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arXiv 2020年

作者： Fu, Qiang Yu, Hongshan Wang, Xiaolong Yang, Zhengeng He, Yong Zhang, Hong Mian, Ajmal The National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University China The Department of Computing Science University of Alberta Canada Quanzhou Institute of Industrial Design and Machine Intelligence Innovation Hunan University China The Southern University of Science and Technology Shenzhen China The School of Mathematics and Information Science Shaanxi Normal University China The Department of Computer Science The University of Western Australia WA6009 Australia

Indirect methods for visual SLAM are gaining popularity due to their robustness to environmental variations. ORB-SLAM2 [1] is a benchmark method in this domain, however, it consumes significant time for computing descriptors that never get reused unless a frame is selected as a keyframe. To overcome these problems, we present FastORB-SLAM which is light-weight and efficient as it tracks keypoints between adjacent frames without computing descriptors. To achieve this, a two stage descriptor-independent keypoint matching method is proposed based on sparse optical flow. In the first stage, we predict initial keypoint correspondences via a simple but effective motion model and then robustly establish the correspondences via pyramid-based sparse optical flow tracking. In the second stage, we leverage the constraints of the motion smoothness and epipolar geometry to refine the correspondences. In particular, our method computes descriptors only for keyframes. We test FastORB-SLAM on TUM and ICL-NUIM RGB-D datasets and compare its accuracy and efficiency to nine existing RGB-D SLAM methods. Qualitative and quantitative results show that our method achieves state-of-the-art accuracy and is about twice as fast as the ORB-SLAM2. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical flows

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Comparator-based gene regulatory network designs for control of expression

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IFAC-PapersOnLine 2018年第19期51卷 38-41页

作者： Kazemi, J. Ozgoli, S. Ingalls, B.P. Advanced Control Systems Laboratory School of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Applied Mathematics University of Waterloo WaterlooOntarioN2L 3G1 Canada

We present comparator-based gene network designs for feedback control of the concentration of gene products. Two gene circuit designs are proposed, each of which compares concentrations of an input transcription factor (the reference input) with the protein product of a target gene (the process output). These designs employ a genetic relay switch and a shifted subtractor as components of a discrete comparator circuit and a bistable comparator circuit, respectively. Numerical simulation results illustrate the potential effectiveness of the designed controllers. © 2018

关键词： Comparator circuits Comparators (optical) Feedback control Integrated circuit manufacture Transcription Gene circuits Gene networks Gene products Gene regulatory networks Process output Reference inputs Relay switches Synthetic biology

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Multi-sensor based Train Localization and Data Fusion in Autonomous Train control System

Multi-sensor based Train Localization and Data Fusion in Aut...

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Chinese Automation Congress (CAC)

作者： Zixing Deng Haifeng Song Hua Huang Yidong Li Hairong Dong State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing P.R. China School of Electronic and Information Engineering Beijing Jiaotong University Beijing P.R. China China Railway Hefei Train Depot Shanghai Group Co. Ltd. Hefei P.R. China School of Computer and Information Technology Beijing Jiaotong University Beijing P.R. China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

It is well known that one of the key technologies in train control system is the localization of train. Since multiple sensors can be installed on the train to obtain real-time data, information fusion is a promising method that can be used to combine each sensor's unique information to calculate the stable and accurate localization results. A multi-sensor based localization system for the train autonomous control is proposed in this paper, which contains the Global Positioning System, Inertial Navigation System and velocity sensor. Covariance Intersection algorithm is proposed to integrate the output results of each sensor. Besides, considering the variety of the train running environment, adaptive Kalman filter is applied to reduce the impact of environmental noise. Finally, simulation results prove the proposed method in this paper improves the positioning accuracy compared with the traditional methods.

