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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Younesi, Abdollah Wang, Zongjie Wang, Liguo University of Connecticut Department of Electrical and Computer Engineering Storrs United States Harbin Institute of Technology Department of Electrical Engineering and Automation Harbin China

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

Due to the increasing rate of high-impact low-frequency (HILF) events, power systems are more vulnerable against the destructive climate events compared to other infrastructures. From this point of view, the primary focus of this article is to investigate the vulnerability of power systems in the face of numerous types of natural disasters in terms of resilience metrics. To achieve this goal, a mesh-structured view of the power system at the transmission level is employed to model the action mechanism from different types of natural disasters on the power system. The Monte Carlo simulation method is further applied to evaluate the resilience metrics of the power system. From the perspective of resilience, the vulnerability of the system against different types of events is finally achieved in this paper. Simulation case studies on the IEEE 30-bus test system have demonstrated that the proposed modeling can not only facilitate in upgraded schemes, but also significantly decrease the amount of damages to the power system after natural extreme events. © 2022 IEEE.

关键词： Climate change

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Auxiliary Control Method for Reducing Vibration of Drive Train of Wind Turbine Caused by Primary Frequency Regulation 3

Auxiliary Control Method for Reducing Vibration of Drive Tra...

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3rd International Conference on Advanced Electrical and Energy Systems, AEES 2022

作者： Bai, Xuechen Wang, Yingwei Sun, Han University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada The School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China

ISBN: (纸本)9781665472081

The primary frequency regulation of the wind turbine can reduce the frequency deviation of the power system to improve the stability of the frequency. However, the primary frequency regulation increases the vibration of the WT's drive train. In this study, auxiliary damping control is proposed to mitigate the vibration of the drive train caused by primary frequency regulation. Firstly, the influence of frequency regulation on the torque of the drive train is analyzed. Then an additional damping control method is proposed, and the influence of the control parameters on the control effect is analyzed. Finally, the effectiveness of the control method is verified by simulation under different wind speeds. The results show that under the same frequency performance, the proposed method can reduce the standard deviation of the drive train torque by more than 50%. © 2022 IEEE.

关键词： Wind turbines

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Improving the Accuracy of Systems for Measuring the Angular Position of Moving Objects with an Adaptive Wiener Filter 34

Improving the Accuracy of Systems for Measuring the Angular ...

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34th International Scientific Symposium Metrology and Metrology Assurance, MMA 2024

作者： Dichev, Dimitar Zhelezarov, Iliya Georgiev, Borislav Hasanov, Hasan Libchev, Kaloyan Kupriyanov, Oleksandr Malakov, Ivo Dicheva, Ralitza Technical University of Gabrovo Department of Machine and Precision Engineering Gabrovo Bulgaria Ukrainian Engineering Pedagogics Academy Department of Information Computer Technologies and Mathematics Kharkiv Ukraine Technical University of Sofia Department of Automation of Discrete Production Engineering Sofia Bulgaria Technical University of Sofia Department of Precision Engineering and Measurement Instruments Sofia Bulgaria

ISBN: (纸本)9798350352832

This paper presents a method for increasing the accuracy of systems for measuring the angular position of moving objects by using an adaptive Wiener filter. In the context of modern technological progress, the accuracy of angular position measurements is important for numerous applications, including aviation, marine, aerospace, robotics, and medical engineering. The method is based on detailed modeling of the dynamic errors arising due to external and internal disturbances and uses the adaptive Wiener filter to correct them. The modeling results demonstrate that the proposed technique significantly reduces the noise and improves the accuracy of the measurements. The method is applicable in various industrial and scientific fields, where high accuracy of angular measurements is required, and offers a significant contribution to the development of measurement technologies and control systems of dynamic objects. © 2024 IEEE.

关键词： Wiener filtering

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Identification of Switched Gated Recurrent Neural Networks Using the EM Algorithm 5

Identification of Switched Gated Recurrent Neural Networks U...

