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2021 IEEE International Conference on Unmanned systems, ICUS 2021

作者： Xiaofei, Dong Zhang, Ren School of Automation Science and Electrical Engineering Beihang University Science and Technology on Complex System Control Intelligent Agent Cooperation Laboratory Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9780738146577

This paper investigates the cooperative guidance issue for multiple unmanned aerial vehicles (UAVs) for simultaneous attacks on maneuvering targets with a prescribed angle formation. Each UAV is accessible to the relative information with respect to the target while the absolute information of the target is unavailable. Also, each UAV just interacts with its neighbors locally. By introducing the integral sliding mode technique, a finite-time observer for the exact estimation of the absolute information of the target is designed. With the help of such an observer, a distributed cooperative guidance algorithm is developed such that both the time-to-go estimate consensus and angle formation objectives are achieved in finite time given that the undirected graph is connected. Finally, a simulation example is presented to validate the efficiency of the suggested guidance algorithm. © 2021 IEEE.

关键词： Antennas

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Manmade Target Scattering Structure Null-Pol Recognition and Application

Manmade Target Scattering Structure Null-Pol Recognition and...

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2021 CIE International Conference on Radar, Radar 2021

作者： Wu, Guoqing Chen, Siwei Li, Yongzhen Wang, Xuesong State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System School of Electronic Science and Engineering National University of Defense Technology ChangSha China

ISBN: (纸本)9781665498142

Target's optimal polarization is the fundamental theory of radar polarization. Null-Pol, also called Co-Pol Null, is one of the significant polarization states, through which other optimal polarization states can be derived. Null-Pol theory is widely applied in polarization reception match, contrast enhancement and so on. This paper revealed the advantage of Null-Pol theory in manmade target scattering structure recognition for the first time. The complex polarization ratio plane and polarization response characteristics were combined and the Null-Pol 3D response patterns were represented, which can reveal the difference of canonical scattering structures. Then, the Null-Pol scattering structure recognition method of manmade targets is proposed. The scattering centers are extracted from radar images. The scattering structure type can be identified by matching the Null-Pol response characteristic vectors with canonical scatters. Through the analysis of electromagnetic computation data of manmade targets, the proposed Null-Pol scattering structure recognition method can identify the scattering structure types of manmade targets. Especially, the results of symmetric targets have better performance than Cameron decomposition, which is more consistent with target's actual scattering structure types. © 2021 IEEE.

关键词： 3D response characteristic pattern manmade target Null-Pol scattering structure recognition target's optimal polarization

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Vehicle detection based on point cloud intensity and distance clustering 5

Vehicle detection based on point cloud intensity and distanc...

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2020 5th International Seminar on Computer technology, Mechanical and Electrical Engineering, ISCME 2020

作者： Wang, Zhao Wang, Xing Fang, Bin Yu, Kun Ma, Jie School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory of Multispectral Information Processing Technology Huazhong University of Science and Technology Wuhan430074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering. © Published under licence by IOP Publishing Ltd.

关键词： Optical radar

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Modeling Method of Aircraft Simulation Based on Formal Description of Petri Net 18th

Modeling Method of Aircraft Simulation Based on Formal Des...

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18th Chinese Intelligent systems Conference, CISC 2022

作者： Zhan, Tianming Huang, Yu Geng, Huapin Tong, Jiahui TAIJI Computer Co. Ltd Beijing100102 China Peking University Beijing100871 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100190 China Beijing Electro-Mechanical Engineering Institute Beijing102299 China

ISBN: (纸本)9789811962028

The simulation of aircraft guidance and control system has characteristics such as multiple model classification, large parameter influence,complex information interaction and so on. The traditional code-level model development method has many limitations, such as time consuming, low reusability, low modeling efficiency, and model quality can not be guaranteed. Based on the concurrency, graphic representation, mathematics and simulation characteristics of Petri net, this paper studies the aircraft simulation modeling method of formal description based on Petri net, constructs a general modeling development and integration software tool that can carry out component, parametric and graphical modeling, solves the problems of multi granularity model modeling and automatic generation of real-time simulation model code, and realizes the visual graphic modeling and comprehensive integration of different granularity models such as components, modules and meta models. The research method has been successfully applied in the simulation modeling of an aircraft. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Petri nets

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Multiplexed model predictive attitude control for nadir-pointing satellite using only magnetorquers

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Control Engineering Practice 2025年 162卷

作者： Yang, Sen Ling, Keck Voon Wang, Zhenhua Li, Huiping School of Astronautic Harbin Institute of Technology Harbin150001 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Harbin150001 China School of Marine Science and Technology Northwestern Polytechnical University Xi'an710072 China

Due to the low failure risk of magnetorquers, the control strategy based on magnetic actuators can significantly enhance the reliability of the satellite attitude control system. In light of this advantage, this paper conducts research on model predictive control (MPC) algorithm for nadir-pointing satellites equipped only with magnetorquers. Firstly, to facilitate controller design, the linear time-varying (LTV) model for nadir-pointing satellites is derived by linearizing the nonlinear equations around the operating point. Then, leveraging this linearized representation, an LTV multiplexed MPC (MMPC) method with exponential weighting is proposed. The MMPC scheme allows for faster sampling rates and quicker responses to disturbances, thereby enhancing the overall performance of the satellite attitude control system while retaining the benefits of MPC. Finally, compared with a state-of-the-art SMPC method, the proposed method is particularly advantageous in situations where computational resources are limited, specifically for satellites that rely only on onboard computing capabilities. © 2025

