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International Conference on Systems and control (ICSC)

作者： Yosra Sayahi Moez Allouche Mariem Ghamgui Sandrine Moreau Driss Mehdi Laboratory of Sciences and Techniques of Automatic Control & Computer Engineering (Lab-STA) National Engineering School of Sfax University of Sfax Sfax Tunisia Laboratory of Computer Science and Automatic Control for Systems University of Poitiers France

This paper highlights the importance of using a Doubly-Fed Induction Generators (DFIG) in the wind industry due to their ability to adapting for all variations in wind speed, thus providing increased efficiency and reliability. However, like any machine, DFIG are not immune to dysfunctional problems and faults (sensor faults, actuator faults and system faults) which affect energy production. To remedy this problem, we develop a Fault Detection and Insolation (FDI) system for sensors fault diagnosis in wind turbine. This work specifically addresses the use of observer's bench to detect and locate faults, such as intermittent sensor faults, inter-coil short circuits, emphasizing a multi-model approach. We use the Dedicated Observer Structure (DOS) and the Generalized Observer Structure (GOS) to solve the complex challenge of multiple and simultaneous sensor fault. Simulation results are presented to assess the effectiveness of the proposed diagnostic methods.

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control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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RANSACs for 3D Rigid Registration:A Comparative Evaluation

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IEEE/CAA Journal of automatica Sinica 2022年第10期9卷 1861-1878页

作者： Jiaqi Yang Zhiqiang Huang Siwen Quan Zhiguo Cao Yanning Zhang the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer ScienceNorthwestern Polytechnical UniversityXi’an 710072China the School of Electronic and Control Engineering Chang’an UniversityXi’an 710064China the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China IEEE

Estimating an accurate six-degree-of-freedom(6-Do F)pose from correspondences with outliers remains a critical issue to 3D rigid *** sample consensus(RANSAC)and its variants are popular solutions to this *** there have been a number of RANSAC-fashion estimators,two issues remain ***,it is unclear which estimator is more appropriate to a particular ***,the impacts of different sampling strategies,hypothesis generation methods,hypothesis evaluation metrics,and stop criteria on the overall estimators remain *** work fills these gaps by first considering six existing RANSAC-fashion methods and then proposing eight variants for a comprehensive *** objective is to thoroughly compare estimators in the RANSAC family,and evaluate the effects of each key stage on the eventual 6-Do F pose estimation *** have been carried out on four standard datasets with different application scenarios,data modalities,and *** provide us with input correspondence sets with a variety of inlier ratios,spatial distributions,and *** on the experimental results,we summarize remarkable outcomes and valuable findings,so as to give practical instructions to real-world applications,and highlight current bottlenecks and potential solutions in this research realm.

关键词： 3D rigid registration performance evaluation point cloud pose estimation

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Carbon-Aware Computing in a Network of Data Centers: A Hierarchical Game-Theoretic Approach

Carbon-Aware Computing in a Network of Data Centers: A Hiera...

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European control Conference (ECC)

作者： Enno Breukelman Sophie Hall Giuseppe Belgioioso Florian Dörfler Division of Decision and Control Systems KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden Department of Electrical Engineering and Information Technology Automatic Control Laboratory ETH Zurich Zürich Switzerland

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Over the past decade, the continuous surge in cloud computing demand has intensified data center workloads, leading to significant carbon emissions and driving the need for improving their efficiency and sustainability. This paper focuses on the optimal allocation problem of batch compute loads with temporal and spatial flexibility across a global network of data centers. We propose a bilevel game-theoretic solution approach that captures the inherent hierarchical relationship between supervisory control objectives, such as carbon reduction and peak shaving, and operational objectives, such as priority-aware scheduling. Numerical simulations with real carbon intensity data demonstrate that the proposed approach success-fully reduces carbon emissions while simultaneously ensuring operational reliability and priority-aware scheduling.

关键词： Data centers Processor scheduling Computational modeling Carbon dioxide Games Supervisory control Resource management

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Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

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IEEE/CAA Journal of automatica Sinica 2023年第12期10卷 2222-2232页

作者： Juanjuan Li Junqing Li Xiao Wang Rui Qin Yong Yuan Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the School of Artificial Intelligence Anhui UniversityHefei 266114 the Qingdao Academy of Intelligent Industries Qingdao 230031China the School of Mathematics Renmin University of ChinaBeijing 100872China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive *** security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing *** this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary ***,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and ***,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic *** incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading *** temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation ***,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

关键词： Auction data trading markets multi-blockchain pricing mechanisms

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Multifunctional control system to enhance the operational capability of the unmanned multirotor platform dedicated to the diagnostics of building structures

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Bulletin of the Polish Academy of sciences: Technical sciences 2025年第3期73卷

作者： Czyba, Roman Domin, Jaroslaw Górski, Marcin Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Silesian University of Technology Faculty of Electrical Engineering Gliwice Poland Silesian University of Technology Faculty of Civil Engineering Gliwice Poland

Drones have become common devices that are increasingly used, from toy mini drones to very advanced autonomous flying platforms used in various industries. The safety of using engineering structures and the need to shorten the time of carrying out diagnostic activities, and thus reduce costs, force the use of innovative tools to carry out an automated and unmanned process of assessing the technical condition of existing engineering structures, especially large-size ones, including bridges strengthened with external FRP (fibre reinforced polymer) composite overlays. There are conventional methods for assessing the technical condition of existing building structures conducted by inspectors and experts, involving the use of typical inspection and diagnostic tools. During the research, it was found that it is possible to conduct an automated and unmanned process of assessing the technical condition of engineering structures using an unmanned flying platform equipped with a thermal imaging diagnostic device. The uniqueness of the presented solution is that the drone is equipped with a docking module that allows the drone to be temporarily attached to the structure being diagnosed for the duration of diagnostic activities. To ensure appropriate conditions for structural diagnostics, a unique system of automated diagnostic processes and a multi-stage structure of the control system in all phases of operation were developed. The article presents a solution for the control system based on control consistent with the reference model. In particular, the focus was on the difficult and dangerous issue of altitude control during the docking phase. Simulation tests conducted in the MATLAB/Simulink environment, as well as laboratory and field tests on a real drone proved the correctness of the proposed solutions, which facilitated the undisturbed operation of the diagnostic module. © 2025 The Author(s).

