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Chinese Automation Congress (CAC)

作者： Longyun Chi Xujun Guan Xiaorong Shen Hai Zhang School of Automation Science and Electrical Engineering Beihang University Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

The naturalness of the panoramic image is an important criterion for evaluating the result of image stitching. When the overlapping region of the image has a low-texture area, the alignment is hard to accomplish due to incorrect and inadequate feature point matching pairs, and the salient structures are easily destroyed due to the unreasonable position of the optimal seam. To solve the problem, we use line segment matching information to assist the alignment of low-texture regions and use superpixel segmentation regions to filter the unreliable point correspondences. An improved seam-finding algorithm is finally used to blend images together. The experimental results demonstrate that the proposed method can get more natural stitching results.

关键词： Image segmentation Strain Feature extraction Information filters Matched filters Optimization

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Application of Neutrosophic Multiple Criteria Decision Making in Unmanned system

Application of Neutrosophic Multiple Criteria Decision Makin...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Yu Zhong Jiajia Zhao School of Electronics and Information Northwestern Polytechnical University Xi’an China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Bejing China

ISBN: (纸本)9781728137933

The multiple criteria decision making (MCDM) is universal and applicable for plenty of unmanned systems which are widely concerned in civil and military fields due to their adaptability and safety. The contributing factors of decision making in unmanned systems are complicated due to the inhomogeneity and nondeterminacy of environment and the relative importance of criteria. Consequently, these critical factors fail to be considered felicitously in many traditional methods. In this paper, a neutrosophic MCDM approach is proposed to mitigate this matter. The neutrosophic set generated from collected information of multiple sources would be fused by the aggregation operator to obtain the decision matrix, then the weights of criteria would be obtained via the proposed method based on the decision matrix, and the decision results could be determined by the aggregation operator and defuzzification method of aggregated neutrosophic set. The application of neutrosophic set can handle numerous uncertain matters in unmanned systems. Finally, an example was provided to illustrate the reasonableness and stability of the proposed approach.

关键词： Decision making Correlation Uncertainty Nonhomogeneous media Stability criteria Space vehicles

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Multi-Stage Marine Ship Recognition Based on Stackable Residual Network

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

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

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

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.

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Flexible Physical Camouflage Generation Based on a Differential Approach

arXiv

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arXiv 2024年

作者： Li, Yang Tan, Wenyi Wang, Tingrui Liang, Xinkai Pan, Quan School of Automation Northwestern Polytechnical University Xi’an710072 China Laboratory of Science and Technology on Complex System Control and Intelligent Agent Cooperation Beijing China

To achieve attacks on object detection in the physical world, many existing studies focus on adversarial camouflage generation techniques, which involve applying adversarial textures onto the surface of the target. However, the neural renderers used in previous works cannot realistically simulate the physical world, and most texture rendering processes are implemented on 2D images, without fully considering the influence of the structural information of 3D models on texture overlay. To address these challenges, we introduce a novel neural rendering approach specifically tailored for adversarial camouflage within a versatile 3D rendering framework. Our method, named Flexible Physical-camouflage Attack (FPA), goes beyond traditional techniques by faithfully simulating lighting conditions, material variations of the camouflage, the angle and distance between the target and the camera, ensuring a nuanced and realistic representation of textures on a 3D target. To achieve this, we employ a generative approach that learns adversarial patterns from a diffusion model. This involves combining specially designed adversarial loss, smoothness loss, non-printable score loss, and concealment constraint loss to ensure the adversarial, printability, and concealable nature of camouflage in the physical world. Furthermore, we showcase the effectiveness of the proposed camouflage in sticker mode, demonstrating its ability to cover the target without compromising adversarial information. Through empirical and physical experiments, FPA exhibits strong performance in terms of attack success rate and transferability. Additionally, the designed sticker-mode camouflage, coupled with a concealment constraint, adapts to the environment, yielding diverse styles of texture. Our findings highlight the versatility and efficacy of the FPA approach in adversarial camouflage applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Camouflage

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Brain Storming Optimization Algorithm for Heating Dispatch Scheduling of Thermal Power Plant 29

Brain Storming Optimization Algorithm for Heating Dispatch S...

