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Annual Conference of Industrial electronics Society

作者： Yunfei Yin Jianxing Liu Sergio Vazquez Ligang Wu Leopoldo G. Franquelo Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China Electronic Engineering Department Universidad de Sevilla Seville Spain

In this paper, a novel scheme of disturbance observer based second order sliding mode (SOSM) control for DC-DC buck converters is proposed. A cascade-control structure is established to regulate the output voltage and force the inductor current to track its reference, which comprises two control loops. The voltage regulation loop which is based on an SOSM controller combined with extended state observer (ESO) is the external loop. The current tracking loop also accomplished by SOSM controller is the internal loop. The fast motion is dominated by the dynamics of the current tracking loop whereas the slow motion stems from the dynamics of the output voltage. In addition, the load resistance that influences significantly the dynamics of whole system is regarded as the external disturbance in this papper. A disturbance observer, ESO, aims at asymptotically rejecting disturbances to converter. The proposed control strategy is verified using simulation test.

关键词： Voltage control Buck converters Tracking loops Inductors Resistance Switches

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Second-order sliding mode control of power converters using different disturbance observers for DC-link voltage regulation

Second-order sliding mode control of power converters using ...

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Annual Conference of Industrial electronics Society

作者： Wensheng Luo Sergio Vazquez Jianxing Liu Ligang Wu Leopoldo G. Franquelo Electronic Engineering Department Universidad de Sevilla Seville Spain Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China

This paper employs second-order sliding mode control (SOSMC) to carry out the current tracking and voltage regulation tasks of a grid-connected three-phase two-level power converter. For dc-link voltage regulation, a disturbance observer is adopted to improve the whole control performance. In this paper, four different types of disturbance observers are proposed for comparison, which are conventional linear observer (LINO), second-order sliding mode observer (SOSMO), linear extended state observer (LESO) and nonlinear extended state observer (NESO). To ensure fair comparison, the parameters of the observers are tuned to make the power converter achieve almost same current total harmonic distortion (THD) and steady error of dc-link voltage. The performances are compared in the term of transient response of the dc-link voltage. The simulation results show: first, the disturbance observers improve the control performance of SOSMC; second, SOSMO outperforms the other three observers.

关键词： Voltage control Tracking loops Sliding mode control Transient response Load modeling

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Distributed Kalman Filters with state equality constraints: Time-based and event-triggered communications

arXiv

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arXiv 2017年

作者： He, Xingkang Hu, Chen Hong, Yiguang Shi, Ling Fang, Haitao Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Rocket Force University of Engineering Shaanxi China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology

In this paper, we investigate a distributed estimation problem for multi-agent systems with state equality constraints (SEC). First, under a time-based consensus communication protocol, applying a modified projection operator and the covariance intersection fusion method, we propose a distributed Kalman filter with guaranteed consistency and satisfied SEC. Furthermore, we establish the relationship between consensus step, SEC and estimation error covariance in dynamic and steady processes, respectively. Employing a space decomposition method, we show the error covariance in the constraint set can be arbitrarily small by setting a sufficiently large consensus step. Besides, we propose an extended collective observability (ECO) condition based on SEC, which is milder than existing observability conditions. Under the ECO condition, through utilizing a technique of matrix approximation, we prove the boundedness of error covariance and the exponentially asymptotic unbiasedness of state estimate, respectively. Moreover, under the ECO condition for linear time-invariant systems with SEC, we provide a novel event-triggered communication protocol by employing the consistency, and give an offline design principle of triggering thresholds with guaranteed boundedness of error covariance. More importantly, we quantify and analyze the communication rate for the proposed event-triggered distributed Kalman filter, and provide optimization based methods to obtain the minimal (maximal) successive non-triggering (triggering) times. Two simulations are provided to demonstrate the developed theoretical results and the effectiveness of the filters. Copyright © 2017, The Authors. All rights reserved.