关键词： Sensors Location awareness Covariance matrices Kalman filters Global Positioning System Sensor systems Mathematical model

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A dynamic early warning method of student study failure risk based on fuzzy synthetic evaluation

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International Journal of Performability engineering 2018年第4期14卷 639-646页

作者： Mi, Chunqiao Deng, Qingyou Lin, Jing Deng, Xiaowu School of Computer Science and Engineering Huaihua University Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China Human Resource Department Huaihua University Huaihua418000 China

As more and more students fail in course studies, higher education is now facing challenges regarding increasingly lower course completion rates as well as overall graduation rates. However, failures in course studies is a comprehensive result of various factors, which is characterized by uncertainty. To deal with this issue, fuzzy sets theory and fuzzy logic are advantageous compared with traditional methods. In this study, based on dynamic student study process data, a fuzzy synthetic evaluation method for dynamic early warning student study failure risk is provided. For each student, three specific early warning factors: 1) student course participation, 2) assignment earned points, and 3) student attendance record, are selected as risk indicators, and the overall risk level is determined by a fuzzy synthetic evaluation approach, which can dynamically give the situation of risk as the evaluation time point changes. Finally, our obtained results show that the employed method is good for identifying at-risk students and exploring the risk reasons by showing the degrees of each early warning factors to the overall risk level. It is of significance for educators to timely apply corresponding strategic pedagogical interventions to help at-risk students avoid academic failure. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Students

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Efficient Multi-robot Exploration via Multi-head Attention-based Cooperation Strategy

arXiv

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arXiv 2019年

作者： Liu, Shuqi Wu, Zhaoxia National Key Laboratory of Big Data Management and Analysis College of Computer and Science Northeastern University Shenyang110004 China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China

The goal of coordinated multi-robot exploration tasks is to employ a team of autonomous robots to explore an unknown environment as quickly as possible. Compared with "human-designed" methods, which began with heuristic and rule-based approaches, "learning-based" methods enable individual robots to learn sophisticated and hard-to-design cooperation strategies through deep reinforcement learning technologies. However, in decentralized multi-robot exploration tasks, learning-based algorithms are still far from being universally applicable to the continuous space due to the difficulties associated with area calculation and reward function designing;moreover, existing "learning-based" methods encounter problems when attempting to balance the "historical trajectory" issue and target area conflict problem. Furthermore, the scalability of these methods to a large number of agents is poor because of the exponential explosion problem of state space. Accordingly, this paper proposes a novel approach Multi-head Attention-based Multi-robot Exploration in Continuous Space (MAMECS) aimed at reducing the state space and automatically "learning" the cooperation strategies required for decentralized multi-robot exploration tasks in continuous space. Computational geometry knowledge is applied to describe the environment in continuous space and to design an improved reward function to ensure a superior exploration rate. Moreover, the multi-head attention mechanism employed helps to solve the "historical trajectory" issue in the decentralized multi-robot exploration task, as well as to reduce the quadratic increase of action space. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Multi-objective optimal control of micro-grid based on economic model predictive control

Multi-objective optimal control of micro-grid based on econo...

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第三十八届中国控制会议

作者： Shijia Ren Jiajun Wang Miaomiao Ma School of Control & Computer Engineering North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University

This paper presents an economic model predictive control(EMPC) scheme to manage the grid-connected micro-grid in an economic way. The grid-connected micro-grid system is composed of wind generation unit, photovoltaic generation unit,energy storage battery, micro-turbines and load. The generation units are scheduled optimally to achieve the three objectives of power balance, economic performance and environmental protection. Moreover, the analytic hierarchy process(AHP) is used to determine the weight coefficients of the objective function. Simulation and analysis results for the economic optimization of micro-grid system are given to illustrate the effectiveness of the proposed economic model predictive control framework.

