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6th International Conference on Renewable Power Generation

作者： Li, Hongtao Ding, Jie Huang, Jingsheng Dong, Yinghua Li, Xiaoyan China Electric Power Search Institute Nanjing210003 China State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems China Electric Power Research Institute Beijing100192 China Hefei University of Technology Hefei230009 China

With the large-scale grid-connected operation of solar photovoltaic (PV) power systems in recent years, the accurate assessment of its reliability level has received increasing focus. This study obtained a probability model for system power output by analysing the structural characteristics of the PV power system and by examining its component failure mode and the effect of the component failure mode on the output power. The algorithmic model for the reliability evaluation of the PV power system was established based on the sequential Monte Carlo method, the weather conditions, component ageing, and light intensity. Finally, the six states model and their reliability indices of the PV power system were studied considering the resource constraint and the structural characteristics of the PV power system. The results showed that the model can fully reflect the actual operation reliability of a PV power system. © 2017 SAGE Publications Ltd. All rights reserved.

关键词： Outages

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Ultralight vector dark matter search using data from the KAGRA O3GK run

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Physical Review D 2024年第4期110卷 042001页

作者： A. G. Abac R. Abbott H. Abe I. Abouelfettouh F. Acernese K. Ackley C. Adamcewicz S. Adhicary N. Adhikari R. X. Adhikari V. K. Adkins V. B. Adya C. Affeldt D. Agarwal M. Agathos O. D. Aguiar I. Aguilar L. Aiello A. Ain P. Ajith T. Akutsu S. Albanesi R. A. Alfaidi A. Al-Jodah C. Alléné A. Allocca S. Al-Shammari P. A. Altin S. Alvarez-Lopez A. Amato L. Amez-Droz A. Amorosi C. Amra S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Andia M. Ando T. Andrade N. Andres M. Andrés-Carcasona T. Andrić J. Anglin S. Ansoldi J. M. Antelis S. Antier M. Aoumi E. Z. Appavuravther S. Appert S. K. Apple K. Arai A. Araya M. C. Araya J. S. Areeda N. Aritomi F. Armato N. Arnaud M. Arogeti S. M. Aronson K. G. Arun G. Ashton Y. Aso M. Assiduo S. Assis de Souza Melo S. M. Aston P. Astone F. Aubin K. AultONeal G. Avallone S. Babak F. Badaracco C. Badger S. Bae S. Bagnasco E. Bagui Y. Bai J. G. Baier R. Bajpai T. Baka M. Ball G. Ballardin S. W. Ballmer S. Banagiri B. Banerjee D. Bankar P. Baral J. C. Barayoga B. C. Barish D. Barker P. Barneo F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta S. D. Barthelmy M. A. Barton I. Bartos S. Basak A. Basalaev R. Bassiri A. Basti M. Bawaj P. Baxi J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale F. Beirnaert M. Bejger D. Belardinelli A. S. Bell V. Benedetto D. Beniwal W. Benoit J. D. Bentley M. Ben Yaala S. Bera M. Berbel F. Bergamin B. K. Berger S. Bernuzzi M. Beroiz D. Bersanetti A. Bertolini J. Betzwieser D. Beveridge N. Bevins R. Bhandare U. Bhardwaj R. Bhatt D. Bhattacharjee S. Bhaumik S. Bhowmick A. Bianchi I. A. Bilenko G. Billingsley A. Binetti S. Bini O. Birnholtz S. Biscoveanu A. Bisht M. Bitossi M.-A. Bizouard J. K. Blackburn C. D. Blair D. G. Blair F. Bobba N. Bode G. Bogaert G. Boileau M. Boldrini G. N. Bolingbroke A. Bolliand L. D. Bonavena R. Bondarescu F. Bondu E. Bonilla M. S. Bonilla A. Bonino R. Bonnand P. Booker A. Borchers V. Boschi S. Bose V. Bossilkov V. Boudart A. Boumerdassi A. Bozzi C. Bradaschia P. R. Brady M. Braglia A. Branch M. Branchesi M. Breschi T. B Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-14476 Potsdam Germany LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Graduate School of Science Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo 152-8551 Japan LIGO Hanford Observatory Richland Washington 99352 USA Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli I-80126 Napoli Italy University of Warwick Coventry CV4 7AL United Kingdom OzGrav School of Physics & Astronomy Monash University Clayton 3800 Victoria Australia The Pennsylvania State University University Park Pennsylvania 16802 USA University of Wisconsin-Milwaukee Milwaukee Wisconsin 53201 USA Louisiana State University Baton Rouge Louisiana 70803 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Stanford University Stanford California 94305 USA Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa I-56127 Pisa Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India 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 Dipartimento di Fisica Università degli Studi di Torino I-10125 Torino Italy INFN Sezione di Torino I-10125 Torino Italy SUPA University of Glasgow Glasgow G12 8QQ United Kingdom OzGrav University of Western Austral

