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International Conference on Mechatronics and Intelligent Robotics, ICMIR 2017

作者： Yu, Hua-dong Ou, Qing-hai Wu, Qing Zhang, Zhe Li, Wen-jing Wang, Yubo Liu, Guohua Lu, Yongling Hu, Chengbo Liu, Na Wang, Rong State Grid Information and Telecommunication Group Co. Ltd. Beijing China State Grid Key Laboratory of Power Industrial Chip Design and Analysis Technology Beijing Smart-Chip Microelectronics Technology Co. Ltd. Beijing100192 China Beijing Engineering Research Center of High-Reliability IC with Power Industrial Grade Beijing Smart-Chip Microelectronics Technology Co. Ltd. Beijing100192 China State Grid Jiangsu Electric Power Company Research Institute Nanjing21110 China Control and Computer Engineering College North China Electric Power University NCEPU Beijing China

ISBN: (纸本)9783319709895

With the rapid development of electric power industry and the expansion of power grid scale, more and more attentions have been paid to the safe operation of electrical equipment. The target recognition method we used now couldn’t recognize power transformer well, which is based on gray information. In this paper, a transformer identification algorithm based on improved Hu moment invariants is proposed. The transformer identification algorithm proposed in this paper has the advantages of good recognition effect and high recognition accuracy. The correctness and feasibility of the proposed algorithm have been verified by experiments. © 2018, Springer International Publishing AG.

关键词： Template matching

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LoRa on the Move: Performance Evaluation of LoRa in V2X Communications

LoRa on the Move: Performance Evaluation of LoRa in V2X Comm...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Li, Yuke Han, Shuangshuang Yang, Linyao Wang, Fei-Yue Zhang, Hui State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266109 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100049 China Qingdao Huituo Intelligent Machine Company Qingdao China Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China Computer Science Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Conviva Inc. Silicon ValleyCA94404 United States

ISBN: (纸本)9781538644522

Recent years have witnessed much interest in Low Power Wide Area (LPWA) technologies, which are gaining unprecedented momentum and commercial interest towards the realisation of the Internet of Things (IoT). Long Range (LoRa), as a representative LPWA technology, has the potential to satisfy the growing demand for the longer range and larger amount connectivities in vehicular communication networks. In this paper, LoRa is firstly applied into two typical vehicular networks, namely Vehicle-to-Infrastructure (V2I)and Vehicle-to-Vehicle (V2V), and performance of LoRa schemes with different parameter configurations are evaluated and compared. Further, Monte Carlo simulations indicate that the schemes equipped with higher bandwidth or lower spreading factor exhibit significant advantages in combating the fast fading caused by Doppler effect in networks. © 2018 IEEE.

关键词： Monte Carlo methods

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Research on unintentional off grid characteristics of microgrid

Research on unintentional off grid characteristics of microg...

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IEEE Conference on Energy Internet and Energy System Integration

作者： Meiyin LIU Bolong SHI Xiaojin LIN Guangxiu YAO Pingping HAN State Key Laboratory of Operation and Control of Renewable Energy Storage Systems China Electric Power Research Institute Beijing China State Grid ZHEJIANG Electric Power Company Hangzhou China HeFei University of Technology Hefei China

With the rapid development of renewable energy generation technology and the influence of government support policies, more and more renewable energy sources are connected to the power grid. However, due to the volatility of wind power and photovoltaic (PV) power, the proportion of high penetration rate will affect the safe and stable operation of the power grid. Microgrid is composed of distributed generation equipment, electricity load, etc. It can realize small power network with basic balance of internal power and electricity. The microgrid is required to maintain voltage and frequency stability and have secure switching function of connecting on/off grid. The transformation of microgrid from grid-connected mode to off-grid mode mainly includes intentional off grid and unintentional off grid. In this paper, studies on the technical specification of grid-connected microgrid, and establishes a grid-connected model of distributed generation. Taking 400V microgrid laboratory as an example, the model of unintentional disconnection of microgrid is established, and the simulation analysis is carried out to verify the accuracy of the model.

