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第42届中国控制会议

作者： Pei Chen Zhongwei Lin Zihan Zhang Zhen Xie State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer EngineeringNorth China Electric Power University

Yaw misalignment is common in the yaw system control of wind turbines,resulting in reduced power generation efficiency and increased *** advantage of LiDAR is that the accuracy of wind measurement is greatly improved,while its disadvantage is that the cost remains *** this paper,an efficient machine learning method for estimating LiDAR measurement,XGBoost,is used to establish the data-driven wind turbine yaw misalignment compensation *** choose a suitable algorithm,four machine learning algorithms are studied,namely Linear Regression,Random Forest,Gradient Boosting and ***,to verify the effectiveness of the proposed method,a 2 MW WT was taken as the research object for filed testing,and the power generation was quantitatively evaluated under the three strategies of wind vane control,LiDAR control and ML algorithm *** results show that the proposed yaw misalignment compensation method is feasible for field *** this way,the yaw-to-wind error can be mitigated even after the LiDAR is removed.

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Output Feedback Control for Deterministic Unknown Dynamics Discrete-Time system using Deep Recurrent Q-Networks

TechRxiv

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TechRxiv 2023年

作者： Putri, Adi Novitarini Hidayat, Egi Mahayana, Dimitri Machbub, Carmadi Control System and Computer Research Group School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia Institut Teknologi Sains Bandung Bekasi Indonesia

The current application of control theory is commonly carried out in systems with a model or known system dynamics. However, in practice this is a formidable task to achieve as not all state information can be known. The use of the Output Feedback (OPFB) scheme in the field of control systems also possesses a weakness because it requires the use of an observer. This appears rather contradictory as the use of an observer requires system dynamics information. This research proposes an optimal control scheme using Deep Recurrent Q-Networks (DRQN) to generate an optimal control signal trajectory based on a collection of input and output data from the system itself. The approach proposed in this study is based on the Q-Learning method from the Reinforcement Learning (RL) scheme. The Long-Short Term Memory (LSTM) is used to approximate the Q-function and determine the control signals for a system without a known model. The method that we proposed in this study has been tested on four case studies. The control signal trajectory generated from our proposed algorithm, is much smaller than the control signal that generated from classical Q-Learning scheme. The results of this research are certainly relevant to the aim of OPFB, namely that the controller is designed to be able to regulate (bring the state trajectory to zero) and minimize control signal energy. It is empirically discovered that the same result is proven by the norm values resulting from the Q-function trajectory. The norm of Q-function trajectory for our proposed algorithm on the 1st, 2nd, 3rd, and 4th case studies are 2.11E-08, 3.15E-06, 3.79E-09, and 1.59E-13, respectively. © 2023, CC BY.

关键词： Trajectories

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MPC Implementation for Optimal Active Power Control of Large-Scale Wind-PV-Battery Hybrid system

MPC Implementation for Optimal Active Power Control of Large...

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第42届中国控制会议

作者： Zihan Zhang Zhongwei Lin Ze Zhao Jiawei Zhou Chenzhi Qu State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University

This paper investigates the operational strategy for real-time optimal active power control of large-scale wind-PV-battery hybrid system. The MPC strategy is established with objective, forecast models and constraints for active power control of wind-PV-battery hybrid system, which simultaneously maximizes tracking accuracy and minimizes settling time. To guarantee the industrial reliability, the corresponding optimization solver and control architecture have been specifically developed and designed. Hardware-in-loop(HIL) experiments are conducted using the real-time digital simulator(RTDS) and programmable logic controllers(PLCs). Under different operating scenarios, HIL-RTDS testing results demonstrate that the MPC strategy significantly improves the tracking speed and accuracy compared to the conventional strategy in both normal and abnormal operating scenarios, which also sustain the feasibility of industrial implementation.

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Optical spatiotemporal vortices

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eLight 2023年第1期3卷 156-168页

作者： Chenhao Wan Andy Chong Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Optical and Electronic Information and Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074HubeiChina Department of Physics Pusan National UniversityGeumjeong-GuBusan 46241South Korea Zhangjiang Laboratory 100 Haike RoadShanghai 201204China Shanghai Key Lab of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China

Spatiotemporal vortices of light,featuring transverse orbital angular momentum(OAM)and energy circulation in the spatiotemporal domain,have received increasing attention *** experimental realization of the controllable generation of spatiotemporal vortices triggers a series of research in this *** review article covers the latest developments of spatiotemporal vortices of light ranging from theoretical physics,experimental generation schemes,and characterization methods,to applications and future *** new degree of freedom in photonic OAM endowed by spatiotemporal vortices paves the way to the discovery of novel physical mechanisms and photonic applications in light science.

