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H∞ control for 2-D Fuzzy Systems With Interval Time-Varying Delays and Missing Measurements

IEEE Transactions on Cybernetics 2017年第2期47卷 365-377页

作者： Luo, Yuqiang Wang, Zidong Liang, Jinling Wei, Guoliang Alsaadi, Fuad E. Shanghai Key Laboratory of Modern Optical System Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China Department of Computer Science Brunel University London UxbridgeUB8 3PH United Kingdom Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Department of Mathematics Southeast University Nanjing210096 China

In this paper, we consider the H∞ control problem for a class of 2-D Takagi-Sugeno fuzzy described by the second Fornasini-Machesini local state-space model with time-delays and missing measurements. The state delays are allowed to be time-varying within a known interval. The measurement output is subject to randomly intermittent packet dropouts governed by a random sequence satisfying the Bernoulli distribution. The purpose of the addressed problem is to design an output-feedback controller such that the closed-loop system is globally asymptotically stable in the mean square and the prescribed H∞ performance index is satisfied. By employing a combination of the intensive stochastic analysis and the free weighting matrix method, several delay-range-dependent sufficient conditions are presented that guarantee the existence of the desired controllers for all possible time-delays and missing measurements. The explicit expressions of such controllers are derived by means of the solution to a class of convex optimization problems that can be solved via standard software packages. Finally, a numerical simulation example is given to demonstrate the applicability of the proposed control scheme. © 2016 IEEE.

关键词： controllers

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Verifiable searchable encryption with aggregate keys for data sharing in outsourcing storage 21st

Verifiable searchable encryption with aggregate keys for dat...

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21st Australasian Conference on Information Security and Privacy, ACISP 2016

作者： Li, Tong Liu, Zheli Li, Ping Jia, Chunfu Jiang, Zoe L. Li, Jin College of Computer and Control Engineering Nankai University Tianjin China School of Mathematics and Computational Science Sun Yat-sen University Guangzhou China Harbin Institute of Technology Shenzhen Graduate School Shenzhen China School of Computer Science Guangzhou University Guangzhou China

ISBN: (纸本)9783319403663

In a secure data sharing system, the keyword search over encrypted files is a basic need of a user with appropriate privileges. Although the traditional searchable encryption technique can provide the privacy protection, two critical issues still should be considered. Firstly, a cloud server may be selfish in order to save its computing resources, and thus returns only a fragment of results to reply a search query. Secondly, since different keys are always used for different document sets, making a search query over massive sets and verifying the search results are both impractical for a user with massive keys. In this paper, we propose a scheme named "verifiable searchable encryption with aggregate keys". In the scheme, a data owner need only distribute a single aggregate key to other users to selectively share both search and verification privileges over his/her document sets. After obtaining such a key, a user can use it not only for generating a single trapdoor as a keyword search query, but for verifying whether the server just conducts a part of computing for the search request. Then, we define the requirements of the scheme and give a valid construction. Finally, our analysis and performance evaluation demonstrate that the scheme are practical and secure. © Springer International Publishing Switzerland 2016.

关键词： Aggregates

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Set-up of active magnetic bearings for control of flexible shaft

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AIP Conference Proceedings 2018年第1期2029卷

作者： Adam Piłat Bartosz Sikora Jan Klocek Jan Cieślik 1AGH University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Automatic Control and Robotics Mickiewicza 30 30-059 Krakow Poland 2Optister Jalowcowa 50 31-329 Krakow Poland 3AGH University of Science and Technology Faculty of Mechanical Engineering and Robotics Department of Manufacturing Systems Mickiewicza 30 30-059 Krakow Poland

This paper presents the new laboratory test rig of the vertical axis flexible shaft supported by two active magnetic bearings. Behind the motivation of establishing this setup is the control of vibration modes of the flexible rotor - shaft assembly, while the rotational speed varies and the unbalanced mass on the whirling disc is introduced to the system. The proposed model is aimed to simulate real operation conditions of the rotating machinery with an applied external load, with a variable structure caused by a change of material properties of the rotor - shaft assembly, or due to a fault of some elements coupled with the rotor.

