explains the principle and harm of islanding, and introduces the main islanding detection methods currently available, analyzing their advantages and disadvantages. Finally, based on the current status of islanding detection development, this paper proposes the problems that need to be addressed and the future development trends to promote further development in this research field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Distributed power generation

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PITD3-based Autonomous Evasion Decision-making Method for Aircraft 43

PITD3-based Autonomous Evasion Decision-making Method for Ai...

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43rd Chinese control Conference, CCC 2024

作者： Liu, Chang Dou, Liqian Zhang, Ruilong Tang, Yifan Ren, Mengyuan Zhang, Lun School of Electrical Engineering and Automation Tianjin300072 China Beijing Aerospace Automatic Control Institute China Tianjin Aerospace Institute of Computing Technology Tianjin300308 China

ISBN: (纸本)9789887581581

Outstanding autonomous evasion decision-making capability has an important significance for ensuring flight safety and enhancing the autonomy of the aircraft. In this paper, a prediction information-based TD3 (PITD3) evasion decision-making algorithm for aircraft is proposed, which can autonomously evade the interceptors' attack in an antagonistic environment. Firstly, an antagonistic environment is described according to environmental constraints and the dynamics of the aircraft and interceptor. Secondly, the autonomous decision-making process of the aircraft facing the threat target is modelled as a Markov decision process to describe the state information of the aircraft and interceptor. Finally, an autonomous evasion decision-making method based on the twin delayed deep deterministic policy gradient (TD3) is designed to find the optimal strategy, and the effectiveness of the proposed autonomous evasion decision-making method is demonstrated by simulation results and comparative experiments. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Markov processes

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INN: A Deep Correction Network for High-Dynamic Inertial Navigation 4

INN: A Deep Correction Network for High-Dynamic Inertial Nav...

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4th International Conference on Industrial automation, Robotics and control engineering, IARCE 2024

作者： Gu, Tao Cong, Li Yang, Jing Guo, Yuzhu Wang, Lina School of Automation Science and Electrical Engineering Beihang University Beijing China School of Electronic Information Beihang University Engineering dept. Beijing China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing China

ISBN: (纸本)9798350380323

Inertial navigation has gained extensive application because it solely relies on built-in Inertial Measurement Unit (IMU) devices. Nevertheless, due to its inherent accumulative integral error, inertial navigation is often integrated with other external devices in practical situations, which unfortunately leads to unavoidable privacy breaches. To address these issues, an Inertial Navigation Neural Network (INN) is designed to generate high-precision position estimations without relying on any other auxiliary information. The INN model consists of attitude correction and velocity regression modules. The attitude module employs a convolutional network to correct the attitude information, and the obtained rotation matrices are then fed into a recurrent neural network to regress incremental velocities and produce high-precision positions. The proposed model is validated on the simulated trajectories based on the Precise Strapdown Inertial Navigation System (PSINS) toolbox. Experimental studies demonstrated that the proposed method has a significant corrective effect on high-dynamic strapdown inertial navigation settings. The accumulated position errors are an order of magnitude smaller than those of traditional track reckoning algorithms and achieved a comparable performance with integrated navigation. © 2024 IEEE.

关键词： Inertial navigation systems

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Pylon Reconstruction Based on NeRF 14

Pylon Reconstruction Based on NeRF

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14th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2024

作者： Hu, Zijian Wang, Chenxing Southeast University School of Automation Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9798350361889

In this paper, we propose a pylon reconstruction method based on Neural Radiance Fields (NeRF) technology. The advantage of this method lies in its ability to reconstruct pylons from images, with a multi-view model maintaining good performance even under complex lighting and occlusion conditions. We demonstrate the process of using NeRF to reconstruct electric power towers through experiments. During the experimental process, we collect images of electric power towers from different environments and angles, then apply NeRF to process these images, using deep learning to learn and generate a network model capable of expressing the scene. The experimental results show that our method can complete the reconstruction of scenes with only image inputs, and the reconstructed models have a certain accuracy in terms of geometric details and textures. © 2024 IEEE.

关键词： Deep learning

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Heterogeneous Image Knowledge Driven Visual Perception

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 255-257页

作者： Lan Yan Wenbo Zheng Fei-Yue Wang IEEE the College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China the School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan 430070China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter is concerned with visual perception closely related to heterogeneous *** the huge challenge brought by different image modalities,we propose a visual perception framework based on heterogeneous image knowledge,i.e.,the domain knowledge associated with specific vision tasks,to better address the corresponding visual perception problems.

关键词： Visual visual knowledge

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Fatigue Testing and Simulation of Hi-Lock Bolted Single Shear Lap Joints 10

Fatigue Testing and Simulation of Hi-Lock Bolted Single Shea...

