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arXiv 2017年

作者： Paccagnan, Dario Marden, Jason R. Department of Electrical and Computer Engineering University of California Santa Barbara United States Automatic Control Laboratory Swiss Federal Institute of Technology Zurich

A fundamental challenge in multiagent systems is to design local control algorithms to ensure a desirable collective behaviour. The information available to the agents, gathered either through communication or sensing, naturally restricts the achievable performance. Hence, it is fundamental to identify what piece of information is valuable and can be exploited to design control laws with enhanced performance guarantees. This paper studies the case when such information is uncertain or inaccessible for a class of submodular resource allocation problems termed covering problems. In the first part of this work we pinpoint a fundamental risk-reward tradeoff faced by the system operator when conditioning the control design on a valuable but uncertain piece of information, which we refer to as the cardinality, that represents the maximum number of agents that can simultaneously select any given resource. Building on this analysis, we propose a distributed algorithm that allows agents to learn the cardinality while adjusting their behaviour over time. This algorithm is proved to perform on par or better to the optimal design obtained when the exact cardinality is known a priori. Copyright © 2017, The Authors. All rights reserved.

关键词： Multi agent systems

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Decentralized adaptive stabilization of large-scale active-passive modular systems

Decentralized adaptive stabilization of large-scale active-p...

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AIAA Guidance, Navigation, and control Conference, 2017

作者： Gruenwald, Benjamin C. Arabi, Ehsan Yucelen, Tansel Chakravarthy, Animesh McNeely, Drew Balakrishnan, S.N. Mechanical Engineering Department Laboratory for Autonomy Control Information and Systems University of South Florida TampaFL33620 United States Electrical Engineering and Computer Science Department Wichita State University WichitaKS67260 United States Mechanical and Aerospace Engineering Department Missouri University of Science and Technology RollaMO65409 United States

ISBN: (数字)9781624104503

ISBN: (纸本)9781624104503

Large-scale active-passive modular systems consist of physically interconnected modules, where local control signals can be applied only to a subset of these modules entitled as active modules and the rest of these modules entitled as passive modules are not subject to any control signal. This paper focuses on a new decentralized adaptive stabilization framework for large-scale active-passive modular systems such that guarantees on not only overall system stability but also practical (i.e., user-defined) performance of active modules are established. An illustrative numerical example is provided to demonstrate the efficacy of the proposed decentralized adaptive stabilization framework. © 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： System stability

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Simulation and practical implementation of direct power control applied on PWM rectifier

Simulation and practical implementation of direct power cont...

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International Conference on systems and control (ICSC)

作者： Sabir Ouchen Achour Betka Jean Paul Gaubert Sabrina Abdeddaim Electrical Engineering Laboratory of Biskra LGEB University of Mohammed Khider Algeria Laboratory of Computer Science and Automatic Control for Systems (LIAS-ENSIP) University of Poitiers France Electrical Engineering Laboratory of Biskra LGEB University of Mohammed Khider Biskra Algeria

In this paper, simulation and practical implementation of direct power control is presented for a PWM Rectifier application. The proposed DPC control has several advantages, including the grid current very close to si... 详细信息

ISBN: (纸本)9781509039616

关键词： Conferences control systems

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AIFORCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. an Italian multicentre study

