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Error analysis of the numerical method for correcting the propagation of EM waves in the troposphere

Journal of Microwaves, Optoelectronics and Electromagnetic Applications 2020年第3期19卷 407-414页

作者： Liu, Yuanzhi Tang, Tao Liu, Guo Olaimat, M. Science and Technology on Electronic Information Control Laboratory Southwest China Research Institute of Electronic Equipment Chengdu610036 China College of Electronic Engineering Chengdu University of Information Technology Chengdu610025 China Department of Electrical and Computer Engineering University of Waterloo WaterlooN2L 3G1 Canada School of Electrical Engineering and Computer Science University of Ottawa OttawaK1N 6N5 Canada Key laboratory of Atmospheric Sounding China Meteorological Administration Chengdu610225 China

Electromagnetic (EM) waves do not travel in straight lines in the troposphere due to refraction. This leads to errors in many engineering applications. In order to correct these errors, a numerical solution based on the difference method has been proposed, which can reveal the degree of EM wave bending owing to atmospheric refraction, moreover, it can predict the actual path of the EM wave propagating in the troposphere. The error of the numerical method is studied here, which reveals the source of the error. Based on this, two improvement methods are proposed. The computational accuracy of the numerical methods is verified by comparison with the analytical solution. The proposed method ensures the computational efficiency and accuracy of the problem of refraction of EM waves in the troposphere. © 2020 SBMO/SBMag

关键词： Error analysis

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Minimum Age-Energy Aware Cost in Wireless Powered Fog Computing Networks

Minimum Age-Energy Aware Cost in Wireless Powered Fog Comput...

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IEEE International Conference on Communications (ICC)

作者： Haina Zheng Ke Xiong Pingyi Fan Zhangdui Zhong Khaled Ben Letaief School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Electronic Engineering Tsinghua University Beijing China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China The Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

This paper investigates the optimal design of wireless powered fog computing networks, where a hybrid access point integrated with fog computing function (F-HAP) first charges an Internet of Things (IoT) device via wireless power transfer (WPT), and then the IoT device uses the harvested energy to compute real-time updates locally or offload the updates to the F-HAP for computing. For such a system, an age-energy aware cost function is defined to evaluate the system performance, based on which, an optimization problem is formulated to explore the minimum age-energy aware cost by jointly optimizing the time assignment, the transmit power, the computing frequency, as well as the computing mode selection, such that the given data processing task can be completed. Since the problem is non-convex with the discrete binary variable, variable substitution and Karush-Kuhn-Tucker (KKT) conditions are applied to solve it and some closed-form results on the optimal solution are derived. Numerical results show that the transmit power has much greater effects on the age-energy performance of the fog offloading mode than on that of the local computing mode. Moreover, when the IoT device is relatively close to the F-HAP, fog offloading is a better choice; Otherwise, local computing should be selected.

关键词： Edge computing Wireless communication Real-time systems Cost function Internet of Things System performance Time-frequency analysis

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Multi-view adaptive fusion network for 3D object detection

arXiv

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arXiv 2020年

作者： Wang, Guojun Tian, Bin Zhang, Yachen Chen, Long Cao, Dongpu Wu, Jian State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130022 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China School of Data and Computer Science Sun Yat-Sen University Guangzhou510275 China Lab University of Waterloo Canada College of Automotive Engineering Jilin University China

3D object detection based on LiDAR-camera fusion is becoming an emerging research theme for autonomous driving. However, it has been surprisingly difficult to effectively fuse both modalities without information loss and interference. To solve this issue, we propose a single-stage multi-view fusion framework that takes LiDAR Birds-Eye View, LiDAR Range View and Camera View images as inputs for 3D object detection. To effectively fuse multi-view features, we propose an Attentive Pointwise Fusion (APF) module to estimate the importance of the three sources with attention mechanisms which can achieve adaptive fusion of multi-view features in a pointwise manner. Besides, an Attentive Pointwise Weighting (APW) module is designed to help the network learn structure information and point feature importance with two extra tasks: foreground classification and center regression, and the predicted foreground probability will be used to reweight the point features. We design an end-to-end learnable network named MVAF-Net to integrate these two components. Our evaluations conducted on the KITTI 3D object detection datasets demonstrate that the proposed APF and APW module offer significant performance gain and that the proposed MVAF-Net achieves state-of-the-art performance in the KITTI benchmark. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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A real-time multi-constraints obstacle avoidance method using LiDAR

arXiv

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arXiv 2020年

作者： Chen, Wei Sun, Jian Li, Weishuo Zhao, Dapeng State Key Laboratory for Strength & Vibration of Mechanical Structures School of Aerospace Xi’an Jiaotong University Xi’an710049 China Shaanxi Engineering Laboratory for Vibration Control of Aerospace Structures Xi’an Jiaotong University Xi’an710049 China School of Mechanical Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Robotics Institute School of Computer Science Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States

