检索结果-内蒙古大学图书馆

Cybersecurity Landscape on Remote State Estimation:A Comprehensive Review

IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 851-865页

作者： Jing Zhou Jun Shang Tongwen Chen the Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 1H9Canada the Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control *** state estimation(RSE)is an indispensable functional module of ***,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance *** paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against ***,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of ***,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from ***,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'***,several challenges and open problems are presented to inspire further exploration and future research in this field.

关键词： Cyber-attacks Kalman filtering remote state estimation unreliable transmission channels

来源：评论

学校读者我要写书评

暂无评论

Wavelet Transformation for Recognizing Various Defects in Diagnosis of Power Transformers

Wavelet Transformation for Recognizing Various Defects in Di...

引用

2024 International Russian automation Conference, RusAutoCon 2024

作者： Kuznetsov, Andrey A. Volchanina, Maria A. Gorlov, Anton V. Department 'Theoretical electrical engineering' Omsk Russia Department 'Automation and control systems' Omsk Russia 'Theoretical electrical engineering' Omsk Russia

The article discusses the use of discrete wavelet transform in diagnosing power oil-filled transformers using automated acoustic monitoring tools. The article is based on the results of experiments using a simulator with an expanded set of defects. Comparison of acoustic signals on a power transformer with signals from a defect simulator made it possible to formulate a criterion for the presence of an insulation defect. An experimental test of the proposed criterion was carried out on signals of various powers and using electrodes of various shapes. The paper presents expressions that implement the criterion for recognizing the type and degree of development of an insulation defect in power transformers. Examples of processing acoustic signals using wavelet transformation in the MatLab program are given. For each type of signals considered, numerical values of the defect recognition criterion are given. At the end of the article, conclusions and prospects for further development of the proposed approach are formulated. © 2024 IEEE.

关键词： MATLAB

来源：评论

学校读者我要写书评

暂无评论

A Review on Cybersecurity Analysis,Attack Detection,and Attack Defense Methods in Cyber-physical Power Systems

引用

Journal of Modern Power Systems and Clean Energy 2023年第3期11卷 727-743页

作者： Dajun Du Minggao Zhu Xue Li Minrui Fei Siqi Bu Lei Wu Kang Li Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronics Engineering and AutomationShanghai UniversityShanghaiChina Department of Electrical Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA School of Electronic and Electrical Engineering University of LeedsLeeds LS29JTUK IEEE

Potential malicious cyber-attacks to power systems which are connected to a wide range of stakeholders from the top to tail will impose significant societal risks and *** timely detection and defense are of crucial importance for safe and reliable operation of cyber-physical power systems(CPPSs).This paper presents a comprehensive review of some of the latest attack detection and defense ***,the vulnerabilities brought by some new information and communication technologies(ICTs)are analyzed,and their impacts on the security of CPPSs are *** malicious cyber-attacks on cyber and physical layers are then analyzed within CPPSs framework,and their features and negative impacts are ***,two current mainstream attack detection methods including state estimation based and machine learning based methods are analyzed,and their benefits and drawbacks are ***,two current mainstream attack defense methods including active defense and passive defense methods are comprehensively ***,the trends and challenges in attack detection and defense strategies in CPPSs are provided.

关键词： Cyber-physical power systems security threat attack detection attack defense state estimation machine learning

来源：评论

学校读者我要写书评

暂无评论

Power Prediction of Wind Farm Considering the Wake Effect and its Boundary Layer Compensation

引用

Protection and control of Modern Power Systems 2024年第6期9卷 19-29页

作者： Zao Tang Jia Liu Jielong Ni Jimiao Zhang Pingliang Zeng Pengzhe Ren Tong Su the Department of Automation Hangzhou Dianzi UniversityHangzhou 310038China the Department of Electrical and Computer Engineering Rowan UniversityGlassboro 08028USA the Jiaxing Power Supply Company of State Grid Zhejiang Electric Power Company Jiaxing 314000China the Department of Electrical and Computer Engineering University of ConnecticutStorrs 06269USA

