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Distributed Optimization and Scaling Design for Solving Sylvester Equations

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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Machine Learning Security Analysis Framework Based on Threat Matrix

Machine Learning Security Analysis Framework Based on Threat...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Hu Li Qiongmin Ma Ming Zhang Xiaohui Kuang National Key Laboratory of Science and Technology on Information System Security Beijing China National Key Laboratory for Complex Systems Simulation Beijing China

Machine Learning and Artificial Intelligence technology accelerates technological progress and promotes social development, but also brings many security problems. Machine learning models may be affected, deceived, controlled or destroyed at different stages, e.g. training and inference, which may lead to serious consequences. In view of the main stages of the life cycle of machine learning, we summarizes the related attack and defense technologies based on threat matrix, makes a comprehensive and systematic analysis on the security issues of machine learning, and puts forward several security protection suggestions for intelligent information systems.

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NUDIF: A Non-Uniform Deployment Framework for Distributed Inference in Heterogeneous Edge Clusters

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Future Internet 2025年第4期17卷 168-168页

作者： Li, Peng Qing, Chen Liu, Hao National Key Laboratory of Complex Aviation System Simulation Chengdu610036 China Southwest China Institute of Electronic Technology Chengdu610036 China Beijing100876 China

Distributed inference in resource-constrained heterogeneous edge clusters is fundamentally limited by disparities in device capabilities and load imbalance issues. Existing methods predominantly focus on optimizing single-pipeline allocation schemes for partitioned sub-models. However, such approaches often lead to load imbalance and suboptimal resource utilization under concurrent batch processing scenarios. To address these challenges, we propose a non-uniform deployment inference framework (NUDIF), which achieves high-throughput distributed inference service by adapting to heterogeneous resources and balancing inter-stage processing capabilities. Formulated as a mixed-integer nonlinear programming (MINLP) problem, NUDIF is responsible for planning the number of instances for each sub-model and determining the specific devices for deploying these instances, while considering computational capacity, memory constraints, and communication latency. This optimization minimizes inter-stage processing discrepancies and maximizes resource utilization. Experimental evaluations demonstrate that NUDIF enhances system throughput by an average of 9.95% compared to traditional single-pipeline optimization methods under various scales of cluster device configurations. © 2025 by the authors.

关键词： Deep neural networks

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A Self-Biased 210-260 GHz Frequency Tripler Based on Planar Schottky Diodes 9

A Self-Biased 210-260 GHz Frequency Tripler Based on Planar ...

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9th IEEE MTT-S International Wireless Symposium, IWS 2022

作者： Wang, Li Zhang, Yong Wei, Haomiao University of Electronic Science and Technology of China Defense Key Laboratory of Ehf Complex System Chengdu611731 China

ISBN: (数字)9781665481977

ISBN: (纸本)9781665481977

This paper describes the design and demonstration of a hybrid integrated 210260 GHz self-biased frequency tripler based on planar Schottky diodes. Two Schottky diodes are placed in reversed parallel on the circuit. Then, a self-bias resistor is connected to the circuit to provide a DC bias. The bias voltage is changed by adjusting the resistances, so that the tripler can fit in the best working state. In addition, the current saturation effect is considered in the nonlinear simulation of the circuit to obtain more accurate simulated results. The proposed tripler was fabricated on a 50-um thickness quartz substrate circuit and housed in split waveguide blocks. Measured results show that the tripler exhibits a peak output power of 6.4 dBm at 220 GHz with 20 dBm pumped power and a wide 3 dB bandwidth of 21 % from 210 GHz to 260 GHz with a conversion efficiency above 2.2%. © 2022 IEEE.

关键词： Schottky barrier diodes

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Antenna Position and Power Allocation Design for Shared Aperture Sparse Linear Array 10

Antenna Position and Power Allocation Design for Shared Aper...

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10th International Conference on Control, Automation and Information sciences, ICCAIS 2021

作者： Liu, Ruitao Wei, Wenqiang Yu, Xianxiang Lu, Qinghui Cui, Guolong Fang, Xueli Zhang, Lei University of Electronic Science and Technology of China Chengdu China Key Laboratory of Complex Aeronautical System Simulation Beijing China

ISBN: (纸本)9781665440295

This paper considers the joint design of the antenna position and power allocation in a shared aperture sparse phased array. We introduce a new optimization model relying on the minimization of the Peak Sidelobe Levels (PSL) of beampattern under the antenna position constraints, mainlobe constraints and transmit power constraints. In order to tackle the non-convex problem, we resort to a Modified Membrane Algorithm based on P systems (MMAPS) along with its each iteration executed in parallel. Finally, numerical simulations verify the effectiveness of the proposed algorithm. The results highlight that MMAPS can obtain a better beampattern in terms of the PSL in comparison with the available methods. © 2021 IEEE.

关键词： Iterative methods

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DOA Estimation of Distributed mmWave Radar system via Fast Iterative Adaptive Approach 10

DOA Estimation of Distributed mmWave Radar System via Fast I...

