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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Liu, Zhenchang Hao, Mingrui Wei, Donghui Liu, Yuting Zhao, Hongyin Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Beijing Electro-Mechanical Engineering Institute Beijing100074 China

ISBN: (纸本)9789811966125

The cooperative search strategy is designed with the distributed model predictive method in this paper. For the rationality of decision-making of UAV swarm in different stages of the cooperative search, a dynamic parameter selection method based on the expert system is proposed. The weight of the objective function is adjusted online through the expert system so that the result of the search decision is more in line with the task requirements. Besides, a jump grid method is proposed for grid selection in cooperative search so that the UAV can select non-adjacent grids as the search point for the next time. The UAV's search frequency and trajectory maneuverability can be changed by adjusting the number of jump grids through the expert system. The simulation shows that the UAV with the expert system can dynamically adjust the search interval and search requirements, making the UAV swarm able to quickly discover targets, explore unknown areas, ensure collision avoidance, and meet the boundary constraints. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Xu, Ruijie Chen, Shichao Sun, Wenqiao Lv, Yisheng Luo, Jialiang Tang, Ying Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences China University of Geosciences Beijing School of Information Engineering The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350337020

Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation system (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance. © 2023 IEEE.

关键词： Cubature Kalman Filter Inertial Pedestrian Navigation Zero-Velocity Update

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Warship Object Detection in Remote Sensing Images with Improved YOLOv5

Warship Object Detection in Remote Sensing Images with Impro...

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2023 China Automation Congress, CAC 2023

作者： Liu, Caiyuan Li, Yuan Chen, Linxiu Guan, Weili School of Automation Beijing Institute of Technology Beijing100081 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9798350303759

Aiming at the task of detecting ships on the sea surface in remote sensing images, there are a lot of disturbances such as variable object sizes, cloud occlusion, complex image backgrounds, and different ship orientations, etc. In this paper, we propose a ship rotating object detection network based on the improved YOLOv5, which is constructed through the strategies of introducing Swin Transformer as a feature extraction network to enhance the feature extraction performance of the network, introducing a rotating detection head to realize the detection of the rotation angle, and modifying the network loss function to accelerate the convergence of the network. The network finally achieves a 71.7% map on ShipRSImageNet validation set, which is an improvement of 2.4% compared with the original network model. The network proposed in this paper solves the problem that the YOLOv5 algorithm is unable to detect rotating objects, and the network based on the self-attention mechanism is used to further enhance the ability to detect small objects. Finally, a ship object detector that can be used in real remote sensing satellite images is obtained. © 2023 IEEE.

关键词： Feature extraction

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Mechanism and Methods for Fault Recovery of Grid-Forming Inverter by Graphical Illustration

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IEEE Transactions on Power systems 2025年第2期40卷 1973-1976页

作者： Yang, Jialun Ye, Hua Wang, Yantuan Liu, Yutian Li, Yujun Du, Zhengchun Shandong Research Institute of Industrial Technology Jinan250061 China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan250061 China Xi'an Jiaotong University Department of Electrical Engineering Xi'an710049 China

The existing priority-based current limiting control (CLC) for grid-forming (GFM) inverters may lead to failures in fault recovery, including being locked in CLC and mode oscillation between CLC and constant voltage control (CVC). To resolve these problems, this letter first reveals the fault recovery mechanism of droop controlled GFM inverters by illustrating the conditions of three fault recovery states in the phasor diagram. Then, two methods including determination of the optimal range of current angle and upper limit of saturation block are presented to guarantee successful fault recovery of GFM inverters. The correctness of theoretical analyses is verified and validated by multiple experimental tests. © 1969-2012 IEEE.

关键词： Fault current limiters

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Distributed Communication Based Cooperative Multi-aircraft Attack Guidance Law

Distributed Communication Based Cooperative Multi-aircraft A...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Lyu, Zhongshi Guo, Xiaoyun Hao, Mingrui Xu, Cheng Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Beijing Institute of Mechanical and Electrical Engineering Beijing100074 China

ISBN: (纸本)9789811966125

New requirements for coordinated aircraft attacks have been introduced with the development of the anti-missile systems. To address the problem of multi-flight collaborative strike, this paper proposes a distributed collaborative guidance strategy that allows for efficient information interaction. Based on the principle of distributed communication topology, the cooperative guidance law is designed to control each vehicle simultaneously in terms of velocity magnitude and direction, so that the state information of each vehicle converges quickly. The purpose of sequential arrival of vehicles is achieved, and a cooperative attack guidance law with good stability and high reliability is constructed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vehicles

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Facial expression recognition under occlusion conditions based on multi-feature cross-attention

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Journal of intelligent and Fuzzy systems 2024年第4期46卷 9841-9856页

作者： Guo, Sheng Tan, Mian Cai, Shan Zhang, Zaijun Liang, Yihui Feng, Hongxi Zou, Xue Wang, Lin School of Data Science and Information Engineering Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China School of Mathematics and Statistics Qiannan Normal University for Nationalities Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province Duyun China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizhou Aerospace Tianma Electromechanical Technology Co LTD Zunyi China

Although facial expression recognition (FER) has a wide range of applications, it may be difficult to achieve under local occlusion conditions which may result in the loss of valuable expression features. This issue has motivated the present study, as a part of which an effective multi-feature cross-attention network (MFCA-Net) is proposed. The MFCA-Net consists of a two-branch network comprising a multi-feature convolution module and a local cross-attention module. Thus, it enables decomposition of facial features into multiple sub-features by the multi-feature convolution module to reduce the impact of local occlusion on facial expression feature extraction. In the next step, the local cross-attention module distinguishes between occluded and unoccluded sub-features and focuses on the latter to facilitate FER. When the MFCA-Net performance is evaluated by applying it to three public large-scale datasets (RAF-DB, FERPlus, and AffectNet), the experimental results confirm its good robustness. Further validation is performed on a real FER dataset with local occlusion of the face. © 2024 – IOS Press.

