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第35届中国控制会议

作者： Ling Wang Xi-yuan Xiao Jian-yao Meng School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education)

ISBN: (纸本)9781509009107

Air pollution is a growing problem arising from various activities and events. Monitoring and prediction of air pollution index remains an important public health concern. In this paper, FCM-HMM Multi-model is proposed to model the air pollution atmosphere system. First, FCM-HMM clustering is developed to mine the inherent states behind the meteorological observation sequence. Second, TS fuzzy inference multi-model is constructed for each state to predict the air pollution index. The experiment results proved the feasibility of the model on air pollution data in Beijing.

关键词： Hidden markov model Fuzzy c-means TS fuzzy inference multi-model Air pollution index(API)

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Monitoring of Multi-mode Industrial Process with Finite Gaussian Mixture Models

Monitoring of Multi-mode Industrial Process with Finite Gaus...

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第27届中国过程控制会议（CPCC2016）

作者： Kaixiang Peng Bingzheng Wang Liang Ma Mengyuan Wang Key Laboratory for Advanced Control of Iron and Steel Process of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology Beijing

In this paper,considering the multi-mode characteristics of industrial process data,a novel multi-mode process monitoring method integrating effectively Gaussian mixture model with total projection to latent structures is *** method aims to handle the problem about performance monitoring of multi-mode *** first,principal component clustering is implemented based on optimal Gaussian distribution and statistical parameters acquired by GMM ***,after building the monitoring covariance model,the multi-mode total projection to latent structures(MTPLS) model is established to describe the statistical properties of the process by using the information of ***,a comprehensive statistics index is defined which is based on Bayesian theory,according to posterior probability of the monitored samples belonging to each Gaussian *** method can ensure the implementation of on-line fault monitoring for multi-mode *** validity of the proposed method is verified by the simulation of TE process data in this paper.

关键词： Gaussian Monitoring of Multi-mode Industrial Process with Finite Gaussian Mixture Models

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Tracking of Stribeck Friction based on Second-Order Linear Extended State Observer

Tracking of Stribeck Friction based on Second-Order Linear E...

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第28届中国控制与决策会议

作者： Lijun Wang Qing Li Ruihua Jiao Yixin Yin Yinan Feng Yichang Liu School of Automation & Electrical Engineering University of Science and Technology Beijing Key Laboratory of Advanced Control of Iron and Steel Process(Ministry of Education) University of Science and Technology Beijing

ISBN: (纸本)9781467397155

To deal with the problem of observing or tracking for discontinuous signal,a kind of second order linear extended state observer(LESO) algorithm is *** ESO estimating the uncertain disturbance,a simple second-order linear ESO with less tunable parameters was designed in the absence of dynamic model,and the method of discrete time realization was *** proposed LESO was applied to the tracking of discontinuous Stribeck Friction in comparison with adaptive state *** results show that the proposed ESO is effective and practical.

关键词： Extended State Observer Adaptive State Observer Stribeck Friction Discontinuous Track

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Radar data processing of blast furnace stock-line based on spatio-temporal data association 34

Radar data processing of blast furnace stock-line based on s...

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34th Chinese control Conference, CCC 2015

作者： Qingwen, Hou Xianzhong, Chen Ping, Chen School of Automation and Electrical Engineering University of Science and Technology Beijing China Key Laboratory of Advanced Control for Iron and Steel Process Beijing China

ISBN: (纸本)9789881563897

A method of spatio-temporal data association processing based on FMCW radar in blast furnace was proposed in this paper. First, model-categories of basic stock-line profile through mining information of process of production are established. Next, predicting measured value on the basis of historical observations and associating with model category of basic stock-line shape-changing using grey relation algorithm, which acquires the expected distribution values. Finally, Welch FFT and peak-searching method of FT continuous spectrum are researched in the calculation of center distance. Simulation results indicate that the proposed method reduces the average rate of outliers from 13% to 4%, and the matching rate of fitting stock-line associating with prior model class library increases from 68% to 87%, compared with traditional interpolation fitting of direct measurement, which guides the operation of blast furnace charging reasonably. © 2015 Technical Committee on control Theory, Chinese Association of Automation.

关键词： data association FMCW radar prior information stock-line measurement

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Radar Data Processing of Blast Furnace Stock-Line Based on Spatio-Temporal Data Association

Radar Data Processing of Blast Furnace Stock-Line Based on S...

