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2nd IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2024

作者： Rovid, Andras Tihanyi, Viktor Cserni, Marton Csontho, Mihaly Domina, Adam Remeli, Viktor Vincze, Zsolt Szanto, Matyas Szalai, Matyas Nagy, Szabolcs Szalay, Zsolt Budapest University of Technology and Economics Faculty of Transportation Engineering and Vehicle Engineering Department of Automotive Technologies Hungary Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Control Engineering and Information Technology Hungary

ISBN: (纸本)9798350307733

Cloud-based, centralized control system architectures have been published in recent years, examining the applications, structure and validity of the idea of controlling a vehicle, or multiple vehicles directly from the cloud. The literature presents possible advantages and disadvantages of control as a Service (CaaS), as well as multiple viable applications, however no real-car real-world implementation exists to the best of our knowledge. In our experiments, we demonstrate a real-life and real-time vehicle control method that runs in the cloud and reacts adaptively to the vehicle's surroundings. In the presented tests, acquisition of the vehicle and environment state is performed from a collective perception-based digital twin that is also produced in our cloud-based Central System using data streams from our distributed sensor network. We conduct real-world physical vehicle tests, and establish that cloud-based centralized control is a viable solution, and an interesting research direction. We test our vehicle control in one mixed reality, and two real-world scenarios on the ZalaZONE proving ground in Hungary, Zalaegerszeg. Our experiments prove that cloud-based low-level vehicle control is feasible. © 2024 IEEE.

关键词： Sensor networks

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AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

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Digital Communications and Networks 2024年第2期10卷 292-303页

作者： Yuteng Xiao Kaijian Xia Hongsheng Yin Yu-Dong Zhang Zhenjiang Qian Zhaoyang Liu Yuehan Liang Xiaodan Li School of Information and Control Engineering China University of Mining and TechnologyXuzhou221116China Department of Informatics University of LeicesterLeicesterLE17RHUK The Affiliated Changshu Hospital of Soochow University Changshu215500China School of Computer Science and Engineering Changshu Institute of TechnologySuzhou215500China

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other ***,it is important to construct a digital twin ***,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted *** this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)***,we fuse the spatial-temporal graph based on the interrelationship of spatial ***,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding ***,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)*** module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like *** dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted *** on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

关键词： Adaptive adjacency matrix Digital twin Graph convolutional network Multivariate time series prediction Spatial-temporal graph

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A Spatial Modes Filtering FETD Method for 3-D Periodic Structures

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Applied Computational Electromagnetics Society Journal 2025年第2期40卷 96-102页

作者： Wang, Yixin Wei, Bing Fan, Kaihang He, Xinbo Zhao, Sihan Department of Information Technology Shaanxi Police College Xi’an710021 China School of Physics Xidian University Xi’an710071 China School of Information and Communications Engineering Xi’an Jiaotong University Xi’an710049 China Science and Technology on Electromechanical Dynamic Control Laboratory Xi’an710065 China

– A spatial modes filtering (SMF) finite-element time-domain (FETD) method with periodic boundary condition (PBC) is proposed for efficiently analyzing the electromagnetic characteristics of 3-D periodic structures with partial fine structures. The system matrices of FETD become asymmetrical because of the introduction of PBC, which prevents the system eigenvalue analysis. By decomposing the system matrix into PBC-independent and PBC-related parts, the unstable spatial modes under the given large time step can be found and removed from the symmetrical PBC-independent system matrices. Then the system matrix equation and time marching of the SMF-FETD and SMF-FETD method based on local eigenvalue solution (LES-SMF-FETD) with PBC are obtained. Numerical results illustrate the efficiency and effectiveness of the SMF-FETD method with PBC based on non-uniform mesh for analyzing the transport properties of 3-D periodic structures. © ACES.

关键词： Eigenvalues and eigenfunctions

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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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Neural Dynamics for Cooperative Motion control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1605-1620页

作者： Yufeng Lian Xingtian Xiao Jiliang Zhang Long Jin Junzhi Yu Zhongbo Sun the Department of Control Engineering Changchun University of TechnologyChangchun 130012China IEEE the College of Information Science and Engineering Northeastern UniversityShenyang 110819 the National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China the School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China the State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

关键词： Cooperative motion control noise-tolerant zeroing neural network(NTZNN) omnidirectional mobile manipulator(OMM) repetitive motion planning

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A Study on the Thermal Deformation Prediction Model of a Motorized Spindle Based on Swarm Intelligence Optimization Algorithms 7

A Study on the Thermal Deformation Prediction Model of a Mot...

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7th IEEE International Conference on information Systems and Computer Aided Education, ICISCAE 2024

作者： Wei, Wei Wang, Ruizhe Department of Information and Control Engineering Shenyang Institute of Science and Technology Shenyang China School of Electrical Engineering Suihua University Suihua China

ISBN: (纸本)9798350350760

Thermal deformation of the motorized spindle significantly impacts the machining accuracy of CNC machine tools. The thermal expansion coefficient, a critical parameter affecting thermal deformation, is highly nonlinear, making it challenging to determine an accurate coefficient that enhances machining precision. This study proposes a thermal deformation model for the motorized spindle based on an improved Grasshopper Optimization Algorithm, aimed at optimizing the thermal expansion coefficient. Experimental studies were conducted on the motorized spindle at various rotational speeds. The results demonstrate that the thermal expansion coefficient obtained using the improved Grasshopper Optimization Algorithm yields more accurate predictions of thermal deformation, with smaller errors compared to actual measurements, than those obtained using an empirical thermal expansion coefficient. ©2024 IEEE.