关键词： Multi agent systems

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Automatic Sleep Spindle Detection with EEG Based on Complex Demodulation Method and Decision Tree Model

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Journal of Biomedical Science and engineering 2017年第5期10卷 10-17页

作者： Jiabin Li Bei Wang Takenao Sugi Yu Zhang Masatoshi Nakamura Department of Automation East University of Science and Technology Shanghai China Department of Electrical and Electronic Engineering Saga University Saga Japan Research Institute of Systems Control Institute for Advanced Research and Education Saga University Saga Japan

Sleep spindle is the characteristic waveform of electroencephalogram (EEG) which is important for clinical diagnosis. In this study, an automatic sleep spindle detection method was developed. The EEG signals were recorded based on the standard polysomnogram (PSG) measurement. A preprocessing procedure is introduced to exclude the unnecessary data segments and normalized the necessary data segments. Complex demodulation method is adopted to detect the candidate sleep spindle waveforms and calculate the features. The sleep spindles are recognized based on a decision tree model. Finally, the detected sleep spindles were utilized to amend the sleep stage recognition results. The sleep EEG data from 3 patients with sleep disorders were analyzed. The obtained results showed that the detected sleep spindles in EEG signal improved the accuracy of sleep stage recognition.

关键词： Sleep Spindle Detection Complex Demodulation Method Decision Tree Mod-el EEG

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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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Automatic Sleep Spindle Detection with EEG Based on Complex Demodulation Method and Decision Tree Model

Automatic Sleep Spindle Detection with EEG Based on Complex ...

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The 11th International Conference on Bioinformatics and Biomedical engineering (ICBBE 2017)

作者： Jiabin Li Bei Wang Takenao Sugi Yu Zhang Masatoshi Nakamura Department of Automation East University of Science and Technology Department of Electrical and Electronic Engineering Saga University Research Institute of Systems Control Institute for Advanced Research and Education Saga University

Sleep spindle is the characteristic waveform of electroencephalogram(EEG) which is important for clinical diagnosis. In this study, an automatic sleep spindle detection method was developed. The EEG signals were recorded based on the standard polysomnogram(PSG) measurement. A preprocessing procedure is introduced to exclude the unnecessary data segments and normalized the necessary data segments. Complex demodulation method is adopted to detect the candidate sleep spindle waveforms and calculate the features. The sleep spindles are recognized based on a decision tree model. Finally, the detected sleep spindles were utilized to amend the sleep stage recognition results. The sleep EEG data from 3 patients with sleep disorders were analyzed. The obtained results showed that the detected sleep spindles in EEG signal improved the accuracy of sleep stage recognition.

关键词： Sleep Spindle Detection Complex Demodulation Method Decision Tree Model EEG

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Multi-view hybrid embedding: A divide-and-conquer approach

arXiv

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arXiv 2018年

作者： Xu, Jiamiao Yu, Shujian You, Xinge Leng, Mengjun Jing, Xiao-Yuan Chen, C. L. Philip School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Science University of Houston HoustonTX77204 United States State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau Macau99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multiview subspace learning (MvSL) that aims to learn a latent subspace shared by multi-view data. Despite promising results obtained on some applications, the performance of existing methods deteriorates dramatically when the multi-view data is sampled from nonlinear manifolds or suffers from heavy outliers. To circumvent this drawback, motivated by the Divideand-Conquer strategy, we propose Multi-view Hybrid Embedding (MvHE), a unique method of dividing the problem of cross-view classification into three subproblems and building one model for each subproblem. Specifically, the first model is designed to remove view discrepancy, whereas the second and third models attempt to discover the intrinsic nonlinear structure and to increase discriminability in intra-view and interview samples respectively. The kernel extension is conducted to further boost the representation power of MvHE. Extensive experiments are conducted on four benchmark datasets. Our methods demonstrate overwhelming advantages against the stateof-the-art MvSL based cross-view classification approaches in terms of classification accuracy and robustness. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

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Population of Merging Compact Binaries Inferred Using Gravitational Waves through GWTC-3