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第三十四届中国控制会议

作者： Hou Qingwen Chen Xianzhong Chen Ping School of Automation & Electrical Engineering University of Science and TechnologyBeijing Key Laboratory of Advanced Control for Iron and Steel Process

ISBN: (纸本)9781467374439

关键词： FMCW radar,stock-line measurement,data association,prior information

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Unit-level modelling for KPI of batch hot strip mill process using dynamic partial least squares

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IFAC-PapersOnLine 2015年第28期48卷 1005-1010页

作者： Zhang, Kai Shardt, Yuri A. W. Chen, Zhiwen Ding, Steven X. Peng, Kaixiang Institute for Automatic Control and Complex Systems University of Duisburg-Essen Bismarckstrasse 81 BB Duisburg47057 Germany Key Laboratory for Advanced Control of Iron and Steel Process School of Automation and Electrical Engineering University of Science and Technology of Beijing Beijing100083 China

This paper proposes two different approaches to the problem of modelling the key performance indicators (KPIs) in a hot strip mill process (HSMP). The first approach takes into consideration the inherent dynamics of the process and develops a nonlinear iterative dynamic partial least squares (PLS) model. The second approach takes into consideration the multibatch property of HSMP to develop an appropriate multibatch modelling framework. Both approaches can be integrated into the dynamic model framework. The proposed framework is examined using industrial data obtained from a steel plant, where it provides a decent model for this process. However, there exists the potential for improvements including dealing with interbatch variability. © 2015, IF AC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Strip mills Benchmarking Least squares approximations steelmaking Strip metal Waste disposal Hot strip mills Industrial datum key performance indicators Model framework Modelling framework Multibatch Partial least square (PLS) Partial least squares models

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Re. ``Discrete Representation of Non-dominated Sets in Multi-objective Linear Programming'' [European Journal of Operational Research 255 (2016) 687–698]

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European Journal of Operational Research 2017年第3期260卷 828-828页

作者： Lizhen Shao Matthias Ehrgott Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 10 0 083 China Department of Management Science Lancaster University Management School Bailrigg Lancaster LA1 4YX United Kingdom

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Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population

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Nature communications 2023年第1期14卷 3043页

