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International Journal of Social robotics 2019年第6期15卷 1077-1077页

作者： Ge, Shuzhi Sam Wykowska, Agnieszka Khatib, Oussama Social Robotics Laboratory Smart Systems Institute Department of Electrical and Computer Engineering The National University of Singapore Singapore Singapore Institute for Future (IFF) Qingdao University Qingdao China Centre for Robotics UESTC Chengdu China Social Cognition in Human-Robot Interaction Istituto Italiano di Tecnologia Genoa Italy Artificial Intelligence Laboratory Department of Computer Science Stanford University Stanford USA

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Energy efficient massive MIMO through distributed precoder design

arXiv

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arXiv 2018年

作者： Zhang, Shuai Liu, Lu Cheng, Yu Cao, Xianghui Zhou, Sheng Niu, Zhisheng Shan, Hangguan Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago United States School of Automation Southeast University Nanjing210018 China Tsinghua National Laboratory for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China

This paper presents an energy-efficient downlink precoding scheme with the objective of maximizing system energy efficiency in a multi-cell massive MIMO system. The proposed precoding design jointly considers the issues of power control, interference management, antenna switching and user throughput in a cluster of base stations (BS). We demonstrate that the precoding design can be formulated into a general sparsity-inducing non-convex problem, which is NP-hard. We thus apply a smooth approximation of zero-norm in the antenna power management to enable the application of the gradient-based algorithms. The non-convexity of the problem may also cause slow convergence or even divergence if some classical gradient algorithms are directly applied. We thus develop an efficient alternative algorithm combining features from augmented multiplier (AM) and quadratic programming (QP) to guarantee the convergence. We theoretically prove the convergence conditions for our algorithm both locally and globally under realistic assumptions. Our proposed algorithms further facilitate a distributed implementation that reduces backhaul overhead by offloading data-intensive steps to local computation at each BS. Numerical results confirm that our methods indeed achieve higher energy efficiency with superior convergence rate, compared to some well-known existing methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Energy efficiency

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A junction-by-junction feedback-based strategy with convergence analysis for dynamic traffic assignment

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Science China(Information Sciences) 2016年第1期59卷 30-46页

作者： Tengfei LIU Xuesong LU Zhong-Ping JIANG State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Department of Electrical and Computer Engineering University of Connecticut Department of Electrical and Computer Engineering Polytechnic School of Engineering New York University

By considering the traffic assignment problem as a control problem, this paper develops a new realtime route guidance strategy for accurate convergence of the traffic flows to user equilibrium(UE) or system optimum(SO), in the presence of drivers’ response uncertainties. With the new guidance strategy, the drivers make routing decisions based on the route guidance information from junction to junction. Specifically, instead of total travel cost of every route from origin to destination, the travel cost of every alternative link plus the average cost to destination from the next junction corresponding to the alternative link is sent to the drivers at each specific junction. The drivers’ response to the route guidance information is directly modeled by the splitting rates at the junctions, which are simply negatively correlated with the comparison of related cost information and are able to take into account the drivers’ response uncertainties. With the proposed route guidance strategy, in the case of fixed travel demands, the accurate convergence of the traffic flows to a UE is guaranteed in the presence of drivers’ response uncertainties by using La Salle’s invariance principle. When marginal travel cost information, instead of travel cost information, is sent to the drivers, a system optimum can be achieved under a mild condition on the marginal cost function.

关键词： dynamic traffic assignment user equilibrium(UE) system optimum(SO) convergence La Salle’s invariance principle

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A Geometric Approach to Second-Order Consensus of Heterogeneous Networked Systems

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IEEE Transactions on Cybernetics 2018年 2018 Jan 9页

作者： Su, Housheng Ye, Yanyan Chen, Xia He, Haibo School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China. China China Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI 02881 USA. United States

This paper investigates second-order consensus of networked systems with heterogeneous intrinsic nonlinear dynamics via a geometrical method, in which the nonlinear dynamics are governed by both velocity and position. First, two necessary conditions are deduced for the existence of consensus solutions by analyzing the inherent nonlinear dynamics of isolated nodes. Then a closed invariant set is constructed via geometrical methods. The nonempty of the set implies the existence of consensus solution. Assuming that the primary and the second dimension about the above invariant set are the same linear subspace, the system can be divided into two simple subsystems through a linear transformation. In addition, some sufficient conditions are proposed for reaching the global consensus based on matrix theory and Lyapunov method. Finally, numerical simulation results are provided to illustrate the validity of theoretical analysis. IEEE

关键词： Multi agent systems

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Fiber orientation estimation guided by a deep network 1

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20th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2017

作者： Ye, Chuyang Prince, Jerry L. National Laboratory of Pattern Recognition & Brainnetome Center Institute of Automation Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States

ISBN: (数字)9783319661827

ISBN: (纸本)9783319661810

Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain’s white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs. However, accurate estimation of complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent diffusion signals. To estimate the mixture fractions of the dictionary atoms, a deep network is designed to solve the sparse reconstruction problem. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding a dense basis of FOs is used and a weighted 1 -norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and typical clinical dMRI data. The results demonstrate the benefit of using a deep network for FO estimation. © 2017, Springer International Publishing AG.

关键词： Magnetic resonance imaging

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Robust ADRC for Nonlinear Time-varying System with Uncertainties 6

Robust ADRC for Nonlinear Time-varying System with Uncertain...