关键词： Nonlinear equations

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Disorder-induced delocalization and reentrance in a Chern-Hopf insulator

arXiv

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arXiv 2024年

作者： Bera, Soumya Dutta, Ivan Moessner, Roderich Saha, Kush Department of Physics Indian Institute of Technology Bombay Mumbai400076 India National Institute of Science Education and Research Jatni752050 India Homi Bhabha National Institute Training School Complex Anushakti Nagar Mumbai400094 India Max-Planck Institute for the Physics of Complex System Dresden Germany

The Chern-Hopf insulator is an unconventional three-dimensional topological insulator with a bulk gap and gapless boundary states without protection from global discrete symmetries. This study investigates its fate in the presence of disorder. We find it stable up to moderate disorder by analyzing the surface states and the zero energy bulk density of states using large-scale numerical simulation and the self-consistent Born approximation. The disordered Chern-Hopf insulator shows reentrant behavior: the disorder initially enhances the topological phase before driving it across an insulator-diffusive metal transition. We examine the associated critical exponents via finite-size scaling of the bulk density of states, participation entropy, and two-terminal conductance. We estimate the correlation length exponent ν 1.0(1), consistent with the clean two-dimensional Chern universality and distinct from the integer quantum Hall exponent. © 2024, CC BY.

关键词： Topological insulators

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Method for constructing multi-dimensional feature map of malicious code 5

Method for constructing multi-dimensional feature map of mal...

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2020 5th International Seminar on Computer technology, Mechanical and Electrical Engineering, ISCME 2020

作者： Ma, Haocong Zhang, Ji Zhou, Junhua Zhai, Xiang Xue, Junjie Ji, Hang State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing100854 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing100854 China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing100854 China

Malicious code is characterized by a large number of types, rapid increase in number, continuous update of transmission routes, and continuous enhancement of back analysis and back detection methods. Therefore, how to effectively detect and analyze malicious code has been a problem of great concern. This paper studies the features of binary file and disassembly file of malicious code, introduces the concept of information gain, and proposes a method to construct the multi-dimensional characteristic graph of malicious code. Finally, the convolutional neural network is used to classify the multi-dimensional feature graph of malicious code, which provides a new idea for the feature extraction of malicious code. © Published under licence by IOP Publishing Ltd.

关键词： Malware

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SAR Image Registration Based on Feature Points Using Improved CSP-DenseNet

SAR Image Registration Based on Feature Points Using Improve...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

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

Synthetic aperture radar (SAR) image registration is widely used in integrated navigation system with development of guidance system in sensors and other applications. However, there exists some difficulty because of the quality and imaginary principle of SAR images. In this article, a brand-new method based on feature points using improved CSP-DenseNet is proposed to solve the problems of SAR image registration with weak and noisy texture. Deep features of interest points in SAR images are extracted using the proposed network from the crops of search image and template image respectively. The method has an advantage of preserving abundant feature information in SAR images under the influence of insufficient image information and noise of SAR images. The CSP-DenseNet architecture has a cross-stage construction optimizing fused by CSP-Net and DenseNet to extract matching features. The network is partially connected in specific convolutional layers for feature reusing and computation resources saving. Then, Brute Force Matcher and RANSAC voting are successively applied for a larger number of matching pairs and high-precise matching vertexes. Experimental results on various SAR image matching methods show that the proposed method provides better performance than other approaches compared.

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DRSC: Dual-Reweighted Siamese Contrastive Learning Network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced Data

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IEEE Internet of Things Journal 2025年

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

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Attention Scale-Aware Deformable Network for Inshore Ship Detection in Surveillance Videos 1st

Attention Scale-Aware Deformable Network for Inshore Ship D...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Liu, Di Zhang, Yan Zhao, Yan Zhang, Yu National Key Laboratory of Science and Technology on Automatic Target Recognition College of Electronic Science and Technology National University of Defense Technology Changsha410073 China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783030930455

Aiming at detecting multi-scale inshore ships in surveillance videos, a one-stage detector namely Attention Scale-aware Deformable Network (ASDN) is proposed in this paper by employing two primary components including Attention Scale-aware Module (ASM) and Deformable Convolutional Network (DCN). Moreover, ASM composed of several branches of convolutions with specially designed kernels and a Convolutional Block Attention Modules (CBAM), is designed for extracting and refining non-local features of multi-scale inshore ships with large aspect ratios at the topmost feature layer. DCN is adopted to capture irregular significant features of ships by modulating input features using parameterized offsets and amplitudes at lateral connections of fine-grained feature pyramid. Experiments conducted on public Seaships7000 dataset demonstrate the contributions of ASM and DCN, and the effectiveness of our method for multi-scale inshore ship detection in surveillance videos in comparison with other Convolutional Neural Network (CNN) based methods, e.g., FPN, Libra R-CNN, SSD, YOLOv3, RefineDet. © 2021, Springer Nature Switzerland AG.

关键词： Convolution

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