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation timescale of DM.

关键词： Dark matter Particle dark matter Gravitational wave detectors

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Dual refinement networks for accurate and fast object detection in real-world scenes

arXiv

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arXiv 2018年

作者： Chen, Xingyu Yu, Junzhi Kong, Shihan Wu, Zhengxing Wen, Li State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 University of Chinese Academy of Sciences Beijing100049 China Beijing Innovation Center for Engineering Science and Advanced Technology Peking University Beijing100871 China School of Mechanical Engineering and Automation Beihang University Beijing100191 China

Object detection has been vigorously studied for years but fast accurate detection for real-world scenes remains a very challenging problem. Overcoming drawbacks of singlestage detectors, we take aim at precisely detecting objects for static and temporal scenes in real time. Firstly, as a dual refinement mechanism, a novel anchor-offset detection including an anchor refinement, a feature offset refinement, and a deformable detection head is designed for two-step regression and capture of accurate detection features. Based on the anchoroffset detection, a dual refinement network (DRN) is developed for high-performance static detection, where a multi-deformable head is further designed to leverage contextual information for describing objects. As for temporal detection in real-world scenes, temporal refinement networks (TRN) and temporal dual refinement networks (TDRN) are developed by propagating the refinement information across time, where we also propose a loose refinement strategy to match object motion with the previous refinement. Our proposed methods are evaluated on PASCAL VOC, COCO, and ImageNet VID datasets. Extensive comparison on static and temporal detection verify the superiority of DRN, TRN, and TDRN. Consequently, our developed approaches run in a fairly fast speed, and in the meantime achieve a significantly enhanced detection accuracy, i.e., a mAP of 82.8% on VOC 2007, 80.6% on VOC 2012, 69.4% on VID 2017, and an AP of 34.3% on COCO. Ultimately, producing encouraging results, our methods are applied to underwater object grasping with an autonomous system. Codes are publicly available at http://***/SeanChenxy/TDRN. Copyright © 2018, The Authors. All rights reserved.

关键词： Computer vision

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Robot Teaching by Teleoperation Based on Visual Interaction and Neural Network Learning

Robot Teaching by Teleoperation Based on Visual Interaction ...

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2017 9th International Conference on Modelling, Identification and control (ICMIC 2017)

作者： Yang Xu Chenguang Yang Junpei Zhong Hongbin Ma Lijun Zhao Min Wang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and EngineeringSouth China University of Technology National Institute of Advanced Industrial Science and Technology School of Automation Beijing Institution of Technology State Key Laboratory of Robotics and System Harbin Institute of Technology

Traditional methods of Robot teaching require human demonstrators to program with a teaching pendant,which is a complex and time-consuming *** this paper,we propose a novel method based on teleoperation which allows a demonstrator to train robot in an intuitive *** specifically,at the beginning the demonstrator controls a robot by visual *** then a learning algorithm based on radial basis function(RBF) network is used to transfer the demonstrator's motions to the *** verify the effectiveness of this developed methods,several simulation experiments have been carried out which based on Microsoft Kinect Sensor and the Virtual Robot Experimentation Platform(V-REP).The experimental results show that this method has achieved satisfactory *** the help of this method,the robot can not only complete the task autonomously after teaching,but also can learn the details of demonstrator's behavior.