关键词： Spatiotemporal optical vortex Orbital angular momentum Spin-orbit interaction Linear momentum density Toroidal vortex Topological charge

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Covert Vehicle Misguidance and Its Detection: A Hypothesis Testing Game over Continuous-Time Dynamics

arXiv

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arXiv 2024年

作者： Tanaka, Takashi Sawada, Kenji Watanabe, Yohei Iwamoto, Mitsugu School of Aeronautics and Astronautics School of Electrical and Computer Engineering Purdue University United States The Info-Powered Energy System Research Center The University of Electro-Communications Japan Department of Informatics The University of Electro-Communications Japan

We formulate a stochastic zero-sum game over continuous-time dynamics to analyze the competition between the attacker, who tries to covertly misguide the vehicle to an unsafe region, versus the detector, who tries to detect the attack signal based on the observed trajectory of the vehicle. Based on Girsanov’s theorem and the generalized Neyman-Pearson lemma, we show that a constant bias injection attack as the attacker’s strategy and a likelihood ratio test as the detector’s strategy constitute the unique saddle point of the game. We also derive the first-order and the second-order exponents of the type II error as a function of the data length. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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AI-Assisted Markerless Activity Tracking system for Supporting Aging and Wellness

AI-Assisted Markerless Activity Tracking System for Supporti...

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Microwaves Antennas and Propagation Conference (MAPCON)

作者： Hajar Abedi Ahmad Ansariyan Plinio P Morita Alexander Wong Jennifer Boger George Shaker Electrical and Computer Engineering University of Waterloo Waterloo Canada School of Public Health Sciences University of Waterloo Waterloo Canada System Design Engineering University of Waterloo Waterloo Canada

This paper presents a mm-Wave radar-based system to create a novel method of tracking seniors and monitoring them using ambient wireless signals. Radar sensors, coupled with deep learning models, facilitate the identification of various physical activities without requiring physical contact or the use of wearable devices. The system employs range-Doppler maps derived from a real-life in-home activities dataset for training deep learning networks. The gated recurrent units (GRU) model is chosen for real-time implementation due to its optimal trade-off between speed and accuracy. The system achieves an overall accuracy of 93% for trained subjects, with 86% accuracy for classifying the in-home physical activities of a new subject. Additionally, the system captures the frequency of washroom use, sedentary, duration of sleep, active and out-of-home periods, the level of activity performed by the subject over a period of time, current activity state, and gait parameters.

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Insights Into the Future: XAI Integration in O-RAN and Space Communication systems

Insights Into the Future: XAI Integration in O-RAN and Space...

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Multimedia University engineering Conference (MECON)

作者： H. Ahmed Tahir Fahad Nabi Muhammad Ziad Tariq Ahmad Fasih Khan Azwan Mahmud Faculty of Engineering Multimedia University Cyberjaya Malaysia Department of Electrical Engineering & Information Technology University of Kiel Kiel Germany Department of Computer Science SZABIST University Islamabad Pakistan Roots School System Islamabad Pakistan

ISBN: (数字)9798331530747

ISBN: (纸本)9798331530754

This paper outlines the merging of Explainable Artificial Intelligence (XAI) with Open Radio Access Network (O-RAN) and space communication systems. The usage of AI drives decision-making in critical domains of telecommunications and space explorations requires transparency and interpretability. However, traditional AI models often operate as “black boxes,” making it difficult to understand their decision-making processes. This lack of explainability poses significant risks, including misinterpretation of signals, undetected anomalies, and erroneous decision-making, which can compromise the integrity of communication systems. Specifically, the challenges include accurately distinguishing between legitimate signals and attacks in anti-jamming scenarios, understanding the behaviour of complex models like LSTM in traffic prediction, and ensuring the reliability of telemetry data despite errors and noise. Integration of XAI techniques within O-RAN architecture and space communication protocols can ensure trust, reliability and safety in AI-enabled systems. This paper investigates the opportunities and challenges of incorporating XAI in O-RAN and space communications. Additionally, it presents the implications of XAI, the need for interpretability in autonomous spacecraft operations, anomaly detection and decision support for mission-critical tasks. By bridging the gap in AI transparency and advanced communication technology, this paper aims to present a detailed analysis of implemented AI and Machine Learning (ML) in the telecommunication domain. It also presents the challenges currently faced in the intersection of XAI and communication. Use cases of XAI in the domain of Open Radio Access Network and Space Communications in light of LIME and SHAP techniques were presented to give a hypothetical modelling for future experimentation to provide local and global interpretation and explanations for the currently employed AI/ML models in the respective domains. Overall, this

关键词： Surveys Space vehicles Explainable AI Space communications Decision making Open RAN Reliability Telemetry Artificial intelligence Virtualization

