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第37届中国控制会议

作者： Zengzeng He Xudong Ye Ke Gu Junfei Qiao Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing University of Technology Huludao Power Supply Corporation Liaoning Electric Power Co.Ltd

PM2.5, which severely affects human health, is one of the most important indices for air quality estimation. There have been limited studies on the simple, fast and cheap PM2.5 concentration prediction and thus this paper presents a model of PM2.5 prediction based on image contrast-sensitive features. Two types of features were extracted from the images and utilized to estimate the PM2.5 concentration. Then, we establish a recurrent fuzzy neural network model, the parameters of which are trained by using the gradient descent algorithm with an adaptive learning rate. Experiment results indicate that the recurrent neural network has better prediction performance than traditional radial basis function and fuzzy neural network.

关键词： PM2.5 prediction contrast-sensitive image features recurrent fuzzy neural network

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Application of Accurate Online Support Vector Regression in Atmospheric SO2 Concentration Prediction

Application of Accurate Online Support Vector Regression in ...

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第30届中国控制与决策会议

作者： Aorong Luo Xiaoli Li Yang Li Jiangeng Li Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Communication University of China

In order to adapt to the characteristics of high nonlinear and time-varying for air pollutants concentration and improve the real-time prediction accuracy of air pollutants concentration, a forecasting model of air pollutants concentration based on accurate online support vector regression（AOSVR） algorithm is established in this paper. According to the hourly SO concentration and meteorological parameters from May 2014 to April 2015 in Wanliu Monitoring Station of beijing in China, the data of 2 months are selected as experimental samples. At the same time, Pearson correlation coefficient method is used to select the exogenous inputs which have strong correlation with the output variable. The results show that the AOSVR algorithm can adjust the prediction model dynamically, and the prediction accuracy is higher than that of the conventional fixed support vector regression（SVR） model.

关键词： AOSVR SO2 Pearson correlation coefficient prediction model

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Nonlinear system identification using deep belief network based on PLSR 36

Nonlinear system identification using deep belief network ba...

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第36届中国控制会议

作者： Wang Gongming Li Wenjing Qiao Junfei Wu Guandi Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Technical Test Center of Shengli Oilfield Branch Sinopec

ISBN: (纸本)9781538629185

Deep learning has been successfully applied into pattern recognition due to its deep architecture and effective unsupervised learning,and deep belief network(DBN) is a popular model based on deep learning *** this paper,a DBN identification model based on partial least square regression(PLSR),named PLSR-DBN,is proposed for nonlinear system *** order to improve the identification accuracy,PLSR is introduced into the supervised fine-tuning of DBN to elimate the overfitting and local minimum resulted from gradients-based learning,and contrastive divergence(CD) algorithm is used in unsupervised ***,the proposed PLSR-DBN is tested on a benchmark nonlinear *** experiment results show that the proposed PLSR-DBN has a better performance on nonlinear system identification than other similar methods.

关键词： Deep learning Deep belief network Nonliear system identification fine-tuning Partial least square regression

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Modeling and Analysis of Material Layer Thickness in Slag Powder Process

Modeling and Analysis of Material Layer Thickness in Slag Po...

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第30届中国控制与决策会议

作者： Shiqi Shen Xiaoli Li Yang Li Jie Gao Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Communication University of China

Dumping the slag into vertical mill, granulated blast furnace slag powder is finally got by the crushing, grinding and separation in the vertical mill. The whole process is a non-linear, multivariate process. In this process, maintaining the stability of material layer thickness can reduce the mill vibration and power consumption. So this paper analyzed the slag powder production process firstly. Then the models of material layer thickness, production and specific surface area are established based on the field data by using least square support vector machine(LSSVM). Finally the suitable range of material layer thickness is obtained by multi-objective optimization of production and specific surface area.

关键词： material layer thickness LSSVM model multi-objective optimization

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Prediction of Urban PM2.5 Concentration Based on Wavelet Neural Network

Prediction of Urban PM2.5 Concentration Based on Wavelet Neu...

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第30届中国控制与决策会议

作者： Shan Zhang Xiaoli Li Yang Li Jianxiang Mei Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Communication University of China

The main objective of this study is to determine the more appropriate computational intelligence(CI) model for the prediction of air pollutants in urban areas. In this paper, in order to emphasize the importance of short-term air quality(AQ) prediction, PM is used as an example to evaluate the concentration of pollutants using a variety of CI methods and tools. According to the data of air quality monitoring stations, the main air pollutants O, CO, NO, SO, PM10, PM and two kinds of meteorological factors temperature and humidity are selected as influencing factors. Comparing with the model of extreme learning machine(ELM), fuzzy neural network(FNN) and least squares support vector machine(LSSVM), wavelet Neural Network(WNN) model is constructed for short time prediction concentration of PM. The experimental results show that the detection results based on WNN are more accurate, higher precision and strong self-learning ability.

关键词： PM2.5 concentration prediction wavelet Neural Network（WNN） air quality computational intelligence （CI）

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Dynamic modeling of a cubical robot balancing on its corner 3

Dynamic modeling of a cubical robot balancing on its corner

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3rd International Conference on Mechanical, Electronic and Information Technology Engineering, ICMITE 2017

作者： Chen, Zhigang Ruan, Xiaogang Li, Yuan Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

Aiming at the problem of self-balancing control of a cubical robot, this paper makes a research on the dynamic modeling of the cubical robot balancing on its corner. Using the prototype of cubical robot we built as the research object, the dynamic model is derived with Lagrangian method on foundation of analysis of coordinate transformation relation of cubical robot. The correctness of the model is verified in theory by numerical simulation. The controller, designed based on reaction torque characteristic of inertia wheels, is used in balance control of the cubical robot. The effectiveness of the controller is verified again with the obtained expect effect. The dynamic model developed and controller can provide a base for further study of balance control of a cubical robot. © The Authors, published by EDP Sciences, 2017.

