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arXiv 2018年

作者： Petković, Tomislav Puljiz, David Marković, Ivan Hein, Björn University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics Unska 3 ZagrebHR-10000 Croatia Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics Intelligent Process Automation and Robotics Lab Kaiserstraße 12 Deutschland KarlsruheDE-76131

With the substantial growth of logistics businesses the need for larger warehouses and their automation arises, thus using robots as assistants to human workers is becoming a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions in real-time. Theory of mind (ToM) is an intuitive human conception of other humans' mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we propose a ToM based human intention estimation algorithm for flexible robotized warehouses. We observe human's, i.e., worker's motion and validate it with respect to the goal locations using generalized Voronoi diagram based path planning. These observations are then processed by the proposed hidden Markov model framework which estimates worker intentions in an online manner, capable of handling changing environments. To test the proposed intention estimation we ran experiments in a real-world laboratory warehouse with a worker wearing Microsoft Hololens augmented reality glasses. Furthermore, in order to demonstrate the scalability of the approach to larger warehouses, we propose to use virtual reality digital warehouse twins in order to realistically simulate worker behavior. We conducted intention estimation experiments in the larger warehouse digital twin with up to 24 running robots. We demonstrate that the proposed framework estimates warehouse worker intentions precisely and in the end we discuss the experimental results. Copyright © 2018, The Authors. All rights reserved.

关键词： Virtual reality

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Author Correction: Frontotemporal coordination predicts working memory performance and its local neural signatures

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Nature communications 2021年第1期12卷 3811页

作者： Ehsan Rezayat Mohammad-Reza A Dehaqani Kelsey Clark Zahra Bahmani Tirin Moore Behrad Noudoost School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran. School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran. dehaqani@ut.ac.ir. Cognitive Systems Laboratory Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran. dehaqani@ut.ac.ir. Department of Ophthalmology and Visual Sciences University of Utah Salt Lake City UT USA. Department of Biomedical Engineering Tarbiat Modares University Tehran Iran. Department of Neurobiology Stanford University Stanford CA USA. Howard Hughes Medical Institute Stanford University Stanford CA USA. Department of Ophthalmology and Visual Sciences University of Utah Salt Lake City UT USA. behrad.noudoost@utah.edu.

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Cellular Computational Extreme Learning Machine Network Based Frequency Predictions in a Power System

Cellular Computational Extreme Learning Machine Network Base...

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International Joint Conference on Neural Networks

作者： Iroshani Jayawardene Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University

ISBN: (纸本)9781509061839

Frequency is one of the most important characteristics in power system monitoring, control and protection. Frequency variations can be observed with significant changes in operating conditions. High penetration levels of renewable energy pose variability and uncertainty challenges for grid operation. It is essential to have innovative methodologies to take necessary actions to overcome these challenges. Power system frequency prediction provides an insight to better system control and protection. In this paper, a cellular computational extreme learning machine network (CCELMN) based frequency prediction approach is presented. Results are compared with those obtained with independent ELM models and persistence model and shown to outperform.

关键词： Cellular computational extreme machine learning network Frequency Prediction Power system Renewable energy

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A Review of Neural Network Based Machine Learning Approaches for Rotor Angle Stability Control

arXiv

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arXiv 2017年

作者： Yousefian, Reza Kamalasadan, S. Department of Electrical and Computer Engineering Power Energy and Intelligent Systems Laboratory University of North Carolina-Charlotte.

This paper reviews the current status and challenges of Neural Networks (NNs) based machine learning approaches for modern power grid stability control including their design and implementation methodologies. NNs are widely accepted as Artificial Intelligence (AI) approaches offering an alternative way to control complex and ill-defined problems. In this paper various application of NNs for power system rotor angle stabilization and control problem is discussed. The main focus of this paper is on the use of Reinforcement Learning (RL) and Supervised Learning (SL) algorithms in power system wide-area control (WAC). Generally, these algorithms due to their capability in modeling nonlinearities and uncertainties are used for transient classification, neuro-control, wide-area monitoring and control, renewable energy management and control, and so on. The works of researchers in the field of conventional and renewable energy systems are reported and categorized. Paper concludes by presenting, comparing and evaluating various learning techniques and infrastructure configurations based on efficiency. Copyright © 2017, The Authors. All rights reserved.

关键词： Supervised learning

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Barrier-certified adaptive reinforcement learning with applications to brushbot navigation

arXiv

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arXiv 2018年

作者： Ohnishi, Motoya Wang, Li Notomista, Gennaro Egerstedt, Magnus School of Electrical Engineering Royal Institute of Technology Stockholm11428 Sweden Georgia Robotics and Intelligent Systems Laboratory Georgia Institute of Technology AtlantaGA30332 United States RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30313 United States

This paper presents a safe learning framework that employs an adaptive model learning algorithm together with barrier certificates for systems with possibly nonstationary agent dynamics. To extract the dynamic structure of the model, we use a sparse optimization technique. We use the learned model in combination with control barrier certificates which constrain policies (feedback controllers) in order to maintain safety, which refers to avoiding particular undesirable regions of the state space. Under certain conditions, recovery of safety in the sense of Lyapunov stability after violations of safety due to the nonstationarity is guaranteed. In addition, we reformulate an action-value function approximation to make any kernel-based nonlinear function estimation method applicable to our adaptive learning framework. Lastly, solutions to the barrier-certified policy optimization are guaranteed to be globally optimal, ensuring the greedy policy improvement under mild conditions. The resulting framework is validated via simulations of a quadrotor, which has previously been used under stationarity assumptions in the safe learnings literature, and is then tested on a real robot, the brushbot, whose dynamics is unknown, highly complex and nonstationary. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

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Influencing behavior of electricity consumers to enhance participation in demand response

Influencing behavior of electricity consumers to enhance par...