With significant expansion in wind farm capacity,wake disturbances from upstream wind turbines have emerged as a detrimental factor,adversely affecting the generated power of downstream ***,the conventional power prediction models usually neglect the wake effect between adjacent wind *** bridge this gap,this paper proposes a novel power prediction model that considers the wake effect and its boundary layer compensation,to enable joint spatial and temporal wind power prediction for wind ***,a two-dimensional convolutional neural network is adopted to extract the key features and reconstruct wind power prediction ***,utilizing historical data,a long short-term memory algorithm is employed to investigate the correlation between elemental characteristics and wind ***,a 3D-Gaussian Frandsen wake model that accounts for the wake effect and boundary layer compensation in wind farms is developed to precisely calculate the spatial wind speed ***,these distributions allow the power outputs of wind turbines in wind farms to be estimated more accurately via the rotor equivalent wind ***,several case studies are conducted to validate the effectiveness of the proposed *** results demonstrate that the suggested approach yields favorable outcomes in predicting both wind speed and wind power.

关键词： Wind farm wind power prediction wake effect 3D-Gaussian Frandsen model spatial-temporal distribution of wind speed.

来源：评论

学校读者我要写书评

暂无评论

Cloud Computing Design Patterns for MLOps: Applications to Virtual Power Plants 49

Cloud Computing Design Patterns for MLOps: Applications to V...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Subramanya, Rakshith Raisanen, Paula Sierla, Seppo Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Department of Electrical Engineering and Automation Department of Computer Science Electrical and Space Engineering Luleå Sweden

ISBN: (纸本)9798350331820

Virtual Power Plants (VPPs) are a key factor in smart grids, and they use cloud computing to integrate and manage Distributed Energy Resources (DERs). VPPs use Machine Learning (ML) methods to optimize various tasks. Machine Learning Operations (MLOps) methodology is a set of techniques that targets to develop, deploy and maintain ML applications smoothly on production. Cloud design patterns (CDPs) are general reusable solutions for common cloud problems that can improve the reliability, scalability, and quality of cloud applications. This paper discusses how CDPs can help in building complex ML applications on cloud with MLOps practices which can help VPPs to optimize their workloads. The paper also provides an example implementation on a public cloud provider. © 2023 IEEE.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Prototype-Based Interpretable Graph Neural Networks

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1486-1495页

作者： Ragno, Alessio Rosa, Biagio La Capobianco, Roberto Sapienza University of Rome Department of Computer Control and Management Engineering Rome00185 Italy Sony AI Zurich8952 Switzerland

Graph neural networks have proved to be a key tool for dealing with many problems and domains, such as chemistry, natural language processing, and social networks. While the structure of the layers is simple, it is difficult to identify the patterns learned by the graph neural network. Several works propose post hoc methods to explain graph predictions, but few of them try to generate interpretable models. Conversely, the topic of the interpretable models is highly investigated in image recognition. Given the similarity between image and graph domains, we analyze the adaptability of prototype-based neural networks for graph and node classification. In particular, we investigate the use of two interpretable networks, ProtoPNet and TesNet, in the graph domain. We show that the adapted networks manage to reach better or higher accuracy scores than their respective black-box models and comparable performances with state-of-the-art self-explainable models. Showing how to extract ProtoPNet and TesNet explanations on graph neural networks, we further study how to obtain global and local explanations for the trained models. We then evaluate the explanations of the interpretable models by comparing them with post hoc approaches and self-explainable models. Our findings show that the application of TesNet and ProtoPNet to the graph domain produces qualitative predictions while improving their reliability and transparency. © 2020 IEEE.

关键词： computer architecture

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Wearable and computer Vision-Based Methods for Human Torso Posture Estimation 9

Comparative Analysis of Wearable and Computer Vision-Based M...

