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8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Applications, ISCIIA and ITCA 2018

作者： Hu, Bo Zhang, Yu Han, Xin-Yang Jiao, Yu-Qiao Ding, Min School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan430074 China State Grid Energy Research Institute Co. LTD. Beijing102200 China

This paper proposes a peak-to-valley time-based electricity price optimization method based on NSGA-II with distributed power supply. First, the curve-fitting method is used to obtain the comprehensive electricity price response curve of users in the whole society. Secondly, based on the use of time-sharing electricity price, the peak-to-valley time-sharing optimization model is established. Then, based on the NSGA-II optimization method, the optimal model is solved, and the optimal electricity price is obtained at different time periods to change the peak-to-valley load and the valley load. Finally, the simulation results verify the effectiveness of the proposed method. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Genetic algorithms

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A pigeon-inspired optimization algorithm for many-objective optimization problems

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Science China(Information Sciences) 2019年第7期62卷 131-138页

作者： Zhihua CUI Jiangjiang ZHANG Yechuang WANG Yang CAO Xingjuan CAI Wensheng ZHANG Jinjun CHEN Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Beijing Institute of Petrochemical Technology Faculty of Information Technology Beijing University of Technology State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences School of Software University of Technology Sydney

Dear editor,Swarm intelligence optimization algorithms are inspired by the behaviour of biological groups in nature. Such algorithms have the advantages of a clear structure, simple operation, comprehensible principles, strong parallelism, effective search abilities, and strong robustness. They can effectively solve difficult problems that traditional methods cannot. Pigeon-inspired optimization (PIO), a novel biomimetic swarm intelligence optimization algorithm, was proposed by Duan and Qiao in

关键词： A pigeon-inspired optimization algorithm for many-objective optimization problems Pareto

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Adaboost-kNN for dynamic emotion recognition 8

Adaboost-kNN for dynamic emotion recognition

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作者： Li, Min Su, Wanjuan Chen, Luefeng Wu, Min Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Tokyo Institute of Technology Yokohama226-8502 Japan

— K-nearest Neighbor based adaptive boosting (AdaBoost-KNN) is proposed for emotion understanding in human-robot interaction (HRI), where the real-time dynamic emotion is recognized according to facial expression. It can make robots capable of understanding human emotions and make HRI run smoothly. The Candide-3 model was adopted to extract the two-dimensional coordinate values of the key points of facial emotion, as the basis for emotion classification. The emotion classification is based on AdaBoost-KNN, which sets up seven basic KNN classifier. Then it iterates seven times to update the weight of samples, and combines the weak classifier with coefficient to establish the final classifier. Finally, the performance of the proposal is verified by using K-fold cross validation. Results show that the proposal achieves higher accuracy than that of a single KNN. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Adaptive boosting

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Fast Satellite Selection Algorithm Based on Satellite Elevation Angle and Satelte Contribution for Geometry Dilution of Precision in GPS/BEIDOU

Fast Satellite Selection Algorithm Based on Satellite Elevat...

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第37届中国控制会议

作者： Guangjun Wang Jianjin Jiang School of Automation China University of Geosciences China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex System

In order to solve the contradiction between positional accuracy and real-time in satellite navigation system, a fast satellite selection algorithm based on satellite elevation angle and satellite contribution for Geometry Dilution of Precision（GDOP） in GPS/BEIDOU is proposed, which dependents on the high dynamic GPS/BEIDOU receiver developed by our laboratory. Through compared with the existing algorithm by real data simulation, our proposed algorithm has the smaller positional error in the X, Y, Z direction and lower complexity. As a result, we can prove that the proposed method has the feasibility and better real-time performance.

关键词： GPS/BEIDOU elevation angle satellite contribution value satellite selection

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Residential virtual power plant with photovoltaic output forecasting and demand response

Residential virtual power plant with photovoltaic output for...

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作者： Cui, Shichang Wang, Yan-Wu Lin, Xiangning Xiao, Jiang-Wen School of Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan China Hubei Provincial Collaborative Innovation Center for New Energy Microgrid China Three Gorges University Yichang China State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Wuhan China

Decreasing conventional power supply is promoting the development of the distributed renewable energy sources, such as solar power and wind power. Recently rooftop photovoltaic has been widely applied, and accordingly efficient energy management is getting increasingly important for fully use of renewable energy and the peak shaving of the main grid. This paper investigates the residential energy management as a small-scale virtual power plant (VPP) connected to the main grid includes distributed energy resources, energy storages and residential loads. The self-organizing map (SOM) and the radical basis function (RBF) networks are adopted to classify the weather types and predict hourly photovoltaic output precisely. In a time-of-use electricity market, price-based demand response is applied to adjust the demand. The residential VPP has two goals: maximum profit by selling surplus power to grid and minimum power purchased from grid. The two goals are integrated as an optimization object by introducing a weight parameter. The algorithm combining receding horizon optimization and linear programming is proposed to solve the optimization problem in residential VPP. Numerical simulation tests can help to find the most suitable value of the weight parameter. Different scenarios are simulated and discussed to demonstrate the performance of the VPP and the proposed algorithm. © 2019 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Energy management

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Defining digital quadruplets in the cyber-physical-social space for parallel driving

arXiv

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arXiv 2020年

作者： Liu, Teng Xing, Yang Chen, Long Cao, Dongpu Wang, Fei-Yue Vehicle Intelligence Pioneers Inc Shandong Qingdao266109 China Qingdao Academy of Intelligent Industries QingdaoShandong266109 China Department of Mechanical and Mechatronics Engineering University of Waterloo N2L 3G1 Canada State Key Laboratory of Management and Control for Complex System Institute of Automation Beijing China

Parallel driving is a novel framework to synthesize the vehicle intelligence and transport automation. This article aims to define the digital quadruplets in parallel driving. In the cyber-physical-social systems (CPSS), based on the ACP method, the names of the digital quadruplets are first given, which are descriptive, predictive, prescriptive and real vehicles. The objectives of the three virtual digital vehicles are interacting, guiding, simulating and improving with the real vehicles. Then, the three virtual components of the digital quadruplets are introduced in detail and their applications are also illustrated. Finally, the real vehicles in parallel driving system and the research process of the digital quadruplets are depicted. The presented digital quadruplets in parallel driving are expected to make the future connected automated driving safety, efficiently and synergistically. Copyright © 2020, The Authors. All rights reserved.