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Operating-Envelopes-Aware Decentralized Welfare Maximization for Energy Communities

arXiv

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arXiv 2023年

作者： Alahmed, Ahmed S. Cavraro, Guido Bernstein, Andrey Tong, Lang School of Electrical and Computer Engineering Cornell University IthacaNY United States Power System Engineering Center National Renewable Energy Laboratory GoldenCO United States

We propose an operating-envelope-aware, prosumer-centric, and efficient energy community that aggregates individual and shared community distributed energy resources and transacts with a regulated distribution system operator (DSO) under a generalized net energy metering tariff design. To ensure safe network operation, the DSO imposes dynamic export and import limits, known as dynamic operating envelopes, on end-users’ revenue meters. Given the operating envelopes, we propose an incentive-aligned community pricing mechanism under which the decentralized optimization of community members’ benefit implies the optimization of overall community welfare. The proposed pricing mechanism satisfies the cost-causation principle and ensures the stability of the energy community in a coalition game setting. Numerical examples provide insights into the characteristics of the proposed pricing mechanism and quantitative measures of its performance. © 2023, CC BY.

关键词： Dynamics

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General Impedance Modeling for Modular Multilevel Converter with Grid-forming and Grid-following Control

arXiv

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arXiv 2024年

作者： Sun, Chu Zhang, Fei Xiao, Huafeng Wang, Na Chen, Jikai The School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden The School of Electrical Engineering Southeast University Nanjing210096 China The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

Modular multilevel converter (MMC) has complex topology, control architecture and broadband harmonic spectrum. For this, linear-time-periodic (LTP) theory, covering multi-harmonic coupling relations, has been adopted for MMC impedance modeling recently. However, the existing MMC impedance models usually lack explicit expressions and general modeling procedure for different control strategies. To this end, this paper proposes a general impedance modeling procedure applicable to various power converters with grid-forming and grid-following control strategies. The modeling is based on a unified representation of MMC circuit as the input and output relation between the voltage or current on the AC side and the exerted modulation index, while the control part vice versa, thereby interconnected as closed-loop feedback. With each part expressed as transfer functions, the final impedance model keeps the explicit form of harmonic transfer function matrix, making it convenient to directly observe and analyze the influence of each part individually. Thereby the submodule capacitance is found as the main cause of difference between MMC impedance compared to two-level converter, which will get closer as the capacitance increases. Effectiveness and generality of the impedance modeling method is demonstrated through comprehensive comparison with impedance scanning using electromagnetic transient simulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Harmonic analysis

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Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography

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光：科学与应用（英文版） 2025年第2期14卷 490-508页

作者： Shiyi Cheng Shuaibin Chang Yunzhe Li Anna Novoseltseva Sunni Lin Yicun Wu Jiahui Zhu Ann C.McKee Douglas L.Rosene Hui Wang Irving J.Bigio David A.Boas Lei Tian Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA Department of Computer Science Boston UniversityBostonMA 02215USA Boston University Alzheimer's Disease Research Center and CTE Center Boston University School of MedicineBostonMA 02118USA Department of Neurology Boston University School of MedicineBostonMA 02118USA VA Boston Healthcare System U.S.Department of Veteran AffairsBostonMA 02130USA Department of Pathology and Laboratory Medicine Boston University School of MedicineBostonMA 02118USA Department of Anatomy & Neurobiology Boston University School of MedicineBostonMAUSA Department of Radiology Athinoula A.Martinos Center for Biomedical ImagingMassachusetts General HospitalBostonMA 02129USA Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA Neurophotonics Center Boston UniversityBostonMA 02215USA

A major challenge in neuroscience is visualizing the structure of the human brain at different *** histology reveals micro-and meso-scale brain features but suffers from staining variability,tissue damage,and distortion,which impedes accurate 3D *** emerging label-free serial sectioning optical coherence tomography(S-OCT)technique offers uniform 3D imaging capability across samples but has poor histological interpretability despite its sensitivity to cortical ***,we present a novel 3D imaging framework that combines S-OCT with a deep-learning digital staining(DS)*** enhanced imaging modality integrates high-throughput 3D imaging,low sample variability and high interpretability,making it suitable for 3D histology *** develop a novel semi-supervised learning technique to facilitate DS model training on weakly paired images for translating S-OCT to Gallyas silver *** demonstrate DS on various human cerebral cortex samples,achieving consistent staining quality and enhancing contrast across cortical layer ***,we show that DS preserves geometry in 3D on cubic-centimeter tissue blocks,allowing for visualization of meso-scale vessel networks in the white *** believe that our technique has the potential for high-throughput,multiscale imaging of brain tissues and may facilitate studies of brain structures.

关键词： tomography serial imaging digital human brain coherence enhanced multiscale optical

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