关键词： Robot Teaching Teleoperation radial basis function(RBF) network

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Weak value amplification via second-order correlated technique

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Chinese Physics B 2016年第2期25卷 58-61页

作者：崔挺黄靖正刘翔曾贵华 State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Laboratory on Navigation and Location-based Serviceand Center of Quantum Information Sensing and Processing Shanghai Jiao Tong University Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai Institute of Spaceflight Control Technology College of Information Science and Technology Northwest University

We propose a new framework combining weak measurement and second-order correlated technique. The theoretical analysis shows that weak value amplification （WVA） experiment can also be implemented by a second-order correlated system. We then build two-dimensional second-order correlated function patterns for achieving higher amplification factor and discuss the signal-to-noise ratio influence. Several advantages can be obtained by our proposal. For instance, detectors with high resolution are not necessary. Moreover, detectors with low saturation intensity are available in WVA setup. Finally, type-one technical noise can be effectively suppressed.

关键词： weak measurement two-dimensional second-order correlated function

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Digital Metamaterials: Designing 3D Digital Metamaterial for Elastic Waves: From Elastic Wave Polarizer to Vibration control (Adv. Sci. 16/2019)

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Advanced Science 2019年第16期6卷 1970097-1970097页

作者： Huan Liu Quan Zhang Kai Zhang Gengkai Hu Huiling Duan State Key Laboratory of Turbulence and Complex Systems Department of Mechanics and Engineering Science BIC-ESAT College of Engineering Peking university Beijing 100871 China School of Aerospace Engineering Beijing Institute of Technology Beijing 100081 China Key Laboratory of Dynamics and Control of Flight Vehicle School of Aerospace Engineering Beijing Institute of Technology Beijing 100081 China CAPT HEDPS and IFSA Collaborative Innovation Center of MoE Peking University Beijing 100871 China

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

Generation method for the PV power time series combining the...

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6th International Conference on Renewable Power Generation

作者： Xu, Shenzhi Ai, Xiaomeng Fang, Jiakun Wen, Jinyu Li, Pai Huang, Yuehui State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan Hubei Province430074 China Department of Energy Technology Aalborg University AalborgDK-9220 Denmark China Electric Power Research Institute State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems Beijing100192 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. © 2017 Institution of Engineering and technology. All rights reserved.

关键词： Time series

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Constraints on dark photon dark matter using data from LIGO’s and Virgo’s third observing run

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Physical Review D 2022年第6期105卷 063030-063030页

