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Distributed sub-optimal resource allocation via a projected form of singular perturbation

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arXiv 2019年

作者： Liang, Shu Zeng, Xianlin Chen, Guanpu Hong, Yiguang Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

Distributed optimization for resource allocation problems is investigated and a sub-optimal continuous-time algorithm is proposed. Our algorithm has lower order dynamics than others to reduce burdens of computation and communication, and is applicable to weight-balanced graphs. Moreover, it can deal with both local constraints and coupled inequality constraints, and remove the requirement of twice differentiability of the cost function in comparison with the existing sub-optimal algorithm. However, this algorithm is not easy to be analyzed since it involves singular perturbation type dynamics with projected non-differentiable righthand side. We overcome the encountered difficulties and obtain results including the existence of an equilibrium of the algorithm, the sub-optimality in the sense that the solution approaches to the optimal solution as an adjustable parameter tends to zero, and the convergence of the algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Resource allocation

Simulating emergent properties of human driving behavior using multi-agent reward augmented imitation learning

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arXiv 2019年

作者： Bhattacharyya, Raunak P. Phillips, Derek J. Liu, Changliu Gupta, Jayesh K. Driggs-Campbell, Katherine Kochenderfer, Mykel J. Stanford Intelligent Systems Laboratory Department of Aeronautics and Astronautics Stanford University StanfordCA94305 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Recent developments in multi-agent imitation learning have shown promising results for modeling the behavior of human drivers. However, it is challenging to capture emergent traffic behaviors that are observed in real-world datasets. Such behaviors arise due to the many local interactions between agents that are not commonly accounted for in imitation learning. This paper proposes Reward Augmented Imitation Learning (RAIL), which integrates reward augmentation into the multi-agent imitation learning framework and allows the designer to specify prior knowledge in a principled fashion. We prove that convergence guarantees for the imitation learning process are preserved under the application of reward augmentation. This method is validated in a driving scenario, where an entire traffic scene is controlled by driving policies learned using our proposed algorithm. Further, we demonstrate improved performance in comparison to traditional imitation learning algorithms both in terms of the local actions of a single agent and the behavior of emergent properties in complex, multi-agent settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Multi agent systems

Air-to-air communications beyond 5G: A novel 3D CoMP transmission scheme

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arXiv 2020年

作者： Li, Yan Miridakis, Nikolaos I. Tsiftsis, Theodoros A. Yang, Guanghua Xia, Minghua School of Electronics and Information Technology Sun Yat-sen University Guangzhou510006 China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai519082 China Institute of Physical Internet School of Electrical and Information Engineering Jinan University Zhuhai519070 China Dept. of Informatics and Computer Engineering University of West Attica 12243 Greece Dept. of Informatics University of Piraeus 18534 Greece School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China Institute of Physical Internet Jinan University Zhuhai519070 China

In this paper, a novel 3D cellular model consisting of aerial base stations (aBSs) and aerial user equipments (aUEs) is proposed, by integrating the coordinated multi-point (CoMP) transmission technique with the theory of stochastic geometry. For this new 3D architecture, a tractable model for aBSs' deployment based on binomial Delaunay-tetrahedralization is developed, which ensures seamless coverage for a given space. In addition, a versatile and practical frequency allocation scheme is designed to eliminate the inter-cell interference effectively. Based on this model, performance metrics including the ergodic data rate and coverage probability are derived for two types of aUEs: i) general aUE (i.e., an aUE having distinct distances from its serving aBSs) and ii) the worst-case aUE (i.e., an aUE having equal distances from its serving aBSs). Simulation and numerical results demonstrate that the proposed approach emphatically outperforms the conventional binomial-Voronoi tessellation without CoMP. Insightfully, it provides a similar performance as to the binomial-Voronoi tessellation which utilizes the conventional CoMP scheme;yet, introducing a considerably reduced computational complexity and backhaul/signaling overhead. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

Systematic Resource Allocation in Cloud RAN with Caching as a Service under Two Timescales

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Systematic Resource Allocation in Cloud RAN with Caching as ...

作者： Tang, Jianhua Quek, Tony Q. S. Chang, Tsung-Hui Shim, Byonghyo Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China Institute of New Media and Communications Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electrical and Computer Engineering Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of

Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CCC) together. In this paper, we dissect the interactions of CCC in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension. In the physical resource dimension, we identify how to segment the baseband unit (BBU) pool resources (i.e., computation and storage) into different types of virtual machines (VMs). In the time dimension, we address how the long-term resource segmentation in the BBU pool impacts on the short-term transmit beamforming at the remote radio heads. We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. After a series of approximation, including sample average approximation, successive convex approximation, and semidefinite relaxation, the SMINLP is approximated as a global consensus problem. The alternating direction method of multipliers (ADMM) is utilized to obtain the solution in a parallel fashion. Simulation results verify the convergence of our proposed algorithm, and also confirm that the proposed scheme is more cost-saving than that without considering the integration of CCC. © 1972-2012 IEEE.

关键词： Integer programming

Predictive Inverse Kinematics: optimizing Future Trajectory through Implicit Time Integration and Future Jacobian Estimation

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Predictive Inverse Kinematics: optimizing Future Trajectory ...

2019 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Ko Ayusawa Wael Suleiman Eiichi Yoshida CNRS-AIST JRL (Joint Robotics Laboratory) UMI13218/RL Intelligent Systems Research Institute National Institute of Advanced Industrial Science and Technology (IS-AIST) Japan Electrical and Computer Engineering Department Faculty of Engineering University of Sherbrooke Canada

This paper presents an inverse kinematics (IK) method which can control future velocities and accelerations for multi-body systems. The proposed IK method is formulated as a quadratic programing (QP) that optimizes future joint trajectories. The features of the proposed IK are: (1) the evaluation of accelerations at future time instances, (2) the trajectory representation that can implicitly integrate the time integral formula into QP, (3) the computation of future Jacobian matrices based on the comprehensive theory of differential kinematics proposed in our previous work. Those features enable a stable and fast IK computation while evaluating the future accelerations. We also conducted thorough numerical studies to show the efficiency of the proposed method.

