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The application of fuzzy pattern fusion based on competitive agglomeration in coal-fired boiler operation optimization 6th

The application of fuzzy pattern fusion based on competitive...

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6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015

作者： Jia, Jining Lu, Yongzai Chu, Jian Su, Hongye State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (纸本)9783319204680

In order to solve the coal-fired boiler operation optimization problem with multiple main controllable parameters, a fuzzy pattern fusion based on competitive agglomeration (FPF-CA) algorithm is developed and applied in this paper. A simplified mathematical model for coal-fired boiler systems is applied in terms of both historical operational and thermal efficiency data under different load conditions. The FPF-CA algorithm can be applied to perform information fusion in terms of combining fuzzy clusters of some quantitative attributes with generated quantitative association rules. The historical data collected from a 130/2.82-M circulating fluidized bed (CFB) boiler being installed in a production scale thermal power plant is used to specifically analyze, and the simulation experiment results show the application of FPF-CA algorithm is a decision oriented intelligent technology and may provide an efficient results for coalfired boiler operation optimization in thermal power plant. © Springer International Publishing Switzerland 2015.

关键词： Agglomeration

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Fuzzy clustering-based quantitative association rules mining in multidimensional data set 6th

Fuzzy clustering-based quantitative association rules mining...

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6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015

作者： Jia, Jining Lu, Yongzai Chu, Jian Su, Hongye State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (纸本)9783319204680

In order to solve the problem of mining quantitative association rules, an algorithm named Fuzzy Pattern Fusion based on Competitive Agglomeration (FPF-CA) is developed in this paper. The proposed algorithm is based on the superior functionalities of Fuzzy Pattern Fusion (FPF) for mining quantitative association rules and Competitive Agglomeration (CA) for finding the optimal number of clusters. The popular data set of UCI machine learning repository is used to demonstrate the feasibility of the FPF-CA algorithm. The simulation experiment results show that the proposed algorithm can efficiently mine quantitative association rules according to the actual data distribution. © Springer International Publishing Switzerland 2015.

关键词： Fuzzy clustering

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Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

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The Lancet Planetary Health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, Md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

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A Novel Tool Wear Measurement Method in Ultra-precision Raster Milling 4th

A Novel Tool Wear Measurement Method in Ultra-precision Rast...

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4th Asia Pacific Conference on Optics Manufacture, APCOM 2014

作者： Zhang, Guoqing To, Suet College of Mechatronics and Control Engineering Shenzhen University Shenzhen China State Key Laboratory of Ultra-precision Machining Technology Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Kowloon Hong Kong Shenzhen Research Institute of The Hong Kong Polytechnic University Shenzhen China

ISBN: (纸本)9783038356387

Tool wear measurement has drawn a significant of attention in the past decades. However, no research has been found on the investigation of tool wear measurement in ultra-precision raster milling (UPRM) process since it is a relative complex cutting process. In the present study, tool wear characteristics were identified by using cutting chip morphologies and a groove cutting. Tool wear investigation using cutting chips is effective because diamond tool wear characteristics can be directly imprinted on the cutting chip surface. Through the inspection of chip surfaces, the profile and location of the tool fracture can be identified. Also, through the groove cutting, the cutting edge retreat due to the tool flank wear can be identified. In this research, a mathematical model was established to calculate the tool retreat. The experimental result shows that the proposed tool wear investigation method is an effective method. © 2016 Trans Tech Publications Ltd, Switzerland.

关键词： Milling (machining)

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Push recovery for the standing under-actuated bipedal robot using the hip strategy

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Frontiers of Information technology & Electronic Engineering 2015年第7期16卷 579-593页

作者： Chao LI Rong XIONG Qiu-guo ZHU Jun WU, Ya-liang WANG Yi-ming HUANG State Key Laboratory of Industrial Control Technology & Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 China

This paper presents a control algorithm for push recovery, which particularly focuses on the hip strategy when an external disturbance is applied on the body of a standing under-actuated biped. By analyzing a simplified dynamic model of a bipedal robot in the stance phase, it is found that horizontal stability can be maintained with a suitably controlled torque applied at the hip. However, errors in the angle or angular velocity of body posture may appear, due to the dynamic coupling of the transla- tional and rotational motions. To solve this problem, different hip strategies are discussed for two cases when （1） external dis- turbance is applied on the center of mass （CoM） and （2） external torque is acting around the CoM, and a universal hip strategy is derived for most disturbances. Moreover, three torque primitives for the hip, depending on the type of disturbance, are designed to achieve translational and rotational balance recovery simultaneously. Compared with closed-loop control, the advantage of the open-loop methods of torque primitives lies in rapid response and reasonable performance. Finally, simulation studies of the push recovery of a bipedal robot are presented to demonstrate the effectiveness of the proposed methods.

关键词： Push recovery Balance control Bipedal robot Hip strategy

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Image Recognition for Steel Ball's Surface Quality Detecting Based on Kernel Extreme Learning Machine 34

Image Recognition for Steel Ball's Surface Quality Detecting...

