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arXiv 2020年

作者： Liu, Bi Huang, Qi Zhao, Junbo Milano, Federico Zhang, Yingchen Sichuan Provincial Key Lab of Power System Wide-area Measurement and Control University of Electronic Science and Technology of China Chengdu Sichuan611731 China Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States School of Electrical and Electronic Engineering University College Dublin Dublin Ireland National Renewable Energy Laboratory GoldenCO80401 United States

Virtual inertia controllers (VICs) for wind turbine generators (WTGs) have been recently developed to compensate the reduction of inertia in power systems. However, VICs can induce low frequency torsional oscillations of the drive train of WTGs. This paper addresses this issue and develops a new nonlinear VIC based on objective holographic feedbacks theory. This approach allows transforming the original nonlinear control into a completely controllable system of Brunovsky’s type. Simulation results under various scenarios demonstrate that the proposed method outperforms existing VICs in terms of suppression of low frequency drive-train torsional oscillations, enhancement of system frequency nadir as well as fast and smooth recovery of WTG rotor speed to the original MPP point before the disturbance. The proposed method is also able to coordinate multiple WTGs. Copyright © 2020, The Authors. All rights reserved.

关键词： Frequency response

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Stochastic Link Flow Model for Signalized Traffic Networks with Uncertainty in Demand

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IFAC-PapersOnLine 2018年第9期51卷 458-463页

作者： Lin, S. Pan, T.L. Lam, W.H.K. Zhong, R.X. Schutter, B. De School of Computer and Control Engineering University of Chinese Academy of Sciences Key Laboratory of System Control and Information Processing Ministry of Education BeijingShanghai China Department of Civil and Structural Engineering The Hong Kong Polytechnic University Hong KongSAR Hong Kong School of Engineering Sun Yat-Sen University Guangzhou China Delft Center for Systems and Control Delft University of Technology Delft Netherlands

In order to investigate the stochastic features in urban traffic dynamics, we propose a Stochastic Link Flow Model (SLFM) for signalized traffic networks with demand uncertainties. In the proposed model, the link traffic state is described using four different link state modes, and the probability for each link state mode is determined based on the stochastic link states. The SLFM model is expressed as a finite mixture approximation of the link state probabilities and the dynamic link flow models for all the four link state modes. Using data from microscopic traffic simulator SUMO, we illustrate that the proposed model can provide a reliable estimation of the link traffic states, and as well as good estimations on the link state uncertainties propagating within a signalized traffic network. © 2018

关键词： Stochastic models Routers Stochastic systems Street traffic control Traffic signals Vehicle actuated signals Demand uncertainty Finite mixtures Stochastic features Stochastic traffic Traffic networks Traffic simulators Uncertainty in demand Urban traffic networks

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Fault Diagnosis of SOFC Stack Based on Neural Network Algorithm

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Energy Procedia 2019年 158卷 1798-1803页

作者： Tao Xue Xiaolong Wu Yuanwu Xu Suwen Jing Zehua Li Jianhua Jiang Zhonghua Deng Xiaowei Fu Li Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan 430074 China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan 430065 China

To realize the commercialization of SOFC, it must be ensured that it can work efficiently and stably. SOFC fault diagnosis becomes an essential part of the research. Due to the strong coupling of faults in the stack, this paper uses neural network algorithm to detect and diagnose faults. The simulation results verify that through the diagnosis of the test sample, the recognition rate of the test sample by the network is found to be 95%, which explains the neural network fault diagnosis model established in this paper on identifying the normal working state of the stack, the electrode stacking of the stack, and the gas leakage fault of the stack has good effectiveness and accuracy.

关键词： SOFC fault diagnosis neural network

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Velocity Coordination of Multi-vehicle systems via Distributed Neighbor Selection

Velocity Coordination of Multi-vehicle Systems via Distribut...

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International Conference on Intelligent Transportation

作者： Haibin Shao Lulu Pan Yugeng Xi Dewei Li Shu Lin Department of Automation and the Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781538670255

In this paper, we examine a data-driven control approach to coordination of multi-vehicle systems based on the distributed neighbor selection. The core of the proposed approach is to utilize a recently developed metric called relative tempo for distributed coordination which is computable from local measurable data for each vehicle in the network. The relative tempo between each agent and its neighbors is shown to be closely related to the directed spanning tree of the underlying network in a quantitative manner. Based on this fact, a local neighbor selection protocol is subsequently provided to construct the global refined communication structure which can both maintain the connectivity and increase the efficiency of the multi-vehicle coordination. The numerical study is finally provided to demonstrate the effectiveness of our approach.

关键词： Protocols Vehicle dynamics control systems Topology Intelligent transportation systems Measurement Heuristic algorithms

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DTMC-Based Performance Evaluation of Mobile-phone User's Application Node 17

DTMC-Based Performance Evaluation of Mobile-phone User's App...

