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The 2015 2nd International Conference on Engineering Technology and Application

作者： Junxiang Cao Jianxu Luo Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

Teaching-Learning-Based optimization(TLBO) is a new swarm intelligence optimization algorithm that simulates the class learning *** to such problems of the traditional TLBO as low optimizing efficiency and poor stability,this paper proposes an improved TLBO algorithm mainly by introducing the elite thought in TLBO and adopting different inertia weight decreasing strategies for elite and ordinary individuals of the teacher stage and the student *** this paper,the validity of the improved TLBO is verified by the optimizations of several typical test functions and the SVM optimized by the weighted elitist TLBO is used in the diagnosis and classification of common failure data of tie TE chemical *** with the SVM combining other traditional optimizing methods,the SVM optimized by the weighted elitist TLBO has a certain improvement in the accuracy of fault diagnosis and classification.

关键词： TLBO algorithm support vector machine fault diagnosis TE chemical process

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Improving the Performance of Online Classifier by Removing the Error Samples from Offline Training Data

Improving the Performance of Online Classifier by Removing t...

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2015 IEEE International Conference on Computer and Communications(ICCC 2015)

作者： Hanhan Zhang Jing Jin Sijie Zhou Yu Zhang Xingyu Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

Brain-computer interface(BCI) plays an important role in helping the people with severe motor disability. In event-related potential(ERP) based BCIs, subjects were asked to count the target stimulus in the offline experiment, the recorded electroencephalogram(EEG) data was used to train the classification mode. However, subjects may make mistakes in counting the target stimulus or be affected by the non-target stimulus. The target trials may not contain expected ERPs and the non-target trials may contain unexpected ERPs, which was called error samples. This paper intends to survey whether the classification accuracy could be improved after removing these error samples from offline training data. The result showed that the online performance of BCI system could be improved after selecting the offline samples for training the classification mode.

关键词： Brain-computer interface(BCI) P300 feature selection algorithm

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Distributed formation control for a multi-agent system with dynamic and static obstacle avoidances

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Chinese Physics B 2014年第7期23卷 337-342页

作者：曹建福凌志浩袁宜峰高冲 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

Formation control and obstacle avoidance for multi-agent systems have attracted more and more attention. In this paper, the problems of formation control and obstacle avoidance are investigated by means of a consensus algorithm. A novel distributed control model is proposed for the multi-agent system to form the anticipated formation as well as achieve obstacle avoidance. Based on the consensus algorithm, a distributed control function consisting of three terms （formation control term, velocity matching term, and obstacle avoidance term） is presented. By establishing a novel formation control matrix, a formation control term is constructed such that the agents can converge to consensus and reach the anticipated formation. A new obstacle avoidance function is developed by using the modified potential field approach to make sure that obstacle avoidance can be achieved whether the obstacle is in a dynamic state or a stationary state. A velocity matching term is also put forward to guarantee that the velocities of all agents converge to the same value. Furthermore, stability of the control model is proven. Simulation results are provided to demonstrate the effectiveness of the proposed control.

关键词： multi-agent system formation control obstacle avoidance consensus theory

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Full Form Dynamic Linearization controller Based Data-driven Model Free Adaptive control

Full Form Dynamic Linearization Controller Based Data-driven...

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第27届中国控制与决策会议

作者： Yuanming Zhu Zhongsheng Hou Shangtai Jin Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology Advanced Control System Laboratory Beijing Jiaotong University

ISBN: (纸本)9781479970186

This paper presents a data-driven model free adaptive control method based on full form dynamic linearization *** this framework,the controller structure is designed by the full form dynamic linearization technique on the ideal controller,and its parameters are online optimized using input and output data of the plant through the simple projection algorithm,where the accurate plant model is not *** effectiveness of the proposed method is verified by numerical simulations.

关键词： Dynamic Linearization,Data-driven,Model Free Adaptive control,Nonlinear System

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Robust optimization for a Multi-Product Integrated Problem of Planning and Scheduling under Products Uncertainty

Robust Optimization for a Multi-Product Integrated Problem o...

