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21st Asia Simulation Conference, AsiaSim 2022

作者： Zhang, Peng Li, Wei Chen, Qingao Tong, Jiahui Control and Simulation Center Harbin Institute of Technology Harbin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9789811991974

When conducting complex simulation experiments, experimental factor screening is of great significance to improve the effectiveness and efficiency of the simulation. Aiming at the problems of unsatisfactory accuracy and efficiency of existing experimental factor screening methods, a simulation experiment factor screening method based on combinatorial optimization (EFS-CO) was designed. EFS-CO abstracts the experimental factor screening problem into a combinatorial optimization problem, designs the experimental factor evaluation criteria based on the support vector machine model, uses it as the fitness function of the genetic algorithm, and finally obtains the significant experimental factor. At the same time, to eliminate the influence of the randomness of the genetic algorithm on the screening results, the screening results of the experimental factors were determined based on the results of multiple experiments. Finally, the effectiveness of the method is verified by example analysis and comparing the results with the traditional experimental factor screening method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Combinatorial optimization

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A credibility allocation approach of complex simulation systems 35

A credibility allocation approach of complex simulation syst...

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35th European Modeling and Simulation Symposium, EMSS 2023

作者： Zhang, Huan Li, Wei Geng, HuaPin Ma, Ping Yang, Ming Control and Simulation Center Harbin Institute of Technology Harbin150080 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150080 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9788885741881

complex simulation systems usually need to satisfy the credibility requirements and the credibility of complex simulation systems is inextricably linked to the credibility of simulation sub-systems. Hence, in order to guarantee the credibility of complex simulation systems meets requirements, it is necessary to allocate the credibility of simulation sub-systems. complex simulation systems are composed of several correlative simulation sub-systems and their credibility has correlation relationships, which makes it difficult to obtain the credibility of simulation sub-systems quickly. The existing works mostly ignore the relationships between the sub-systems and are difficult to generate the credibility allocation results of simulation sub-systems. In this paper, we model the credibility allocation problem as a probabilistic inference problem based on the pairwise Markov random field (PMRF). In addition, we apply several inference algorithms to perform the inference exactly and approximately in the credibility PMRF. Finally, experiments are conduted on the air defense combat complex simulation system, and the experiment results demonstrate that the proposed method can obtain the credibility allocation results quickly and accurately. © 2023 The Authors.

关键词： Inference engines

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Scribble-Supervised Video Object Segmentation

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IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 339-353页

作者： Peiliang Huang Junwei Han Nian Liu Jun Ren Dingwen Zhang Brain and Artificial Intelligence Laboratory School of AutomationNorthwestern Polytechnical UniversityXi’an 710072China IEEE Department of Engagement Services Mohamed Bin Zayed University of Artificial IntelligenceAbuDhabiUnited Arab Emirate Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory BeijingChina

Recently,video object segmentation has received great attention in the computer vision *** of the existing methods heavily rely on the pixel-wise human annotations,which are expensive and time-consuming to *** tackle this problem,we make an early attempt to achieve video object segmentation with scribble-level supervision,which can alleviate large amounts of human labor for collecting the manual ***,using conventional network architectures and learning objective functions under this scenario cannot work well as the supervision information is highly sparse and *** address this issue,this paper introduces two novel elements to learn the video object segmentation *** first one is the scribble attention module,which captures more accurate context information and learns an effective attention map to enhance the contrast between foreground and *** other one is the scribble-supervised loss,which can optimize the unlabeled pixels and dynamically correct inaccurate segmented areas during the training *** evaluate the proposed method,we implement experiments on two video object segmentation benchmark datasets,You Tube-video object segmentation(VOS),and densely annotated video segmentation(DAVIS)-*** first generate the scribble annotations from the original per-pixel ***,we train our model and compare its test performance with the baseline models and other existing *** experiments demonstrate that the proposed method can work effectively and approach to the methods requiring the dense per-pixel annotations.