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第29届中国控制与决策会议

作者： Lu Kang Yali Wu Xinrui Wang Xiaohua Feng School of Automation and Information Engineering Xi'an University of Technology Shannxi Province Key Laboratory of Complex System Control and Intelligent Information Processing School of Marine Science and Technology Northwestern Polytechnical University

ISBN: (纸本)9781509046584

In the traditional scheduling mode of the current thermal power plants,only the power load distribution of the unit was *** there was no enough guidance to the heat source *** can not meet the current energy-saving and emission-reduction *** this paper,an optimal heat load dispatching model is set up under the premise of power load distribution of thermal power *** kinds of heat loads including to high-pressure,medium-pressure and low-pressure are taken into account in the scheduling *** to the characteristics of the problem,Brain Storming Optimization(BSO) algorithm is used to get the optimal distribution *** comparison with the original scheme shows the correctness of the model and the optimization ***,the optimization ability and effectiveness of brain storming optimization algorithm in solving complex optimization problems are verified by comparing the proposed algorithm with the other different optimization algorithms,such as Particle Swarm Optimization(PSO) and Differential Evolution(DE) algorithm.

关键词： Heating Dispatching Brain Storming Optimization Algorithm Load Distribution

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Multi-Source Dynamic Time Series Data Processing Method Based on Comprehensive Weighted Fusion 12

Multi-Source Dynamic Time Series Data Processing Method Base...

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12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2022

作者： Geng, Huapin Yuan, Feimeng Suo, Bin Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China Beijing Electro-mechanical Engineering Institute Beijing China School of Mathematics Southwest Jiaotong University Chengdu China School of Information Engineering Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9781839538360

Aiming at the fusion problem of multi-source heterogeneous dynamic data sources, based on the subjective and objective comprehensive weighting idea, an efficient multi-source dynamic data fusion algorithm is proposed. First, the outlier detection method based on linear regression model is used to preprocess the multi-source dynamic time series data. Then, determine the weight of the dynamic data. The prior weight of the data source is determined by the AHP method, cyclic scoring method, etc., and the posterior weight is determined according to the similarity between the time series data. Based on the Euclidean distance, a similarity measure between different data sources and a similarity measure between different dynamic data samples are constructed respectively. The former is used for the fusion of dynamic data standard deviation expectations, and the latter is used for the fusion of mean expectations. Finally, the comprehensive weight of each dynamic time series of each data source is obtained, and the comprehensive weighted fusion method is used to obtain the expected value and standard deviation of the dynamic response. The analysis of the calculation example shows that the proposed algorithm has higher computational efficiency than the method that treats dynamic data as several discrete static data. © 2022 IEEE.

关键词： Data fusion

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Path Planning for Anti-Ship Missile Using Tangent Based Dubins Path

Path Planning for Anti-Ship Missile Using Tangent Based Dubi...

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International Conference on intelligent Autonomous systems (ICoIAS)

作者： Long Cheng Hao Lu Teng Lei Jun Chen School of Electronics and Information Northwestern Polytechnical University Xi’an China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

Path planning is a significant component to achieve successful operation of anti-ship missile in dynamic battlefield environment. A threat-free path planning algorithm based on geometric tangent Dubins path is proposed in this paper. First, the constraints need to be satisfied for threat-free path planning are presented, and the problem model of missile path planning is constructed. Then, the geometric tangent between two threat circles is taken advantage of guiding the missile to avoid the threat, and the basic turn and straight line path is generated based on Dubins path. At last, the adaptability of the proposed algorithm in complex battlefield environment is verified by simulations. The results show that the Dubins path avoidance method based on geometric tangent can meet the requirements of flight path, obstacle avoidance and real-time planning, which make it have a strong application prospect.

