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The design of gas concentration detection system based on acoustic relaxation method 8

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The design of gas concentration detection system based on ac...

8th International Congress of Information and Communication Technology, ICICT 2018

作者： Qing, Zhao Wanting, Zhou Shi, Liu Teng, Long School of Control and Computer Engineering North China Electric Power University Beijing102206 China Electrical Engineering Division Department of Engineering University of Cambridge CB3 0FA United Kingdom

The concentration detection of the mixed gas is significant to operate safely and efficiently as well as reduce the emission of pollutants. In this paper, a concentration detection system based on acoustic relaxation attenuation is designed in order to reconstruct the three-dimensional images. The system is based on FPGA, supplemented by other hardware circuits. The consists of the system, the choice of the ultrasonic transducer, the calculation of amplifier magnification, and the specific design of circuit are elaborated in the paper. Experiments certificate that the hardware system can measure the attenuation intensity and flight time of ultrasonic waves in real time accurately. It is the base to detect the concentration of the gas mixture. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Field programmable gate arrays (FPGA)

Distance distribution between two random nodes in arbitrary polygons

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arXiv 2019年

作者： Pure, Ross Durrani, Salman Tong, Fei Pan, Jianping Research School of Electrical Energy and Materials Engineering Australian National University CanberraACT2615 Australia Department of Control Science and Engineering Zhejiang University Hangzhou China Department of Computer Science University of Victoria BC Canada

Distance distributions are a key building block in stochastic geometry modelling of wireless networks and in many other fields in mathematics and science. In this paper, we propose a novel framework for analytically computing the closed form probability density function (PDF) of the distance between two random nodes each uniformly randomly distributed in respective arbitrary (convex or concave) polygon regions (which may be disjoint or overlap or coincide). The proposed framework is based on measure theory and uses polar decomposition for simplifying and calculating the integrals to obtain closed form results. We validate our proposed framework by comparison with simulations and published closed form results in the literature for simple cases. We illustrate the versatility and advantage of the proposed framework by deriving closed form results for a case not yet reported in the literature. Finally, we also develop a Mathematica implementation of the proposed framework which allows a user to define any two arbitrary polygons and conveniently determine the distance distribution numerically. Copyright © 2019, The Authors. All rights reserved.

关键词： Geometry

Fast ORB-SLAM without Keypoint Descriptors

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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

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

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

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

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

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

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

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

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

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

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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

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

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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