关键词： Convolution

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Multi-Stage Marine Ship Recognition Based on Stackable Residual Network 19

Multi-Stage Marine Ship Recognition Based on Stackable Resid...

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19th International Conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2023

作者： Yu, Wanwan Zhang, Junping Wang, Linlin Xi, Yunqiao Xu, Hongfeng Chen, Linxiu Hao, Mingrui Harbin Institute of Technology School of Electronics and Information Engineering Harbin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9798350304398

Marine ship recognition has always been an important research field. The ships can be usually recognized by analyzing their attribute characteristics. However, in the actual process of recognition, many ship recognition methods using a single model cannot meet the requirements of this task due to the complexity of ship types, diversity of interference factors and different information obtained from distance differences. Therefore, we propose a multi-stage ship recognition scheme. First, the single radiation emitter feature recognition network model is designed for long-distance recognition. Second, the fusion feature network model for short distance recognition. The proposed network is mainly constructed with the residual units, which not only simplify the training of deep networks and facilitate the spread of information, but also can solve the vanishing gradient and exploding gradient problem. The simulation experimental results show that the proposed network model has better performance than typical methods and has great potential in applications. © 2023 IEEE.

关键词： Ships

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Graph-based Selection-Activation Reinforcement Learning for Heterogenous Multi-agent Collaboration 43

Graph-based Selection-Activation Reinforcement Learning for ...

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43rd Chinese control Conference, CCC 2024

作者： Chen, Hao-Xiang Zhang, Xi-Wen Shen, Jun-Nan Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System School of Automation Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401135 China

ISBN: (纸本)9789887581581

In practical applications of multi-agent systems, agents are often heterogeneous, and each type of them typically has different task objectives. For heterogeneous multi-agent reinforcement learning (HMARL), the diversity of agent types and the unbalanced agent number of each type can lead to the curse of dimensionality and non-stationary. Moreover, the increase in the number of heterogeneous agents may result in slow convergence during training. This paper proposes a graph-based selection-activation reinforcement learning (GSARL) method for training heterogenous multi-agent collaboration strategies. It first constructs agents based on their types, then extracts the global adjacency matrices and the ally adjacency matrices from the agents' observations, and calculates the global feature matrices. Afterwards, GSARL utilizes hierarchical graph convolutional network to sequentially convolve the global information and ally information, obtaining action logits based on agent types. By using the neural topology graph and the selection-activation method, the optimal multi-agent collaboration configuration is obtained through combinatorial optimization. Experiments are conducted in an adversarial combat simulation environment involving collaborative Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). Simulation results show that the proposed method can accelerate convergence while allowing that each type of heterogeneous agents can leverage its unique advantages. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Unmanned aerial vehicles (UAV)

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Bayesian Network Based Imprecise Probability Estimation Method for Wind Power Ramp Events

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Journal of Modern Power systems and Clean Energy 2021年第6期9卷 1510-1519页

作者： Yuanchun Zhao Wenli Zhu Ming Yang Mengxia Wang Key Laboratory of Power System Intelligent Dispatch and Control Shandong University State Grid Jinan Power Company

Although wind power ramp events(WPREs)are relatively scarce,they can inevitably deteriorate the stability of power system operation and bring risks to the trading of electricity *** this paper,an imprecise conditional probability estimation method for WPREs is proposed based on the Bayesian network(BN)*** method uses the maximum weight spanning tree(MWST)and greedy search(GS)to build a BN that has the highest fitting degree with the observed ***,an extended imprecise Dirichlet model(IDM)is developed to estimate the parameters of the BN,which quantificationally reflect the ambiguous dependencies among the random ramp event and various meteorological *** BN is then applied to predict the interval probability of each possible ramp state under the given meteorological conditions,which is expected to cover the target probability at a specified confidence *** proposed method can quantify the uncertainty of the probabilistic ramp event ***,by using the extracted dependencies and Bayesian rules,the method can simplify the conditional probability estimation and perform reliable prediction even with scarce *** results on a real wind farm with three-year operation data illustrate the effectiveness of the proposed method.

关键词： Bayesian network(BN) conditional probability imprecise Dirichlet model(IDM) imprecise probability wind power ramp events

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Multi-pulse parallel demodulation for high-precision measurement in Raman-distributed optical fiber sensing

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Journal of Lightwave Technology 2025年第11期43卷 5260-5269页

作者： Xu, Yang Li, Jian Chai, Mingyuan Zhang, Mingjiang Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Taiyuan University of Technology College of Physics Taiyuan030024 China Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan030032 China

Raman distributed temperature sensors enables distributed measurement of temperature along the sensing fiber, which is widely used in production. Due to the limitation of the optical time-domain reflection principle, the temperature measurement accuracy of the sensors will be seriously affected when the length of the temperature variation region is shorter than the pulse width. In this work, the relationship between pulse width, fiber under test (FUT) length and FUT temperature is firstly studied. Then, a multi-pulse parallel demodulation scheme is proposed. The experiments depict that a temperature measurement accuracy is optimized from over 30°C to 1.55 °C with a 0.2 m temperature variation region over 5.6 km. The scheme is based on multi-pulse parallel measurements combined with numerical simulations to achieve equivalent short pulse measurements of the small-region temperature variation without changing the hardware structure of the sensors. This scheme reduces the hardware requirements of the sensors and avoids the problems of sensing distance reduction and signal-to-noise ratio deterioration caused by shortening the pulse width. © 2025 IEEE. All rights reserved.

关键词： Demodulation

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