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2016 IEEE Energy Conversion Congress and Exposition, ECCE 2016

作者： Metry, Morcos Bayhan, Sertac Shadmand, Mohammad B. Balog, Robert S. Rub, Haitham Abu Renewable Energy and Advanced Power Electronics Research Laboratory Department of Electrical and Computer Engineering Texas AandM University College StationTX77843 United States Electrical and Computer Engineering Program Texas AandM University at Qatar Qatar Foundation Doha23874 Qatar Department of Electronics and Automation Gazi University Ankara Turkey

ISBN: (纸本)9781509007370

Stochastic dynamic behavior of solar energy necessitates the use of robust controllers for photovoltaic (PV) power electronics interfaces to maximize the energy harvest by continuous operation at maximum power point (MPP). This paper proposes a sensorless current model predictive control maximum power point tracking (SC-MPC-MPPT) algorithm. By predicting the future behavior of the power conversion stage, the proposed controller features fast and stable performance under dynamic ambient condition and negligible oscillation around MPP at steady state. Moreover, it does not require expensive sensing and communication equipment and networks to directly measure the changing solar insolation level. The power conversion stage includes an upstream boost dc/dc power conversion to a dc-link capacitor, and a downstream seven-level sub-Multilevel Inverter (sMI) from the dc-link capacitor to the grid. The sMI is using three power arms cascaded with an H-bridge inverter. This topology brings considerable benefits such as reduced number of power switches and their gate drivers when compared to the traditional multilevel inverters. Model Predictive Control (MPC) is employed for current regulation of the sMI, thus eliminating the need of cascaded classical control loops and modulator. The proposed SC-MPC-MPPT technique for a boost converter is implemented experimentally using the dSPACE DS1007 platform. © 2016 IEEE.

关键词： Model predictive control

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Author Correction: Observation of an ultra-high-energy cosmic neutrino with KM3NeT

