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Interval analysis for neural networks with application to fault detection

science China(Information sciences) 2024年第11期67卷 222-237页

作者： Zhenhua WANG Youdao MA Song ZHU Thach Ngoc DINH Yi SHEN Department of Control Science and Engineering Harbin Institute of Technology National Key Laboratory of Complex System Control and Intelligent Agent Cooperation School of Mathematics China University of Mining and Technology The CEDRIC-Lab Conservatoire National des Arts et Métiers (CNAM)

This paper investigates an interval analysis method for neural networks and applies it to fault detection for systems with unknown but bounded measurement noise. First, a novel interval analysis method is presented, which can compute the bounds of the output of a feedforward neural network subject to a bounded input. By applying the proposed interval analysis method to a network trained with fault-free system data, adaptive thresholds for fault detection are computed. Finally, one can acquire fault detection results via a fault detection strategy. The proposed method can achieve tight bounds of the network output and employ simple operations, which leads to accurate fault detection results and a low computational burden.A numerical simulation and an experiment on an AC servo motor are given to illustrate the effectiveness and superiority of the proposed method.

关键词： interval analysis feedforward neural network fault detection adaptive thresholds

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Cooperative Search Strategy of Multi-UAVs Based on Hexagonal Grid 7th

Cooperative Search Strategy of Multi-UAVs Based on Hexagonal...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Zhao, Hongyin Hao, Mingrui Zhang, Hang Dong, Xiaofei Liu, Yuting Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9789819733316

Aiming at the cooperative search problem in a wide-area unknown region, a cooperative search strategy of multi-UAVs based on hexagonal grid is proposed for the fairness of decision-making, This strategy is trained by reinforcement learning method, and adopts centralized training and distributed execution, and deals with the dynamic environment problem by fusing pheromone models. Simulation shows that the cooperative search algorithm has the functions of unknown region cooperative search and online threat avoidance replanning. Compared with the square grid training method, the reinforcement learning search algorithm based on the hexagonal grid after training expands the feasible directions in each decision planning cycle, which is more suitable for low-slow-small UAVs, and the performance is more stable after convergence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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A cluster positioning architecture and relative positioning algorithm based on pigeon fock bionics

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Satellite Navigation 2023年第1期4卷 92-112,I0004页

作者： Zhongliang Deng Hang Qi Chengfeng Wu Enwen Hu Runmin Wang Beijing University of Posts and Telecommunications Beijing 100876China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing 100074China.

Unmanned clusters can realize collaborative work,fexible confguration,and efcient operation,which has become an important development trend of unmanned *** positioning is important for ensuring the normal operation of unmanned *** existing solutions have some problems such as requiring external system assistance,high system complexity,poor architecture scalability,and accumulation of positioning errors over *** the aid of the information outside the cluster,we plan to construct the relative position relationship with north alignment to adopt formation control and achieve robust cluster relative *** on the idea of bionics,this paper proposes a cluster robust hierarchical positioning architecture by analyzing the autonomous behavior of pigeon *** divide the clusters into follower clusters,core clusters,and leader nodes,which can realize fexible networking and cluster *** at the core cluster that is the most critical to relative positioning in the architecture,we propose a cluster relative positioning algorithm based on spatiotemporal correlation *** the design idea of low cost and large-scale application,the algorithm uses intra-cluster ranging and the inertial navigation motion vector to construct the positioning equation and solves it through the Multidimensional Scaling(MDS)and Multiple Objective Particle Swarm Optimization(MOPSO)*** cluster formation is abstracted as a mixed direction-distance graph and the graph rigidity theory is used to analyze localizability conditions of the *** designed the cluster positioning simulation software and conducted localizability tests and positioning accuracy tests in diferent *** with the relative positioning algorithm based on Extended Kalman Filter(EKF),the algorithm proposed in this paper has more relaxed positioning conditions and can adapt to a variety of *** also has higher relative positioning accuracy

关键词： Cluster positioning architecture Cluster relative positioning Multidimensional scaling Multiple objective particle swarm optimization Unmanned aerial vehicles positioning Localizability analysis Rigid graph

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A Combat Intention Reasoning Method for Warship Formation Targets Based on Deep Learning 1

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Xu, Hongfeng Zhao, Jiajia Zhang, Hang Jiang, Jixiang Chen, Linxiu Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (数字)9789819733361

ISBN: (纸本)9789819733354

With the rapid development of science and technology, modern war presents the characteristics of mathematics and information. Grasping the operational intention of battlefield targets plays a crucial role in the trend of war. The target of warship formation is an important fighting force in modern war. How to analyze and reason the target’s fighting intention accurately and timely is one of the most important parts of modern battlefield situation cognition system. Aiming at the problems of too strong subjectivity, lack of objectivity and poor model robustness existing in traditional intention reasoning methods, this paper introduces the idea of deep learning data-driven, and takes the electromagnetic, state and formation characteristic information of ship formation targets as input, designs a deep learning-based ship formation target intention reasoning model. The effectiveness of the algorithm is verified by mathematical simulation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Relative Navigation of Unmanned Aerial Vehicles Based on Data Link Ranging and Partial Guidance Information 1