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Driving fatigue prediction with pre-event electroencephalography (EEG) via a recurrent fuzzy neural network

Driving fatigue prediction with pre-event electroencephalogr...

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2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016

作者： Liu, Yu-Ting Wu, Shang-Lin Chou, Kuang-Pen Lin, Yang-Yin Lu, Jie Zhang, Guangquan Lin, Wen-Chieh Lin, Chin-Teng Institute of Electrical Control Engineering Taiwan Brain Research Center Taiwan Institute of Computer Science and Engineering Taiwan Department of Computer Science National Chiao-Tung University Hsinchu Taiwan Taoyuan Taiwan Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

ISBN: (纸本)9781509006250

We propose an electroencephalography (EEG) prediction system based on a recurrent fuzzy neural network (RFNN) architecture to assess drivers' fatigue degrees during a virtual-reality (VR) dynamic driving environment. Prediction of fatigue degrees is a crucial and arduous biomedical issue for driving safety, which has attracted growing attention of the research community in the recent past. Meanwhile, combined with the benefits of measuring EEG signals facilitates, many EEG-based brain-computer interfaces (BCIs) have been developed for use in real-Time mental assessment. In the literature, EEG signals are severely blended with stochastic noise;therefore, the performance of BCIs is constrained by low resolution in recognition tasks. For this rationale, independent component analysis (ICA) is usually used to find a source mapping from original data that has been blended with unrelated artificial noise. However, the mechanism of ICA cannot be used in real-Time BCI design. To overcome this bottleneck, the proposed system in this paper utilizes a recurrent self-evolving fuzzy neural work (RSEFNN) to increase memory capability for adaptive noise cancellation when assessing drivers' mental states during a car driving task. The experimental results without the use of ICA procedure indicate that the proposed RSEFNN model remains superior performance compared with the state-of-Thearts models. © 2016 IEEE.

关键词： Electroencephalography

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Corrigendum: Dielectric cross-shaped-resonator-based metasurface for vortex beam generation at mid-IR and THz wavelengths (Nanophotonics (2019) 8:7 (1263–1270) DOI: 10.1515/nanoph-2019-0112)

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Nanophotonics 2019年

作者： Dharmavarapu, Raghu Izumi, Ken-ichi Katayama, Ikufumi Ng, Soon Hock Vongsvivut, Jitraporn Tobin, Mark J. Kuchmizhak, Aleksandr Nishijima, Yoshiaki Bhattacharya, Shanti Juodkazis, Saulius Centre for NEMS and Nanophotonics (CNNP) Department of Electrical Engineering Indian Institute of Technology Madras Chennai 600036 India Centre for Micro-Photonics Faculty of Science Engineering and Technology Swinburne University of Technology Hawthorn VIC 3122 Australia Melbourne Centre for Nanofabrication ANFF 151 Wellington Road Clayton VIC 3168 Australia Physics Department Yokohama National University 79-5 Tokiwadai Hodogaya-ku Yokohama 240-8501 Japan Infrared Microspectroscopy Beamline Australian Synchrotron Clayton VIC 3168 Australia Institute of Automation and Control Processes Far Eastern Branch Russian Academy of Sciences Vladivostok 690041 Russia Far Eastern Federal University Vladivostok 690090 Russia Department of Electrical and Computer Engineering Graduate School of Engineering Yokohama National University 79-5 Tokiwadai Hodogaya-ku Yokohama 240-8501 Japan Institute of Advanced Sciences Yokohama National University 79-5 Tokiwadai Hodogaya-ku Yokohama 240-8501 Japan Tokyo Tech World Research Hub Initiative (WRHI) School of Materials and Chemical Technology Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo 152-8550 Japan

There was a mistake in the grant number used in the recently published paper (DOI: https://***/10.1515/ nanoph-2019-0112. Published Online: 2019-07-09). The text should read: A.K. is grateful for support via the Russian science Foundation (grant no. 17-19-01325), and R.D. thanks the IIT Madras-Swinburne joint PhD program. © 2019 De Gruyter. All rights reserved.