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Nature 2025年第8057期640卷 E3页

作者： S. Aiello R. Bruno G. Ferrara E. Leonora F. Longhitano N. Randazzo A. Sinopoulou I. Tosta e Melo A. Albert D. Drouhin T. Pradier A. R. Alhebsi S. Thoudam A. van Vliet M. Alshamsi V. Bertin M. Breuhaus J. Brunner J. Busto L. Cerisy P. Coyle B. De Martino D. Dornic J.-G. Ducoin A. Enzenhöfer S. Henry D. Joly S. Lancelin S. Le Stum N. Lumb F. Magnani C. Paries M. Perrin-Terrin G. Vannoye S. Alves Garre A. Bariego-Quintana D. Calvo V. Cecchini A. Garcia Soto S. R. Gozzini J. J. Hernández-Rey A. Lazo N. Lessing J. Palacios González E. J. Pastor Gomez J. Prado D. Real A. Šaina F. Salesa Greus A. Sánchez Losa J. D. Zornoza J. Zúñiga A. Ambrosone M. Bendahman M. Benhassi C. Bozza R. M. Bozza R. Buompane A. Condorelli A. De Benedittis L. A. Fusco L. Gialanella W. Idrissi Ibnsalih A. Manfreda A. Marinelli F. Marzaioli S. Mastroianni G. Miele P. Migliozzi M. L. Mitsou C. M. Mollo L. Morales-Gallegos M. R. Musone V. Oliviero C. Poirè I. C. Rea A. Simonelli B. Spisso D. Vivolo F. Ameli A. Capone S. Celli I. Di Palma S. Gagliardini M. Mastrodicasa C. A. Nicolau A. Tudorache A. Veutro A. Zegarelli M. Andre M. Anghinolfi F. Badaracco B. Caiffi V. Ellajosyula C. Guidi V. Kulikovskiy L. Malerba M. Marconi B. Ó Fearraigh V. Parisi A. Romanov M. Sanguineti M. Taiuti S. Zavatarelli L. Aphecetche F. Bretaudeau R. Dallier V. Decoene L. Martin M. Ardid S. Ardid J. García Méndez C. A. Quiroz-Rangel C. Argüelles N. Kamp A. Y. Wen H. Atmani J. Boumaaza A. Eddymaoui Y. Tayalati J. Aublin B. Baret Y. Becherini T. Cartraud J. A. B. Coelho A. Coleiro S. Colonges A. Creusot C. Donzaud M. Eff S. El Hedri I. Goos P. Goswami A. Ilioni A. Kouchner M. Lindsey Clark E. Oukacha S. Peña Martínez R. Pestes A. Sharma B. Trocmé V. Van Elewyck C. Verilhac I. Lhenry-Yvon L. Bailly-Salins M. Bennani B. Guillon M. Labalme C. Lastoria G. Lehaut I. Mozun Mateo V. Pestel A. Vacheret Z. Bardačová E. Eckerová F. Mamedov Y. Shitov I. Štekl R. Dvornický F. Šimkovic F. Benfenati Gualandi F. Carenini P. Castaldi T. Chiarusi I. Del Rosso G. Levi A. Margiotta R. Muller INFN Sezione di Catania (INFN-CT) Catania Italy INFN Laboratori Nazionali del Sud (LNS) Catania Italy Dipartimento di Fisica e Astronomia “Ettore Majorana” Università di Catania (INFN-CT) Catania Italy Université de Strasbourg CNRS IPHC UMR 7178 Strasbourg France Université de Haute Alsace Mulhouse France Department of Physics Khalifa University Abu Dhabi United Arab Emirates Aix Marseille Université CNRS/IN2P3 CPPM Marseille France IFIC - Instituto de Física Corpuscular (CSIC - Universitat de València) Paterna Spain INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Napoli Italy Università di Napoli “Federico II” Dip. Scienze Fisiche “E. Pancini” Complesso Universitario di Monte S. Angelo Napoli Italy Dipartimento di Matematica e Fisica Università degli Studi della Campania “Luigi Vanvitelli” Caserta Italy Dipartimento di Fisica Università di Salerno e INFN Gruppo Collegato di Salerno Fisciano Italy INFN Sezione di Roma Roma Italy Dipartimento di Fisica Università La Sapienza Roma Italy Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Universitat Politècnica de Catalunya Vilanova i la Geltrú Spain INFN Sezione di Genova Genova Italy Università di Genova Genova Italy LPC CAEN Normandie Université ENSICAEN UNICAEN CNRS/IN2P3 Caen France Subatech IMT Atlantique IN2P3-CNRS Nantes Université Nantes France Instituto de Investigación para la Gestión Integrada de las Zonas Costeras Universitat Politècnica de València Gandia Spain Department of Physics Harvard University Cambridge MA USA Laboratory for Particle Physics and Cosmology Lyman Laboratory Harvard University Cambridge MA USA Faculty of Sciences University Mohammed V in Rabat Rabat Morocco Institute of Applied Physics Mohammed VI Polytechnic University Ben Guerir Morocco Astroparticule et Cosmologie Université Paris Cité CNRS Paris France Institut Universitaire de France Paris France Institute of Experimental and Applied Physics

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Strategic Air Traffic Flow Management under Uncertainties using Scalable Sampling-based Dynamic Programming and Q-Learning Approaches

Strategic Air Traffic Flow Management under Uncertainties us...

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Asian Control Conference

作者： Junfei Xie Yan Wan F. L. Lewis Department of Computer Science Texas A&M University at Corpus Christi Corpus Christi TX Department of Electrical Engineering University of Texas at Arlington Arlington TX State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China UTA Research Institute University of Texas at Arlington Fort Worth Texas USA

ISBN: (纸本)9781509015740;9781509015733

Many Cyber-Physical Systems (CPSs) demonstrate high-dimensional environmental uncertainties that modulate the physical system dynamics and complicate the computational decision-making tasks. As an example, strategic air traffic flow management (ATFM) aims to resolve air traffic congestion through managing traffic flows at a long look-ahead time. The convective weather, a dominant factor of traffic congestion, is very uncertain and significantly complicates the decision-making process. Other uncertainties such as traffic demands further expand the uncertainty space and complicate the design of optimal control solutions that are robust to uncertainties. In this paper, we formulate the strategic ATFM as a stochastic optimal control problem, and use scalable sampling based dynamic programming and Q-learning approaches to address high-dimensional uncertainties. Simulation studies demonstrate the effectiveness of these approaches. These approaches generally apply to CPSs that operate under high-dimensional environmental uncertainties.