关键词： Drones

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An Efficient and Secured Energy Management System for Automated Guided Vehicles

An Efficient and Secured Energy Management System for Automa...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Mrozek, Dariusz National Taiwan University Department of Computer Science and Information Engineering Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Norway Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Poland

ISBN: (纸本)9781665480451

In this paper, we propose a Secure Energy Management System (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning network to simultaneously classify suppliers and predict the real supply quantities. The Q-learning decision module can then determine operating reserve and subsidies to manage the energy grid. Experimental results illustrate that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers, excels at shaping supply distribution, and outperforms the existing benchmark systems. © 2022 IEEE.

关键词： Energy management

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A Personality Detection Method Based on Semantic and Psycholinguistic of Social Texts

A Personality Detection Method Based on Semantic and Psychol...

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computer Information science and Application technology (CISAT), International Conference on

作者： Chundong Wang Yuhan Yang Hao Lin Lu Han School of Computer Science and Engineering National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin University of Technology Tianjin China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

At the intersection of personality psychology, computer science, and linguistics, more and more researchers are paying attention to personality detection based on content analysis of texts on social media. However, existing social text-based personality detection methods rely heavily on pre-trained language models, overlook multi-dimensional sentiment in social texts, and lack of effective feature selection of psycholinguistic features. To address these limitations, we propose a method for Myers-Briggs type indicator (MBTI) personality detection. Our approach extracts and selects high-dimensional psycholinguistic feature, utilizes an optimal intermediate layer output from pre-trained language models for semantic representation, and fuses these features to enhance detection accuracy. The experiment results indicate that our proposed method improves the state-of-art results for MBTI personality traits by 1.65% on average on Kaggle MBTI dataset.

关键词： computer science Information science Social networking (online) Fuses Computational modeling Semantics Psychology Linguistics Media Feature extraction

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Dual-view Label and Feature Supervision Network for Semi-supervised Medical Image Segmentation 24

Dual-view Label and Feature Supervision Network for Semi-sup...

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7th International Conference on Signal Processing and Machine Learning, SPML 2024

作者： Zhou, Zhen Li, Zuoyong Wang, Zhengyuan Teng, Shenghua Wang, Tao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (纸本)9798400717192

Semi-supervised learning has garnered significant attention, particularly in medical image segmentation, owing to its capacity to leverage a large number of unlabeled data and a limited amount of labeled data to improve performance. However, most existing semi-supervised segmentation methods exhibit shortcomings in supervising unlabeled data, both in the label space due to potential noise in pseudo-labels, and the feature space due to indistinct class boundaries. To address the issues above, we propose Dual-view Label and Feature Supervision Network, termed DLFS-Net, to enhance label and feature supervision for unlabeled data. Our approach is based on a dual-view learning strategy that incorporates two modules: the Confidence Fusion Supervision Module (CFS) and the Dual-view Prototype Learning Module (DPL). Specifically, the CFS module generates weight matrices to integrate complementary information from two model outputs, producing more accurate pseudo-labels in the label space. The DPL module reduces intra-class variations in the feature space from two different views through prototype learning to generate clear class boundaries. Experiments on the LA and Pancreas-CT datasets demonstrate that our framework show solid gains (e.g.,1.76% Dice and 2.37% Jaccard improvement on Pancreas-CT dataset with 20% labeled data) compared with the state-of-the-art methods. © 2024 ACM.

关键词： Self-supervised learning

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Resource allocation in UAV assisted air ground intelligent inspection system

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Cognitive Robotics 2022年 2卷 1-12页

作者： Zhang, Zhuoya Xu, Fei Qin, Zengshi Xie, Yue School of ordnance science and technology Xi'an Technological University Xi'An China Engineering Laboratory The State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'An China School of Computer Science and Engineering Xi'an Technological University Xi'An China

With the progress of power grid technology and intelligent technology, intelligent inspection robot (IR) came into being and are expected to become the main force of substation inspection in the future. Among them, mobile edge computing provides a promising architecture to meet the explosive growth of communication and computing needs of inspection robot. Inspection robot can transmit the collected High Definition (HD) video to adjacent edge servers for data processing and state research and judgment. However, the communication constraints of long-distance transmission, high reliability and low delay pose challenges to task offloading optimization. Therefore, this paper introduced Unmanned Aerial Vehicle (UAV) and established UAV assisted mobile edge computing system. UAV assisted and mobile edge computing are combined to form edge computing nodes. In this way, it provided communication and computing services to the IR for fast data processing. Specifically, in order to optimize the system energy consumption, a resource allocation strategy based on genetic algorithm is proposed. By optimizing the offloading decision and computing resource allocation of the IRs, the computing task of the IRs are offloaded to an energy-efficient UAV. The experimental results show that the resource allocation strategy based on genetic algorithm can effectively reduce the energy consumption and cost of UAVs and IRs, and effectively realize the reasonable allocation of resources. The results verify the effectiveness and reliability of the algorithm in the real scene. © 2021

关键词： Resource allocation

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