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10th International Conference on Control, Automation and Information sciences, ICCAIS 2021

作者： Wei, Wenqiang Liu, Ruitao Yu, Xianxiang Lu, Qinghui Cui, Guolong Fang, Xueli Zhang, Lei University of Electronic Science and Technology of China Chengdu China Key Laboratory of Complex Aeronautical System Simulation Beijing China

ISBN: (纸本)9781665440295

In this paper, we propose a fast algorithm based on iterative adaptive approach to address the DOA estimation for the distributed mmWave radar system under the challenging conditions of a single snapshot and coherent sources. In order to improve angular resolution, let each transmit sensor emit the same signal in time-sharing to construct a large aperture virtual array. We first roughly derive azimuth angles with one mmWave radar using conventional beamforming. Then, resorting to iterative adaptive approach, we search for the accurate DOA estimations within a very small region based on the estimated azimuth to improve computational efficiency and angular resolution. Finally, numerical simulation results are provided to demonstrate superior performance of the proposed algorithm over some counterparts. © 2021 IEEE.

关键词： Computational efficiency

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Two-Stage Point Cloud Super Resolution with Local Interpolation and Readjustment via Outer-Product Neural Network

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Journal of systems science & complexity 2021年第1期34卷 68-82页

作者： WANG Guangyu XU Gang WU Qing WU Xundong School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Key Laboratory of Complex Systems Modeling and Simulation Ministry of EducationHangzhou 310018China

This paper proposes a two-stage point cloud super resolution framework that combines local interpolation and deep neural network based readjustment. For the first stage, the authors apply a local interpolation method to increase the density and uniformity of the target point cloud. For the second stage, the authors employ an outer-product neural network to readjust the position of points that are inserted at the first stage. Comparison examples are given to demonstrate that the proposed framework achieves a better accuracy than existing state-of-art approaches, such as PU-Net, Point Net and DGCNN(Source code is available at https://***/qwerty1319/PC-SR).

关键词： Neural network outer-product network point cloud super resolution

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Length Estimation of Ships through Dual-radar Collaborative Observation

Length Estimation of Ships through Dual-radar Collaborative ...

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IET International Radar Conference 2023, IRC 2023

作者： Hu, Yihang Ai, Xiaofeng Wang, Manxi Zheng, Yuqing 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 Luoyang471003 China

ISBN: (数字)9781837240982

ISBN: (纸本)9781839539954

The length of ships plays an important role in target classification and recognition, but to estimate ships' length in real time is difficult by traditional single radar observation. To solve this problem, the dual-radar collaborative observation method is proposed in this study to jointly calculate the ship length, by using the obtained HRRP (High Range Resolution Profile). The effect of SNR (signal-to-noise ratio) on estimation error of system parameters and the ship length estimated is simulated. The results show that the method can estimate the ship length effectively, the estimation error of the ship length is not more than one meter under high SNR (more than 10dB), which provides a reference for feature extraction and target recognition of ships. © The Institution of Engineering & technology 2023.

关键词： Radar target recognition

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A Method for Marine Ship Formation Intention Recognition based on BiConvLSTM-Attention 3

A Method for Marine Ship Formation Intention Recognition bas...

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3rd International Conference on Electronic Information Engineering and Computer science, EIECS 2023

作者： Xi, Yunqiao Zhang, Junping Liu, Jian Wang, Linlin Xu, Hongfeng Chen, Linxiu Jiang, Jixiang Harbin Institute of Technology School of Electronics and Information Engineering Harbin China Institute of Electrical and Mechanical Engineering Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9798350316674

To solve the problem that current dynamic intention recognition methods fail to make full use of time domain variation information between multi-temporal group targets, which leads to low accuracy of intention recognition. This paper proposes a bidirectional convolutional long short term memory-attention network for marine formation target intention recognition. The network takes the multi-source trajectory data of the marine ship formation target as the input, extracts and uses the target change rule and time domain characteristics of the Marine formation data, and trains the model to have the ability to independently learn the weight of information in different time periods. The simulation results show that the method has good performance and can meet the needs of practical application. © 2023 IEEE.

关键词： intention recognition long short-term marinship formation temporal features

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Multi-view and Multi-target Association Algorithm for Unmanned Aerial Vehicle Clusters Based on Siamese Network

Multi-view and Multi-target Association Algorithm for Unmann...

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Zhang, Hang Song, Chuang Zheng, JiangBin Hao, MingRui School of Software Northwestern Polytechnical University Shaanxi Xi’an710072 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9789811966125

The multi-target association algorithm of unmanned aerial vehicles (UAV) clusters in unknown environments is used to solve the problem of multiple UAVs, which can correctly determine whether the data from different perspectives contain the same target and eliminate redundant targets. This problem is different from that of multi-target tracking under the same perspective. However, the inherent relationship and differences of data from various perspectives of UAV clusters contribute to the problem of inaccurate target positioning information caused by low-cost detectors and inertial measurement units of UAVs and the constraints of limited communication capability between UAV clusters. Thus, using multi-perspective data is the key to improving the accuracy of multi-perspective situational awareness. This paper proposes a multi-view and multi-target association algorithm for UAV clusters based on the Siamese network. This algorithm can realise the problem of multi-view and multi-target identity judgement with high accuracy, achieve the requirements of high accuracy and high generalisation performance with the depth model based on convolution neural networks, extract the depth features from the optical image and map the original data to the depth feature space. Thus, the same target is matched by comparing the distance measurement of multi-view data in the feature space. The algorithm is verified by an aircraft cluster flight test, which proves the feasibility and accuracy of the algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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