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Wang, Ziwen Song, Chuang Hao, Mingrui Liang, Xinkai Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (数字)9789819733323

ISBN: (纸本)9789819733316

In GNSS-denied environments, precise relative navigation between unmanned aerial vehicles (UAVs) is a key technology for achieving cooperative tasks. The relative navigation method based on data link ranging information combines altitude, airspeed measurements, and attitude information to ensure the observability of UAVs’ relative states. However, due to the significant constraints imposed by observability conditions, a novel approach is proposed that utilizes partial guidance information from the UAV swarm towards the target as additional measurements. This approach enhances the observability capability of relative navigation and alleviates the constraints imposed by the observability conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Observability

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Target Tracking Method Based on LSTM-EKF 1

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Xu, Hongfeng Zhao, Jiajia Zhang, Hang Jiang, Jixiang Chen, Linxiu Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (数字)9789819733323

ISBN: (纸本)9789819733316

With the development of science and technology, target tracking technology has been widely used in many fields. Through target tracking technology, the accurate estimation of target state can be obtained, and the perception ability can be effectively improved. However, due to the increasing maneuvering ability of the target, the traditional target tracking algorithm is more and more difficult to meet the requirements of maneuvering target tracking. This article in view of the traditional filtering algorithm based on bayesian theory problems of nonlinear error model, in combination with the advantages of EKF and deep learning, introducing the idea of hybrid data model, puts forward the algorithm of target tracking based on LSTM - EKF, and design a mathematical simulation experiment on the performance of the algorithm is verified. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Target tracking

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A Review of Target Association Methods 1

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Xu, Hongfeng Zhao, Jiajia Zhang, Hang Jiang, Jixiang Chen, Linxiu Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (数字)9789819733361

ISBN: (纸本)9789819733354

With the continuous development of information technology, target association technology has been more and more widely used in military and civilian fields. How to achieve effective and reliable target data association has always been a difficult and hot spot in the field of target tracking. In this paper, the research status of target association technology is summarized, the existing target association algorithm is sorted and summarized according to the method principle, and the existing problems in the research of target association technology are analyzed, and the further research direction is prospected. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Target tracking

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Low-Light Image Enhancement Method for Consistency Judgment of Multi-view Multi-target for UAV Swarm 7th

Low-Light Image Enhancement Method for Consistency Judgment ...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Li, WeiXue Song, Chuang Hao, MingRui Zhang, Hang Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9789819733392

To address the challenges posed by low-light images in multi-drone detection tasks, which lead to significant differences in target image features across different perspectives and poor accuracy in target association under multiple viewpoints, this paper proposes an enhanced network architecture based on MIRNet for low-light image enhancement. The goal is to improve the stability of target features in multi-view scenarios. By incorporating Sobel convolution to capture target edges and texture features, and by designing a residual contextual block combined with an attention mechanism and a pixel loss function that emphasizes the central region, the model not only acquires richer features but also focuses more on the central region's target characteristics. Experimental results demonstrate that the proposed approach effectively enhances image brightness, contrast, and target texture, exhibiting substantial advantages in both objective and subjective evaluation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image enhancement

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Cooperative Formation control of Fixed-Wing UAV Based on L1 Guidance Law Considering Collision Avoidance 7th

Cooperative Formation Control of Fixed-Wing UAV Based on L1 ...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Yue, Keyuan Hao, Mingrui Zhen, Yan Liu, Xinyu Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9789819733392

This article proposes a formation control scheme based on L1 guidance law for fixed wing unmanned aerial vehicle clusters while considering collision avoidance issues between them. The fixed wing aircraft cluster adopts L1 guidance and tracking method to track their respective desired waypoints. By controlling the speed of the fixed-wing UAVs, the entire cluster forms a formation configuration on their individual desired trajectories. During the formation and transformation processes, an improved artificial potential field method is proposed based on the characteristics of fixed-wing UAVs to achieve collision avoidance between clusters. The effectiveness of the proposed approach is validated through numerical simulations based on a six-degree-of-freedom nonlinear UAV model, providing a reference for practical engineering applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fixed wings

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Combat Intention Recognition of Aerial Battlefield Targets Based on LSTM 3rd

Combat Intention Recognition of Aerial Battlefield Targets B...

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3rd International Conference on Autonomous Unmanned systems, ICAUS 2023

作者： Xu, Hongfeng Zhao, Jiajia Zhang, Hang Jiang, Jixiang Chen, Linxiu Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9789819710867

In the course of military operations, the battlefield situation changes rapidly, and the combat time is fleeting. How to quickly and accurately identify the enemy’s combat intention is one of the important preconditions to ensure the commander’s correct command decision and obtain the initiative in the battlefield. In this paper, aiming at the problems of low accuracy and insufficient algorithm robustness of traditional target combat intention recognition methods in air battlefield, a combat intention recognition method based on LSTM is proposed according to the characteristics of sequence and multi-dimension of target characteristic data. Based on the historical and current state characteristics, electromagnetic characteristics and action characteristics of the target obtained by the sensor, the method uses the LSTM network to build the target combat intention recognition model, and learns the changes of the target characteristics through the network model to realize the target combat intention recognition. Simulation results show that the proposed method can effectively improve the accuracy of target combat intention recognition. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Military operations

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