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IEEE General Meeting Power& Energy Society

作者： Chetan Mishra Virgilio A. Centeno Anamitra Pal Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg Virginia USA Network Dynamics and Simulation Science Laboratory Virginia Polytechnic Institute and State University Blacksburg Virginia USA

In this paper estimation of three-phase transmission line parameters is done with the help of synchrophasor measurements by using a recursive regression technique based on the Kalman filter. The errors in the regression vector due to presence of noise in the synchrophasor data are also accounted for while estimating the parameters. The performance of this technique is demonstrated for a medium length transmission line. The results indicate that the proposed approach is able to successfully compute three phase transmission line parameters of non-transposed, non-symmetric lines.

关键词： Transmission line measurements Power transmission lines Phasor measurement units Measurement uncertainty Estimation Transmission line matrix methods Noise measurement

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Direct and third-body mediated resonance energy transfer in dimensionally constrained nanostructures

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Physical Review B 2015年第3期92卷 035128-035128页

作者： Dilusha Weeraddana Malin Premaratne David L. Andrews Advanced Computing and Simulation Laboratory (A L) Department of Electrical and Computer Systems Engineering Monash University Clayton 3800 VIC Australia School of Chemistry University of East Anglia Norwich Research Park Norwich NR4 7TJ United Kingdom

The process of resonance energy transfer (RET) in a nanostructure influenced by a vicinal, nonabsorbing third body is studied within the framework of molecular quantum electrodynamics. Direct RET and the influence of neighboring matter have been studied previously, mainly for molecules. However, a complete study or unified understanding of direct and indirect RET in nanostructures with different dimensionalities is still lacking. Therefore, there is a strong need for a complete theory that models RET for the cases of quantum wells, nanowires, and quantum dots. We construct a detailed picture of excitation energy transfer in nanostructures and how it is affected by another quantum object, which includes the derivation of quantum amplitudes based on second- and fourth-order time-dependent perturbation theories, and the derivation of transfer rates and distance dependencies, providing a complete picture and understanding of RET in nanostructures. The results of the derivations indicate that the dimensionality of the nanostructure determines the controllability of the RET rate. Furthermore, third-body mediation leads to a nonvanishing RET in the coupling of nanowire to nanowire and quantum dot to quantum dot.

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ADRC based cascade controller design for urea selective catalytic reduction system

ADRC based cascade controller design for urea selective cata...

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SICE Annual Conference

作者： Yunfeng Hu Zhigang Chen Jinghua Zhao Hong Chen Department of Control Science and Engineering Jilin University Changchun Jilin China State Key Laboratory of Automotive Simulation and Control Jilin University Changchun Jilin China Computer College JiLin Normal University Siping Jilin China

Urea selective catalytic reduction (urea-SCR) control is very important for diesel engine emissions. In this paper, a Continuous Stirred Tank Reactor(CSTR) is used to represent the SCR model, then a cascade control system is deduced based on active disturbance rejection control (ADRC) strategy. The cascade control system consists of two ADRC controllers, and every ADRC controller contains a Extended State Observer (ESO) to estimate the total disturbance and a PID feedback control law to handle the ammonia coverage ratio tracking. Finally, the simulation results are given to demonstrate the effectiveness of the proposed control scheme tracking the reference and the robustness to the noise.

关键词： Thyristors Mathematical model Engines Noise Control systems Observers Robustness

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High-energy-density electron beam from interaction of two successive laser pulses with subcritical-density plasma

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Physical Review Accelerators and Beams 2016年第2期19卷 021301-021301页

作者： J. W. Wang W. Yu M. Y. Yu H. Xu J. J. Ju S. X. Luan M. Murakami M. Zepf S. Rykovanov Helmholtz Institute Jena Jena 07743 Germany State Key Laboratory of High Field Laser Physics Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai 201800 China Institute for Fusion Theory and Simulation and the Department of Physics Zhejiang University Hangzhou 310027 China Institute for Theoretical Physics I Ruhr University Bochum D-44780 Germany National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 China Institute of Laser Engineering Osaka University Osaka 565-0871 Japan Centre for Plasma Physics School of Mathematics and Physics Queen’s University Belfast Belfast BT7 1NN United Kingdom

It is shown by particle-in-cell simulations that a narrow electron beam with high energy and charge density can be generated in a subcritical-density plasma by two consecutive laser pulses. Although the first laser pulse dissipates rapidly, the second pulse can propagate for a long distance in the thin wake channel created by the first pulse and can further accelerate the preaccelerated electrons therein. Given that the second pulse also self-focuses, the resulting electron beam has a narrow waist and high charge and energy densities. Such beams are useful for enhancing the target-back space-charge field in target normal sheath acceleration of ions and bremsstrahlung sources, among others.

关键词： Plasma acceleration & new acceleration techniques

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A Learning-By-Examples Approach for Non-Destructive Localization and Characterization of Defects Through Eddy Current Measurements

A Learning-By-Examples Approach for Non-Destructive Localiza...

