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Numerical simulation of the coupled dynamics model of the projectile engraving process

International Journal of Mechanical system Dynamics 2024年第2期4卷 188-201页

作者： Shujun Zhang Xiaoting Rui Hailong Yu Xiaoli Dong Institute of Launch Dynamics Nanjing University of Science and TechnologyNanjingChina National Key Laboratory of Complex Multibody System Dynamics NanjingChina

The projectile engraving process directly influences the projectile motion in-bore and impacts the firing accuracy,firing safety,and barrel life of the *** this reason,attention has been focused on this research *** address the limitations of the“instantaneous engraving”hypothesis adopted in the classical interior ballistic theory,the VUAMP user subroutine,one of ABAQUS's secondary development interfaces,is utilized in this paper to realize the modeling and numerical simulation of a coupled dynamics model of the projectile engraving *** addition to facilitating engineering applications,a polynomial fitting formula of the engraving resistance obtained by simulation is proposed and then used as a supplement to establish a closed and solvable interior ballistic model considering the projectile engraving *** comparing with test data,the simulation accuracy of the coupled dynamics model is *** results reveal that the engraving process takes 3.8 ms,accounting for 26%of the whole launch process,which takes 14.6 ms,demonstrating that the process is not *** results of this paper can serve as a reference for future studies on the coupled solution of the projectile engraving process and interior ballistics of guns or gun-like equipment.

关键词： projectile engraving process engraving resistance finite element method polynomial fitting interior ballistic simulation

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Distributed Optimization and Scaling Design for Solving Sylvester Equations

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Journal of systems science & complexity 2024年第6期37卷 2487-2510页

作者： CHENG Songsong YU Xin ZENG Xianlin LIANG Shu HONG Yiguang Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China

This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.

关键词： Distributed optimization least squares solution linear convergence rate step-size interval Sylvester equation

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Fractal autoencoder with redundancy regularization for unsupervised feature selection

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science China(Information sciences) 2025年第2期68卷 89-102页

作者： Meiting SUN Fangyu LI Honggui HAN School of Information Science and Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing Institute of Artificial Intelligence

Feature selection is a crucial step in data preprocessing because feature selection reduces the dimensionality of data by eliminating irrelevant and redundant features. Since manual labeling is expensive, unsupervised feature selection has received increasing attention in recent years. However, existing unsupervised feature selection methods tend to prioritize selecting highly correlated features over exploring feature diversity. Thus, a regularized fractal autoencoder(RFAE) method is proposed to select informative features in an unsupervised way. Specifically, the fractal autoencoder network extends autoencoders to construct a correspondence neural network and a selection neural network. The correspondence neural network exploits interfeature correlations and the selection neural network selects the informative features. A redundancy regularization strategy consists of a redundancy elimination regularization term based on the dependency between features and a sparse regularization term based on the group lasso. The redundancy regularization strategy eliminates feature subset redundancy and enhances network generalization ability. Extensive experimental results on six publicly available datasets show that the proposed RFAE outperforms the compared methods regarding clustering accuracy and classification accuracy. Moreover, the proposed RFAE achieves acceptable computation efficiency.

关键词： unsupervised feature selection fractal autoencoder correspondence neural network selection neural network redundancy regularization strategy

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Time-Varying Formation Tracking Control of Heterogeneous Multi-Agent systems With Intermittent Communications and Directed Switching Networks

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 294-296页

作者： Yuhan Wang Zhuping Wang Hao Zhang Huaicheng Yan the Shanghai Research Institute for Intelligent Autonomous Systems Tongji University the National Key Laboratory of Autonomous Intelligent Unmanned Systems the Frontiers Science Center for Intelligent Autonomous System of Ministry of Education the College of Electronic and Information Engineering the Department of Control Science and Engineering Tongji University the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education the School of Information Science and Engineering East China University of Science and Technology

Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so... 详细信息

关键词： Switches Topology Observers Formation control Event detection Switched systems Multi-agent systems Control theory Autonomous systems Stability

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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge Computing

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Ming Northeastern University School of Computer Science and Engineering Engineering Research Center of Security Technology of Complex Network System Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University School of Computer Science and Engineering Shenyang110819 China

Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system information. We first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multi-per-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Unsupervised Low-Light Image Enhancement Based on Explicit Denoising and Knowledge Distillation

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Computers, Materials & Continua 2025年第2期82卷 2537-2554页

作者： Wenkai Zhang Hao Zhang Xianming Liu Xiaoyu Guo Xinzhe Wang Shuiwang Li College of Computer Science and Engineering Guilin University of TechnologyGuilin541006China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of TechnologyGuilin541004China

Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images. Supervised methods, which utilize paired high-low light images as training sets, can enhance the PSNR to around 20 dB, significantly improving image quality. However, such data is challenging to obtain. In recent years, unsupervised low-light image enhancement (LIE) methods based on the Retinex framework have been proposed, but they generally lag behind supervised methods by 5–10 dB in performance. In this paper, we introduce the Denoising-Distilled Retine (DDR) method, an unsupervised approach that integrates denoising priors into a Retinex-based training framework. By explicitly incorporating denoising, the DDR method effectively addresses the challenges of noise and artifacts in low-light images, thereby enhancing the performance of the Retinex framework. The model achieved a PSNR of 19.82 dB on the LOL dataset, which is comparable to the performance of supervised methods. Furthermore, by applying knowledge distillation, the DDR method optimizes the model for real-time processing of low-light images, achieving a processing speed of 199.7 fps without incurring additional computational costs. While the DDR method has demonstrated superior performance in terms of image quality and processing speed, there is still room for improvement in terms of robustness across different color spaces and under highly resource-constrained conditions. Future research will focus on enhancing the model’s generalizability and adaptability to address these challenges. Our rigorous testing on public datasets further substantiates the DDR method’s state-of-the-art performance in both image quality and processing speed.