arXiv

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arXiv 2020年

作者： Soda, Paolo D’Amico, Natascha Claudia Tessadori, Jacopo Valbusa, Giovanni Guarrasi, Valerio Bortolotto, Chandra Akbar, Muhammad Usman Sicilia, Rosa Cordelli, Ermanno Fazzini, Deborah Cellina, Michaela Oliva, Giancarlo Callea, Giovanni Panella, Silvia Cariati, Maurizio Cozzi, Diletta Miele, Vittorio Stellato, Elvira Carrafiello, Gian Paolo Castorani, Giulia Simeone, Annalisa Preda, Lorenzo Iannello, Giulio Bue, Alessio Del Tedoldi, Fabio Alì, Marco Sona, Diego Papa, Sergio Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Via Alvaro del Portillo 21 Rome00128 Italy Department of Diagnostic Imaging and Stereotactic Radiosurgey Centro Diagnostico Italiano S.p.A. Via S. Saint Bon 20 Milan20147 Italy Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Via Morego 30 Genoa16163 Italy Bracco Imaging S.p.A. Via Caduti di Marcinelle 13 Milan20134 Italy Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Radiology Institute Fondazione IRCCS Policlinico San Matteo Viale Golgi 19 Pavia27100 Italy Department of Naval Electrical Electronic and Telecommunications Engineering - University of Genova Via All’Opera Pia 11 A Genoa16145 Italy Radiology Department ASST Fatebenefratelli Sacco Piazza Principessa Clotilde 3 Milan20121 Italy Diagnostic and interventional radiology unit ASST Santi Paolo e Carlo - San Paolo Hospital Via Antonio di Rudinì 8 Milan20142 Italy Department of Advanced Diagnostic Technologies - Therapeutic Diagnostic and Radiology Units ASST Santi Paolo e Carlo - San Paolo Hospital Via Antonio di Rudinì 8 Milan20142 Italy Department of Emergency Radiology Careggi University Hospital Largo Piero Palagi 1 Florence50139 Italy Operative Unit of Radiology Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico of Milan Via della Commenda 10 Milan20122 Italy Department of Health Sciences Univeristy of Milan Via Festa del Perdono 7 Milan20122 Italy Diagnostic Imaging Postgraduate Medical School University of Foggia Via Antonio Gramsci 89 Foggia71122 Italy Department of Diagnostic Imaging IRCCS Ospedale Casa Sollievo della Sofferenza Viale Cappuccini 1 San Giovanni Rotondo71013 Italy Radiology Unit Department of Clinical Surgical Diagnostic and Pediatric Sciences University of Pavia Corso Str. Nuova 65 Pavia27100 Italy Neuroinformatics Laboratory Fondazione Bru

Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether chest X-ray (CXR) can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. CXR is a radiological technique that compared to computed tomography (CT) it is simpler, faster, more widespread and it induces lower radiation dose. We present a dataset including data collected from 820 patients by six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. We investigate the potential of artificial intelligence to predict the prognosis of such patients, distinguishing between severe and mild cases, thus offering a baseline reference for other researchers and practitioners. To this goal, we present three approaches that use features extracted from CXR images, either handcrafted or automatically by convolutional neuronal networks, which are then integrated with the clinical data. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, implying that clinical data and images have the potential to provide useful information for the management of patients and hospital resources. © 2020, CC BY-NC-ND.

关键词： Deep learning

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An Intelligent Gripper Design for Autonomous Aerial Transport with Passive Magnetic Grasping and Dual-Impulsive Release

An Intelligent Gripper Design for Autonomous Aerial Transpor...

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IEEE/ASME International Conference on Advanced Intelligent Mechatronics

作者： Usman A. Fiaz M. Abdelkader Jeff S. Shamma Department of Electrical & Computer Engineering University of Maryland(UMD) College Park. MD USA KAUST The Robotics Intelligent Systems & Control (RISC) Laboratory Thuwal KSA

We present a novel gripper design for autonomous aerial transport of ferrous objects with unmanned aerial vehicles (UAVs). The proposed design uses permanent magnets for grasping, and a novel dual-impulsive release mechanism, for achieving drop. The gripper can simultaneously lift up to four objects of arbitrary shape, in fully autonomous mode, with a 100% rate of successful drops. We optimize the system subject to realistic constraints, such as the simplicity of design and its sturdiness to aerial maneuvers, payload limits for multi-rotor UAVs, reliability of autonomous grasping irrespective of the environment of operation, active power consumption of the gripper, and its comparison with the existing technologies. We describe the design concepts, and the hardware, and perform extensive experiments in both indoor and outdoor environments, with two multi-rotor configurations. Several results, showcasing superior performance of the proposed system are provided as well.