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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Bai, Wentao Gu, Suhang Yan, Chao Zhang, Haoyu Jiang, Chunli Guo, Fan School of Computer Science and Engineering Changshu Institute of Technology Changshu China School of Electrical Engineering and Automation Changshu Institute of Technology Changshu China School of Information Science and Technology Hangzhou Normal University Hangzhou China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Automation Nanjing Institute of Technology Nanjing China

ISBN: (纸本)9798350393521

In the domain of nonlinear hybrid dynamic system modeling, the effectiveness of switched autoregressive exogenous (SARX) systems may face certain restrictions. To address this issue, this paper presents an enhanced switched system framework. In this framework, all SARX subsystems are replaced with gated recurrent neural networks, aiming to overcome these limitations. Importantly, the proposed switched system does not rely on any prior assumptions about the knowledge of operating modes. Finally, a new identification method is proposed based on the expectation-maximization (EM) algorithm, and its effectiveness is validated through a simulation example. © 2023 IEEE.

关键词： Recurrent neural networks

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Distributed data-driven fault detection for industrial interconnected systems with unknown topology structure ⁎

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IFAC-PapersOnLine 2024年第4期58卷 670-675页

作者： Jingjing Gao Xu Yang Xian Zhou Qing Li Jian Huang Jiarui Cui Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing PR China School of Computer and Communication Engineering University of Science and Technology Beijing PR China

Driven by the enhancing requirements for ensuring stable operation of complex industrial processes, this paper investigates a distributed data-driven fault detection (FD) method for industrial interconnected systems. The topology structure of the industrial interconnected system is presumed to be unknown. To this end, the input/output (I/O) data-set model for the interconnected system and the distributed input/output data-set models for the subsystems are derived first. Then, the topology structure of the interconnected system is reconstructed through data-driven realization of gap metric by utilizing the subspace Identification method. Subsequently, the distributed data-driven fault detection method with enhanced information exchange topology is developed. Finally, we validate the feasibility and efficiency of the proposed method by conducting a case study on a chemical process consisting of four continuous-stirred tank reactors (CSTRs).

关键词： Fault detection industrial interconnected systems subspace Identification method gap metric data-driven

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Phase-Shift Algorithm for Two Nonorthogonal Interferograms

Phase-Shift Algorithm for Two Nonorthogonal Interferograms

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Informatics, Electronics, and Radio engineering (PIERE), International Conference Problems of

作者： Vladimir I. Guzhov Sergey P. Ilinykh Dmitry S. Khaidukov Faculty of Automation and Computer Engineering Novosibirsk State Technical University Novosibirsk Russian Federation

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

The phase step method is widely used to analyze interferograms and extract the phase distribution from them. Its limitation is the large number of interferograms required for this purpose. The minimum number is three interferograms. This paper describes a general analysis algorithm that requires only two interferograms, the phase shifts between which may not be orthogonal. The idea of the paper is to obtain an additional interferogram by transforming a two-dimensional elliptical cross section that is formed by the two interferograms into a threedimensional cross section with respect to the base of some spatial cylinder. After determining the parameters of the ellipse, a projection vector parallel to the center axis of the cylinder is found. Then the two-dimensional section is projected onto a three-dimensional plane perpendicular to the center axis of the cylinder. Each of the projections of this plane onto the coordinate planes forms one of the interferograms. After obtaining three interferograms, phase demodulation was performed using a three-point algorithm. The RMS error of the phase calculation was 0.015 radians.

关键词： Vectors Informatics Demodulation

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models

MamMIL: Multiple Instance Learning for Whole Slide Images wi...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Fang, Zijie Wang, Yifeng Zhang, Ye Wang, Zhi Zhang, Jian Ji, Xiangyang Zhang, Yongbing Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen China Shenzhen China School of Computer Science and Technology Shenzhen China Peking University School of Electronic and Computer Engineering Shenzhen Graduate School Shenzhen China Tsinghua University Department of Automation Beijing China

ISBN: (纸本)9798350386226

Recently, pathological diagnosis has achieved superior performance by combining deep learning models with the multiple instance learning (MIL) framework using whole slide images (WSIs). However, the giga-pixeled nature of WSIs poses a great challenge for efficient MIL. Existing studies either do not consider global dependencies among instances, or use approximations such as linear attentions to model the pair-to-pair instance interactions, which inevitably brings performance bottlenecks. To tackle this challenge, we propose a framework named MamMIL for WSI analysis by cooperating the selective structured state space model (i.e., Mamba) with MIL, enabling the modeling of global instance dependencies while maintaining linear complexity. Specifically, considering the irregularity of the tissue regions in WSIs, we represent each WSI as an undirected graph. To address the problem that Mamba can only process 1D sequences, we further propose a topology-aware scanning mechanism to serialize the WSI graphs while preserving the topological relationships among the instances. Finally, in order to further perceive the topological structures among the instances and incorporate short-range feature interactions, we propose an instance aggregation block based on graph neural networks. Experiments show that MamMIL can achieve advanced performance than the state-of-the-art frameworks. The code can be accessed at https://***/Vison307/MamMIL. © 2024 IEEE.