关键词： Microgrid Modeling Renewable energy Unintentional off grid

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Generation method for the PV power time series combining the decomposition technique and Markov chain theory

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The Journal of Engineering 2018年第13期2017卷 2026-2031页

作者： Shenzhi Xu Xiaomeng Ai Jiakun Fang Jinyu Wen Pai Li Yuehui Huang State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan 430074 Hubei Province People's Republic of China Department of Energy Technology Aalborg University Aalborg DK-9220 Denmark State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems China Electric Power Research Institute Beijing 100192 People's Republic of China

Photovoltaic (PV) power generation has made considerable developments in recent years. However, its intermittent and volatility of its output have seriously affected the security operation of the power system. In order to better understand the PV generation and provide sufficient data support for analysis the impacts, a novel generation method for PV power time series combining decomposition technique and Markov chain theory is presented here. It digs important factors from historical data from existing PV plants and then reproduce new data with similar patterns. In detail, the proposed method first decomposes the PV power time series into ideal output curve, amplitude parameter series, and random fluctuating component three parts. Then generating daily ideal output curve by the extraction of typical daily data, amplitude parameter series based on the Markov chain Monte Carlo (MCMC) method, and random component based on random sampling, respectively. Finally, the generated three parts are recombined into new PV power time series by the decomposition formula. Data obtained from real-world PV plants in Gansu, China, validates the effectiveness of the proposed method. The generated series can simulate the basic statistical, distribution, and fluctuation characteristics of the measured series.

关键词： time series photovoltaic power systems power system security Markov processes Monte Carlo methods PV power time series decomposition technique photovoltaic power generation power system security operation amplitude parameter series random fluctuating component Markov chain Monte Carlo method MCMC method random component random sampling real-world PV plants Gansu China basic statistical characteristics distribution characteristics fluctuation characteristics

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Perceptual visual interactive learning

arXiv

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arXiv 2018年

作者： Liu, Shenglan Liu, Xiang Liu, Yang Feng, Lin Qiao, Hong Zhou, Jian Wang, Yang Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian Liaoning116024 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Supervised learning methods are widely used in machine learning. However, the lack of labels in existing data limits the application of these technologies. Visual interactive learning (VIL) compared with computers can avoid semantic gap, and solve the labeling problem of small label quantity (SLQ) samples in a groundbreaking way. In order to fully understand the importance of VIL to the interaction process, we re-summarize the interactive learning related algorithms (e.g. clustering, classification, retrieval etc.) from the perspective of VIL. Note that, perception and cognition are two main visual processes of VIL. On this basis, we propose a perceptual visual interactive learning (PVIL) framework, which adopts gestalt principle to design interaction strategy and multi-dimensionality reduction (MDR) to optimize the process of visualization. The advantage of PVIL framework is that it combines computer's sensitivity of detailed features and human's overall understanding of global tasks. Experimental results validate that the framework is superior to traditional computer labeling methods (such as label propagation) in both accuracy and efficiency, which achieves significant classification results on dense distribution and sparse classes dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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Fidelity-based probabilistic q-learning for control of quantum systems

arXiv

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arXiv 2018年

作者： Chen, Chunlin Dong, Daoyi Li, Han-Xiong Chu, Jian Tarn, Tzyh-Jong Department of Control and System Engineering Nanjing University Nanjing210093 China Department of Chemistry Princeton University PrincetonNJ08544 United States School of Information Technology and Electrical Engineering University of New South Wales Australian Defence Force Academy CanberraACT2600 Australia Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Department of Systems Engineering and Engineering Management City University of Hong Kong Hong Kong Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO63130 United States

The balance between exploration and exploitation is a key problem for reinforcement learning methods, especially for Q-learning. In this paper, a fidelity-based probabilistic Qlearning (FPQL) approach is presented to naturally solve this problem and applied for learning control of quantum systems. In this approach, fidelity is adopted to help direct the learning process and the probability of each action to be selected at a certain state is updated iteratively along with the learning process, which leads to a natural exploration strategy instead of a pointed one with configured parameters. A probabilistic Q-learning (PQL) algorithm is first presented to demonstrate the basic idea of probabilistic action selection. Then the FPQL algorithm is presented for learning control of quantum systems. Two examples (a spin- 1/2 system and a Λ-type atomic system) are demonstrated to test the performance of the FPQL algorithm. The results show that FPQL algorithms attain a better balance between exploration and exploitation, and can also avoid local optimal policies and accelerate the learning process. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