关键词： Convolutional neural networks(CNNs) scribble self-attention video object segmentation weakly supervised

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Phase Unwrapping Based on CFAR Detection for Multi-baseline InISAR

Phase Unwrapping Based on CFAR Detection for Multi-baseline ...

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2021 CIE International Conference on Radar, Radar 2021

作者： Ma, Meng Qu, Guangzhou Liu, Shuaishuai Yang, Yanjiao Pei, Hucheng Chi, Qingxi Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9781665498142

A phase unwrapping method based on constant false alarm rate (CF AR) detection is proposed for multi-baseline interferometric inverse synthetic aperture radar (InISAR). Aiming at the problem of ambiguity number estimation error under the condition of phase noise, Chinese remainder theorem (CRT) and cluster analysis are utilized to acquire the probability distribution of phase noise. By introducing CFAR detection, the novel method reduced the error probability of ambiguity number estimation. Through simulation, it is verified that the novel method can effectively suppress phase noise and obtain accurate phase unwrapping results. © 2021 IEEE.

关键词： CFAR multi-baseline InISAR interferometric phase phase noise phase unwrapping

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Image Matching Method for SAR and Optical Image Based on Siamese Network 1

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3rd International Conference on Autonomous Unmanned systems, ICAUS 2023

作者： Yang, Yanjiao Wang, Ziwei Qu, Guangzhou Ma, Meng Liu, Shuaishuai Chi, Qingxi Beijing Electro-Mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9789819710874

ISBN: (纸本)9789819710867

Aiming at the difficulty of the similarity extraction between the SAR and optical satellite image, an image Matching method for SAR and Optical image based on siamese network is studied. The radar system can provide SAR images while also providing SAR distance and other measurement information. Optical satellites can obtain real-time optical images and provide corresponding longitude and latitude information. Based on the satellite positioning algorithm, the cumulative error of sins can be corrected by kalman filter using SAR and optical image matching results and SAR measurements. Simulation results show the effectiveness of the algorithm. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Synthetic aperture radar

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Finite Time Cooperative Guidance Law for Multiple UAVs with Intermittent Communication Topologies

Finite Time Cooperative Guidance Law for Multiple UAVs with ...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Lyu, Teng Xu, Cheng Li, Hongchun Hu, Song Zhang, Daochi System Control and Intelligent Agent Cooperation Laboratory Science and Technology on Complex Beijing China Beijing Electro-mechanical Engineering Institute Beijing China

ISBN: (纸本)9781728137926

To solve the problem of multiple Unmanned Aerial Vehicles (UAVs) with intermittent communication topologies simultaneously attack a stationary target, a time cooperative guidance law is studied in this paper. First, a novel time cooperative guidance model for multiple UAVs is constructed based on UAVs-target relative motion equations. Then, a cooperative guidance law is designed based on consensus theory, which can ensure multiple UAVs' impact times realize finite time consensus under intermittent topologies. After UAVs' impact times become to be the same, traditional PN guidance law is applied in all UAVs to guarantee that the UAVs can attack the target precisely without influencing the consensus of impact times. Finally, the validity of the proposed method is shown by numerical simulations. © 2019 IEEE.

关键词： Topology

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Solving Ascent Trajectory Optimization Problems by Radua Pseudospectral Method

Solving Ascent Trajectory Optimization Problems by Radua Pse...