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Are state-of-the-art visual place recognition techniques any good for aerial robotics?

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arXiv 2019年

作者： Zaffar, Mubariz Khaliq, Ahmad Ehsan, Shoaib Milford, Michael Alexis, Kostas McDonald-Maier, Klaus Embedded and Intelligent Systems Laboratory in Computer Science Electronic Engineering Department University of Essex Colchester United Kingdom Australian Centre for Robotic Vision and School of Electrical Engineering and Computer Science Queensland University of Technology BrisbaneQLD Australia Autonomous Robots Lab University of Nevada United States

Visual Place Recognition (VPR) has seen significant advances at the frontiers of matching performance and computational superiority over the past few years. However, these evaluations are performed for ground-based mobile platforms and cannot be generalized to aerial platforms. The degree of viewpoint variation experienced by aerial robots is complex, with their processing power and on-board memory limited by payload size and battery ratings. Therefore, in this paper, we collect 8 state-of-the-art VPR techniques that have been previously evaluated for ground-based platforms and compare them on 2 recently proposed aerial place recognition datasets with three prime focuses: a) Matching performance b) Processing power consumption c) Projected memory requirements. This gives a birds-eye view of the applicability of contemporary VPR research to aerial robotics and lays down the the nature of challenges for aerial-VPR. Copyright © 2019, The Authors. All rights reserved.

关键词： Antennas

Comparison of Learning Cellular Computational Networks with EKF and CPSO for Multi-Location Wind Speed Prediction 8

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Comparison of Learning Cellular Computational Networks with ...

8th IEEE Symposium Series on Computational Intelligence, SSCI 2018

作者： Pathiravasam, Hirath Venayagamoorthy, Ganesh Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States Department of Electrical Engineering University of Moratuwa Katubedda Sri Lanka School of Engineering University of KwaZulu-Natal Durban South Africa

ISBN: (纸本)9781538692769

Multiple-location wind speed prediction is a challenging and important problem in the modern power system operations. A distributed computing approach is implemented for spatio-temporal wind speed predictions. The multi-location wind speed prediction problem is carried out by respective cells in the Cellular Computational Network (CCN) framework. CCN based systems capture spatial dynamics via connections to neighboring cells. CCN is a framework for learning of learning systems. A Jordan-Elman hybrid recurrent neural network is used as the computational unit in the CCN. In this study, the effectiveness of Extended Kalman Filter (EKF) algorithm and Cooperative Particle Swarm optimization (CPSO) based learning algorithms in cell parameter estimation is compared. CCNs with neural networks (NNs) as computational units are essentially deep NNs via several neighbor cell connections. Three spatially distributed wind power stations are modeled using CCN framework to predict the respective wind speeds. Typical results illustrates that skill factors with EKF learning are positive and those with CPSO learning are negative. © 2018 IEEE.

关键词： Learning systems

Time Series Forecasting of New Cases and New Deaths Rate for COVID-19 using Deep Learning Methods

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arXiv 2021年

作者： Ayoobi, Nooshin Sharifrazi, Danial Alizadehsani, Roohallah Shoeibi, Afshin Gorriz, Juan M. Moosaei, Hossein Khosravi, Abbas Nahavandi, Saeid Chofreh, Abdoulmohammad Gholamzadeh Goni, Feybi Ariani Klemeš, Jiří Jaromír Mosavi, Amir Department of Mathematics Savitribai Phule Pune University Pune411007 India Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain Department of Mathematics Faculty of Science University of Bojnord Iran Sustainable Process Integration Laboratory - SPIL NETME Centre Faculty of Mechanical Engineering Brno University of Technology - VUT Brno Technická 2896/2 Brno616 69 Czech Republic Department of Management Faculty of Business and Management Brno University of Technology VUT Brno Kolejní 2906/4 Brno612 00 Czech Republic Faculty of Civil Engineering Technische Universität Dresden Dresden01069 Germany John von Neumann Faculty of Informatics Obuda University Budapest1034 Hungary School of Economics and Business Norwegian University of Life Sciences 1430 Norway

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. Several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organizations working against COVID-19 and determining their long-term plans. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

A procedure to generate discrete MIMO closed-loop benchmark via LFT with application to state space identification 15

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A procedure to generate discrete MIMO closed-loop benchmark ...

15th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2018

作者： Puerto Acosta, Jorge A. Bottura, Celso P. Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering University of Campinas/UNICAMP Cidade Universitria Zeferino Vaz Av. Albert Einstein 400 Campinas/SPCEP 13083-852 Brazil

ISBN: (纸本)9789897583216

In this paper we use the conformal transformation known as linear fractional transformation (LFT), with the purpose of generating a discrete multivariable closed-loop benchmark from continuous multivariable closed-loop control system, having in mind state space identification. To reach this objective we propose a procedure based on the general framework representation (GFR) and on the multi input multi output (MIMO) LFT bilin-ear discretization process. We first use the LFT tool to obtain the continuous joint control-output (augmented) system form for representing the canonical closed-loop continuous system. Afterwards, we discretize the augmented continuous closed-loop system in order to obtain an augmented discrete model, then, we calculate the discrete plant and controller in the state space form. An application to the multivariable control of a continuous chemical reactor is presented and also we use the discrete benchmark generated to identify a state space model an example of the potential of the our proposal. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Closed loop control systems