关键词： Missiles Path planning Collision avoidance Planning Radar detection Turning Real-time systems

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Leader-follower Formation control for a Multi-missile system via Deep Reinforcement Learning

Leader-follower Formation Control for a Multi-missile System...

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Chinese control and Decision Conference, CCDC

作者： Jianfeng Li Keyuan Yu Shenmin Song Center for Control Theory and Guidance Technology School of Astronautics Harbin Institute of Technology Harbin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The methods for the formation tracking control problem are mainly based on nonlinear control theories. Usually, the control effect of such methods heavily depends on an accurate control model and a lot of controller parameters need to be selected properly. To cope with these shortcomings, a novel reinforcement learning-based formation controller is proposed for a leader-follower multi-missile system. The deep deterministic policy gradient (DDPG) algorithm is implemented to generate the formation control signals. An interaction environment containing a leader and a follower is formulated, in which a model-free optimal control policy is obtained by a training process. The convergent actor-network is tested for a multi-missile system. Simulation results show that the formation control objective can be rapidly achieved in spite of the change of leader maneuvers. There is no need to change the hyperparameters of the policy for different kinds of formation scenarios, which shows great advantages over the conventional nonlinear control theory-based methods in terms of parameter selection.

关键词： Training Three-dimensional displays Target tracking Simulation Heuristic algorithms Process control Optimal control

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Vibration-Based Fault Diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy

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Chinese Journal of Electronics 2024年第3期33卷 803-813页

作者： Yongkui SUN Yuan CAO Peng LI Guo XIE Tao WEN Shuai SU National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Electronic and Information Engineering Beijing Jiaotong University Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

As one of the most important railway signaling equipment,railway point machines undertake the major task of ensuring train operation *** fault diagnosis for railway point machines becomes a hot *** the advantage of the anti-interference characteristics of vibration signals,this paper proposes an novel intelligent fault diagnosis method for railway point machines based on vibration signals.A feature extraction method combining variational mode decomposition(VMD) and multiscale fluctuation-based dispersion entropy is developed,which is verified a more effective tool for feature ***,a two-stage feature selection method based on Fisher discrimination and ReliefF is proposed,which is validated more powerful than single feature selection ***,support vector machine is utilized for fault *** comparisons show that the proposed method performs *** diagnosis accuracies of normal-reverse and reverse-normal switching processes reach 100% and 96.57% ***,it is a try to use new means for fault diagnosis on railway point machines,which can also provide references for similar fields.

关键词： Fault diagnosis Railway point machine Vibration signal Variational mode decomposition Two-stage feature selection

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Distributed Multi-Sensor Multi-View Fusion of PHD Filter for Maneuvering Targets

Distributed Multi-Sensor Multi-View Fusion of PHD Filter for...

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IEEE International Conference on Radar

作者： Cong Peng Lei Chai Wei Yi Linxiu Chen School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781665468893

This paper addresses the problem of maneuvering multi-target tracking by a network of sensors having different and limited fields of view (FoV s). Each local sensor runs the Gaussian Mixture Probability Hypothetical Density (GMPHD) filter. Due to the target maneuver, based on a single motion model, severe tracking performance degradation can be observed. We propose to use multi-model (MM) method to realize the adaptation to motion characteristic so as to overcome maneuverability of targets. Then considering FoV s of sensors in the network are different and limited, the standard weighted arithmetic average (WAA) fusion is no longer applicable and leads to an underestimation of the target number. Therefore, we use state-dependent WAA (SD-WAA) fusion rule, which performs the WAA in a more robust way by calculating a set of state-related fusion weights. Numerical experiment is designed to demonstrate the efficacy of the proposed method.

关键词： Degradation Adaptation models Target tracking Fuses Radar Sensor fusion Sensor phenomena and characterization

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