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第41届中国控制会议

作者： Zhenyong Zhang Xin Che Xuguo Jiao Jingpei Wang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou University College of Control Science and Engineering Zhejiang University School of Information and Control Engineering Qingdao University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Smart grid,as one of the largest evolutionary critical infrastructures,witnesses the deep integration of cyber technologies and physical ***,recent events show that the vulnerabilities exposed in the cyber side and the physical side can be exploited to cause coupled and severe ***,the adversaries are able to mask the pernicious physical attack using the cyber attack by compromising the digitized data transmitted to the control *** cooperative cyber-physical attack(CCPA) is stealthy to the current defense *** this article,we propose a cyber-physical defensive mechanism(CPDM) to thwart the *** changing the physical value of the line admittance,the model parameters known by the adversary are not useful anymore,which is the basic idea to design the *** implementation of CPDM depends on the deployment of the distributed flexible AC transmission system(D-FACTS).To reduce the infrastructure cost for the CPDM,we investigate the minimum number of required D-FACTS devices to thwart the *** consider both weak constraints on the CCPA and CPDM and practical limitations for the CCPA and *** each case,we propose a method to compute the optimal deployment of D-FACTS devices,that is,where the DFACTS device should be deployed.

关键词： CPA Enhance Smart Grid Security With A Coordinated Cyber-Physical Defensive Mechanism Security

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Enhancing Septic Shock Detection through Interpretable Machine Learning

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computer Modeling in engineering & Sciences 2024年第12期141卷 2501-2525页

作者： Md Mahfuzur Rahman Md Solaiman Chowdhury Mohammad Shorfuzzaman Lutful Karim Md Shafiullah Farag Azzedin Department of Information and Computer Science King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Department of Electrical and Computer Engineering North South UniversityDhaka1229Bangladesh Department of Computer Science Taif UniversityTaif21974Saudi Arabia College of Applied Arts and Technology Seneca PolytechnicTorontoM2J 2X5Canada Department of Control&Instrumentation Engineering King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia

This article presents an innovative approach that leverages interpretable machine learning models and cloud computing to accelerate the detection of septic shock by analyzing electronic health *** traditional methods,which often lack transparency in decision-making,our approach focuses on early detection,offering a proactive strategy to mitigate the risks of *** integrating advanced machine learning algorithms with interpretability techniques,our method not only provides accurate predictions but also offers clear insights into the factors influencing the model’s ***,we introduce a preference-based matching algorithm to evaluate disease severity,enabling timely interventions guided by the analysis *** innovative integration significantly enhances the effectiveness of our *** leverage a clinical health dataset comprising 1,552,210 Electronic Health Records(EHR)to train our interpretable machine learning models within a cloud computing *** techniques like feature importance analysis and model-agnostic interpretability tools,we aim to clarify the crucial indicators contributing to septic shock *** transparency not only assists healthcare professionals in comprehending the model’s predictions but also facilitates the integration of our system into existing clinical *** validate the effectiveness of our interpretable models using the same dataset,achieving an impressive accuracy rate exceeding 98%through the application of oversampling *** findings of this study hold significant implications for the advancement of more effective and transparent diagnostic tools in the critical domain of sepsis management.

关键词： Sepsis prediction machine learning cloud computing

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Eliminating control Plane Overload via Measurement Task Placement

Eliminating Control Plane Overload via Measurement Task Plac...

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作者： Chen, Xiang Liu, Hongyan Zhang, Dong Huang, Qun Zhou, Haifeng Wu, Chunming Yang, Qiang College of Computer Science and Technology Zhejiang University Hangzhou310007 China College of Computer Science and Big Data Fuzhou University Fuzhou350116 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China College of Control Science and Engineering Zhejiang University Hangzhou310007 China College of Electrical Engineering Zhejiang University Hangzhou310007 China

Recent efforts in network measurement place measurement tasks on programmable switches to measure high-speed traffic. These tasks extract flow data, i.e., events, from packets and send events to the control plane. However, the tasks may generate massive events in a short time. In this context, the links transferring events to the control plane and the control plane servers that handle events may be overloaded, i.e., control plane overload. None of existing solutions can eliminate control plane overload. In this paper, we propose MTP, a framework that eliminates control plane overload via careful measurement task placement. Our key idea is to allocate enough resources for each task during task placement to avoid control plane overload at runtime. For each task, MTP estimates its maximum possible rate of sending events to the control plane. Then its optimization framework addresses the resource restrictions of both switches and the control plane. The experiments on Tofino switches indicate that MTP outperforms existing solutions with higher accuracy in several use cases. © 2022 IEEE.