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2017 IEEE 6th Data Driven Control and Learning Systems Conference (DDCLS’17)

作者： Xiangyang Li Wei Ai Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering South China University of Technology IEEE Center for Advanced Control Technologies Department of Electrical and Computer EngineeringCleveland State University

ISBN: (纸本)9781509054626

Active disturbance rejection control(ADRC) exemplifies the spirit of the data-driven control(DDC) design strategy and shows much promise in obtaining consistent applications in industrial control systems with uncertainties, without the premise that the detailed mathematical model of the controlled system is given. Instead, it is shown that the information needed for the control system to work at high level of effectiveness can be extracted from the input-output data by the use of the extended state observer(ESO). On the other hand, it is shown in this paper that the robustness of ADRC depends on the effectiveness of ESO. Furthermore, taking advantage of the rich body of knowledge in the existing field of robust control, the estimation error in ESO is analysed and, for the purpose of improved robustness, a unique nonlinear component is added to the conventional ADRC law. The modified ADRC which is a kind of robust ADRC law is validated in simulation for a nonlinear time-varying system with parametric and functional uncertainties. It is shown that the proposed robust ADRC law provides more effective tracking performance than the conventional ADRC when the bandwidth of ESO is not wide enough.

关键词： Active Disturbance Rejection Control(ADRC) Robust Control Extended State Observer(ESO) Nonlinear Time-varying System

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Preventive Soot Blowing Strategy Based on State of Health Prediction for Coal-fired Power Plant Boiler

Preventive Soot Blowing Strategy Based on State of Health Pr...

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Chinese Control Conference (CCC)

作者： Yuanhao Shi Jie Wen Xiaoqiong Pang Jianfang Jia Fangshu Cui Jianchao Zeng Jingcheng Wang North University of China School of Electrical and Control Engineering Taiyuan China North University of China School of Computer Science and Technology Taiyuan China Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

This paper seeks the optimization of soot-blowing operations for heat transfer surfaces in coal- fired power plant boiler. A preventive soot blowing strategy based on state of health prediction for heat transfer surface was proposed. The average state of health of heat transfer surface was forecasted as the threshold to draft preventive soot blowing strategy. According to the theory of renewal process, an optimization model with the system prediction interval and the threshold of preventive soot blowing operation as the optimization variables and minimize average energy losses as the target function was established. By using Particle Swarm Optimization (PSO), the optimal preventive cycle and soot-blowing threshold were obtained, and the long-run average cost rate was the lowest. The model is validated with experiment data of a 300MW coal-fired power plant boiler and the parameters of prediction model are obtained. The results can verify the feasibility of proposed strategy. It can be used as the guide for the optimization of soot blowing in coal-fired power plant to improve the energy conservation level.

关键词： Thermal engineering Radio frequency Coal Combustion Heat engines Heat transfer

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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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Minimum uncertainty latent variable models for robot recognition of sequential human activities

Minimum uncertainty latent variable models for robot recogni...

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2017 IEEE International Conference on robotics and automation, ICRA 2017

作者： Han, Fei Reardon, Christopher Parker, Lynne E. Zhang, Hao Human-Centered Robotics Lab Department of Computer Science Colorado School of Mines GoldenCO80401 United States US Army Research Laboratory AdelphiMD20783 United States Distributed Intelligence Laboratory Department of Electrical Engineering and Computer Science University of Tennessee TN37996 United States

ISBN: (纸本)9781509046331

Recognition of sequential human activities, such as 'sitting down' and 'standing up', is a common but challenging problem in human-robot interaction, which requires modeling their underlying temporal patterns. Although previous sequence modeling methods, such as Hidden Conditional Random Fields (HCRFs), demonstrated satisfactory recognition accuracy, they do not explicitly model the uncertainty in underlying temporal patterns, which can provide valuable information to characterize sequential activities. To address this problem, we introduce a novel Minimum Uncertainty HCRF (MU, or μHCRF). Different from traditional HCRF-based techniques that only utilize the negative log-likelihood of the categories' conditional probability as the loss function, the proposed μ-HCRF also introduces a regularization term to model the underlying temporal pattern of the latent variables. As another theoretical contribution, we provide a derivation to show that the formulated problem has a closed-form solution, and prove that inference of the proposed μHCRF is tractable. Extensive empirical study is performed to evaluate our approach, using four public benchmark datasets. Experimental results have shown that our μHCRFs outperform previous techniques and achieve state-of-the-art performance on human activity recognition, especially on sequential activities. © 2017 IEEE.

关键词： Human robot interaction

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Model Learning Based on Grid Cell Representations

Model Learning Based on Grid Cell Representations

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中国神经科学学会第十二届全国学术会议

作者： Guan-Wen HUANG Bai-Lu SI Automation and Electrical Engineering Department Shenyang Ligong University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences

Mammals are able to form internal representations of their environments. Place cells found in the hippocampus fire stingily only at a couple of locations of the environment. One synapse away from the hippocampus, grid cells in medial entorhinal cortex discharge bountifully at many locations of the environment, expressing periodic triangular grid firing maps in two-dimensional open field maze. In this study, we investigate the functional advantage of grid codes in the hippocampal-entorhinal circuit from the perspective of model learning. We build neural network models to learn the mapping from space to an Abstract variable, which could be used in cognitive processes such as decision-making or motor control. The network using grid code as spatial input achieves better learning accuracy with fewer number of cells than the radial basis function network, which assumes place cell inputs. Our result shows that grid representations constitute better spatial representation in the task of model learning, and may help associative cortex better read out the information held in memory circuits.

关键词： grid cell place cell model learning

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