关键词： Undirected graphs

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Indirect adaptive observer control (I-AOC) design for truck–trailer model based on T–S fuzzy system with unknown nonlinear function

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Complex and Intelligent Systems 2024年第5期10卷 7311-7331页

作者： Aslam, Muhammad Shamrooz Bilal, Hazrat Chang, Wer-Jer Yahya, Abid Badruddin, Irfan Anjum Kamangar, Sarfaraz Hussien, Mohamed Artificial Intelligence Research Institute China University of Mining and Technology Department of Automation University of Science and Technology of China Department of Marine Engineering of National Taiwan Ocean University (NTOU) Department of Electrical Computer and Telecommunications Engineering Botswana International University of Science and Technology Department of Mechanical Engineering College of Engineering King Khalid University Department of Chemistry Faculty of Science King Khalid University

Tracking is a crucial problem for nonlinear systems as it ensures stability and enables the system to accurately follow a desired reference signal. Using Takagi–Sugeno (T–S) fuzzy models, this paper addresses the problem of fuzzy observer and control design for a class of nonlinear systems. The Takagi–Sugeno (T–S) fuzzy models can represent nonlinear systems because it is a universal approximation. Firstly, the T–S fuzzy modeling is applied to get the dynamics of an observational system in order to estimate the unmeasurable states of an unknown nonlinear system. There are various kinds of nonlinear systems that can be modeled using T–S fuzzy systems by combining the input state variables linearly. Secondly, the T–S fuzzy systems can handle unknown states as well as parameters known to the indirect adaptive fuzzy observer. A simple feedback method is used to implement the proposed controller. As a result, the feedback linearization method allows for solving the singularity problem without using any additional algorithms. A fuzzy model representation of the observation system comprises parameters and a feedback gain. The Lyapunov function and Lipschitz conditions are used in constructing the adaptive law. This method is then illustrated by an illustrative example to prove its effectiveness with different kinds of nonlinear functions. A well-designed controller is effective and its performance index minimizes network utilization—this factor is particularly significant when applied to wireless communication systems. © The Author(s) 2024.

关键词： Adaptive observer control Feedback control design Indirect method Lyapunov–Krasovskii candidate T–S fuzzy models

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When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

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Green Energy and Intelligent Transportation 2024年第3期3卷 24-41页

作者： Malak Abid Ali Khan Hongbin Ma Arshad Farhad Asad Mujeeb Imran Khan Mirani Muhammad Hamza National Key Lab of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Computer Science Namal UniversityPakistan Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China

LoRa technology contributes to green energy by enabling efficient,long-range communication for the Internet of Things(IoT).This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa,where the network performance is affected by the density of gateways(GWs)and end devices(EDs),as well as environmental *** mitigate interference,data throughput losses,and high-power consumption,the proposed spreading factor(SF)and hybrid(data rate|SF)models dynamically adjust the transmission *** orchestration of concurrent data modifications within the network server(NS)is crucial for uninterrupted communication between GWs and EDs,especially in monitoring electric vehicle(EV)stations to reduce traffic congestion and *** K-means and density-based spatial clustering of applications with noise(DBSCAN)algorithms optimizes ED allocation,averts data congestion,and improves the signal-tointerference noise ratio(SINR).These methods ensure seamless information reception by meticulously allocated EDs across various GW *** estimate the free-space losses(FSL),a log-distance path loss model(log-PL)is *** various bandwidths(BWs),bidirectional communications,and duty cycles(DCs)helps to prevent saturation,thus prolonging the operational lifespan of *** findings reveal a notable packet rejection rate(PRR)of 0%for the DBSCAN(hybrid model).In contrast,the K-means exhibits a PRR ranging from 5%(hybrid model)to 35.29%(SF model)for the ten GWs ***,the network saturation is reduced to 10.185%and 9.503%,respectively,highlighting an improvement in the average efficiency of slotted ALOHA(91.1%)and pure ALOHA(90.7%).These enhancements increase the lifespan of EDs to 15,465.27 days.

关键词： DBSCAN Green transportation Hybrid model Intelligent transportation K-means

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