关键词： Job analysis

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Analysis of Electric Field Distribution of Insulators YD-JZ 10 kV Oil-Immersed Transformers 2

Analysis of Electric Field Distribution of Insulators YD-JZ ...

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2022 2nd International Conference on Power System and Energy Internet, PoSEI 2022

作者： Feng, Qi Shao, Zhenhua College of Electrical and Electronic Engineering Wenzhou University Wenzhou China College of Computer and Control Engineering Minjiang University Fuzhou China

The electric field distribution of the YD-JZ 10 kV oil-immersed transformer insulator is analyzed by COMSOL simulation, calculation, and actual measurement methods. The result shows that the first shed of the insulator accounts for 18% of the voltage drop. After that, the voltage of each umbrella skirt decreased gradually, and the proportion of voltage drop of the last two umbrella skirts increased again. The electric field of the insulator tip burr is 1.16 × 106 V/m spherical burr is 2.84 × 105 V/m. The simulation result shows that the maximum electric field of spherical air gap defect in an insulator is 1.38 × 105 V/m conical air gap defect is 1.61 × 105 V/m metal particle defect is 1.49 × 105 V/m. The simulated insulator umbrella skirt is covered with water. When the whole insulator umbrella skirt is short-circuited, there may be ground discharge. The measured results and simulation results show that the insulator can work normally if the umbrella skirt is not completely short-circuited. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Defects

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Multi-Objective Optimization of Multi-Product Parallel Disassembly Line Balancing Problem Considering Multi-Skilled Workers Using a Discrete Chemical Reaction Optimization Algorithm

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computers, Materials & Continua 2024年第9期80卷 4475-4496页

作者： Xiwang Guo Liangbo Zhou Zhiwei Zhang Liang Qi Jiacun Wang Shujin Qin Jinrui Cao College of Information and Control Engineering Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Research Center of the Economic and Social Development of Henan East Provincial Joint Shangqiu Normal UniversityShangqiu476000China Department of Computer Science New Jersey City UniversityJersey CityNJ 07305USA

This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle *** on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is *** enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular *** established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,*** a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.

关键词： Parallel disassembly line balancing problem multi-product multiskilled workers discrete chemical reaction optimization algorithm

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Research on the Mechanism and Suppression Methods of Commutation Failure in High-Voltage Direct Current Transmission Systems 7

Research on the Mechanism and Suppression Methods of Commuta...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Li, Ming Shen, Xiaolin Li, Xuanlang Liu, Yiqi State Grid Economic and Technology Research Institute Co. Ltd. Beijing China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798350359558

High-voltage Direct Current (HVDC) transmission technology has obvious advantages in power transmission, so it has been developed rapidly. Commutation failure is one of the most common faults in HV AC-DC hybrid transmission systems, which can cause DC lock and power oscillation. This paper takes commutation failure as the research object analyzes its causes and main influencing factors and puts forward corresponding suppression strategies, which are significant for the safe and stable operation of HVDC transmission systems. © 2024 IEEE.

关键词： HVDC power transmission

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On-Device Learning with Raspberry Pi for GCN-Based Epilepsy EEG Classification

On-Device Learning with Raspberry Pi for GCN-Based Epilepsy ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： He, Zhuoli Wang, Chuansheng Huang, Jiayan Grau, Antoni Guerra, Edmundo Yan, Jiaquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University College of New Engineering Industry Putian China Minjiang University School of Computer and Big Data Fuzhou China

ISBN: (纸本)9798350386226

Epilepsy is a chronic brain disease characterized by recurrent and transient seizures, which is accompanied by super-synchronous abnormal discharge of electroencephalogram (EEG) signals. As a non-invasive auxiliary diagnostic technique, EEG is currently important means of seizure detection. However, due to ignoring the spatial topological relationship between electrodes, existing data-driver methods fail to fully reflect the interaction between signals. Meanwhile, their models usually be designed with a large number of redundant parameters, making it difficult to deploy to micro-embedded devices with limited-resources. In this paper, we propose a on-device learning with edge device for epilepsy EEG classification network based on GCN (oDLGCN-EEG). Specifically, to analyze the spatial relationships between various electrodes and their temporal dependencies, we design a Brain Topology Network (BTN) for the spatiotemporal dependency feature map construction. To capture the internal activity during epilepsy seizures, we design a Neural Feature Extraction Module (NFEM) for the neural activity feature map construction. Besides, we propose a pruning scheme to optimize the model, which successfully deploys the optimized oDLGCN-EEG on the embedded Raspberry Pi device for efficient and low-power consumption intelligent epilepsy classification. Experiments in comparison with state-of-the-art methods show that oDLGCN-EEG achieves the best classification accuracy and with the smallest parameter number on baseline EEG dataset. The code is available at https://***/cathnat/Epileptic-Classification. © 2024 IEEE.