关键词： Micro-grid multi-objective optimization EMPC AHP power balance

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Communication-efficient federated deep learning with asynchronous model update and temporally weighted aggregation

arXiv

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arXiv 2019年

作者： Chen, Yang Sun, Xiaoyan Jin, Yaochu School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

—Federated learning obtains a central model on the server by aggregating models trained locally on clients. As a result, federated learning does not require clients to upload their data to the server, thereby preserving the data privacy of the clients. One challenge in federated learning is to reduce the client-server communication since the end devices typically have very limited communication bandwidth. This paper presents an enhanced federated learning technique by proposing a synchronous learning strategy on the clients and a temporally weighted aggregation of the local models on the server. In the asynchronous learning strategy, different layers of the deep neural networks are categorized into shallow and deeps layers and the parameters of the deep layers are updated less frequently than those of the shallow layers. Furthermore, a temporally weighted aggregation strategy is introduced on the server to make use of the previously trained local models, thereby enhancing the accuracy and convergence of the central model. The proposed algorithm is empirically on two datasets with different deep neural networks. Our results demonstrate that the proposed asynchronous federated deep learning outperforms the baseline algorithm both in terms of communication cost and model accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Observer Design for Boundary Coupled Fractional Order Distributed Parameter Systems

Observer Design for Boundary Coupled Fractional Order Distri...

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International Conference on control, Mechatronics and Automation (ICCMA)

作者： Yanjiu ZHOU Juan CHEN Baotong CUI Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) School of IoT Engineering Jiangnan University Wuxi China Department of Computer Systems Tallinn University of Technology Tallinn Estonia

This paper studies the observer design for the fractional order boundary coupled distributed parameter systems. Firstly, we obtain the decoupled observer error systems from the state system and observer system. Secondly, the advection terms are eliminated through the invertible transformation and we can obtain the general reaction-diffusion partial differential equations, which are more convenient for discussions. Thirdly, Backstepping method and target systems are employed to give kernel partial differential equations and then the related observer gains can be derived from the corresponding kernels. Finally, Mittag-Leffler stability of the designed fractional order observers is analyzed and the main results are illustrated by the simulation graphically.

关键词： control nonlinearities distributed parameter systems observers partial differential equations reaction-diffusion systems stability

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QoE-aware user association and resource allocation for mixed services in heterogeneous networks

QoE-aware user association and resource allocation for mixed...

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第二届材料科学应用与能源材料国际研讨会

作者： Shuxian Li Liangrui Tang Jiangyu Yan State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University School of Control and Computer Engineering North China Electric Power University

In heterogeneous networks(HetNets),user association and resource allocation(UARA) is a great challenge for radio resource management(RRM).In this paper,a novel QoE-aware UARA scheme is *** delay factor is introduced to QoE metric to weight delays' effect on service *** the UARA scheme based on QoE is formulated as an optimization problem which is a mixed integer nonlinear programming(MINLP) *** solve the problem,we adopt the genetic algorithm(GA) and associate the user association and the resource with population and fitness *** the evolutions of genetic algorithm,the optimal UARA scheme can be worked *** simulation results indicate that the proposed scheme can reduce packet loss rate,and improve the system load balancing performance.

关键词： service QoE-aware user association and resource allocation for mixed services in heterogeneous networks

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Background subtraction on depth videos with convolutional neural networks

arXiv

引用

arXiv 2019年

作者： Wang, Xueying Liu, Lei Li, Guangli Dong, Xiao Zhao, Peng Feng, Xiaobing State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100049 China College of Computer Science and Technology Jilin University ChangchunJilin130012 China

Background subtraction is a significant component of computer vision systems. It is widely used in video surveillance, object tracking, anomaly detection, etc. A new data source for background subtraction appeared as the emergence of low-cost depth sensors like Microsoft Kinect, Asus Xtion PRO, etc. In this paper, we propose a background subtraction approach on depth videos, which is based on convolutional neural networks (CNNs), called BGSNet-D (BackGround Subtraction neural Networks for Depth video). The method can be used in color unavailable scenarios like poor lighting situations, and can also be applied to combine with existing RGB background subtraction methods. A preprocessing strategy is designed to reduce the influences incurred by noise from depth sensors. The experimental results on the SBM-RGBD dataset show that the proposed method outperforms existing methods on depth data, and even reaches the performance of the methods that use RGB-D data. Copyright © 2019, The Authors. All rights reserved.

关键词： Anomaly detection

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