引用

9th International Conference on Mathematics and computers in Sciences and Industry, MCSI 2024

作者： Muntean, Emanuel Leba, Monica University of Petrosani Department of System Control and Computer Engineering Petrosani Romania

ISBN: (纸本)9798350377224

This paper conducts a comparative analysis of human torso posture estimation methodologies, focusing on an inertial measurement unit (IMU) sensor coupled with an Arduino UNO as a wearable approach, and Kinect V2, utilizing OpenCV for posture analysis. The core objective of this study is to ascertain which method yields greater precision in the estimation of human torso posture. The IMU sensor, characterized by its wearability, provides the distinct advantage of unobtrusiveness and the capability to record motion across diverse environments. Conversely, Kinect V2 leverages computer vision techniques to derive posture estimations from visual data in real-time. Through comprehensive experimentation, this research evaluates the accuracy of both methodologies by juxtaposing their posture estimation outcomes. The findings of this investigation aim to significantly contribute to the enhancement of human posture estimation systems, with wide-ranging implications for health, sports, and ergonomics. © 2024 IEEE.

关键词： Ergonomics

来源：评论

学校读者我要写书评

暂无评论

A Real-Time Machine Learning Based Solution for Privacy Enforcement in Video Recordings and Live Streaming 9

A Real-Time Machine Learning Based Solution for Privacy Enfo...

引用

9th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2024

作者： Conforti, Pietro Manganelli Emanuele, Matteo Mandelli, Lorenzo Department of Computer Control and Management Engineering Sapienza University of Rome Italy

These past years the world had to deal with a whole new situation brought by Covid-19. Everyone’s routine changed and we started passing way more time than before on virtual meeting, virtual chats and similar. With this, many privacy problems arised from all the video data generated by a single user. Google and Zoom introduced the possibility to blur out the background while using a front face camera, but this did not solve many privacy concerns ranging from showing people in videos without their permission, to the leaking of sensible data and information from videos uploaded online. We propose a solution build over the use of computer vision techniques like image segmentation and classification for context recognition for a privacy enforcement solution capable of fitting the user’s personal need, blurring out selectively specific objects from a video based on the user’s preferences for each room in which they are. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

STGNN: a new self-trained generalized neural networks task space control for robot manipulators

引用

Neural Computing and Applications 2025年 1-26页

作者： Elmogy, Ahmed Elawady, Wael El-Ghaish, Hany Computer Engineering Department Prince Sattam Ibn Abdelaziz University Alkharj16273 Saudi Arabia Computers and Automatic Control Department Faculty of Engineering Tanta University Tanta31527 Egypt

The inherent uncertainties in the dynamic and kinematic parameters of robot manipulators pose significant challenges for their control in task space. This paper introduces an innovative adaptive sliding mode control strategy, leveraging self-trained generalized neural networks to enhance the precision and robustness of robot manipulator control. The proposed framework called STGNN integrates deep neural networks for the real-time estimation and adjustment of dynamics’ and kinematics’ parameters. Key contributions of this work include the development of semi-supervised learning models for adaptive dynamics and kinematics estimation. By training the generalized neural network on different trajectories, the same models can be utilized to operate on a variety of tasks, significantly improving the robustness, scalability, and adaptability of the control system. The STGNN approach allows the models to leverage both labeled and unlabeled data, incorporating a self-training mechanism that enhances their ability to generalize and adapt to diverse operational scenarios. This capability is particularly beneficial for real-time applications, where the ability to learn and adapt on the fly is crucial. Simulation results demonstrate the superior performance of the STGNN framework in controlling robot manipulators in operational space, showcasing its potential for real-world applications. The proposed method significantly advances the adaptability, accuracy, and efficiency of robot control systems, offering notable improvements over conventional approaches. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Self adjusting control systems

来源：评论

学校读者我要写书评

暂无评论

A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

引用

IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multi-physical analysis and motor design optimization. This paper proposes a new method for thermal steady state modeling and analysis based on transfer learning, which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on transfer learning is developed: the model is pre-trained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R2 and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：