关键词： Grasping Force Servomotors Grippers Payloads Torque Magnetic separation

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Optimization of hydrogen yield of a high-temperature electrolysis system with coordinated temperature and feed factors at various loading conditions: a model-based study☆

arXiv

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arXiv 2018年

作者： Xing, Xuetao Lin, Jin Song, Yonghua Hu, Qiang Zhou, You Mu, Shujun State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100084 China Department of Electrical and Computer Engineering University of Macau Macau China Tsinghua-Sichuan Energy Internet Research Institute Chengdu610213 China National Institute of Clean and Low Carbon Energy Beijing102211 China Zhejiang Zhentai Energy Tech. Co. Ltd. Lishui Zhejiang Province321400 China

High-temperature electrolysis (HTE) is a promising technology for achieving high-efficiency power-to-gas, which mitigates the renewable curtailment while promoting decarbonization by transforming wind or solar energy into fuels. Different from low-temperature electrolysis, a considerable amount of the input energy is consumed by auxiliary equipment in an HTE system for maintaining the temperature, so the studies on systematic energy description and parameter optimization of HTE are essentially required. A few published studies investigated HTE’s systematic optimization based on simulation, yet there is not a commonly used analytical optimization model which is more suitable for integration with power grid. To fill in this blank, a concise analytical operation model is proposed in this paper to coordinate the necessary power consumptions of auxiliaries under various loading conditions of an HTE system. First, this paper develops a comprehensive energy flow model for an HTE system based on the fundamentals extracted from the existing work, providing a quantitative description of the impacts of condition parameters, including the temperature and the feedstock flow rates. A concise operation model is then analytically proposed to search for the optimal operating states that maximize the hydrogen yield while meeting the desired system loading power by coordinating the temperature, the feedstock flows and the electrolysis current. The evaluation of system performance and the consideration of constraints caused by energy balances and necessary stack requirements are both included. In addition, analytical optimality conditions are obtained to locate the optimal operating states without performing nonlinear programming by further investigating the optimization method. In the case study, a numerical case of an HTE system is employed to validate the proposed operation model and optimization method, which proves that the proposed operation strategies not only improve the overal

关键词： Hydrogen production

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Large Scale Rollout of Smart Grid Services

Large Scale Rollout of Smart Grid Services

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Global Internet of Things Summit (GIoTS)

作者： Florian Kintzler Tobias Gawron-Deutsch Stephan Cejka Judith Schulte Mathias Uslar Eric MSP Veith Ewa Piatkowska Paul Smith Friederich Kupzog Henrik Sandberg Michelle S. Chong David Umsonst Marco Mittelsdorf Corporate Technology Siemens AG Austria Vienna Austria R&D Division Energy OFFIS e.V. - Institut fur Informatik Oldenburg Germany Center for Digital Safety Security AIT Austrian Institute of Technology Vienna Austria School of Electrical Engineering and Computer Science Department of Automatic Control KTH Royal Institute of Technology Stockholm Sweden Fraunhofer Institute for Solar Energy Systems Freiburg Germany

In the domain of energy automation, where a massive number of software-based IoT services interact with a complex dynamic system, processes for software installation and software update become more important and more complex. These processes have to ensure that the dependencies on all layers are fulfilled, including dependencies arising due to the energy system controlled by IT components being a hidden communication channel between these components. In addition, the processes have to be resilient against faults in and attacks to both the energy grid and the communication network. The ERA-Net funded project LarGo! aims at developing and testing processes for the large scale rollout of software applications in the power grid domain as well as the user domain. This article describes a work in progress and the project's roadmap to solve the technical issues. It investigates the problems that arise from the interlocking of the two networks- the power grid and the communication network. Based on this analysis a first set of requirements for a rollout process in such a Smart Grid is derived and the chosen approach to verify the resilience of the developed processes under research is described.

关键词： Smart grids Substations Software Resilience Maintenance engineering Internet of Things

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Distributed Continuous-Time Algorithm for Robust Resource Allocation Problems using Output Feedback

Distributed Continuous-Time Algorithm for Robust Resource Al...

