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Parallel Dispatch:A New Paradigm of electrical Power System Dispatch

IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 311-319页

作者： Jun Jason Zhang Fei-Yue Wang Qiang Wang Dazhi Hao Xiaojing Yang David Wenzhong Gao Xiangyang Zhao Yingchen Zhang Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer Science University of Denver Denver 80210 CO USA State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Science (SKL-MCCS CASIA) Beijing 100190 China Research Center of Computational Experiments and Parallel Systems The National University of Defense Technology Changsha 410073 China State Grid Tianjin Electric Power Company Tianjin 300010 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China National Renewable Energy Laboratory Golden CO 80401 USA IEEE

Modern power systems are evolving into sociotechnical systems with massive complexity, whose real-time operation and dispatch go beyond human capability. Thus,the need for developing and applying new intelligent power system dispatch tools are of great practical significance. In this paper, we introduce the overall business model of power system dispatch, the top level design approach of an intelligent dispatch system, and the parallel intelligent technology with its dispatch applications. We expect that a new dispatch paradigm,namely the parallel dispatch, can be established by incorporating various intelligent technologies, especially the parallel intelligent technology, to enable secure operation of complex power grids,extend system operators' capabilities, suggest optimal dispatch strategies, and to provide decision-making recommendations according to power system operational goals.

关键词： ACP knowledge automation power dispatch parallel dynamic programming parallel intelligence paralle learning situational awareness

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CrowdWatcher: an open-source platform to catch the eye of the crowd

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Quality and User Experience 2019年第1期4卷 1-17页

作者： Lebreton, Pierre Hupont, Isabelle Hirth, Matthias Mäki, Toni Skodras, Evangelos Schubert, Anton Raake, Alexander Group of Networked Sensing and Control (NeSC) Zhejiang University Hangzhou China Institute of Intelligent Systems and Robotics Sorbonne University Paris France User-Centric Analysis of Multimedia Data Group Technische Universität Ilmenau Ilmenau Germany Department of Computer Science Aalto University Espoo Finland Department of Electrical and Computer Engineering University of Patras Patras Greece Audio Visual Technology Group Technische Universität Ilmenau Ilmenau Germany

This paper presents the open-source eye tracking platform CrowdWatcher. It enables researchers to measure gaze location and user engagement in a crowdsourcing context through traditional RGB webcams. The proposed platform particularly advances the field of Quality of Experience (QoE) research, as it allows the experimenter to collect remotely and with very limited effort novel information from crowds of participants, such as their commitment towards a task, attention and decision-making processes. Two different experiments are described that were conducted to demonstrate the platform’s potential. The first experiment addresses the measurement of participants’ behavior while performing a movie selection task. Results show that the platform provides complementary information to traditional self-reported data by taking gaze analysis into account. This is of particular relevance, since in a crowdsourcing context decision processes and attention are difficult to assess, and there is often limited control over the engagement of the test user with the task. A second experiment is conducted in the scenario of a multimedia QoE test. Prediction accuracy is compared to a professional infrared eye tracker. While CrowdWatcher performs less well than the professional eye tracker, it is still able to collect valuable gaze information in the far more challenging environment of crowdsourcing. As an outlook to further application domains, the usage of the platform to measure user engagement allows participants who do not pay attention to the task to be identified.

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A rapid fault reconstruction strategy using a bank of sliding mode observers

arXiv

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arXiv 2019年

作者： Shakarami, Mehran Esfandiari, Kasra Suratgar, Amir Aboulfazl Talebi, Heidar Ali Engineering and Technology Institute Groningen University of Groningen AG Groningen 9747 Netherlands Center for Systems Science Yale University New HavenCT United States Center of Excellence on Control and Robotics Department of Electrical Engineering Tehran Polytechnic Tehran Iran

This paper deals with the design of a model-based rapid fault detection and isolation strategy using sliding mode observers. To address this problem, a new scheme is proposed by adaptively combining the information provided by a bank of observers. In this regard, a new structure for sliding mode observers is considered. Then, the well-known recursive least square algorithm is utilized to merge individual state estimations suitably such that the system fault is detected faster. The required condition for enhancing perfect state estimation is derived, and the stability of the overall system is proven via Lyapunov's direct method. The supremacy of proposed scheme is fully discussed through mathematical analyses as well as simulations. Copyright © 2019, The Authors. All rights reserved.