作者： R. Abbott T. D. Abbott F. Acernese K. Ackley C. Adams N. Adhikari R. X. Adhikari V. B. Adya C. Affeldt D. Agarwal M. Agathos K. Agatsuma N. Aggarwal O. D. Aguiar L. Aiello A. Ain P. Ajith T. Akutsu S. Albanesi A. Allocca P. A. Altin A. Amato C. Anand S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando T. Andrade N. Andres T. Andrić S. V. Angelova S. Ansoldi J. M. Antelis S. Antier S. Appert Koji Arai Koya Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud S. M. Aronson K. G. Arun H. Asada Y. Asali G. Ashton Y. Aso M. Assiduo S. M. Aston P. Astone F. Aubin C. Austin S. Babak F. Badaracco M. K. M. Bader C. Badger S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer A. Balsamo G. Baltus S. Banagiri D. Bankar J. C. Barayoga C. Barbieri B. C. Barish D. Barker P. Barneo F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta J. Bartlett M. A. Barton I. Bartos R. Bassiri A. Basti M. Bawaj J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale M. Bejger I. Belahcene V. Benedetto D. Beniwal T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi D. Bersanetti A. Bertolini J. Betzwieser D. Beveridge R. Bhandare U. Bhardwaj D. Bhattacharjee S. Bhaumik I. A. Bilenko G. Billingsley S. Bini R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini L. D. Bonavena F. Bondu E. Bonilla R. Bonnand P. Booker B. A. Boom R. Bork V. Boschi N. Bose S. Bose V. Bossilkov V. Boudart Y. Bouffanais A. Bozzi C. Bradaschia P. R. Brady A. Bramley A. Branch M. Branchesi J. E. Brau M. Breschi T. Briant J. H. Briggs A. Brillet M. Brinkmann P. Brockill A. F. Brooks J. Brooks D. D. Brown S. Brunett G. Bruno R. Bruntz J. Bryant T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic C. Buy R. L. Byer L. Cadonati G. Cagnoli C. Cahillane J. Calderón Bustillo J. D. Callaghan T. A. Callis LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy OzGrav School of Physics and Astronomy Monash University Clayton 3800 Victoria Australia LIGO Livingston Observatory Livingston Louisiana 70754 USA University of Wisconsin-Milwaukee Milwaukee Wisconsin 53201 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA) Northwestern University Evanston Illinois 60208 USA Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa I-56127 Pisa Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan INFN Sezione di Torino I-10125 Torino Italy Università di Napoli “Federico II” Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy Université de Lyon Université Claude Bernard Lyon 1 CNRS Institut Lumière Matière F-69622 Villeurbanne Franc

We present a search for dark photon dark matter that could couple to gravitational-wave interferometers using data from Advanced LIGO and Virgo’s third observing run. To perform this analysis, we use two methods, one based on cross-correlation of the strain channels in the two nearly aligned LIGO detectors, and one that looks for excess power in the strain channels of the LIGO and Virgo detectors. The excess power method optimizes the Fourier transform coherence time as a function of frequency, to account for the expected signal width due to Doppler modulations. We do not find any evidence of dark photon dark matter with a mass between mA∼10−14–10−11 eV/c2, which corresponds to frequencies between 10–2000 Hz, and therefore provide upper limits on the square of the minimum coupling of dark photons to baryons, i.e., U(1)B dark matter. For the cross-correlation method, the best median constraint on the squared coupling is ∼1.31×10−47 at mA∼4.2×10−13 eV/c2; for the other analysis, the best constraint is ∼2.4×10−47 at mA∼5.7×10−13 eV/c2. These limits improve upon those obtained in direct dark matter detection experiments by a factor of ∼100 for mA∼[2–4]×10−13 eV/c2, and are, in absolute terms, the most stringent constraint so far in a large mass range mA∼2×10−13–8×10−12 eV/c2.

关键词： Dark matter Gravitational wave detectors

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Search for continuous gravitational waves from 20 accreting millisecond x-ray pulsars in O3 LIGO data

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Physical Review D 2022年第2期105卷 022002-022002页