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2015 Workshop 1

作者： Mengwen Chen Cuiwen Cao Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology

This paper presents robust optimization models for a multi-product integrated problem of planning and scheduling(based on the work of Terrazas-Moreno & Grossmann(2011) [1]) under products prices uncertainty. With the objective of maximizing the total profit in planning time horizon, the planning section determines the amount of each product, each product distributed to each market, and the inventory level in each manufacturing site during each scheduling time period;the scheduling section determines the products sequence, start and end time of each product running in each production site during each scheduling time period. The uncertainty sets used in robust optimization model are box set, ellipsoidal set, polyhedral set, combined box and ellipsoidal set, combined box and polyhedral set, combined box, ellipsoidal and polyhedral *** genetic algorithm is utilized to solve the robust optimization models. Case studies show that the solutions obtained from robust optimization models are better than the solutions obtained from the original integrated planning and scheduling when the prices are changed.

关键词： Uncertainty Robust optimization Integrated Problem of Planning and Scheduling GA

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Sensor Scheduling for Distributed Filtering under Limited Communication Resources

Sensor Scheduling for Distributed Filtering under Limited Co...

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IEEE Annual Conference on Decision and control

作者： Wen Yang Chao Yang Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes(East China University of Science and Technology) Ministry of Education

ISBN: (纸本)9781479978878

In this paper we consider the problem of sensor scheduling for distributed estimation with power constraint. To satisfy the requirement of energy consumption, we assume that each sensor sends data to its adjacent sensors with a given probability, where the state of the system is estimated by means of distributed estimators. First, we design optimal estimator gain to minimize the estimation error covariance for each sensor, and then we find an upper bound of the expected state error covariance, provide a sufficient condition to guarantee the stability of the proposed estimator. To find the optimal communication probability, we further formulate the sensor scheduling problem as an optimization problem, which is relaxed to a convex optimization in the form of LMIs.

关键词： Sensors communication resources Distribute Convex function Covariance Filtration energy consumption

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Partial stabilization for Boolean network with state feedback control 34

Partial stabilization for Boolean network with state feedbac...

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第三十四届中国控制会议

作者： LI Fangfei ZHAO Shouwei LI Chunxiang YU Zhaoxu Department of Mathematics East China University of Science and Technology School of Fundamental Studies Shanghai University of Engineering Science Department of Mathematics and Physics Second Military Medical University Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

ISBN: (纸本)9781467374439

In the literatures, to design state feedback controller to partially stabilize Boolean control network is rarely *** by this, this paper studies partial stabilization for Boolean network with state feedback control. A sufficient condition for the existence of a state feedback controller to achieve the partial stabilization is established first. Then, the feedback control law is proposed. Finally, examples are presented to show the proposed design procedure.

关键词： Boolean control network,partial stabilization,feedback control design,semi-tensor product

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Data-driven Model Free Adaptive control for a Class of Interconnected Systems

Data-driven Model Free Adaptive Control for a Class of Inter...

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Asian control Conference

作者： Yuanming Zhu Feng Qian Zhongsheng Hou Shangtai Jin Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Advanced Control System Laboratory Beijing Jiaotong University

ISBN: (纸本)9781479978632

This paper recalls a novel data-driven model-free adaptive control (MFAC) method for a class of interconnected discrete-time nonlinear systems, whose model is unavailable and interactions between each subsystem are measurable. Then, under some mild conditions, stability of the closed-loop system is analyzed theoretically. Compared with original MFAC, the proposed MFAC for interconnected systems belongs to decentralized control method, and makes full use of the interacted data to achieve better performance. The effectiveness and superiority are verified by simulation result.

关键词： Data-driven control Model Free Adaptive control Interconnected System

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Hellinger distance based probability distribution approach to performance monitoring of nonlinear control systems

Hellinger distance based probability distribution approach t...

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作者： Li, Chen Huang, Biao Qian, Feng Key Laboratory of Advanced Control and Optimization Ministry of Education East China University of Science and Technology Shanghai200237 China Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G 2G6 Canada

control performance monitoring has attracted great attention in both academia and industry over the past two decades. However, most research efforts have been devoted to the performance monitoring of linear control systems, without considering the pervasive nonlinearities (e.g. valve stiction) present in most industrial control systems. In this work, a novel probability distribution distance based index is proposed to monitor the performance of non-linear control systems. The proposed method uses Hellinger distance to evaluate change of control system performance. Several simulation examples are given to illustrate the effectiveness of the proposed method. © 2015 The chemical Industry and Engineering Society of China, and chemical Industry Press. All rights reserved.

关键词： Probability distributions

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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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