关键词： State estimation

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Astronomical source detection in radio continuum maps with deep neural networks

arXiv

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arXiv 2022年

作者： Riggi, S. Magro, D. Sortino, R. De Marco, A. Bordiu, C. Cecconello, T. Hopkins, A.M. Marvil, J. Umana, G. Sciacca, E. Vitello, F. Bufano, F. Ingallinera, A. Fiameni, G. Spampinato, C. Zarb Adami, K. INAF-Osservatorio Astrofisico di Catania Via Santa Sofia 78 Catania95123 Italy Institute of Space Sciences and Astronomy University of Malta MsidaMSD2080 Malta Department of Electrical Electronic and Computer Engineering University of Catania Viale Andrea Doria 6 Catania95125 Italy Complex Systems and Artificial Intelligence research center University of Milano-Bicocca Viale Sarca 336 Milano20126 Italy Australian Astronomical Optics Macquarie University 105 Delhi Rd North RydeNSW2113 Australia National Radio Astronomy Observatory P.O. Box O SocorroNM87801 United States INAF-Istituto. di Radioastronomia Via Gobetti 101 Bologna40127 Italy NVIDIA AI Technology Centre Italy Department of Astrophysics University of Oxford OxfordOX1 2JD United Kingdom

Source finding is one of the most challenging tasks in upcoming radio continuum surveys with SKA precursors, such as the Evolutionary Map of the Universe (EMU) survey of the Australian SKA Pathfinder (ASKAP) telescope. The resolution, sensitivity, and sky coverage of such surveys is unprecedented, requiring new features and improvements to be made in existing source finders. Among them, reducing the false detection rate, particularly in the Galactic plane, and the ability to associate multiple disjoint islands into physical objects. To bridge this gap, we developed a new source finder, based on the Mask R-CNN object detection framework, capable of both detecting and classifying compact, extended, spurious, and poorly imaged sources in radio continuum images. The model was trained using ASKAP EMU data, observed during the Early Science and pilot survey phase, and previous radio survey data, taken with the VLA and ATCA telescopes. On the test sample, the final model achieves an overall detection completeness above 85%, a reliability of ∼65%, and a classification precision/recall above 90%. Results obtained for all source classes are reported and discussed. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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A Framework for the Sustainable Control and Optimization of Resources in Agriculture

A Framework for the Sustainable Control and Optimization of ...

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IEEE Annual Conference on Decision and Control

作者： Fernando Lobo Pereira Ismael Pena Geraldo Nunes Silva SYSTEC Faculty of Electrical Engineering Porto University and with the Institute for Systems and Robotics Porto Portugal Department of Applied Mathematics São Paulo State University (UNESP) Brasil Department of Applied Mathematics Sao Paulo State University (UNESP) Brasil

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

This article concerns the issue of optimizing strategies of resources management in Agriculture. The increasing human population, and the anthropogenic environmental impacts resulting from the current agricultural food production practice raise tremendous challenges as the latter constitutes a huge threat to the overarching goal of eliminating hunger from the Earth's face. The contribution of this article consists in showing the stability and convergence of a bi-level optimization based control architecture to de-conflict the short-term profit-seeking goals of each agricultural production unit with the long-term goal of attaining the collective environmental equilibrium required to preserve the necessary food production efficiency level. Given the complexity of any realistic scenario, we consider a very simple context clarifying the core mathematical issues of a bi-level control architecture previously proposed by the authors that, under reasonable assumptions, generates control strategies ensuring the asymptotic convergence to an environmentally healthy equilibrium while ensuring the economic (and, thus, social) short term sustainability.

关键词： Production Optimal control Agriculture Economics Optimization Sustainable development Decision making

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Assis - cicerone robot with visual obstacle avoidance using a stack of odometric data

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2018年第1期45卷 219-227页

作者： Mendesy, Mateus Coimbray, A. Paulo Crisóstomo, Manuel M. ESTGOH Polytechnic Institute of Coimbra Portugal ISR - Institute of Systems and Robotics Dept. of Electrical and Computer Engineering University of Coimbra Portugal

Modern research has shown that intelligent behaviour is, to a great extent, strongly supported on the use of a sophisticated memory. Additionally, vision is the main source of information for the average human. Constant increase of sensing and processing power and constant decrease of the cost of memory have made vision-based approaches in robotics more attractive. ASSIS is a prototype of an assistant robot which uses vision and memory-based navigation, including intelligent obstacle avoidance. ASSIS uses a parallel implementation of a Sparse Distributed Memory to store images and use them later for localisation and navigation. Obstacle avoidance is achieved using a stack-based method and odometric data. Users can command the robot through a web-based interface. ASSIS implements behaviours such as courier or cicerone.

关键词： Navigation

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Context-aware latency reduction protocol for secure encryption and decryption

Context-aware latency reduction protocol for secure encrypti...