作者： R. Abbott T. D. Abbott F. Acernese K. Ackley C. Adams N. Adhikari R. X. Adhikari V. B. Adya C. Affeldt D. Agarwal M. Agathos K. Agatsuma N. Aggarwal O. D. Aguiar L. Aiello A. Ain T. Akutsu S. Albanesi A. Allocca P. A. Altin A. Amato C. Anand S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando T. Andrade N. Andres T. Andrić S. V. Angelova S. Ansoldi J. M. Antelis S. Antier S. Appert Koji Arai Koya Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud S. M. Aronson K. G. Arun H. Asada Y. Asali G. Ashton Y. Aso M. Assiduo S. M. Aston P. Astone F. Aubin C. Austin S. Babak F. Badaracco M. K. M. Bader C. Badger S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer A. Balsamo G. Baltus S. Banagiri D. Bankar J. C. Barayoga C. Barbieri B. C. Barish D. Barker P. Barneo F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta J. Bartlett M. A. Barton I. Bartos R. Bassiri A. Basti M. Bawaj J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale M. Bejger I. Belahcene V. Benedetto D. Beniwal T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi D. Bersanetti A. Bertolini J. Betzwieser D. Beveridge R. Bhandare U. Bhardwaj D. Bhattacharjee S. Bhaumik I. A. Bilenko G. Billingsley S. Bini R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini L. D. Bonavena F. Bondu E. Bonilla R. Bonnand P. Booker B. A. Boom R. Bork V. Boschi N. Bose S. Bose V. Bossilkov V. Boudart Y. Bouffanais A. Bozzi C. Bradaschia P. R. Brady A. Bramley A. Branch M. Branchesi J. E. Brau M. Breschi T. Briant J. H. Briggs A. Brillet M. Brinkmann P. Brockill A. F. Brooks J. Brooks D. D. Brown S. Brunett G. Bruno R. Bruntz J. Bryant T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic C. Buy R. L. Byer L. Cadonati G. Cagnoli C. Cahillane J. Calderón Bustillo J. D. Callaghan T. A. Callister E. Ca LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 Victoria Australia LIGO Livingston Observatory Livingston Louisiana 70754 USA University of Wisconsin-Milwaukee Milwaukee Wisconsin 53201 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration & Research in Astrophysics (CIERA) Northwestern University Evanston Illinois 60208 USA Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa I-56127 Pisa Italy Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan INFN Sezione di Torino I-10125 Torino Italy Università di Napoli “Federico II” Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy Université de Lyon Université Claude Bernard Lyon 1 CNRS Institut Lumière Matière F-69622 Villeurbanne France Department of Physics The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Un

Results are presented of searches for continuous gravitational waves from 20 accreting millisecond x-ray pulsars with accurately measured spin frequencies and orbital parameters, using data from the third observing run of the Advanced LIGO and Advanced Virgo detectors. The search algorithm uses a hidden Markov model, where the transition probabilities allow the frequency to wander according to an unbiased random walk, while the J-statistic maximum-likelihood matched filter tracks the binary orbital phase. Three narrow subbands are searched for each target, centered on harmonics of the measured spin frequency. The search yields 16 candidates, consistent with a false alarm probability of 30% per subband and target searched. These candidates, along with one candidate from an additional target-of-opportunity search done for SAX J1808.4−3658, which was in outburst during one month of the observing run, cannot be confidently associated with a known noise source. Additional follow-up does not provide convincing evidence that any are a true astrophysical signal. When all candidates are assumed nonastrophysical, upper limits are set on the maximum wave strain detectable at 95% confidence, h095%. The strictest constraint is h095%=4.7×10−26 from IGR J17062−6143. Constraints on the detectable wave strain from each target lead to constraints on neutron star ellipticity and r-mode amplitude, the strictest of which are ε95%=3.1×10−7 and α95%=1.8×10−5 respectively. This analysis is the most comprehensive and sensitive search of continuous gravitational waves from accreting millisecond x-ray pulsars to date.

关键词： Gravitational waves Binary stars Neutron stars & pulsars

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On the nonexistence of event-based triggers that preserve Gaussian state in presence of package-drop

On the nonexistence of event-based triggers that preserve Ga...

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American control Conference

作者： Enoch Kung Junfeng Wu Dawei Shi Ling Shi Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Automatic Control The Royal Institute of Technology (KTH) State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

ISBN: (纸本)9781509045839

state estimation is a core objective in cyber-physical systems. In the state estimation problem over linear systems, the Kalman filter is the standard solution. The filter is the format on which the solutions to subsequent estimation problems are based. Among these problems are the estimation problem in the presence of packet drops and estimation problem involving event-based triggers. We study in this paper both phenomena simultaneously. In an attempt to find the Kalman-like filter, which proves the Gaussianity of the state and offers a set of update equations, our paper shows that no such filter exists. More precisely, one cannot find an event-based trigger such that under possible packet drops, the state variable remains a Gaussian variable. This conclusion can be reasonably extended to a more general setting.

关键词： state estimation Packet drops Event-based triggers state estimation Event-based Filters state variable TRIGGERS Gauss

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