关键词： Swarm intelligence

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Enhanced Nature Inspired-Support Vector Machine for Glaucoma Detection

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Computers, Materials & Continua 2023年第7期76卷 1151-1172页

作者： Jahanzaib Latif Shanshan Tu Chuangbai Xiao Anas Bilal Sadaqat Ur Rehman Zohaib Ahmad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of Technology100124BeijingChina College of Information Science Technology Hainan Normal UniversityHainan ProvinceHaikou571158China Department of Computer Science University of SalfordManchesterUK Faculty of Information Technology Beijing University of Technology Chaoyang District BeijingChina

Glaucoma is a progressive eye disease that can lead to blindness if left *** detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require specialized *** study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine(EGWO-SVM)*** proposed method involves preprocessing steps such as removing image noise using the adaptive median filter(AMF)and feature extraction using the previously processed speeded-up robust feature(SURF),histogram of oriented gradients(HOG),and Global *** enhanced Grey Wolf Optimization(GWO)technique is then employed with SVM for *** evaluate the proposed method,we used the online retinal images for glaucoma analysis(ORIGA)database,and it achieved high accuracy,sensitivity,and specificity rates of 94%,92%,and 92%,*** results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of *** study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes.

关键词： Glaucoma detection grey golf optimization support vector machine feature extraction image classification

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Improving graph collaborative filtering with network motifs

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Neural Computing and Applications 2025年第16期37卷 9413-9432页

作者： Zhang, Yuqi Yu, Jian Liu, Zhizhong Wang, Guiling Nguyen, Minh Sheng, Quan Z. Wang, Nancy Department of Computer and Information Sciences Auckland University of Technology Auckland Central1010 New Zealand School of Computer and Control Engineering Yantai University Shandong Yantai264005 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Computing Macquarie University SydneyNSW2109 Australia

Deep learning on graphs, specifically graph convolutional networks (GCNs), has exhibited exceptional efficacy in the domain of recommender systems. Most GCNs have a message-passing architecture that enables nodes to aggregate information from neighbours iteratively through multiple layers. This enables GCNs to learn from higher-order information, but the model does not allow for direct captions of the local structural patterns. Our rationale is to investigate the effectiveness of capturing such local patterns for graph-based collaborative filtering to enhance model’s learning ability per layer. This technique combines lower-order and higher-order interactions during layer-wise propagation. In this paper, we propose MotifGCN to aggregate both lower-order and higher-order information in each graph convolution layer. Specifically, we develop dedicated algorithms of generating motif adjacency matrices. The matrices are then used for motif-enhanced neighbourhood aggregation in each layer. As this paper focuses on recommender systems, MotifGCN is built on the basis of bipartite graphs. Our experiments on four real-world datasets show that MotifGCN has a superior performance compared to various state-of-the-art methods. © The Author(s) 2025.

关键词： Collaborative filtering

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Real-Time Intrusion Detection Based on Decision Fusion in Industrial control Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 143-153页

作者： Xue, Yawen Pan, Jie Geng, Yangyang Yang, Zeyu Liu, Mengxiang Deng, Ruilong Information Engineering University Zhengzhou450001 China Singapore University of Technology and Design ITrust 487372 Singapore The University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS10 2TN United Kingdom Zhejiang University State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China

Industrial control systems (ICSs) are becoming increasingly interconnected as the rapid convergence of information technology (IT) and operation technology (OT) networks, and meanwhile massive attack surfaces have been exposed. However, traditional intrusion detection systems (IDSs) are difficult to be directly deployed in ICSs due to the hard real-time requirement and rare patching chance. Besides, the design of effective and practical IDSs is hampered by the lack of benchmarking ICS cybersecurity datasets. To bridge the gaps, this paper makes the first attempt by open-sourcing the developed ICS cybersecurity datasets and proposing a decision fusion based real-time IDS. Firstly, we design a customized cybersecurity dataset in a full-hardware and high-fidelity platform, including 7 types of cyber threats tailored for ICSs. The collected dataset includes network traffic, sensor readings, actuator status, and system parameters, providing the state-of-the-art benchmark dataset for ICSs consisting of cross-layer characteristics. Furthermore, we design an online decision fusion-based IDS by strategically integrating 4 widely-used machine learning models. The proposed IDS is deployed on a real-time running ethanol distillation, surpassing the performance of single detection models in terms of precision and F1-score, which substantially enhances intrusion detection accuracy and cybersecurity of ICS. © 2023 IEEE.

关键词： Intrusion detection

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Finite Sample Frequency Domain Identification 63

Finite Sample Frequency Domain Identification

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Tsiamis, Anastasios Abdalmoaty, Mohamed Smith, Roy S. Lygeros, John Automatic Control Laboratory Department of Information Technology and Electrical Engineering Eth Zürich Zürich8092 Switzerland

ISBN: (纸本)9798350316339

We study non-parametric frequency-domain system identification from a finite-sample perspective. We assume an open loop scenario where the excitation input is periodic and consider the Empirical Transfer Function Estimate (ETFE), where the goal is to estimate the frequency response at certain desired (evenly-spaced) frequencies, given input-output samples. We show that under sub-Gaussian colored noise (in timedomain) and stability assumptions, the ETFE estimates are concentrated around the true values. The error rate is of the order of O((du+Sdu dy) SM / N tot ), where N tot is the total number of samples, M is the number of desired frequencies, and du, dy are the dimensions of the input and output signals respectively. This rate remains valid for general irrational transfer functions and does not require a finite order state-space representation. By tuning M, we obtain a N tot -1 / 3 finite-sample rate for learning the frequency response over all frequencies in the H∞ norm. Our result draws upon an extension of the Hanson-Wright inequality to semi-infinite matrices. We study the finite-sample behavior of ETFE in simulations. © 2024 IEEE.

关键词： Frequency response

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