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arXiv 2023年

作者： Li, Yushan Wang, Zitong He, Jianping Chen, Cailian Guan, Xinping Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

Preserving the topology from being inferred by external adversaries has become a paramount security issue for network systems (NSs), and adding random noises to the nodal states provides a promising way. Nevertheless, recent works have revealed that the topology cannot be preserved under i.i.d. noises in the asymptotic sense. How to effectively characterize the non-asymptotic preservation performance still remains an open issue. Inspired by the deviation quantification of concentration inequalities, this paper proposes a novel metric named trace-based variance-expectation ratio. This metric effectively captures the decaying rate of the topology inference error, where a slower rate indicates better non-asymptotic preservation performance. We prove that the inference error will always decay to zero asymptotically, as long as the added noises are non-increasing and independent (milder than the i.i.d. condition). Then, the optimal noise design that produces the slowest decaying rate for the error is obtained. More importantly, we amend the noise design by introducing one-lag time dependence, achieving the zero state deviation and the non-zero topology inference error in the asymptotic sense simultaneously. Extensions to a general class of noises with multi-lag time dependence are provided. Comprehensive simulations verify the theoretical findings. Copyright © 2023, The Authors. All rights reserved.

关键词： Topology

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Tracking Performance of Networked systems with Limited Bandwidth and Colored Noise

Tracking Performance of Networked Systems with Limited Bandw...

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2022 Chinese Automation Congress, CAC 2022

作者： Zhang, Bin Jiang, Xiaowei Sun, Li Zhang, Xianhe School of Automation China University of Geosciences Wuhan430074 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Statistics and Mathematics Zhongnan University of Economics and Law Wuhan430074 China School of Electrical Engineering and Automation Hubei Normal University Huangshi435002 China

ISBN: (纸本)9781665465335

This study investigated the optimal tracking performance (OTP) of multi-input multi-output (MIMO), discrete- time networked control systems (NCSs). The limits of tracking performance (TP) under the influences of bandwidth, encoding- decoding, and additive coloured Gaussian noise (ACGN) are derived using the techniques of coprime decomposition and all-pass decomposition. The results reveal the negative impact of non-minimum phase (NMP) zeros and unstable poles of the plant as well as network communication constraints on the TP of NCSs. Finally, a numerical simulation is discussed and verifies our conclusions. © 2022 IEEE.

关键词： Networked control systems

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Differential Private Discrete Noise-Adding Mechanism: Conditions, Properties, and Optimization

Differential Private Discrete Noise-Adding Mechanism: Condit...

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作者： Qin, Shuying He, Jianping Fang, Chongrong Lam, James Shanghai Jiao Tong University Department of Automation Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Beijing100816 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Shenzhen Institute of Research and Innovation Shenzhen518055 China The University of Hong Kong Department of Mechanical Engineering Hong Kong999077 Hong Kong

Differential privacy is a widely used framework for evaluating privacy loss in data anonymization. While the continuous noise-adding mechanism has been extensively studied, there is a dearth of research on discrete random mechanisms for discretely distributed data. This study addresses this gap by examining the primary differential privacy conditions and properties for general discrete random mechanisms, and investigating the trade-off between data privacy and data utility. We establish sufficient and necessary conditions for discrete ϵ-differential privacy and sufficient conditions for discrete (ϵδ)-differential privacy, with closed-form expressions for differential privacy parameters. These conditions can be applied to evaluate the differential privacy properties of discrete noise-adding mechanisms with various types of noise. Moreover, we propose an optimal discrete ϵ-differential private noise-adding mechanism under the utility-maximization framework. Here, the utility is characterized by the similarity of the statistical properties between the mechanism's input and output. Our findings suggest that the optimal class of discrete noise probability distributions in the mechanism is staircase-shaped. © 1991-2012 IEEE.

关键词： Probability distributions

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Iterative-Reweighting-Based Robust Iterative-Closest-Point Method

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Journal of Shanghai Jiaotong university(Science) 2021年第5期26卷 739-746页

作者： ZHANG Jianlin ZHOU Xuejun YANG Ming Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240China Noblelift Intelligent Equipment Co. Ltd.Huzhou 313100ZhejiangChina

In point cloud registration applications,noise and poor initial conditions lead to many false *** matches significantly degrade registration accuracy and speed.A penalty function is adopted in many robust point-to-point registration methods to suppress the influence of false ***,after applying a penalty function,problems cannot be solved in their analytical forms based on the introduction of ***,most existing methods adopt the descending *** this paper,a novel iterative-reweighting-based method is proposed to overcome the limitations of existing *** proposed method iteratively solves the eigenvectors of a four-dimensional matrix,whereas the calculation of the descending method relies on solving an eight-dimensional ***,the proposed method can achieve increased computational *** proposed method was validated on simulated noise corruption data,and the results reveal that it obtains higher efficiency and precision than existing methods,particularly under very noisy *** results for the KITTI dataset demonstrate that the proposed method can be used in real-time localization processes with high accuracy and good efficiency.