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10th International Symposium on System Security, Safety, and Reliability, ISSSR 2024

作者： Wang, Lei Zhang, Ruijin Qian, Wenxue Li, Jun Li, Jia Northeastern University School of Mechanical Engineering and Automation Shenyang China Northeastern University Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education Shenyang China

ISBN: (纸本)9798350362930

There are many parts of the aircraft with Hi-Lock bolted lap structures, such as wings and tails. The joints are subjected to alternating loads during the service of the aircraft, and the main failure form is fatigue failure. Taking the Hi-locking bolt single shear lap joint as the research object, the factors affecting the fatigue life of the single shear lap joint are investigated from both test and finite element analysis. Firstly, the fatigue test of the single shear joint of Hi-Lock bolt is conducted to obtain crack initiation location, fatigue life and the mechanical property parameters needed for simulation;secondly, the three-dimensional solid modeling and finite element analysis of the test specimen are carried out based on ABAQUS, and multiple influencing factors, such as two types of interference and clearance fits, load level, interference sizes, preload magnitude, etc., are studied on the fatigue life of the shear joint structure. The results of the finite element analysis show that the interference fit at low load is good for the improvement of the joint life, and the optimal interference size is below 2%;the high preload level applied is advantageous to obtain a higher life. © 2024 IEEE.

关键词： Locks (fasteners)

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Inverse Reinforcement Learning Integrated Reinforcement Learning for Single Intersection Traffic Signal control 5

Inverse Reinforcement Learning Integrated Reinforcement Lear...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Gu, Shiyi Zhang, Tingting Zhang, Ya Southeast University School of Automation Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing China Army Engineering University College of Command and Control Engineering Nanjing210017 China

ISBN: (纸本)9798350325294

Traffic Signal control (TSC) optimization is vital for efficient traffic flow in urban intersections. Recent research has explored the application of Reinforcement Learning (RL) agents to tackle TSC challenges. However, the selection of an appropriate reward function in RL agent training still relies on subjective judgment and experience. To address this issue, an Inverse Reinforcement Learning (IRL) integrated RL algorithm is proposed. In the IRL learning section, expert trajectory data are collected and analyzed by Relative Entropy IRL (REIRL). The latent reward of expert policy is reconstructed and utilized in RL agent training process. In the RL control section, a Double Dueling Deep Q Network is applied under a cycle control framework. As verified in simulations, the introduction of expert experience improves the performance of the RL agent to the expert level and concurrently enables robustness to expert policy noises. © 2023 IEEE.

关键词： Reinforcement learning

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Torsional Vibration Suppression Method Design for Variable-Speed Wind Turbine Based on UIO-SMC 22

Torsional Vibration Suppression Method Design for Variable-S...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Yu, Jiajun Xia, Jinhui Su, Jinya Li, Ze Huang, Wentao The School of Automation Southeast University Nanjing210096 China The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310058 China The College of Electronic and Information Engineering Hebei University Baoding071002 China The School of Internet of Things Engineering Jiangnan University Wuxi214122 China

ISBN: (纸本)9798331527471

With the continuous development of the wind power industry, variable-speed wind turbines (VSWT) are moving towards large-scale, and their applications have been expanded to offshore areas. However, the operating environment of offshore wind farm is even more harsh, and the potential torsional vibration of the drivetrain caused by disturbances in the offshore wind farm may introduce fatigue damage to the VSWT, resulting in unexpected faults that may lead to prolonged shutdown of the VSWT and loss of power generation efficiency. This study proposes a torsional vibration suppression strategy based on unknown input observer and sliding mode control. The proposed suppression strategy provides an appropriate electromagnetic torque compensation for the generator to mitigate disturbances in the whole control system. The effectiveness of the proposed strategy is validated by extensive simulation studies. © 2024 IEEE.

关键词： Sliding mode control

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Spatio-Temporal Communication Compression in Distributed Prime-Dual Flows 63

Spatio-Temporal Communication Compression in Distributed Pri...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Ren, Zihao Wang, Lei Yuan, Deming Su, Hongye Shi, Guodong Zhejiang University College of Control Science and Engineering China Nanjing University of Science and Technology School of Automation China University of Sydney Australia Centre for Field Robotics Australia

ISBN: (纸本)9798350316339

In this paper, we study distributed prime-dual flows for multi-agent optimization with spatio-temporal compressions. The central aim of multi-agent optimization is for a network of agents to collaboratively solve a system-level optimization problem with local objective functions and node-to-node communication by distributed algorithms. The scalability of such algorithms crucially depends on the complexity of the communication messages, and a number of communication compressors for distributed optimization have recently been proposed in the literature. First of all, we introduce a general spatio-temporal compressor characterized by the stability of the resulting dynamical system along the vector field of the compressor. We show that several important distributed optimization compressors such as the greedy sparsifier, the uniform quantizer, and the scalarizer all fall into the category of this spatio-temporal compressor. Next, we propose two distributed prime-dual flows with the spatio-temporal compressors being applied to local node states and local error states, respectively, and prove (exponential) convergence of the node trajectories to the global optimizer for (strongly) convex cost functions. Finally, a few numerical examples are present to illustrate our theoretical results. © 2024 IEEE.

关键词： Cost functions

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