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Satellite-relayed intercontinental quantum network

arXiv

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arXiv 2018年

作者： Liao, Sheng-Kai Cai, Wen-Qi Handsteiner, Johannes Liu, Bo Yin, Juan Zhang, Liang Rauch, Dominik Fink, Matthias Ren, Ji-Gang Liu, Wei-Yue Li, Yang Shen, Qi Cao, Yuan Li, Feng-Zhi Wang, Jian-Feng Huang, Yong-Mei Deng, Lei Xi, Tao Ma, Lu Hu, Tai Li, Li Liu, Nai-Le Koidl, Franz Wang, Peiyuan Chen, Yu-Ao Wang, Xiang-Bin Steindorfer, Michael Kirchner, Georg Lu, Chao-Yang Shu, Rong Ursin, Rupert Scheidl, Thomas Peng, Cheng-Zhi Wang, Jian-Yu Zeilinger, Anton Pan, Jian-Wei Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics University of Science and Technology of China Hefei230026 China Center for Excellence Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China Vienna Center for Quantum Science and Technology Faculty of Physics University of Vienna Vienna1090 Austria Institute for Quantum Optics and Quantum Information Austrian Academy of Sciences Vienna1090 Austria School of Computer National University of Defense Technology Changsha410073 China Key Laboratory of Space Active Opto-Electronic Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai200083 China National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China Key Laboratory of Optical Engineering Institute of Optics and Electronics Chinese Academy of Sciences Chengdu610209 Shanghai Engineering Center for Microsatellites Shanghai201203 China State Key Laboratory of Astronautic Dynamics Xi'an Satellite Control Center Xi’an710061 China Xinjiang Astronomical Observatory Chinese Academy of Sciences Urumqi830011 China National Space Science Center Chinese Academy of Sciences Beijing100080 China Space Research Institute Austrian Academy of Sciences Graz8042 Austria

We perform decoy-state quantum key distribution between a low-Earth-orbit satellite and multiple ground stations located in Xinglong, Nanshan, and Graz, which establish satellite-to-ground secure keys with ~kHz rate per passage of the satellite Micius over a ground station. The satellite thus establishes a secure key between itself and, say, Xinglong, and another key between itself and, say, Graz. Then, upon request from the ground command, Micius acts as a trusted relay. It performs bitwise exclusive OR operations between the two keys and relays the result to one of the ground stations. That way, a secret key is created between China and Europe at locations separated by 7600 km on Earth. These keys are then used for intercontinental quantum-secured communication. This was on the one hand the transmission of images in a one-time pad configuration from China to Austria as well as from Austria to China. Also, a videoconference was performed between the Austrian Academy of sciences and the Chinese Academy of sciences, which also included a 280 km optical ground connection between Xinglong and Beijing. Our work points towards an efficient solution for an ultra-long-distance global quantum network, laying the groundwork for a future quantum internet. Copyright © 2018, The Authors. All rights reserved.

关键词： Satellites

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Sampled-data stability criterion for T-S fuzzy systems with time-varying delay 3

Sampled-data stability criterion for T-S fuzzy systems with ...

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3rd International Conference on Informative and Cybernetics for Computational Social Systems, ICCSS 2016

作者： Du, Zhenbin Jia, Qiang Qin, Zhenku Qu, Zifang School of Computer and Control Engineering Yantai University Yantai China Faculty of Science Jiangsu University Zhenjiang China College of Mathematics and Information Science Shandong Institute of Business and Technology Yantai China

ISBN: (纸本)9781509033676

According to the existing researches, a new Takagi-Sugeno fuzzy systems under the time-varying delay were constructed. To reaching a superior sampled-data stability, the approach of input delay with free-weighting matrix is used. New criterion of stability is proposed. Lyapunov-Krasovskii function's derivative also included, it means a feasible condition. Fuzzy sampled-data controller is designed with asymptotically stability. Truck-trailer system as a simulation illustrated the superiority of fuzzy sampled-data control design. © 2016 IEEE.