关键词： point cloud registration iterative reweighting iterative closest-point(ICP) robust localization

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Dynamic Event-Triggered Consensus on Matrix-weighted Networks:Leadless Case

Dynamic Event-Triggered Consensus on Matrix-weighted Network...

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第42届中国控制会议

作者： Lintao Dou Haoyu Wei Jian Huang Lulu Pan Haibin Shao Dewei Li the College of Intelligent Science National University of Defense Technology the Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of Chinaand Shanghai Engineering Research Center of Intelligent Control and Management

This paper examines the event-triggered consensus of the multi-agent system on matrix-weighted networks,where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the ***,a novel distributed dynamic event-triggered coordination strategy is proposed for this category of generalized networks,in which an auxiliary system is employed for each agent to dynamically adjust the triggering threshold,which plays an essential role in guaranteeing that the triggering time sequence does not exhibit Zeno *** event-triggered control protocols are proposed to guarantee leaderless consensus for multi-agent systems on matrix-weighted networks,***,the spectrum of matrix-valued weights is crucial in event-triggered mechanism design for matrix-weighted networks,generalizing those results only applicable for scalar-weighted *** proposed approach allows each agent to broadcast and receive information only at its triggering ***,simulation examples are provided to demonstrate the theoretical results.

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Active Target Tracking Using Bearing-only Measurements With Gaussian Process Learning

arXiv

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arXiv 2024年

作者： Fu, Yingbo Yang, Ziwen Zhu, Shanying Guo, Yi Chen, Cailian The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

This paper studies the tracking problem of a target with the partially unknown motion model by an active agent with bearing-only measurements using Gaussian process learning. To address this problem, a learning-planning-control framework is proposed. First, to learn and predict the target motion under mild assumptions, a Gaussian-process-based scheme is proposed, and a probabilistic uniform prediction error bound can be rigorously proved. Second, by analyzing the data dependence of the posterior covariance, we obtain an optimal relative trajectory to achieve efficient sampling. Third, to realize efficient learning, a controller to track the planned path is proposed based on the learned target motion, which can provide guaranteed tracking performance. Theoretical analysis is conducted to prove the the given probabilistic error bounds. Numerical examples and comparison with other typical methods verify the feasibility and superior performance of our proposed framework. Copyright © 2024, The Authors. All rights reserved.

关键词： Target tracking

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Infinite-Horizon Optimal Wireless control Over Shared State-Dependent Fading Channels for IIoT systems

arXiv

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arXiv 2024年

作者： Wang, Shuling Li, Peizhe Zhu, Shanying Chen, Cailian Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Heterogeneous systems consisting of a multiloop wireless control system (WCS) and a mobile agent system (MAS) are ubiquitous in Industrial Internet of Things systems. Within these systems, the positions of mobile agents may lead to shadow fading on the wireless channel that the WCS is controlled over and can significantly compromise its performance, requiring joint coordination between the WCS and MAS. Such coordination introduces different time steps and hybrid state spaces consisting of logical components and continuous components. This paper focuses on the infinite-horizon optimal control of MAS to ensure the performance of WCS while minimizing an average cost for the heterogeneous system subject to safety constraints. A state-dependent fading channel is modeled to capture interference among transmission links, as well as the effects of mobile agents’ movements on successful wireless transmission. In order to address the heterogeneous system dynamics, the optimal control problem is formulated as the optimal constrained set stabilization of the MAS by establishing a necessary and sufficient condition for the Lyapunov-like performance of WCS with the expected decay rates. Using the semi-tensor product of matrices, a constrained optimal state transition graph is constructed to encode the constrained system dynamics as well as objective function, which further reduces the problem into a minimum-mean cycle problem for the graph. By studying the properties of the graph, the feasibility is proven, and an effective algorithm is proposed for the construction of optimal input sequences. An illustrative example is provided to demonstrate effectiveness of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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Safety-Aware Scheduling of Real-Time control systems with Burst Computing Tasks

SSRN

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SSRN 2024年

作者： Cheng, Ting Liang, Yonghui Xu, Qimin Zhu, Shanying Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

In industrial sites, multiple real-time control systems often share computing resources. However, burst computing tasks may lead to the dropping of control computing subtasks, resulting in potential hazards. To address this issue, we propose a safety-aware task scheduling scheme. When burst computing tasks are triggered, this scheduling scheme adaptively releases resources from low-criticality control computing subtasks, ensuring the timely completion of burst computing tasks and keeping the control system state deviation within a safe threshold. To achieve the scheduling scheme, we propose an efficient algorithm called ADP-Opt. It decouples the complex original problem into multiple subproblems, which greatly reduces the search space and improves the solution efficiency. In the suboptimization problem, safety is maintained by limiting the upper bound of the system state's deviation to below the threshold. By introducing a penalty term in the cost function related to computing resources, we aim to balance the impact of control computing subtasks on control performance and the consumption of computing resources. Consequently, the overall cost of the control system can be minimized while releasing enough resources for burst computing tasks. Simulation results show that the scheduling decisions made by ADP-Opt can approach the optimal decision by approximately 99% efficiently. © 2024, The Authors. All rights reserved.

关键词： Cost functions

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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