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作者： Almiani, Muder Razaque, Abdul Aidja, Toufik Al-Dmour, Ayman Department of Computer Information Systems Al-Hussein Bin Talal University Ma'an Jordan Department of Computer Science New York Institute of Technology United States Department of Electrical and Computer Engineering Cleveland State University OH United States

Security is one of the biggest challenges for data communication. As the use of mobile phones has increased, people started using several mobile applications to handle the internet-ofthings. On other hand, the frequent use of mobile phones in the business caught the attention of researchers to protect the manufacturers and customers. Handling these issues, several paradigms for encrypting and decrypting the data-outsourcing are proposed. However, there are still new threats that challenge researchers due to new attacking methods and malicious actions of adversaries. Though, the manufacturers have implemented an industry standard to protect the customers' privacy, but existing standards do not provide comfortable zone for the customers, particularly reducing the latency of the emerging applications being used in internet-of-things. In this paper, we introduce context-aware security (ConSec) protocol to support the internet-ofthings applications to reduce the latency while encrypting and decrypting the applications. Furthermore, elliptic curve cryptography is used to fully secure the encryption and decryption processes. The proposed method is implemented using Java platform and results are verified and compared with full disk method from the latency perspective. © 2018 Inderscience Enterprises Ltd.

关键词： Cellular telephones

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A closed-loop all-electronic pixel-wise adaptive imaging system for high dynamic range video

arXiv

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arXiv 2019年

作者： Zhang, Jie [Jack] Newman, Jonathan P. Wang, Xiao Thakur, Chetan Singh Rattray, John Etienne-Cummings, Ralph Wilson, Matthew A. Picower Institute for Learning and Memory Department of Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States Department of Computer Science Boston University BostonMA United States Department of Electronic Systems Engineering Indian Institute of Science Bangalore India

We demonstrated a CMOS imaging system that adapts each pixels exposure and sampling rate to capture high dynamic range (HDR) videos. The system consist of a custom designed image sensor with pixel-wise exposure configurability and a real-time pixel exposure controller. These parts operate in a closed-loop to sample, detect and optimize each pixels exposure and sampling rate to minimize local regions underexposure, overexposure and motion blurring. Exposure control is implemented using all-integrated electronics without external optical modulation. This reduces overall system size and power consumption. The image sensor is implemented using a standard 130nm CMOS process while the exposure controller is implemented on a computer. We performed experiments under complex lighting and motion condition to test performance of the system, and demonstrate the benefit of pixel-wise adaptive imaging on the performance of computer vision tasks such as segmentation, motion estimation and object recognition. Copyright © 2019, The Authors. All rights reserved.

关键词： Pixels

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DAGMaR: A DAG-based Robust Road Membership Estimation Framework for Scenario Mining

DAGMaR: A DAG-based Robust Road Membership Estimation Framew...

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International Conference on Internet of Things: systems, Management and Security

作者： Lars Klitzke Johannes Meyer Tilman Leune Carsten Koch Frank Köster Department of Electrical Engineering and Computer Science University of Applied Sciences Emden Germany Deutsches Zentrum für Luft-und Raumfahrt (DLR) Institute of Transportation Systems Braunschweig Germany

For the identification of scenarios in real-world testdrive data (scenario mining) knowledge about the road, the vehicle drives on, is required because the set of possible scenarios varies according to the road type. Since these scenarios are needed for the scenario-based validation of automated driving functions, a robust approach for road membership estimation is *** that purpose, the vehicle location is estimated using a particle filter and mapped on a digital map providing the required road information. But, since particle filter-based solutions lack precision in ambiguous situations, reliable road membership estimation is not *** this work, a map matching framework is proposed to provide high accuracy and complete trajectories of vehicle poses by utilizing particle filter for vehicle localization and a directed acyclic graph of mapped roads (DAGMaR). The presented approach is evaluated with an inner-city, a mixed and a motorway drive showing promising results.

关键词： Roads Global Positioning System Estimation Standards Probability density function Internet Security

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Single perceptron operating at 12 GigaOPs based on a Kerr soliton crystal microcomb for versatile, high-speed, scalable, optical neural networks

TechRxiv

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TechRxiv 2021年

作者： Moss, David J. Tan, M. Xu, X. Wu, J. Boes, A. Corcoran, B. Nguyen, T.G. Chu, S.T. Little, B.E. Morandotti, R. Mitchell, A. Hicks, D.G. Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia Dept. of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia School of Engineering RMIT University MelbourneVIC3001 Australia Department of Physics and Material Science City University of Hong Kong Tat Chee Avenue Hong Kong Xi’an Institute of Optics and Precision Mechanics of CAS Xi’an China INRS-Énergie Matériaux et Télécommunications 1650 Boulevard Lionel-Boulet Varennes QCJ3X 1S2 Canada University of Electronic Science and Technology of China Chengdu610054 China Bioinformatics Division Walter & Eliza Hall Institute of Medical Research ParkvilleVIC3052 Australia

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high speeds. We demonstrate the basic building block ONNs — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 109 operations per second, or Giga-OPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing. © 2021, CC BY.

关键词： Data handling

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