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2017 North American Power Symposium, NAPS 2017

作者： Ballenger, Anna P. Herath, Pramod Caceres, Maria Navarro Venayagamoorthy, Ganesh K. Corchado, Juan Manuel Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States School of Engineering University of Kwazulu-Natal Durban South Africa University of Salamanca c/Espejo s/n Salamanca37007 Spain

ISBN: (纸本)9781538626993

Demand response (DR) provides an opportunity for consumers to change their electricity usage during peak periods by applying time-based rates or other forms of financial incentives. Currently the majority of DR resources are based on commercial loads in which substantial amounts of DR resources for residential purposes are wasted. Despite the multiple advantages of demand response to such residential customers, they are often reluctant to participate in such programs because of privacy concerns or because they are unaware of time-based rates. In this study, a customer dashboard was developed to enhance and persuade customers to participate in demand response programs. The online dashboard was used to increase awareness of all customer types, which is essential for ensuring a significant increase of DR participants. Incorporating flexibility, ease-of-use, and rewards programs will attract customers to use the dashboard thus expanding customer awareness to consciously or subconsciously change energy consumption behaviors. Making such cultural changes in customer behaviors leads to a long term solution for participation in demand response programs. With enhanced DR participation a sustainable power system operation and environment can be realized. © 2017 IEEE.

关键词： Sales

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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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Real-time force control of an SEA-based body weight support unit with the 2-DOF control structure

arXiv

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arXiv 2018年

作者： Sun, Yubo Lei, Yuqi Zou, Wulin Li, Jianmin Yu, Ningbo Institute of Robotics and Automatic Information Systems Nankai University Haihe Education Park Tianjin300353 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Haihe Education Park Tianjin300353 China Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States School of Mechanical Engineering Tianjin University Tianjin300072 China

Body weight support (BWS) is a fundamental technique in rehabilitation. Along with the dramatic progressing of rehabilitation science and engineering, BWS is quickly evolving with new initiatives and has attracted deep research effort in recent years. We have built up a novel gravity offloading system, in which the patient is allowed to move freely in the three-dimensional Cartesian space and receives support against gravity. Thus, the patients, especially for those that suffer from neurological injury such as stroke or spinal cord injury, can focus their residual motor control capabilities on essential therapeutic trainings of balance and gait. The realtime force control performance is critical for the BWS unit to provide suitable support and avoid disturbance. In this work, we have re-designed our BWS unit with a series elastic actuation structure to improve the human-robot interaction performance. Further, the 2 degrees of freedom (2-DOF) control approach was taken for accurate and robust BWS force control. Both simulation and experimental results have validated the efficacy of the BWS design and real-time control methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Force control

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Low Complexity Automatic Modulation Classification Based on Order-Statistics

Low Complexity Automatic Modulation Classification Based on ...

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作者： Han, Lubing Gao, Feifei Li, Zan Dobre, Octavia A. State Key Laboratory of Intelligent Technologies and Systems Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing100084 China State Key Laboratory of Integrated Service Networks School of Telecommunications Engineering Xidian University Xi'an710071 China Department of Electrical and Computer Engineering Memorial University St. John'sNLA1B3X9 Canada

In this paper, we propose three automatic modulation classification classifiers based on order-statistics and reduced order-statistics, where the order-statistics are the random variables sorted by ascending order and the reduced order-statistics represent a subset of the original order-statistics. Specifically, the linear support vector machine classifier applies the linear combination of the order-statistics of the received signals, while the approximate maximum likelihood and the backpropagation neural networks (BPNNs) classifier resort to the reduced order-statistics to decrease the computational complexity. Moreover, BPNN is applicable for modulation classification both in known and unknown channel scenarios. It is shown that in the known channel scenario, the proposed classifiers provide a good tradeoff between performance and computational complexity, while in the unknown channel scenario, the proposed BPNN classifier outperforms the expectation maximization classifier in terms of both classification performance and computational complexity. Simulations results are provided to evaluate the proposed classifiers. © 2016 IEEE.

关键词： Computational complexity

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Transformative role of photovoltaics in phasing out alternating current based grid by local DC power networks for sustainable global economic growth 44

Transformative role of photovoltaics in phasing out alternat...

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44th IEEE Photovoltaic Specialist Conference, PVSC 2017

作者： Singh, Rajendra Asif, Amir A. Venayagamoorthy, Ganesh K. Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States

ISBN: (纸本)9781509056057

For sustainable global economic growth, eradication of global energy poverty and addressing climate challenges, free fuel based solar and wind energy sources are the only viable solution for electricity generation. Due to inherent advantages, photovoltaics has emerged as the major source of electrical power. Local generation of direct current (DC) power by PV and the use of batteries for storing DC electrical power have the potential of transforming global electricity infrastructure to address the problems faced by alternating current (AC) based centralized power generation and long haul transmission and distribution. © 2017 IEEE.

关键词： Electric power transmission networks

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