关键词： Vehicles

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A resonant cavity based on birdcage coil for overhauser geomagnetic sensor

A resonant cavity based on birdcage coil for overhauser geom...

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2018 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2018

作者： Bai, Bingjie Dong, Haobin Ge, Jian Liu, Huan Zhang, Cheng Wang, Luo Institute of Geophysics and Geomatics China University of Geosciences Hubei Wuhan China School of Automation China University of Geosciences Hubei Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Hubei Wuhan China Science and Technology on Near-Surface Detection Laboratory Jiangsu Wuxi China

ISBN: (纸本)9781538622223

The resonant cavity is an important component of Overhauser magnetometer sensor. Its function is to make the working substance generate dynamic nuclear polarization effect in the sensor. An alternative design of resonant cavity based on the birdcage coil structure was proposed to make the resonant frequency in accordance with the polarization frequency of the working substance. The structure and equivalent circuit of resonant cavity were analyzed. The model of resonant cavity was established and S-parameter is calculated to verify the frequency consistency of the resonant cavity and working substance. In addition, output signal experiment and simultaneous comparison testing of sensor prototype were conducted. Overall, this design has the advantages of high performance and strong anti-interference. © 2018 IEEE.

关键词： Equivalent circuits

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Fog Removal Method of Slope Monitoring Image Based On Vision Detection

Fog Removal Method of Slope Monitoring Image Based On Vision...

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Chinese control Conference

作者： Xianzhen Sang Yue Yang Xianhe Hou Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan

In the slope monitoring based on image detection, the main work is to process the acquired slope image. The landslide occurred mostly in the rain, fog and other complex weather conditions. If we can process effectively and fast fog images according to the fog horizon slope vision images. It would be helpful for subsequent image segmentation, object extraction, positioning, and improving the accuracy and efficiency of detection of slope deformation. Based on the visual technology in slope monitoring, we compared two kinds of defogging algorithm of slope images. One is a kind of image enhancement method of non-physical model, mainly including: equalization algorithm and homomorphic filtering algorithm, McCann Retinex algorithm and multi-scale Retinex algorithm and a global histogram. The other is the image restoration method based on physical model, including the dark channel prior bilateral filtering algorithm, and combined with the theory of dark channel prior to fog algorithm. The experimental results show that the histogram equalization method has the advantage of fast imaging quality in slope visual image processing, and is more suitable for slope monitoring.

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An optimization method for coupling matrix elements of microwave filter

An optimization method for coupling matrix elements of micro...

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Chinese control Conference

作者： Xiao-Long Zhuang Wei-Hua Cao Yan Yuan Can Liu Yu-Sheng Guo China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper proposes a coupling matrix element optimization method for microwave filters. The traditional method is more complex and does not directly optimize the filter coupling matrix elements. The firefly algorithm optimization method used in this paper directly optimizes the elements of the N + 2 order coupling matrix. Compared with the traditional method, it has better speed and optimization effect. First, the elements of the specific coupling matrix are introduced into the optimization algorithm to be executed, then the matrix is iterated through the set objective function. Finally, when the optimized data is within the allowable range, the optimization of the elements of the coupling matrix is stopped and optimization is performed. The resulting coupling matrix outputs the response. To prove the effectiveness of the proposed method, three methods were used to optimize the coupling matrix elements of the fourth-order filter and compare the final optimization results.

关键词： Couplings Tuning Transmission line matrix methods Integrated circuit modeling Optimization methods Cats

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Hyperspectral Remote Sensing Image Classification Based on Convolutional Neural Network

Hyperspectral Remote Sensing Image Classification Based on C...

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Chinese control Conference

作者： Xiangyang Dai Wei Xue Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan

Remote sensing hyperspectral imaging can obtain rich spectral information of terrestrial objects, which allows the indistinguishable matter in the traditional wideband remote sensing to be distinguished in hyperspectral remote sensing. Hyperspectral image has the characteristics of "combining image with spectrum". Making full use of spectral information and spatial information in hyperspectral image is the premise of obtaining accurate classification results. At present, most of hyperspectral data feature extraction algorithms mainly utilize local spatial information in the same channel and spectral information in the same spatial location of different channels. However, these methods require a large amount of prior knowledge, it is difficult to fully grasp the hyperspectral data of all spatial and spectral information, and the model generalization ability is poor. With the development of deep learning, convolutional neural network shows superior performance in all kinds of visual tasks, especially in the two-dimensional image classification, and could get a high classification accuracy. In this paper, an image classification method based on three-dimensional convolution neural network is proposed based on the structural properties of hyperspectral data. In the proposed method, first the stereo image blocks of hyperspectral data are intercepted, then multi-layer convolution and pooling operation of extracted blocks by convolutional neural network are implemented to obtain the essential information of hyperspectral data, finally the classification of hyperspectral data is completed. The experimental results show the proposed method could provide better feature expression and classification accuracy for hyperspectral image.

关键词： Hyperspectral imaging Image classification Convolution Feature extraction Support vector machines

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