关键词： Deep learning low-light image enhancement real-time processing knowledge distillation

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Combining Local Electromagnetic Scattering and Global Structure Features for SAR Open Set Recognition

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12572-12587页

作者： Lin, Qinjie Sun, Hao Xu, Yanjie Wang, Junyu Ji, Kefeng Kuang, Gangyao National University of Defense Technology State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology Changsha410073 China

Automatic target recognition (ATR) is crucial for synthetic aperture radar (SAR) image interpretation. However, existing SAR ATR primarily rely on algorithms from the field of computer vision, many methods have not adequately considered the imaging sensitivity of SAR images. Moreover, SAR ATR typically follows the closed set assumption, but in dynamic and open real-world environments, SAR systems may encounter target classes that have not been seen during training. To address these issues, this paper proposes a SAR open set recognition (OSR) method that combines local electromagnetic scattering and global structure features. The global structural features focus on the overall relationship between target and background, while the local electromagnetic scattering features can effectively capture the scattering characteristics of target. Hence, we construct a global and local feature guidance (GLGE) network, fuses these complementary features as prior knowledge to guide deep features. Then, a class-anchor-based loss function is constructed by Anchor loss and Tuple loss, which cluster the features of known classes targets around predefined class anchors. Finally, the distance ratio is calculated to identify known classes targets and reject unknown classes targets. The experimental results on the MSTAR and SAR-ACD datasets show that our method outperforms other OSR methods in recall, precision, F1, and accuracy, demonstrating a superior performance in OSR. © 2008-2012 IEEE.

关键词： Automatic target recognition

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Time-varying formation tracking control of high-order multi-agent systems with multiple leaders and multiplicative noise

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science China(Information sciences) 2024年第12期67卷 191-207页

作者： Ruru JIA Xiaofeng ZONG Qing WANG College of Artificial Intelligence Nankai University School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation Science and Electrical Engineering Beihang University

This work discusses the time-varying formation(TVF) tracking control problem of high-order multi-agent systems(MASs) with multiple leaders and multiplicative measurement noise. With the help of Lyapunov function tools and stochastic analysis methods, the TVF tracking protocol with multiple leaders and multiplicative noise is developed based on the relative state measurements, where followers are driven to realize the target TVF while tracking the convex combination formed by multiple leaders. Then, the TVF tracking problem is converted into the mean square asymptotic stability problem of a stochastic differential equation(SDE); sufficient conditions related to the control gains are given by stabilizing the corresponding stochastic system. Moreover, a TVF tracking algorithm is presented to outline the steps of protocol ***, the theoretical results are illustrated in terms of simulation examples.

关键词： multi-agent systems multiple leaders multiplicative measurement noise stochastic differential equation formation tracking control

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Review of Formation Control and Cooperative Guidance Technology of Multiple Unmanned Aerial Vehicles

Review of Formation Control and Cooperative Guidance Technol...

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Liu, Xinyu Hao, Mingrui Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9789811966125

Aiming at the research status of formation control and cooperative guidance technology of multi unmanned aircrafts, this paper introduces various methods of formation control technology and cooperative guidance technology. It also expounds the characteristics of formation control methods such as graph theory, leader-follower formation, behavior strategy, virtual structure, artificial potential field mode and consistency mode, as well as independent, centralized and distributed cooperative guidance methods. Then the application status and technical development of intelligent methods in multi vehicles formation control and cooperative guidance are introduced. At the same time, this paper compares the advantages and problems of various methods, and prospects the future development direction of this field. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graph theory

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Collaborative Estimation of Multi-source Heterogeneous Moving Sensors for UAV Cluster 5th

Collaborative Estimation of Multi-source Heterogeneous Movin...

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5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021

作者： Yang, Xiangyu Song, Chuang Chen, Linxiu Hao, Mingrui Zhang, Hang Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9789811939976

In the cooperative combat mission of UAV cluster, the collaborative estimation of targets by airborne multi-source heterogeneous sensors can make the utmost of the multivariate detection performance of moving sensors placed in multiple spatial positions in complex confrontation environments, in order to deal with the problem of independent estimation failure of sensors carried by single aircraft under the multiple compound interference of non-cooperative targets. In this paper, the typical target motion model, estimation model and interference model are established, then a collaborative estimation method of airborne target detection sensors with different detection accuracy and different interference countermeasure capability is designed for each aircraft in the UAV cluster, based on spatial allocation strategy, nonlinear filtering, interference detection, consistency correlation and information fusion. It is verified by Monte Carlo simulation study that this method can guarantee the obtainment of continuous and accurate guidance information for each aircraft in the cluster, so as to improve the strike accuracy of each UAV and complete the strike mission. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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