关键词： Stability criteria

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The effects of machining strategies of magnetic assisted roller burnishing on the resulted surface structure

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IOP Conference Series: Materials science and engineering 2018年第1期448卷

作者： Zsolt Kovács Zsolt János Viharos János Kodácsy John von Neumann University GAMF Faculty of Engineering and Computer Science Dep. of Vehicle Technology Kecskemét H-6000 Hungary Department of Manufacturing Science and Technology Budapest University of Technology and Economics Budapest H-1111 Hungary Institute for Computer Science and Control of the Hungarian Academy of Sciences Kende u. 13-17 Budapest H-1111 Hungary John von Neumann University Faculty of Economics and Business Department of Economics and Law Kecskemét H-6000 Hungary

The aim of this paper is to investigate the effect of different burnishing strategy on a C45 steel machining. Burnishing is a well-known, cold working surface improving technology, it is usually applied for cylindrical workpiece. In the reported research a novel developed magnetic assisted ball burnishing (MABB) tool was applied which was designed for MABB machining of flat and harmonic surfaces. To increase its efficiency, different types of machining strategies were applied. After the burnishing process the machined surfaces were measured by surface roughness tester to determine the most important Rsk and Rku tribological parameters. According to the results it can be stated that all different burnishing strategies have special application areas where they can be used efficiently (e.g. sliding surface, moulding tool and after- or pre-machining for ultra-precision machining).

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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First cryogenic test operation of underground km-scale gravitational-wave observatory KAGRA