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Physical Review X 2023年第1期13卷 011048-011048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 P. F. de Alarcón S. Akcay 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 F. Antonini S. Appert Koji Arai Koya Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud M. Arogeti 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 C. P. L. Berry 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. Brandt 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 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 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 Universitat de les Illes Balears IAC3–IEEC E-07122 Palma de Mallorca Spain University College Dublin Dublin 4 Ireland INFN Sezione di Torino I-10125 Torino Italy Università di Napoli “Federico II” Complesso Universitario di Monte S. Angelo I-80126 Napoli

We report on the population properties of compact binary mergers inferred from gravitational-wave observations of these systems during the first three LIGO-Virgo observing runs. The Gravitational-Wave Transient Catalog 3 (GWTC-3) contains signals consistent with three classes of binary mergers: binary black hole, binary neutron star, and neutron star–black hole mergers. We infer the binary neutron star merger rate to be between 10 and 1700 Gpc−3 yr−1 and the neutron star–black hole merger rate to be between 7.8 and 140 Gpc−3 yr−1, assuming a constant rate density in the comoving frame and taking the union of 90% credible intervals for methods used in this work. We infer the binary black hole merger rate, allowing for evolution with redshift, to be between 17.9 and 44 Gpc−3 yr−1 at a fiducial redshift (z=0.2). The rate of binary black hole mergers is observed to increase with redshift at a rate proportional to (1+z)κ with κ=2.9−1.8+1.7 for z≲1. Using both binary neutron star and neutron star–black hole binaries, we obtain a broad, relatively flat neutron star mass distribution extending from 1.2−0.2+0.1 to 2.0−0.3+0.3M⊙. We confidently determine that the merger rate as a function of mass sharply declines after the expected maximum neutron star mass, but cannot yet confirm or rule out the existence of a lower mass gap between neutron stars and black holes. We also find the binary black hole mass distribution has localized over- and underdensities relative to a power-law distribution, with peaks emerging at chirp masses of 8.3−0.5+0.3 and 27.9−1.8+1.9M⊙. While we continue to find that the mass distribution of a binary’s more massive component strongly decreases as a function of primary mass, we observe no evidence of a strongly suppressed merger rate above approximately 60M⊙, which would indicate the presence of a upper mass gap. Observed black hole spins are small, with half of spin magnitudes below χi≈0.25. While the majority of spins are preferentially aligned wi

关键词： Gravitational wave sources Gravitational waves Gravitational wave detection

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Native diversity buffers against severity of non-native tree invasions ( vol 621,pg 773 , 2023 )

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NATURE 2023年第7982期622卷 E2-E2页