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Preparation of CdTe Nanocrystals Doped Fluorescent Silica Spheres by Sol-gel Method and Their Surface Modification via Thiol-ene Chemistry

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Chemical Research in Chinese Universities 2017年第3期33卷 327-332页

作者： LIU Yan YU Dongdong ZHU Wei BAI Xiao SHEN Qihui LIU Xiaoyang ZHOU Jianguang Department of Chemistry and Pharmaceutical Engineering Jilin Institute of Chemical Technology Jilin 132022 P. R. China State Key Laboratory of Inorganic Synthesis and Preparative Chemistry College of Chemistry Jilin University Changehun 130012 P. R. China Hospital of Zhejiang University Hangzhou 310027 P. R. China State Key Laboratory of Industrial Control Technology Research Center for Analytical Instrumentation College of Control Science and Engineering Zhejiang University Hangzhou 310058 P. R. China

A thcile one-step sol-gel method was used to prepare CdTe nanocrystals（NCs） doped silica microspheres with luxuriant thiol group on their surface using 3-mercaptopropyl trimethoxysilane（MPTMS） as organosilane in the aqueous solution. As the ligand and organosilane, MPTMS ensured the doping efficiency of CdTe NCs by ligand exchange and a large thiol group exposed to the solution to activate the surface of silica microspheres. The remodeled sol-gel process was performed at a lower temperature, which protected the thiol groups from degradation. Meanwhile, the particle size and size distribution can be controlled by varying synthesis conditions, such as the stirring rate and the reaction temperature. This user-friendly method provides a plattbrm tbr immobilization of chemical groups such as carboxyl for combination of bioactive and diagnostic molecules at the surface of CdTe NCs doped silica micro- spheres through thiol-ene chemistry for high-throughput microarrays.

关键词： Fluorescent silica sphere Semiconductor nanocrystal Sol-gel method

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Seasonal Scheduling of Office Electricity Use via Adaptive Dynamic Programming

Seasonal Scheduling of Office Electricity Use via Adaptive D...

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第37届中国控制会议

作者： Guang Shi Bo Zhao Chao Li Xin Jin Derong Liu National Computer Network Emergency Response Technical Team/Coordination Center of China The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Automation Guangdong University of Technology

In this paper, an adaptive dynamic programming（ADP） based optimization method is proposed to schedule the electricity use of an office, where a battery is considered as the control variable, while solar and wind energies are included as additional energy supplies besides the grid. The electricity demand of an office generally contains socket, lighting and airconditioning demands. Based on the periodic models of electricity price, electricity demand, and solar and wind energies, the optimal control strategies of the battery are determined by the proposed ADP based optimization method, so that the electricity cost from the grid can be saved. Simulation analysis demonstrates that the proposed method can achieve optimal real-time scheduling of office electricity use in different seasons of a year.

关键词： Smart grid electricity scheduling optimal control adaptive dynamic programming

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An Improved Page Rank Algorithm Based on Fuzzy C-Means Clustering and Information Entropy 3

An Improved Page Rank Algorithm Based on Fuzzy C-Means Clust...

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2017 3rd IEEE International Conference on control Science and systems Engineering (ICCSSE)

作者： Wenbo Zheng Shaocong Mo Pengfei Duan Xiaotian Jin School of Software Engineering Xi’an Jiaotong University The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Computer Science and Technology Wuhan University of Technology Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology

ISBN: (纸本)9781538604854

This paper proposes an improvement to the Page Rank algorithm. Most existing Page Rank algorithms expect a strong correlation among consecutively accessed webpages, which in reality should be a fuzzy relationship when a user accesses pages on an arbitrarily basis. We mine data from search-behavior logs by analyzing chronological sequential patterns, and cluster all webpages using fuzzy C clustering. The weight of each cluster is identified with information entropy, which is then used to adjust the average weight. A sample of 1 million pages is used for testing. Compared with traditional Page Rank, the new algorithm decreases search time by 34.83% and increases search accuracy by 41.88%;when compared with HITS, the improvements are 31.82% and 64.04% respectively.