作者： Jianxin Shi Kouya Shiraishi Jiyeon Choi Keitaro Matsuo Tzu-Yu Chen Juncheng Dai Rayjean J Hung Kexin Chen Xiao-Ou Shu Young Tae Kim Maria Teresa Landi Dongxin Lin Wei Zheng Zhihua Yin Baosen Zhou Bao Song Jiucun Wang Wei Jie Seow Lei Song I-Shou Chang Wei Hu Li-Hsin Chien Qiuyin Cai Yun-Chul Hong Hee Nam Kim Yi-Long Wu Maria Pik Wong Brian Douglas Richardson Karen M Funderburk Shilan Li Tongwu Zhang Charles Breeze Zhaoming Wang Batel Blechter Bryan A Bassig Jin Hee Kim Demetrius Albanes Jason Y Y Wong Min-Ho Shin Lap Ping Chung Yang Yang She-Juan An Hong Zheng Yasushi Yatabe Xu-Chao Zhang Young-Chul Kim Neil E Caporaso Jiang Chang James Chung Man Ho Michiaki Kubo Yataro Daigo Minsun Song Yukihide Momozawa Yoichiro Kamatani Masashi Kobayashi Kenichi Okubo Takayuki Honda Dean H Hosgood Hideo Kunitoh Harsh Patel Shun-Ichi Watanabe Yohei Miyagi Haruhiko Nakayama Shingo Matsumoto Hidehito Horinouchi Masahiro Tsuboi Ryuji Hamamoto Koichi Goto Yuichiro Ohe Atsushi Takahashi Akiteru Goto Yoshihiro Minamiya Megumi Hara Yuichiro Nishida Kenji Takeuchi Kenji Wakai Koichi Matsuda Yoshinori Murakami Kimihiro Shimizu Hiroyuki Suzuki Motonobu Saito Yoichi Ohtaki Kazumi Tanaka Tangchun Wu Fusheng Wei Hongji Dai Mitchell J Machiela Jian Su Yeul Hong Kim In-Jae Oh Victor Ho Fun Lee Gee-Chen Chang Ying-Huang Tsai Kuan-Yu Chen Ming-Shyan Huang Wu-Chou Su Yuh-Min Chen Adeline Seow Jae Yong Park Sun-Seog Kweon Kun-Chieh Chen Yu-Tang Gao Biyun Qian Chen Wu Daru Lu Jianjun Liu Ann G Schwartz Richard Houlston Margaret R Spitz Ivan P Gorlov Xifeng Wu Ping Yang Stephen Lam Adonina Tardon Chu Chen Stig E Bojesen Mattias Johansson Angela Risch Heike Bickeböller Bu-Tian Ji H-Erich Wichmann David C Christiani Gadi Rennert Susanne Arnold Paul Brennan James McKay John K Field Sanjay S Shete Loic Le Marchand Geoffrey Liu Angeline Andrew Lambertus A Kiemeney Shan Zienolddiny-Narui Kjell Grankvist Mikael Johansson Angela Cox Fiona Taylor Jian-Min Yuan Philip Lazarus Matthew B Schabath Melinda C Aldrich Hyo-Sung Jeon Shih Sheng Jiang Jae Sook Sung C Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA. jianxin.shi@nih.gov. Division of Genome Biology National Cancer Research Institute Tokyo Japan. Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA. Division of Cancer Epidemiology and Prevention Aichi Cancer Center Research Institute Nagoya Japan. Institute of Population Health Sciences National Health Research Institutes Zhunan Taiwan. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Jiangsu Key Lab of Cancer Biomarkers Prevention and Treatment Collaborative Innovation Center for Cancer Medicine Nanjing Medical University Nanjing China. Prosserman Centre for Population Health Research Lunenfeld-Tanenbaum Research Institute Sinai Health Toronto ON Canada. Department of Epidemiology and Biostatistics National Clinical Research Center for Cancer Key Laboratory of Molecular Cancer Epidemiology of Tianjin Tianjin Medical University Cancer Institute and Hospital Tianjin Medical University Tianjin China. Division of Epidemiology Department of Medicine Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center Nashville TN USA. Cancer Research Institute Seoul National University College of Medicine Seoul Republic of Korea. Department of Etiology & Carcinogenesis and State Key Laboratory of Molecular Oncology Cancer Institute and Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China. Department of Epidemiology School of Public Health China Medical University Shenyang China. Department of Clinical Epidemiology and Center of Evidence Based Medicine The First Hospital of China Medical University Shenyang China. Department of Oncology Shandong Cancer Hospital and Institute Shandong Academy of Medical Sciences Jinan China. Ministry of Education Key Laboratory of Contemporary Anthropology School of Life Sciences Fudan University Shanghai China. S

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (P = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications.

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Fuzzy Inference Algorithm based on Quantitative Association Rules

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Procedia Computer Science 2015年 61卷 388-394页

作者： Ling Wang Ji-Yuan Dong Shu-Lin Li School of Automation & Electrical Engineering University of Science and Technology Beijing 100083 China Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing 100083 China

In order to develop a data mining system to extract the fuzzy inference rules from the data, in this paper a fuzzy inference algorithm based on quantitative association rule (FI-QAR) is proposed. First, a discretization algorithm based on an improved clustering for each dimension data is adopted, and then the quantitative results are represented in the form of a Nominal variables matrix to compute the support and confidence level in the Apriori algorithm for quantitative association rules mining. On the basis of this, the quantitative association fuzzy rules are reconstructed by combing with TS fuzzy model to realize fuzzy inference, which can be applied to predict the output class and precise output. Experiment results demonstrated that the proposed algorithm is feasible and practical.

关键词： quantitative association rules apriori algorithm discretization TS fuzzy rules

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A high-order sliding mode stator flux observer for asynchronous motor based on feedback linearization

A high-order sliding mode stator flux observer for asynchron...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Yuedou Pan Tao Chen School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China

ISBN: (纸本)9781467376839

This paper proposes a high-order sliding mode stator flux estimation method for asynchronous motor based on feedback linearization. Selecting the rotor flux as the system's output, the coordinate change matrix and the nonlinear state feedback are derived based on the affine nonlinear model of asynchronous motors by using the nonlinear system's differential geometry theory. Then the asynchronous motor system is input-output linearized and the feedback linearization model of asynchronous motors is obtained. On this basis, a high-order sliding mode rotor flux observer for asynchronous motor is designed to estimate rotor flux, and this paper analysis the stability of the designed observer, then we can calculate stator flux by using the rotor flux. The designed observer is applied to Direct Torque control (DTC) of the asynchronous motor and achieves a good control performance. Simulation results verify the effectiveness and feasibility of the proposed method.

关键词： Stators Observers Rotors DC motors Nonlinear systems State feedback

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