关键词： Uncertainty Mathematical model Meteorology Optimal control Dynamic programming Atmospheric modeling Aerospace electronics

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Optoelectronic Devices: Thin Metallic Heat Sink for Interfacial Thermal Management in Biointegrated Optoelectronic Devices (Adv. Mater. Technol. 11/2018)

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Advanced Materials Technologies 2018年第11期3卷

作者： Han Hee Jung Juwon Song Shuang Nie Han Na Jung Min Seok Kim Jae-Woong Jeong Young Min Song Jizhou Song Kyung-In Jang Department of Robotics Engineering Department of Information and Communication Engineering Brain Engineering Convergence Center Daegu Gyeongbuk Institute of Science and Technology Daegu 49288 Republic of Korea Department of Engineering Mechanics Soft Matter Research Center and Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Zhejiang University Hangzhou 310027 China School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology 123 Cheomdan-gwagiro Buk-gu Gwangju 61005 Republic of Korea School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon 34141 Republic of Korea

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Nonlinear observability of grinding mill conditions

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IFAC-PapersOnLine 2016年第20期49卷 13-18页

作者： le Roux, J.D. Steinboeck, A. Kugi, A. Craig, I.K. Department of Electrical Electronic and Computer Engineering University of Pretoria Pretoria South Africa Automation and Control Institute Vienna University of Technology Gußhausstraße 27-29 Vienna1040 Austria Christian Doppler Laboratory for Model-Based Control in the Steel Industry Automation and Control Institute Vienna University of Technology Gußhausstraße 27-29 Vienna1040 Austria

A nonlinear observer model of a grinding mill is developed. The model distinguishes between the volumetric hold-up of water, solids, rocks and balls in the mill, where solids are all ore small enough to discharge through the end-discharge grate, and rocks are all ore too large to discharge. The model includes the discharge rate, abrasion rate of rocks and the abrasion rate of balls as parameters. It is shown that with mill discharge flow-rate, discharge density and volumetric hold-up measurements the model states and parameters are locally (weakly) nonlinearly observable, but not linearly observable. Although instrumentation at the mill discharge is not yet included in industrial circuits because of space restrictions, this study motivates the benefits to be gained from including instrumentation at the mill discharge. If mills are fitted with adequate measurement instrumentation at the discharge, important mill states and parameters can be estimated. © 2016

关键词： Ball mills Abrasion Comminution Flow rate Grinding (comminution) Grinding (machining) Grinding mills Observability Parameter estimation State estimation Abrasion rates Discharge density Discharge flow rates Discharge rates Industrial circuits Measurement instrumentation Non linear observer Nonlinear observability

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Full body human motion estimation on lie groups using 3D marker position measurements

Full body human motion estimation on lie groups using 3D mar...

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IEEE-RAS International Conference on Humanoid Robots

作者： Josip Ćesić Vladimir Joukov Ivan Petrović Dana Kulić Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia Department of Electrical and Computer Engineering Adaptive Systems Laboratory Canada

This paper proposes a new algorithm for full body human motion estimation using 3D marker position measurements. The joints are represented with Lie group members, including special orthogonal groups SO(2) and SO(3), and a special euclidean group SE(3). We employ the Lie Group Extended Kalman Filter (LG-EKF) for stochastic inference on groups, thus explicitly accounting for the non-euclidean geometry of the state space, and provide the derivation of the LG-EKF recursion for articulated motion estimation. We evaluate the performance of the proposed algorithm in both simulation and on real-world motion capture data, comparing it with the Euler angles based EKF. The results show that the proposed filter significantly outperforms the Euler angles based EKF, since it estimates human motion more accurately and is not affected by gimbal lock.

关键词： Algebra Manifolds Three-dimensional displays Position measurement Motion estimation Kinematics Stochastic processes

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3D action recognition using multi-temporal depth motion maps and fisher vector 25

3D action recognition using multi-temporal depth motion maps...

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Chen, Chen Liu, Mengyuan Zhang, Baochang Han, Jungong Junjun, Jiang Liu, Hong Department of Electrical Engineering University of Texas at Dallas United States Key Laboratory of Machine Perception Shenzhen Graduate School Peking University China School of Automation Science and Electrical Engineering Beihang University China Department of Computer Science and Digital Technologies Northumbria University United Kingdom School of Computer Science China University of Geosciences China