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IEEE International Symposium on Antennas and Propagation

作者： M. Salucci G. Oliveri F. Viani R. Miorelli C. Reboud P. Calmon A. Massa ELEDIA Research Center Department of Information Engineering and Computer Science University of Trento via Sommarive 9 38123 Trento Italy Laboratoire de Simulation et de Modelisation Electromagnetique CEA LIST 91191 Gif-sur-Yvette Cedex France

ISBN: (纸本)9781479978168

This work presents an innovative inversion method for non-destructive localization and characterization of defects based on a Learning-by-Example (LBE) approach. The qualitative imaging of the electromagnetic anomalies affecting a conductive structure is based on the exploitation of Eddy Current Testing (ECT) measurements through Support Vector Regression (SVR), enabling an evaluation of the structure under test almost in real-time. Some preliminary numerical results are shown in order to validate the effectiveness of the proposed method.

关键词： Estimation Eddy current testing Support vector machines Inspection Inverse problems Real-time systems Noise measurement

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Corrigendum: “A high order predictor–corrector integration algorithm for first principle chemical dynamics simulations”

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Journal of Computational Biophysics and Chemistry 2016年第7期15卷

作者： Heng Wu Shaofei Lu Ningjia Zhu Jialin Liu Eduardo Colmenares Yin Lu Yu Zhuang Yong Chen William L. Hase Department of Computer Science Texas TECH University Lubbock USA College of Computer Science and Electronic Engineering Hunan University Changsha P.R. China Department of Design Science Jiaxing University Jiaxing P.R. China Contribution: Provided original idea and correct formula. Contribution: Revised the original manuscript and corrected programmer problems. Department of Chemistry & Biochemistry Texas TECH University Lubbock USA Contribution: Provided original idea and cluster for simulation.

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Novel Multi-output Support Vector Regression Model via Double Regularization

Novel Multi-output Support Vector Regression Model via Doubl...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yanyan Yang Degang Chen Ze Dong School of Control and Computer Engineering North China Electric Power University Beijing China Department of Mathematics and Physics North China Electric Power University Beijing China Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation North China Electric Power University Baoding P. R. China

ISBN: (纸本)9781479986989

Multi-output regression estimation aims at mining a vector-valued function from multi-dimensional input vector to multi-dimensional output vector. However, the output variables may be correlative. It is desirable to develop a multi-dimensional regression model taking advantage of the possible correlations. Therefore, this paper proposes a novel multi-output support vector regression model via double regularization. For each output variable, we first introduce an influential level vector with the dimensionality equal to the one of the input vector, in order to characterize the correlation between this variable and other output variables. As a second regularization term, 2-norms of all influential level vectors are then added into the objective function. Each influential level vector is also considered in constraints of our model. Finally, experimental comparisons demonstrate that our proposed model in this paper has a better generalization performance as well as a better robustness.

关键词： Yttrium Support vector machines Correlation Mathematical model Linear programming Kernel Computational modeling

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A clustering method for identifying regions of interest in turbulent combustion tensor fields

Mathematics and Visualization

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Mathematics and Visualization 2015年 40卷 323-338页

作者： Maries, Adrian Luciani, Timothy Pisciuneri, P.H. Nik, Mehdi B. Yilmaz, S. Levent Givi, Peyman Marai, G. Elisabeta Learning Research and Development Center University of Pittsburgh PittsburghPA15260 United States Department of Computer Science Brown University ProvidenceRI02912 United States Center for Simulation and Modeling University of Pittsburgh PittsburghPA15260 United States Department of Mechanical Engineering and Materials Science University of Pittsburgh PittsburghPA15260 United States Mathworks NatickMA01760 United States Department of Computer Science University of Illinois at Chicago ChicagoIL60647 United States

Production of electricity and propulsion systems involve turbulent combustion. Computational modeling of turbulent combustion can improve the efficiency of these processes. However, large tensor datasets are the result of such simulations;these datasets are difficult to visualize and analyze. In this work we present an unsupervised statistical approach for the segmentation, visualization and potentially the tracking of regions of interest in large tensor data. The approach employs a machine learning clustering algorithm to locate and identify areas of interest based on specified parameters such as strain tensor value. Evaluation on two combustion datasets shows this approach can assist in the visual analysis of the combustion tensor field. © Springer International Publishing Switzerland 2015.

关键词： Combustion

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On the Shortest Path Planning for the Carangiform Robotic Fish 33

On the Shortest Path Planning for the Carangiform Robotic Fi...