Obstacle avoidance is one of the essential and indispensable functions for autonomous mobile robots. Most of the existing solutions are typically based on single condition constraint and cannot incorporate sensor data in a real-time manner, which often fail to respond to unexpected moving obstacles in dynamic unknown environments. In this paper, a novel real-time multi-constraints obstacle avoidance method using Light Detection and Ranging(LiDAR) is proposed, which is able to, based on the latest estimation of the robot pose and environment, find the sub-goal defined by a multi-constraints function within the explored region and plan a corresponding optimal trajectory at each time step iteratively, so that the robot approaches the goal over time. Meanwhile, at each time step, the improved Ant Colony Optimization(ACO) algorithm is also used to re-plan optimal paths from the latest robot pose to the latest defined sub-goal position. While ensuring convergence, planning in this method is done by repeated local optimizations, so that the latest sensor data from LiDAR and derived environment information can be fully utilized at each step until the robot reaches the desired position. This method facilitates real-time performance, also has little requirement on memory space or computational power due to its nature, thus our method has huge potentials to benefit small low-cost autonomous platforms. The method is evaluated against several existing technologies in both simulation and real-world experiments. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical radar

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Multi-dispatch for Grid-connected Microgrid with Robust Optimization Algorithm

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IFAC-PapersOnLine 2018年第28期51卷 474-479页

作者： Shi, Ruifeng Li, Shaopeng Sun, Changhao Lee, Kwang Y. School of Control and Computer Engineering North China Electric Power University Beijing China Smart Energy Institute Nanjing SAC Power Grid Automation Co. Ltd Nanjing211100 China Department of Electrical and Computer Engineering Baylor University TX76798 United States

Increasing penetration of uncoordinated electric vehicles (EVs) and intermittent renewable energy generation in microgrid systems motivates us to explore an effective strategy for the safe and economic operation of such distributed generation (DG) systems. This paper presents a robust economic dispatch strategy for grid-connected microgrids. Uncertainty from wind power and EV charging loads is considered as an uncertain set of interval predictions. Considering the worst application scenario, the proposed strategy can help to schedule the EV charging behaviors and DG outputs in order to reduce operation cost under practical constraints. In addition, in order to facilitate the decision makers to make decision strategies from the perspective of economy, this paper explores the relationship between the volatility of uncertain parameters and economy based on the theory of interval forecast. Numerical case study shows the feasibility of the proposed dispatch strategy. © 2018

关键词： Electric load dispatching Decision making Decision theory Economic analysis Electric vehicles Optimization Scheduling Uncertainty analysis Wind power Distributed generation system Electric Vehicles (EVs) Ev charging behaviors Micro grid Multi Dispatch Renewable energy generation Robust optimization Robust optimization algorithm

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CNN-based Broad Learning with Efficient Incremental Reconstruction Model for Facial Emotion Recognition ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10236-10241页

作者： Luefeng Chen Min Li Xuzhi Lai Kaoru Hirota Witold Pedrycz School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Tokyo Institute of Technology Yokohama 226-8502 Japan Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada

Convolutional neural network-based broad learning with efficient incremental reconstruction model (CNNBL) is proposed to recognize emotions in human-robot interaction. It aims to extract deep and abstract features from facial emotional images, and reduce the influence of the complex structure and slow network updates on facial emotion recognition in deep learning. Feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotion recognition. When the network needs to expand, the network is dynamically updated by incremental learning algorithm. We verified the experimental performance through k -fold cross validation. According to the recognition results, the accuracy on JAFFE database of our proposal is greater than that of the state of the art, such as the Local Binary Patterns with Softmax and Deep Attentive Multi-path convolutional neural network.