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第三十四届中国控制会议

作者： LI Xiaodong MAO Weijie JIANG Wei School of Software Engineering Hangzhou Dianzi University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Yuquan Campus

ISBN: (纸本)9781467374439

Steel ball's surface detection plays a significant role in computer vision applications. An efficient and accurate defect detection approach is implemented in this paper. The defect region on steel ball's surface is achieved by region growing technique on steel ball's surface images. The defect region such as spot, cluster spots, scratch and strip defect is classified by kernel extreme learning machine. The experimental results are carried out by using steel ball's surface images and perfect performance is achieved by the proposed defect detection technique.

关键词： Kernel Extreme Learning Machine,Surface Quality Detecting,Steel Ball

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Spatial batch optimal design based on self-learning Gaussian process models for LPCVD processes

Spatial batch optimal design based on self-learning Gaussian...

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作者： Sun, Pei Xie, Lei Chen, Junghui State Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China

Low pressure chemical vapor deposition (LPCVD) is one of the most important processes during semiconductor manufacturing. However, the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process. Besides, due to the properties of this process, the reliability of the model must be taken into consideration when optimizing the MVs. In this work, an optimal design strategy based on the self-learning Gaussian process model (GPM) is proposed to control this kind of spatial batch process. The GPM is utilized as the internal model to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data. Unlike the conventional model based design, the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent. Besides, the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties. The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process. © 2015 Elsevier B.V.

关键词： Low pressure chemical vapor deposition

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Mining scheduling knowledge for job shop scheduling problem

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IFAC-PapersOnLine 2015年第3期28卷 800-805页

作者： Wang, C.L. Rong, G. Weng, W. Feng, Y.P. State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Graduate School of Information Production and Systems Waseda University 2-7 Hibikino Wakamatsu-ku Kitakyushu Fukuoka808-0135 Japan

The optimal or near-optimal schedules generated by traditional optimization or approximation methods for job shop scheduling problems (JSSP) contain valuable scheduling patterns about this kind of scheduling problems. These patterns could be used to improve the dispatching performance and provide insights into the corresponding scheduling problems. This paper uses timed Petri nets to describe the dispatching processes of the job shop scheduling scenarios. On this basis, a data mining based scheduling knowledge extraction framework is developed to mine the expected scheduling knowledge from the solutions generated by traditional optimization or approximation method for JSSP. Based on this, we show how to use the extracted knowledge as a new dispatching rule to generate complete schedules. A novel method is further developed to combine the extracted knowledge with traditional heuristics to construct new composite dispatching rules which could gain better performance. Besides, we propose a novel approach to utilize the extracted knowledge to improve a Petri net based branch and bound algorithm used in this paper. A series of experiments is carried out to evaluate the performance of the proposed methods. © 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Job shop scheduling Approximation theory Branch and bound method Data mining Heuristic methods Machine shops Petri nets Scheduling Approximation methods Branch and bound algorithms Composite dispatching rules Dispatching rules Job shop scheduling problems Scheduling knowledge Scheduling problem Timed Petri Net

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Modeling and optimization methods of integrated production planning for steel plate mill with flexible customization

Modeling and optimization methods of integrated production p...

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作者： Lu, Shan Su, Hongye Johnsson, Charlotta Wang, Yue Xie, Lei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Department of Automatic Control Lund University Lund Sweden

With diversified requirements and varying manufacturing environments, the optimal production planning for a steel mill becomes more flexible and complicated. The flexibility provides operators with auxiliary requirements through an implementable integrated production planning. In this paper, a mixed-integer nonlinear programming (MINLP) model is proposed for the optimal planning that incorporates various manufacturing constraints and flexibility in a steel plate mill. Furthermore, two solution strategies are developed to overcome the weakness in solving the MINLP problem directly. The first one is to transform the original MINLP formulation to an approximate mixed integer linear programming using a classic linearization method. The second one is to decompose the original model using a branch-and-bound based iterative method. Computational experiments on various instances are presented in terms of the effectiveness and applicability. The result shows that the second method performs better in computational efforts and solution accuracy. © 2015 The Chemical Industry and Engineering Society of China, and Chemical Industry Press. All rights reserved.

关键词： Iterative methods

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Spatial batch optimal design based on self-learning Gaussian process models for LPCVD processes

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Chinese Journal of Chemical Engineering 2015年第12期23卷 1958-1964页

作者：孙培谢磊陈荣辉 State Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University

Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor ***,the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch ***,due to the properties of this process,the reliability of the model must be taken into consideration when optimizing the *** this work,an optimal design strategy based on the self-learning Gaussian process model(GPM) is proposed to control this kind of spatial batch *** GPM is utilized as the internal model to predict the thicknesses of thin films on all spatial-distributed wafers using the limited *** the conventional model based design,the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent Besides,the GPM is also actively enhanced using as little data as possible based on the predictive *** effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.

关键词： Batchwise LPCVD Transport processes Spatial distribution Gaussian process model Optimal design

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