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17th IEEE/ACIS International Conference on computer and Information Science, ICIS 2018

作者： Wang, Danyu Shi, Minyong Wang, Yongbin Hong, Zhiguo School of Computer Communication University of China Beijing100024 China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture China Beijing Key Laboratory of Modern Entertainment Technology Beijing100024 China

ISBN: (纸本)9781538658925

New media news client now largely replaces traditional newspaper media. Mobile-phone application node is the key component of distributed mobile phone, which offers reading newspaper on mobile phone. The performance mobile-phone application node has great impact on telephone user's experience. This paper uses DTMC-based model checking methodology to evaluate mobile-phone application node's performance such as Sina or Tencent News client. Based on the analysis of news node's working process, the probability of steady-state is chosen as the performance index to study different parameters' influence on it. Furthermore, trend that the probability of steady-state of News node varies with the effect of probability from WeChat state to Weibo state and probability from Game state to Video state is derived thereby. © 2018 IEEE.

关键词： Model checking

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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Modelling of gas turbine via discrete state-space neural networks

Modelling of gas turbine via discrete state-space neural net...

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Chinese Automation Congress (CAC)

作者： Jianhua Zhang Guanglei Cui Guolian Hou Zhiqiang Wu Jiiun Cao Penghao Fan State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

In this paper, one kind of multivariate system identification algorithm based discrete state-space neural network (DSSNN) is presented for modelling gas turbine systems with non-Gaussian disturbances. An improved performance index is proposed to train DSSNNs by using Survival Information Potential (SIP) of identification errors. Compared with the traditional Mean Square Error (MSE) based identification algorithm, simulation results demonstrate that the proposed system identification algorithm can obtain better model for 8OkW gas turbine.

关键词： Power systems Turbines system identification Fuels Renewable energy sources Neural networks Handheld computers

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Path planning of mobile robot based on improved DDQN

引用

Journal of Physics: Conference Series 2021年第1期2024卷

作者： Yang yunxiao Wangjun Zhang Hualiang Dai shilong School of Computer Science Shenyang University of Chemical Technology Shenyang 110142 China. Industrial Control Network and System Laboratory Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China. Institudes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang 110169 China.

Aiming at the problem of overestimation and sparse rewards of deep Q network algorithm in mobile robot path planning in reinforcement learning, an improved algorithm HERDDQN is proposed. Through the deep convolutional neural network model, the original RGB image is used as input, and it is trained through an end-to-end method. The improved deep reinforcement learning algorithm and the deep Q network algorithm are simulated in the same two-dimensional environment. The experimental results show that the HERDDQN algorithm solves the problem of overestimation and sparse reward better than the DQN algorithm in terms of success rate and reward convergence speed, Which shows that the improved algorithm finds a better strategy than the DQN algorithm.

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An Analysis of the Effects of Flexibility Modification on system Frequency Regulation 2

An Analysis of the Effects of Flexibility Modification on Sy...

引用

2nd IEEE International Conference on Power and Energy Applications, ICPEA 2019

作者： Yuguang, Niu Ming, Du Enfu, Dong Ke, Zhou Weichun, Ge Huanhuan, Luo State Key Laboratory for Alternate Electric Power System with Renewable Energy Source North China Electric Power University Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China State Grid Liaoning Electric Power Supply CO. LTD Shenyang Liaoning Province110004 China Xi'an Thermal Power Research Institute CO. LTD Xi'an Shanxi Province710054 China

ISBN: (纸本)9781728115962

With the flexibility modification of thermal power plant, the minimum technical output of units has been reduced. Different kinds of operating modes can be chosen by operators of power grids, which will influence the frequency regulation of power grids. The mathematical model of reheater steam turbine and load frequency control (LFC) are established considering flexibility modification of thermal power plants. Two scenarios are created and simulations are carried out. The results indicate that with the increase of units investigated in power system, the frequency deviation decrease obviously in both a stand-alone control area and two interconnected control areas. The conclusions presented here provide some guidance on operation of thermal power plants in regions to reduce the amplitude of frequency oscillation. © 2019 IEEE.

关键词： Frequency response

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control-Oriented Wind Farm Wake Modeling with Near Wake Performance and LES Validation

Control-Oriented Wind Farm Wake Modeling with Near Wake Perf...

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Chinese Automation Congress (CAC)

作者： Zhen Xie Zhongwei Lin Zhenyu Chen Chuanxi Wang Qingru Cui Gengda Li Weidong Wang Daizhen Li State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control Computer and Engineering North China Electric Power University Beijing P. R. China Guodian New Energy Technology Research Institute Co. Ltd Beijing P. R. China CHN ENERGY (Shandong) New Energy Co. Ltd. Jinan P. R. China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

With the deepening research on wind farms, wake effects have gradually been paid attention to and used in wind farm optimization. During this development, the engineering wake models are adopted, which could be further improved in modeling accuracy. This paper adopts a control-oriented wake model to improve the modeling accuracy, especially in the near wake region. A double-Gaussian wake model is incorporated with appropriate wake deflection and combination models and a vertical wind profile function. Based on a Large-eddy simulated dataset, the proposed model is adopted and verified. The widely-used Gaussian wake model is also adopted and compared with the proposed model. Results show good agreement between the presented model and LES results, especially in the focused near wake region.

关键词： Wind turbines Computational modeling Wind farms Rotors Electron tubes Wind speed Mathematical model

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