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第三十三届中国控制会议

作者： LIU Chang CHEN Songlin Jianxun Wang Xiaobo Tan Control and Simulation Center Harbin Institute of Technology Smart Microsystems Laboratory Department of Electrical and Computer EngineeringMichigan State UniversityEast LansingMI 48824USA

ISBN: (纸本)9781479947249

Considering the under-actuated characteristic of the carangiform robotic fish whose forward movement and turning are carried out by its single caudal fin, this paper investigates its shortest path planning problem subjected to the terminal constrains. First, a simplified kinematics model is proposed in view of its locomotion characteristic. With this model, the shortest path planning is transformed into the shortest time optimal control problem and the maximum principle is employed to obtain the necessary condition. Furthermore, based on the analysis on the relationship between the length of all feasible paths and the terminal condition, the sufficient and necessary conditions are derived for the shortest path under any terminal constrains. Finally, a program developed with Matlab verifies the effectiveness of the proposed results.

关键词： carangiform robotic fish,shortest path,maximum principle

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 1

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BMC Neurosci 2017年第SUPPL 1期18卷 58-58页

作者： Sue Denham Panayiota Poirazi Erik De Schutter Karl Friston Ho Ka Chan Thomas Nowotny Dongqi Han Sungho Hong Sophie Rosay Tanja Wernle Alessandro Treves Sarah Goethals Romain Brette Tomas Van Pottelbergh Rodolphe Sepulchre Alex D. Bird Hermann Cuntz Pedro J. Gonçalves Jan-Matthis Lueckmann Giacomo Bassetto Marcel Nonnenmacher Jakob H. Macke Audrey J. Sederberg Jason N. MacLean Stephanie E. Palmer Ulisse Ferrari Christophe Gardella Olivier Marre Thierry Mora Emina Ibrahimovic Martin Müller Jean-Pascal Pfister Tushar Chauhan Timothée Masquelier Alexandre Montlibert Benoit R. Cottereau Moritz Helias Jannis Schuecker David Dahmen Sven Goedeke Alexandre Hyafil Ainhoa Hermoso-Mendizabal Pavel E. Rueda-Orozco Santiago Jaramillo David Robbe Jaime de la Rocha Marie Rooy Fani Koukouli David DiGregorio Uwe Maskos Boris Gutkin Andrey Yu Verisokin Darya V. Verveyko Dmitry E. Postnov Willy Wong Omid Talakoub Robert Chen Milos Popovic Dmitriy Lisitsyn Eric Drebitz Iris Grothe Sunita Mandon Andreas Kreiter Udo Ernst Peter A. Robinson Xuelong Zhao Kevin M. Aquino John D. Griffiths Grishma Mehta-Pandejee Natasha Gabay James MacLaurin Somwrita Sarkar Tim Kunze Jens Haueisen Thomas R. Knösche Subutai Ahmad Yuwei Cui Marcus Lewis Jeff Hawkins Simona Olmi Spase Petkoski Fabrice Bartolomei Maxime Guye Viktor Jirsa Hazem Toutounji Daniel Durstewitz Matteo Cantarelli Adrian Quintana Boris Marin Matt Earnshaw Padraig Gleeson Robert Court Robert McDougal R. Angus Silver Salvador Dura-Bernal Stephen Larson William W. Lytton Giovanni Idili Lorenzo Posani Simona Cocco Karel Ježek Rémi Monasson School of Psychology Faculty of Health & Human Sciences Plymouth University Plymouth Devon PL4 8AA UK Institute of Molecular Biology and Biotechnology (IMBB) Foundation for Research and Technology-Hellas (FORTH) Heraklion Crete Greece Okinawa Institute of Science and Technology Graduate University Okinawa Japan Wellcome Trust Centre for Neuroimaging Institute of Neurology UCL London WC1N 3BG UK School of Engineering and Informatics University of Sussex Brighton BN1 9QJ UK Graduate School Okinawa Institute of Science and Technology Okinawa 904-0495 Japan Computational Neuroscience Unit Okinawa Institute of Science and Technology Okinawa 904-0495 Japan Cognitive Neuroscience Area Scuola Internazionale Superiore di Studi Avanzati Trieste 34136 Italy Kavli Institute for Systems Neuroscience NTNU Trondheim 7030 Norway Institut de la Vision Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Paris 75012 France Department of Engineering University of Cambridge Cambridge Cambridgeshire CB2 1PZ UK Ernst Strüngmann Institute for Neuroscience Max Planck Society Frankfurt Germany Frankfurt Institute for Advanced Studies Frankfurt Germany Neural Systems Analysis Research Center Caesar An Associate of the Max Planck Society Bonn Germany Max Planck Institute for Biological Cybernetics Tübingen Germany Bernstein Center for Computational Neuroscience Tübingen Germany Present Address: Biomedical Engineering Department Georgia Institute of Technology Atlanta GA 30306 USA Department of Neurobiology University of Chicago Chicago IL 60637 USA Department of Organismal Biology and Anatomy University of Chicago Chicago IL 60637 USA Committee on Computational Neuroscience University of Chicago Chicago IL 60637 USA Institut de la Vision Sorbonne Universités INSERM Paris France Laboratoire de Physique Statistique Ecole Normale Supérieure Paris France Institute of Molecular Life Sciences University of Zürich 8057 Zürich Switzerland Institute of Neuroinformatics Univer

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