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American control Conference

作者： Xianlin Zeng Peng Yi Yiguang Hong Key Laboratory of Systems and Control Institute of Systems Science Chinese Academy of Sciences Beijing 100190 China Department of Electrical and Computer Engineering University of Toronto Canada

ISBN: (纸本)9781509045839

This paper proposes a novel distributed continuous-time algorithm for the resource allocation problem with uncertainty parameters, which is a robust optimization problem. The considered objective function is the sum of local convex functions assigned to agents in a multi-agent network, with private set constraints and global inequality constraint involving uncertain parameters. Each agent only knows its local objective function, local constraint set, and neighbor information. We propose a novel continuous-time distributed subgradient-based algorithm with projected output feedback to solve the optimization problem. Finally, we show that the algorithm is able to find the optimal solution under some mild conditions.

关键词： Output feedback Resource utilization inequality constraint algorithms management of resources Constraint Set Objective function NETWORKING AGENT Convex function Distribute Agents optimal solution

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Data based reconstruction of complex multiplex networks

arXiv

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arXiv 2018年

作者： Ma, Chuang Chen, Han-Shuang Li, Xiang Lai, Ying-Cheng Zhang, Hai-Feng School of Mathematical Science Anhui University Hefei230601 China School of Physics and Material Science Anhui University Hefei230601 China Adaptive Networks and Control Laboratory Department of Electronic Engineering Center of Smart Networks and Systems School of Information Science and Engineering Fudan University Shanghai200433 China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Department of Communication Engineering North University of China Taiyuan Shanxi030051 China

It has been recognized that many complex dynamical systems in the real world require a description in terms of multiplex networks, where a set of common, mutually connected nodes belong to distinct network layers and play a different role in each layer. In spite of recent progress towards data based inference of single-layer networks, to reconstruct complex systems with a multiplex structure remains largely open. We articulate a mean-field based maximum likelihood estimation framework to solve this outstanding and challenging problem. We demonstrate the power of the reconstruction framework and characterize its performance using binary time series from a class of prototypical duplex network systems that host two distinct types of spreading dynamics. In addition to validating the framework using synthetic and real-world multiplex networks, we carry out a detailed analysis to elucidate the impacts of structural and dynamical parameters as well as noise on the reconstruction accuracy and robustness. Copyright © 2018, The Authors. All rights reserved.

关键词： Network layers

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Fault diagnosis for multi-energy flows of energy internet: Framework and prospects

Fault diagnosis for multi-energy flows of energy internet: F...

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2017 IEEE Conference on Energy Internet and Energy System Integration, EI2 2017

作者： Wang, Tao Wang, Jun Fan, Zhennan Wei, Xiaoguang Zang, Tianlei Pérez-Jiménez, Mario J. Huang, Tao School of Electrical Engineering and Electronic Information Sichuan Province Key Laboratory of Power Electronics Energy-saving Technologies and Equipment Key Laboratory of Fluid and Power Machinery Ministry of Education Xihua University Chengdu China School of Electrical Engineering Southwest Jiaotong University Chengdu China State Key Laboratory of Security Control and Simulation of Power Systems and Large Scale Generation Department of Electrical Engineering Tsinghua University Beijing China Research Groupon Natural Computing Department of Computer Science and Artificial Intelligence University of Sevilla Sevilla Spain Department of Energy Politecnico di Torino Torino Italy

ISBN: (纸本)9781538614273

Energy Internet (EI) is an inevitable development trend of energy systems under the background of technology development, environmental pressure and energy transition. Multi-energy flow coupling is one of the key characteristics of the EI, which enhances the interoperability of different types of energy flows while consequently increases the probability of cascading failures. Therefore it is of great significance to study the multi-energy flow fault diagnosis of the EI to ensure its safe and stable operation as well as the continuous energy supply. This paper introduces the concept of multi-energy flow cascading fault of the EI for the first time. The energy internet framework for multi-energy flow cascading fault diagnosis is firstly proposed, and then characteristics of various energy networks in the EI are analyzed from the perspective of fault diagnosis. Finally, future research prospects are discussed. © 2017 IEEE.

关键词： Failure analysis

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