关键词： Topology

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Motion control of Underactuated Mechanical System via Singular Perturbations 41

Motion Control of Underactuated Mechanical System via Singul...

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第41届中国控制会议

作者： ZHOU Zhen GENG Chenchen CUI Ruogeng XIAO Jinzhuang WANG Hongbin College of Electronic Information Engineering Hebei University Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this work,a novel two-time-scale control structure is proposed to perform the motion control problem of the underactuated mechanical *** allows us to analyze the system dynamics in each time scale ***,the underactuated mechanical system is divided into the actuated and the unactuated parts,since that the singular perturbations theorem could be applied ***,in order to facilitate the different speed of convergence,a small positive constant is designed in the auxiliary control variable of actuated *** the low gains can make sure that the actuated states can converge to the desired position in its domain ***,the PD controller with high gains in contrast could have a fast speed,meanwhile the reference trajectory is settled for the unactuated ***,the final controller constructed by incorporating the characteristics of system model has a low *** results on the underactuated system are given to verify the efficacy of the strategy.

关键词： Underactuated mechanical systems Decoupled control Singular perturbations Motion cotrol

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Fast and Accurate LSTM Meta-modeling of TNF-induced Tumor Resistance In Vitro

Fast and Accurate LSTM Meta-modeling of TNF-induced Tumor Re...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Abrate, Marco P. Smeriglio, Riccardo Bardini, Roberta Savino, Alessandro Di Carlo, Stefanoi University College London Dept. of Cell and Developmental Biology London United Kingdom Politecnico di Torino Dept. of Control and Computer Engineering Turin Italy

ISBN: (纸本)9798350386226

Multi-level, hybrid models and simulations, among other methods, are essential to enable predictions and hypothesis generation in systems biology research. However, the computational complexity of these models poses a bottleneck, limiting the applicability of methodologies relying on large number of simulations, such as the Optimization via Simulation (OvS) of complex biological processes. Meta-models based on approximate surrogate models simplify multi-level simulations, maintaining accuracy while reducing computational costs. Among Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM) networks are well suited to handle sequential data, which often characterizes biological simulations. This paper presents an LSTM-based surrogate modeling approach for multi-level simulations of complex biological processes. Validation relies on the simulation of Tumor Necrosis Factor (TNF) administration to a 3T3 mouse fibroblasts tumor spheroid based on PhysiBoSS 2.0, a hybrid agent-based multi-level modeling framework. Results show that the proposed LSTM meta-model is accurate and fast compared with the simulator. In fact, it infers simulated behavior with an average relative error of 7.5%. Moreover, it is at least five orders of magnitude faster. Even considering the cost of training, this approach provides a faster, more accurate, and reusable surrogate of multi-scale simulations in computationally complex tasks, such as model-based OvS of biological processes. © 2024 IEEE.

关键词： Tumors

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Research on the Expert Database System of Characteristic Species in Wuling Mountain Area 21

Research on the Expert Database System of Characteristic Spe...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Yujun, Yang Yimei, Yang Wang, Zhou Liyun, Li Ling, Peng Jin, Xiang Huixia, Shu College of Computer and Artificial Intelligence Software College Huaihua University Huaihua418008 China Hunan Provincial Key Laboratory of Ecological Agriculture Intelligent Control Technology Huaihua418008 China School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9798331519254

The Wuling Mountains Area(WMA) is one of the important ecological protection areas in central China, known for its rich biodiversity and unique ecological environment. To effectively protect the species resources in this area and strengthen ecological monitoring and research, this paper proposes and designs the 'Wuling Mountains Region Characteristic Species Expert Database System.' By integrating expert resources, species data, ecological environment information, and modern information technologies, an intelligent system has been developed that combines data management, expert support, species identification, and research analysis. This paper discusses in detail the system's requirements analysis, design concept, implementation technologies, and potential applications, aiming to provide technological support for biodiversity conservation and sustainable ecological development in the region. © 2024 IEEE.

关键词： Characteristic species Expert database system Intelligent system Species data Wuling Mountain Area

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