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2022 International Symposium on intelligent Robotics and systems, ISoIRS 2022

作者： Deng, Liaochao Xu, Cheng You, Weishuai Beijing Institute of Mechanical and Electrical Engineering Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781665455213

Ascent trajectory optimization problem of air-breathing hypersonic vehicles is a highly nonlinear and nonconvex problems. Most of the early works focus on the traditional indirect method, which needs to derive the complete first-order necessary conditions of the trajectory optimization problem. The derivation process is too complicated and error-prone. Additionally, indirect method has a high demand on the initial guess, and it needs to give the initial guess of covariant variables without physical significance. In this paper, we solve the ascent trajectory optimization problem directly using Radau Pseudospectral Method (RPM). Firstly, the complex three-dimensional ascent trajectory optimization problem is established in detail. Conmmon inequality path constraints including those on dynamic pressure and aerodynamic bending moment are taken into account. The performance index is given as maximizing the final mass considering minimizing the fuel consumption. Subsequently, the ascent trajectory optimization problem is transformed into a nonlinear programming problem (NLP) by RPM. Finally, the ascent trajectory optimization for Generic Hypersonic Aerodynamic Model Example (GHAME) is solved by RPM and the optimal results demonstrate the rapidity, effectiveness and high precision of RPM. The comparison between optimal trajectories with and without path constraints shows that path constraints increase fuel consumption. © 2022 IEEE.

关键词： Hypersonic vehicles

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complex-Valued Signal Capsule Network for Automatic Modulation Classification

Complex-Valued Signal Capsule Network for Automatic Modulati...

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International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Yang, Shuyuan Feng, Zhixi Wang, Nengguo Bu, Yunong Hu, Hao Luoyang471003 China Xidian University Shannxi China Science and Technology On Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9789811694912

Most current deep learning (DL) based AMC methods often require a large number of labeled samples to drive itself optimization study, so as to achieve a more superior performance. However, for many AMC tasks with a small amount of data, the AMC method based on deep learning still has many challenges, which makes the deep neural network model trained on a small dataset lack effective migration effect, leading to the failure to achieve better performance effect in the testing process. In this paper, we propose a novel AMC method based on complex-valued Signal Capsule Network (CSCN). Compared with other DL based methods, the proposed CSCN can achieve a better performance under the condition of small dataset and low SNR on the well-known deepsig dataset RML2016.10a and RML2016.10b. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Design of Reinforcement Learning Environment for Multiple UAV Area Coverage Search

Design of Reinforcement Learning Environment for Multiple UA...

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International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Sun, Yinjiang Zhang, Rui Liu, Xiaoheng Liang, Wenbao Xu, Cheng Beijing Electro-Mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9789811694912

Unmanned aerial vehicle (UAV) plays an increasingly important role both in civil and military fields. As a typical mission, area coverage search is a primary measure for reconnaissance and surveillance of unknown areas. Due to the limited information about the mission area, UAV needs adaptiveness to the dynamic changes of the area and makes decisions based on local observation. It puts higher demands on the response speed of UAV, and traditional planning methods are tough to be directly applied. The problem becomes more noticeable for multiple UAV because of the heterogeneous function and large scale, and new solutions are urgently required. In this paper, the feasibility of reinforcement learning (RL) for performing area coverage search is explored, and an environment is designed for RL training and evaluation. Simulation results show that multiple UAV can cooperate and perform the mission with pretty high coverage. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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Adversarial Example Attack and Defence of Object Recognition: A Survey

Adversarial Example Attack and Defence of Object Recognition...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Tan, Wei Zhao, Jiajia Liang, Xinkai Lu, Hanchen Song, Baogang Guan, Hao Science and Technology On Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Mechano-Electronic Engineering Institute Beijing China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

Deep learning-based intelligent object recognition algorithm has been widely applied in object detection, auto driving, ect. However, deep neural network is very vulnerable because of its high-dimensional linearization. The object recognition results can be easily mis-leaded through a tiny disturbance in the original image. This kind of sample that adds tiny disturbance to the original image is called adversarial example. The adversarial example attack is a factor that must be considered in the design of robust object recognition algorithm, that is, the need for adversarial defence. An in-depth understanding of adversarial attack can help the model defend successfully. In this survey, an introduction of adversarial attack and defense aiming to object recognition algorithm at the present stage is summarized, and the challenges to be faced and the development trend in the future are pointed out. © 2022 IEEE.

关键词： Object recognition

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