关键词： fuzzy c-means information entropy PageRank

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Input optics systems of the KAGRA detector during O3GK (vol 2023, 023F01, 2023)

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PROGRESS OF THEORETICAL AND EXPERIMENTAL PHYSICS 2023年第5期2023卷

作者： Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Bae, Y. Baiotti, L. Bajpai, R. Barton, M. A. Cannon, K. Cao, Z. Capocasa, E. Chan, M. Chen, C. Chen, K. Chen, Y. Chiang, C-I Chu, H. Chu, Y-K Eguchi, S. Enomoto, Y. Flaminio, R. Fujii, Y. Fujikawa, Y. Fukunaga, M. Fukushima, M. Furuhata, T. Gao, D. Ge, G-G Ha, S. Hagiwara, A. Haino, S. Han, W-B Hasegawa, K. Hattori, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hiranuma, Y. Hirata, N. Hirose, E. Hong, Z. Hsieh, B-H Huang, G-Z Huang, H-Y Huang, P. Huang, Y-C Huang, Y-J Hui, D. C. Y. Ide, S. Ikenoue, B. Imam, S. Inayoshi, K. Inoue, Y. Ioka, K. Ito, K. Itoh, Y. Izumi, K. Jeon, C. Jin, H-B Jung, K. Jung, P. Kaihotsu, K. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kang, G. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawasaki, T. Kim, C. Kim, J. Kim, J. C. Kim, Ws Kim, Y-M Kimura, N. Kita, N. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A. K. H. Kotake, K. Kozakai, C. Kozu, R. Kumar, R. Kume, J. Kuo, C. Kuo, H-S Kuromiya, Y. Kuroyanagi, S. Kusayanagi, K. Kwak, K. Lee, H. K. Lee, H. W. Lee, R. Leonardi, M. Li, K. L. Lin, L. C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H. L. Liu, G. C. Luo, L-W Majorana, E. Marchio, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyazaki, Y. Miyo, K. Miyoki, S. Mori, Y. Morisaki, S. Moriwaki, Y. Nagano, K. Nagano, S. Nakamura, K. Nakano, H. Nakano, M. Nakashima, R. Nakayama, Y. Narikawa, T. Naticchioni, L. Negishi, R. Quynh, L. Nguyen Ni, W-T Nishizawa, A. Nozaki, S. Obuchi, Y. Ogaki, W. Oh, J. J. Oh, S. H. Ohashi, M. Ohishi, N. Ohkawa, M. Ohta, H. Okutani, Y. Okutomi, K. Oohara, K. Ooi, C. Oshino, S. Otabe, S. Pan, K-C Pang, H. Parisi, A. Park, J. Arellano, F. E. Pena Pinto, I. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sakuno, Y. Sato, S. Sato, T. Sawada, T. Sekiguchi, T. Sekiguchi, Y. Shao, L. Shibagaki, S. Shimizu, R. Shimoda, T. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Son, E. J. Sotani, H. Sugimoto, R. Suresh, J. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Graduate School of Science and Technology Hirosaki University 3 Bunkyo-cho Hirosaki Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Republic of Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Road Danshui Dist. New Taipei City 25137 Taiwan Department of Physics

KAGRA, the underground and cryogenic gravitational-wave detector, was operated for its solo observation from February 25 to March 10, 2020, and its first joint observation with the GEO 600 detector from April 7 to April 21, 2020 (O3GK). This study presents an overview of the input optics systems of the KAGRA detector, which consist of various optical systems, such as a laser source, its intensity and frequency stabilization systems, modulators, a Faraday isolator, mode-matching telescopes, and a high-power beam dump. These optics were successfully delivered to the KAGRA interferometer and operated stably during the observations. The laser frequency noise was observed to limit the detector sensitivity above a few kilohertz, whereas the laser intensity did not significantly limit the detector sensitivity.

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