作者： Delavaux, Camille S. Crowther, Thomas W. Zohner, Constantin M. Robmann, Niamh M. Lauber, Thomas van den Hoogen, Johan Kuebbing, Sara Liang, Jingjing de-Miguel, Sergio Nabuurs, Gert-Jan Reich, Peter B. Abegg, Meinrad Yao, Yves C. Adou Alberti, Giorgio Zambrano, Angelica M. Almeyda Alvarado, Braulio Vilchez Alvarez-Davila, Esteban Alvarez-Loayza, Patricia Alves, Luciana F. Ammer, Christian Anton-Fernandez, Clara Araujo-Murakami, Alejandro Arroyo, Luzmila Avitabile, Valerio Aymard, Gerardo A. Baker, Timothy R. Balazy, Radomir Banki, Olaf Barroso, Jorcely G. Bastian, Meredith L. Bastin, Jean-Francois Birigazzi, Luca Birnbaum, Philippe Bitariho, Robert Boeckx, Pascal Bongers, Frans Bouriaud, Olivier Brancalion, Pedro H. S. Brandl, Susanne Brienen, Roel Broadbent, Eben N. Bruelheide, Helge Bussotti, Filippo Gatti, Roberto Cazzolla Cesar, Ricardo G. Cesljar, Goran Chazdon, Robin Chen, Han Y. H. Chisholm, Chelsea Cho, Hyunkook Cienciala, Emil Clark, Connie Clark, David Colletta, Gabriel D. Coomes, David A. Valverde, Fernando Cornejo Corral-Rivas, Jose J. Crim, Philip M. Cumming, Jonathan R. Dayanandan, Selvadurai de Gasper, Andre L. Decuyper, Mathieu Derroire, Geraldine DeVries, Ben Djordjevic, Ilija Dolezal, Jiri Dourdain, Aurelie Obiang, Nestor Laurier Engone Enquist, Brian J. Eyre, Teresa J. Fandohan, Adande Belarmain Fayle, Tom M. Feldpausch, Ted R. Ferreira, Leandro V. Fischer, Markus Fletcher, Christine Frizzera, Lorenzo Gamarra, Javier G. P. Gianelle, Damiano Glick, Henry B. Harris, David J. Hector, Andrew Hemp, Andreas Hengeveld, Geerten Herault, Bruno Herbohn, John L. Herold, Martin Hillers, Annika Coronado, Euridice N. Honorio Hui, Cang Ibanez, Thomas T. Amaral, Ieda Imai, Nobuo Jagodzinski, Andrzej M. Jaroszewicz, Bogdan Johannsen, Vivian Kvist Joly, Carlos A. Jucker, Tommaso Jung, Ilbin Karminov, Viktor Kartawinata, Kuswata Kearsley, Elizabeth Kenfack, David Kennard, Deborah K. Kepfer-Rojas, Sebastian Keppel, Gunnar Khan, Mohammed Latif Killeen, Timothy J. Kim, Hyun Seok Kitayama, Kanehiro Kohl, Michael Korju Institute of Integrative Biology ETH Zurich (Swiss Federal Institute of Technology) Zurich Switzerland Department of Genetics Evolution and Environment University College London London UK The Forest School at The Yale School of the Environment Yale University New Haven CT USA Department of Forestry and Natural Resources Purdue University West Lafayette IN USA Department of Agriculture Forestry and Bioresources Seoul National University Seoul South Korea Department of Crop and Forest Sciences University of Lleida Lleida Spain Joint Research Unit CTFC–AGROTECNIO–CERCA Solsona Spain Wageningen University and Research Wageningen The Netherlands World Agroforestry (ICRAF) Nairobi Kenya Department of Forest Resources University of Minnesota St Paul MN USA Hawkesbury Institute for the Environment Western Sydney University Penrith New South Wales Australia Institute for Global Change Biology and School for Environment and Sustainability University of Michigan Ann Arbor MI USA Swiss Federal Institute for Forest Snow and Landscape Research WSL Birmensdorf Switzerland UFR Biosciences University Félix Houphouët-Boigny Abidjan Côte d’Ivoire Department of Agricultural Food Environmental and Animal Sciences University of Udine Udine Italy Faculty of Science and Technology Free University of Bolzano Bolzano Italy Spatial Ecology and Conservation Laboratory Department of Tourism Recreation and Sport Management University of Florida Gainesville FL USA Forestry School Tecnológico de Costa Rica TEC Cartago Costa Rica Fundacion ConVida Universidad Nacional Abierta y a Distancia UNAD Medellin Colombia Field Museum of Natural Histiory Chicago IL USA Center for Tropical Research Institute of the Environment and Sustainability UCLA Los Angeles CA USA Silviculture and Forest Ecology of the Temperate Zones University of Göttingen Göttingen Germany Division of Forest and Forest Resources Norwegian Institute of Bioeconomy Research (NIBIO) Ås Norway Museo de Hi

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Distributed Online Optimization of High-Order Multi-Agent systems 35

Distributed Online Optimization of High-Order Multi-Agent Sy...

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第35届中国控制会议

作者： DENG Zhenhua ZHANG Yangqiong HONG Yiguang Key Lab of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology

ISBN: (纸本)9781509009107

Distributed online optimization problem for continuous-time multi-agent systems with the form of high-order integrator is studied in this *** order to minimize the regret,a distributed online optimization algorithm is developed via consensus idea,PI control idea and gradient ***,we show that under the proposed algorithm,the regret bound approaches a constant when the operating time tends to infinity.

关键词： Distributed online optimization multi-agent systems regret analysis high-order integrator

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