arXiv

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arXiv 2019年

作者： Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Atsuta, S. Awai, K. Bae, S. Baiotti, L. Barton, M.A. Cannon, K. Capocasa, E. Chen, C.-S. Chiu, T.-W. Cho, K. Chu, Y.-K. Craig, K. Creus, W. Doi, K. Eda, K. Enomoto, Y. Flaminio, R. Fujii, Y. Fujimoto, M.-K. Fukunaga, M. Fukushima, M. Furuhata, T. Hagiwara, A. Haino, S. Hasegawa, K. Hashino, K. Hayama, K. Hirobayashi, S. Hirose, E. Hsieh, B.H. Huang, C.-Z. Ikenoue, B. Inoue, Y. Ioka, K. Itoh, Y. Izumi, K. Kaji, T. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kanemura, S. Kaneyama, M. Kang, G. Kasuya, J. Kataoka, Y. Kawai, N. Kawamura, S. Kawasaki, T. Kim, C. Kim, J. Kim, J.C. Kim, W.S. Kim, Y.-M. Kimura, N. Kinugawa, T. Kirii, S. Kitaoka, Y. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A.K.H. Kotake, K. Kozu, R. Kumar, R. Kuo, H.-S. Kuroyanagi, S. Lee, H.K. Lee, H.M. Lee, H.W. Leonardi, M. Lin, C.-Y. Lin, F.-L. Liu, G.C. Liu, Y. Majorana, E. Mano, S. Marchio, M. Matsui, T. Matsushima, F. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyamoto, T. Miyo, K. Miyoki, S. Morii, W. Morisaki, S. Moriwaki, Y. Morozumi, T. Murakami, I. Musha, M. Nagano, K. Nagano, S. Nakamura, K. Nakamura, T. Nakano, H. Nakano, M. Nakao, K. Namai, Y. Narikawa, T. Naticchioni, L. Nguyen Quynh, L. Ni, W.-T. Nishizawa, A. Obuchi, Y. Ochi, T. Oh, J.J. Oh, S.H. Ohashi, M. Ohishi, N. Ohkawa, M. Okutomi, K. Ono, K. Oohara, K. Ooi, C.P. Pan, S.-S. Park, J. Peña Arellano, F.E. Pinto, I. Sago, N. Saijo, M. Saito, Y. Saitou, S. Sakai, K. Sakai, Y. Sakai, Y. Sasai, M. Sasaki, M. Sasaki, Y. Sato, N. Sato, S. Sato, T. Sekiguchi, Y. Seto, N. Shibata, M. Shimoda, T. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Son, E.J. Suemasa, A. Suzuki, T. Suzuki, T. Tagoshi, H. Tahara, H. Takahashi, H. Takahashi, R. Takamori, A. Takeda, H. Tanaka, H. Tanaka, K. Mitaka City Tokyo181-8588 Japan Mitaka City Tokyo181-8588 Japan Department of Physics University of Tokyo Bunkyo-ku Tokyo113-0033 Japan University of Tokyo Bunkyo-ku Tokyo113-0033 Japan KAGRA Observatory University of Tokyo Kashiwa CityChiba277-8582 Japan Tsukuba CityIbaraki305-0801 Japan Earthquake Research Institute University of Tokyo Bunkyo-ku Tokyo113-0032 Japan Department of Mathematics and Physics Hirosaki University Hirosaki City Aomori036-8561 Japan Kamioka-cho Hida City Gifu506-1205 Japan Mitaka City Tokyo181-8588 Japan Graduate School of Science and Technology Tokyo Institute of Technology Meguro-ku Tokyo152-8551 Japan KAGRA Observatory University of Tokyo Kamioka-cho Hida City Gifu506-1205 Japan Yuseong-gu Daejeon34141 Korea Republic of Graduate School of Science Osaka University Toyonaka CityOsaka560-0043 Japan Department of Physics National Taiwan Normal University sec. 4 Taipei116 Taiwan Department of Physics Sogang University Mapo-Gu Seoul121-742 Korea Republic of Institute of Physics Academia Sinica Nankang Taipei11529 Taiwan Department of Physics University of Toyama Toyama CityToyama930-8555 Japan Université Savoie Mont Blanc CNRS/IN2P3 AnnecyF-74941 France Department of Astronomy University of Tokyo Bunkyo-ku Tokyo113-0033 Japan Tsukuba CityIbaraki305-0801 Japan Department of Applied Physics Fukuoka University Jonan Fukuoka CityFukuoka814-0180 Japan Faculty of Engineering University of Toyama Toyama CityToyama930-8555 Japan University of Tokyo Kashiwa CityChiba277-8582 Japan Department of Physics National Central University Zhongli District Taoyuan City32001 Taiwan Center for High Energy and High Field Physics Zhongli District Taoyuan City32001 Taiwan Kyoto University Sakyou-ku Kyoto City Kyoto606-8502 Japan Graduate School of Science Osaka City University Sumiyoshi-ku Osaka CityOsaka558-8585 Japan JAXA Institute of Space and Astronautical Sc

KAGRA is a second-generation interferometric gravitational-wave detector with 3-km arms constructed at Kamioka, Gifu in Japan. It is now in its final installation phase, which we call bKAGRA (baseline KAGRA), with scientific observations expected to begin in late 2019. One of the advantages of KAGRA is its underground location of at least 200 m below the ground surface, which brings small seismic motion at low frequencies and high stability of the detector. Another advantage is that it cools down the sapphire test mass mirrors to cryogenic temperatures to reduce thermal noise. In April–May 2018, we have operated a 3-km Michelson interferometer with a cryogenic test mass for 10 days, which was the first time that km-scale interferometer was operated at cryogenic temperatures. In this article, we report the results of this "bKAGRA Phase 1" operation. We have demonstrated the feasibility of 3-km interferometer alignment and control with cryogenic mirrors. Copyright © 2019, The Authors. All rights reserved.

关键词： Mirrors

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