关键词： Robots

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Spatial-temporal correlation based fast intra mode decision for HEVC

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Journal of Information Hiding and Multimedia Signal Processing 2018年第5期9卷 1137-1147页

作者： Feng, Zeqi Liu, Pengyu Jia, Kebin Duan, Kun Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing10012 China Beijing Advanced Innovation Center for Future Internet Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China

In high efficiency video coding (HEVC), up to 35 intra prediction modes are adopted to improve encoding performance. However, large numbers of modes lead to increased coding complexity inevitably, which hinders the promotion of HEVC in real-time environment. To reduce the encoding complexity, a spatial-temporal correlation based fast intra mode decision algorithm is proposed in this paper. Herein, the rough mode decision (RMD) candidate list and the rate distortion optimization (RDO) candidate list are narrowed respectively by strong spatial correlation and temporal correlation between prediction units (PUs). For RMD, the proposed algorithm early determines the impossible range of the PU optimal mode in line with spatial correlation, which would narrow RMD candidate list and speed up RMD process. For RDO, the proposed algorithm tries to combine temporal correlation with intra coding to simplify RDO candidate list and save RDO time consuming. Experimental results demonstrate that the proposed algorithm yields average 28.64% encoding time reduction with 1.19% BDBR gain or 0.06dB BDPSNR loss compared with HM16.9. The proposed algorithm drives HEVC coding efficiency under real-time environments. © 2018, Ubiquitous International. All rights reserved.

关键词： Image coding

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Attributes guided feature learning for vehicle re-identification

arXiv

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arXiv 2019年

作者： Li, Hongchao Lin, Xianmin Zheng, Aihua Li, Chenglong Luo, Bin He, Ran Hussain, Amir School of Computer Science and Technology Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Edinburgh Napier University School of Computing EdinburghEH10 5DT United Kingdom

Vehicle Re-ID has recently attracted enthusiastic attention due to its potential applications in smart city and urban surveillance. However, it suffers from large intra-class variation caused by view variations and illumination changes, and inter-class similarity especially for different identities with a similar appearance. To handle these issues, in this paper, we propose a novel deep network architecture, which guided by meaningful attributes including camera views, vehicle types and colors for vehicle Re-ID. In particular, our network is end-to-end trained and contains three subnetworks of deep features embedded by the corresponding attributes. For network training, we annotate the view labels on the VeRi-776 dataset. Note that one can directly adopt the pre-trained view (as well as type and color) subnetwork on the other datasets with only ID information, which demonstrates the generalization of our model. Extensive experiments on the benchmark datasets VeRi-776 and VehicleID suggest that the proposed approach achieves the promising performance and yields to a new state-of-the-art for vehicle Re-ID. Copyright © 2019, The Authors. All rights reserved.

关键词： Vehicles

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CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI

arXiv

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arXiv 2024年

作者： Wang, Zi Wang, Fanwen Qin, Chen Lyu, Jun Ouyang, Cheng Wang, Shuo Li, Yan Yu, Mengyao Zhang, Haoyu Guo, Kunyuan Shi, Zhang Li, Qirong Xu, Ziqiang Zhang, Yajing Li, Hao Hua, Sha Chen, Binghua Sun, Longyu Sun, Mengting Li, Qin Chu, Ying-Hua Bai, Wenjia Qin, Jing Zhuang, Xiahai Prieto, Claudia Young, Alistair Markl, Michael Wang, He Wu, Lianming Yang, Guang Qu, Xiaobo Wang, Chengyan Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University Xiamen China Department of Bioengineering and Imperial-X Imperial College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital London United Kingdom Department of Electrical and Electronic Engineering & Imperial-X Imperial College London London United Kingdom Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computing Department of Brain Sciences Imperial College London London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Human Phenome Institute Fudan University Shanghai China Department of Radiology Zhongshan Hospital Fudan University Shanghai China School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China GE Healthcare Beijing China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Department of Radiology Ren Ji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China Siemens Healthineers Ltd. Shanghai China School of Nursing The Hong Kong Polytechnic University Hong Kong School of Data Science Fudan University Shanghai China School of Engineering Pontificia Universidad Católica de Chile Santiago Chile School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Millenium Institute for Intelligent Healthcare Engineering Santiago Chile Department of Radiology Feinberg School of Medicine Northwestern University Chicago Unit

The released CMRxRecon2024 dataset is currently the largest and most protocol-diverse publicly available k-space dataset including multi-modality and multi-view cardiac MRI data from 330 healthy volunteers, and each o... 详细信息

关键词： Adversarial machine learning

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Analysis of Longitudinal data using Varying-Coefficient Model 36

Analysis of Longitudinal data using Varying-Coefficient Mode...

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第36届中国控制会议

作者： Nan Yu Pu Wang Liying Fang Ministry of Information Technology Beijing University of Technology Beijing Laboratory For Urban Mass Transit Engineering Research Center of Digital Community Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781538629185

In this study,a model selection procedure for varying-coefficient model based on longitudinal data is proposed to distinguish three types of variables:variables not in the model,variables in the model with time-independent coefficients and variables in the model with time-varying *** identify these three kinds of variables simultaneously,we extend the present variable selection method from cross-sectional data to longitudinal *** method combines the B-spline function approximation and Adaptive-Lasso penalty to perform variable selection and do nonparametric estimation *** is illustrated with a set of simulation experiments,and results indicate the proposed variable selection procedure performs well in distinguishing the real type of independent variables.

关键词： varying-coefficient model variable selection longitudinal data B-spline Adaptive-Lasso

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