This paper presents an effective local spatiotemporal descriptor for action recognition from depth video sequences. The unique property of our descriptor is that it takes the shape discrimination and action speed variations into account, intending to solve the problems of distinguishing different pose shapes and identifying the actions with different speeds in one goal. The entire algorithm is carried out in three stages. In the first stage, a depth sequence is divided into temporally overlapping depth segments which are used to generate three depth motion maps (DMMs), capturing the shape and motion cues. To cope with speed variations in actions, multiple frame lengths of depth segments are utilized, leading to a multi-temporal DMMs representation. In the second stage, all the DMMs are first partitioned into dense patches. Then, the local binary patterns (LBP) descriptor is exploited to characterize local rotation invariant texture information in those patches. In the third stage, the Fisher kernel is employed to encode the patch descriptors for a compact feature representation, which is fed into a kernel-based extreme learning machine classifier. Extensive experiments on the public MSRAction3D, MSRGesture3D and DHA datasets show that our proposed method outperforms state-of-the-art approaches for depth-based action recognition.

关键词： Local binary pattern

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DAC-h3: A proactive robot cognitive architecture to acquire and express knowledge about the world and the self

arXiv

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arXiv 2017年

作者： Moulin-Frier, Clément Fischer, Tobias Petit, Maxime Pointeau, Grégoire Puigbo, Jordi-Ysard Pattacini, Ugo Low, Sock Ching Camilleri, Daniel Nguyen, Phuong Hoffmann, Matej Chang, Hyung Jin Zambelli, Martina Mealier, Anne-Laure Damianou, Andreas Metta, Giorgio Prescott, Tony J. Demiris, Yiannis Dominey, Peter Ford Verschure, Paul F.M.J. Laboratory for Synthetic Perceptive Emotive and Cognitive Systems Universitat Pompeu Fabra Barcelona08002 Spain Barcelona Spain Personal Robotics Laboratory Department of Electrical and Electronic Engineering Imperial College London SW7 2AZ United Kingdom Robot Cognition Laboratory INSERM U846 Stem Cell and Brain Research Institute Bron69675 France Italian Institute of Technology iCub Facility Via Morego 30 Genova Italy Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Department of Computer Science University of Sheffield United Kingdom

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users. Copyright © 2017, The Authors. All rights reserved.

关键词： Human robot interaction

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Research on Dust Evolution Simulation System Based on Dynamic Data Driven

Research on Dust Evolution Simulation System Based on Dynami...

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2016 International Conference on Sustainable Energy, Environment and Information engineering (SEEIE 2016)

作者： Xiao-mei HU He-wei QU Jun-jun HU Yuan TIAN Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and AutomationShanghai University Electrical Engineering and Computer Science College of EngineeringUniversity of Michigan

The dust evolution simulation provides an important way to analyze the impact of dust on the environment. Dynamic data driven application system framework is applied and dust evolution simulation model based on DDDAS framework is proposed. Compared with the simulation model of dust evolution without DDDAS, the functions of the inputs of real-time data and modification of simulation parameters ensure the improvement of the accuracy of dust evolution simulation. The experimental results show that the dust evolution simulation with DDDAS framework is more precise than the simulation without DDDAS, which proves the effectiveness of the dust evolution simulation system based on dynamic data driven.

关键词： Dynamic data driven Dust evolution simulation Kinetic monte carlo Dust accumulation

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Nonsmooth Dynamic Filtering for Mechanical Servo-systems

Nonsmooth Dynamic Filtering for Mechanical Servo-systems

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IEEE International Conference on Control and automation

作者： Yonghong Tan Ruili Dong Yanyan Li Xiang Chen Hong He Department of Electrical Engineering Shanghai Normal University Institute of Robotics and Automatic Information System Tianjin Key Laboratory of Intelligent Robotics Nankai University Department of Electrical and Computer Engineering University of Windsor

ISBN: (纸本)9781509017393

In this paper, a nonsmooth dynamic filtering approach is proposed for stochastic noise filtering of mechanical servo-systems. In this approach, a stochastic nonsmooth state-space model with sandwich structure is developed to describe the nonsmooth dynamic feature of the system in random noisy case. Then, an approach for observability test of the nonsmooth system with sandwich architecture is proposed. Afterwards, a nonsmooth dynamic filtering method motivated by the structure of Kalman filter is proposed for noise suppression. The proposed nonsmooth Kalman filtering method is applied to random noise filtering of a servo-system with dead-zone feature.

关键词： CONTRAST FILTERS Servo systems noise filtering Noise suppression Kalman filters sandwich structures Models, Space Sandwiches

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