关键词： Convolution Neural Networks Broad Learning Emotion Recognition Human-Robot Interaction

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PID2018 Benchmark Challenge: learning feedforward control

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IFAC-PapersOnLine 2018年第4期51卷 663-668页

作者： Zhao, Yang Dehghan, Sina Ates, Abdullah Yuan, Jie Zhou, Fengyu Li, Yan Chen, YangQuan School of Control Science and Engineering Center for Robotics Shandong University JinanShandong250061 China University of California Merced MercedCA95340 United States School of Automation Southeast University Nanjing210096 China Engineering Faculty Computer Engineering Department Inonu University Malatya44280 Turkey

The design and application of learning feedforward controllers (LFFC) for the one-staged refrigeration cycle model described in the PID2018 Benchmark Challenge is presented, and its effectiveness is evaluated. The control system consists of two components: 1) a preset PID component and 2) a learning feedforward component which is a function approximator that is adapted on the basis of the feedback signal. A B-spline network based LFFC and a low-pass filter based LFFC are designed to track the desired outlet temperature of evaporator secondary flux and the superheating degree of refrigerant at evaporator outlet. Encouraging simulation results are included. Qualitative and quantitative comparison results evaluations show that, with little effort, a high-performance control system can be obtained with this approach. Our initial simple attempt of low-pass filter based LFFC and B-spline network based LFFC give J=0.4902 and J=0.6536 relative to the decentralized PID controller, respectively. Besides, the initial attempt of a combination controller of our optimized PI controller and low-pass filter LFFC gives J=0.6947 relative to the multi-variable PID controller. © 2018

关键词： controllers Benchmarking Electric control equipment Evaporators Feedforward control Interpolation Learning systems Low pass filters Proportional control systems Refrigeration Three term control systems Combination controllers Conditional integrations Decentralized PID controller Feed forward controllers High performance control systems PID 2018 Benchmark Challenge Quantitative comparison Vapour compression refrigeration systems

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Single pixel fourier computed tomography

Single pixel fourier computed tomography

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Novel Techniques in Microscopy, NTM 2019

作者： Stockton, Patrick A. Wernsing, Keith A. Field, Jeff J. Squier, Jeff Bartels, Randy A. Department of Electrical and Computer Engineering Colorado State University United States Department of Biochemistry and Molecular Biology Colorado State University United States Microscope Imaging Network Foundational Core Facility Colorado State University United States School of Biomedical Engineering Colorado State University United States Center for Microintegrated Optics for Advanced Biological Control Department of Physics Colorado School of Mines United States

ISBN: (纸本)9781943580545

关键词： computerized tomography

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Ice Detection for Wind Turbine Blades Based on PSO-SVM Method

Ice Detection for Wind Turbine Blades Based on PSO-SVM Metho...

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1st International Conference on Advanced Algorithms and control engineering, ICAACE 2018

作者： Guangfei, Zhou Wen, Tan Da, Zhang School of Control and Computer Engineering North China Electric Power University Beijing102206 China

Detection of ice formation on wind turbine blades is still in the research and development stage. Generally, wind turbines have to be shut down to deice the blades when the icing is serious. This paper adopts a data-driven approach to detect the icing blades by analyzing a lot of actual wind field data collected by SCADA system to create a prediction model. Considering the characteristics of industrial big data, firstly, we preprocess the field data by eliminating the singular data, and then select suitable features through mechanism analysis. Finally, the characteristic data are fed into the support vector machine (SVM) which uses the particle swarm optimization (PSO) algorithm to optimize the parameters. Experimental results show that the proposed approach can achieve good performance and provide a new idea for fault diagnosis of ice formation on wind turbine blades. © Published under licence by IOP Publishing Ltd.

关键词： Turbomachine blades

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Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling

arXiv

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arXiv 2021年

作者： Huang, Chen He, Ruisi Ai, Bo Molisch, Andreas F. Lau, Buon Kiong Haneda, Katsuyuki Liu, Bo Wang, Cheng-Xiang Yang, Mi Oestges, Claude Zhong, Zhangdui The Purple Mountain Laboratories Nanjing211111 China The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China The State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China Peng Cheng Laboratory Shenzhen518055 China The Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States The Department of Electrical and Information Technology Lund University Lund22100 Sweden The Department of Radio Science and Engineering Aalto University Aalto00076 Finland The School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom The Institute of Information and Communication Technologies Electronics and Applied Mathematics Universite Catholique de Louvain Louvain-la-Neuve 1348 Belgium

This two-part paper investigates the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. In Part I, we introduced AI and ML as well as provided a comprehensive survey on ML enabled channel characterization and antenna-channel optimization, and in this part (Part II) we review state-of-the-art literature on scenario identification and channel modeling here. In particular, the key ideas of ML for scenario identification and channel modeling/prediction are presented, and the widely used ML methods for propagation scenario identification and channel modeling and prediction are analyzed and compared. Based on the state-of-art, the future challenges of AI/ML-based channel data processing techniques are given as well. © 2021, CC BY-NC-ND.

关键词： Machine learning

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