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industrial Cyber-Physical systems (ICPS)

作者： Sirui Du Shumei Zhang Yang Zhao Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin China Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China

With the rapid development of space technology, the demand for satellite reliability is getting higher and higher. Momentum wheel is a key component of satellite attitude control system, and its reliability is an important factor affecting the life of satellite. The condition monitoring of momentum wheel bearing (MWB) is of great significance to ensure the long life and high reliable operation of the satellite. In this paper, a new monitoring method based on multivariate statistics and canonical variable analysis (CVA) is proposed, and a new health degree function is defined from both dynamic and static aspects. First, the time-frequency domain analysis technique is used to extract the features of MWB in time domain, frequency domain and time-frequency domain, and the multi-domain high-dimensional health condition feature set is constructed. Then, in order to reduce the complexity of the problem, feature reduction is realized based on CVA. On the basis of considering steady-state error and sliding interval variance (SIV), the health degree (HD) characterizing the performance condition of MWB is defined. Finally, the experimental results based on the bearing test-bed show that the proposed method is feasible and effective, and the data-driven health condition monitoring of satellite momentum wheel bearing is realized.

关键词： Condition monitoring Time-frequency analysis Analytical models Satellites Wheels Feature extraction Complexity theory

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An Early Fault Detection Method of the Induced Draft Fan Based on Long-Short Term Memory Network and Double Warning Thresholds

An Early Fault Detection Method of the Induced Draft Fan Bas...

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第34届中国控制与决策会议

作者： Wei Wei You Lv Yizhuo Liu Qing Li Shisen Xu Jun Li School of Control and Computer Engineering North China Electric Power University Key Laboratory of Power Station Energy Transfer Conversion and System North China Electric Power University Beijing Key Laboratory of New Technology and System on Measuring and Control for Industrial Process North China Electric Power University Huaneng Technology Innovation Center Xian Thermal Power Research Institute Company Limited

The induced draft(ID) fan is a significant piece of auxiliary equipment in thermal power *** early detection of possible faults in the ID fan may be used for effective predictive maintenance,thereby reducing the overall maintenance cost and improving the reliability and availability of thermal power *** paper proposed a genetic algorithm and long-short term memory network(GA-LSTM)-based condition monitoring approach using normal operation data of the ID ***,the LSTM network and the sliding window technique were introduced to construct double thresholds,which contains a dynamic threshold and a static *** GA-LSTM-double thresholds method was applied to a coal-fired power plant to achieve early fault detection and *** results demonstrates that the proposed method is capable of detecting minor anomalies in advance.

关键词： Early Fault Detection Long-Short Term Memory(LSTM) Genetic Algorithm(GA)

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An Early Fault Detection Method of the Induced Draft Fan Based on Long-Short Term Memory Network and Double Warning Thresholds

An Early Fault Detection Method of the Induced Draft Fan Bas...

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Chinese control and Decision Conference, CCDC

作者： Wei Wei You Lv Yizhuo Liu Qing Li Shisen Xu Jun Li School of Control and Computer Engineering North China Electric Power University Beijing P. R. China Key Laboratory of Power Station Energy Transfer Conversion and System North China Electric Power University Beijing P. R. China Beijing Key Laboratory of New Technology and System on Measuring and Control for Industrial Process North China Electric Power University Beijing P. R. China Huaneng Technology Innovation Center Beijing P. R. China Xian Thermal Power Research Institute Company Limited Xian P. R. China

ISBN: (纸本)9781665478977

The induced draft (ID) fan is a significant piece of auxiliary equipment in thermal power plants. An early detection of possible faults in the ID fan may be used for effective predictive maintenance, thereby reducing the overall maintenance cost and improving the reliability and availability of thermal power plants. This paper proposed a genetic algorithm and long-short term memory network (GA-LSTM)-based condition monitoring approach using normal operation data of the ID fan. Furthermore, the LSTM network and the sliding window technique were introduced to construct double thresholds, which contains a dynamic threshold and a static threshold. The GA-LSTM-double thresholds method was applied to a coal-fired power plant to achieve early fault detection and diagnosis. The results demonstrates that the proposed method is capable of detecting minor anomalies in advance.

关键词： Fans Fault detection Neurons Predictive models Indexes Reliability State estimation

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Improve the Conversion Rate and Selectivity of Aromatic Monomers in Lignin Photocatalytic Conversion Reaction Via Phase Junction Cadmium Sulphide Nanoparticles and External Hydrogen Supplier

SSRN

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SSRN 2023年

作者： Yue, Zongyang Shao, Shibo Yu, Jialin Lu, Guanchu Wei, Wenjing Huang, Yi Zhang, Kai Wang, Ke Fan, Xianfeng Institute for Materials and Processes School of Engineering The University of Edinburgh EdinburghEH9 3BF United Kingdom Petrochemical Research Institute PetroChina Company Limited Beijing102206 China Beijing Key Laboratory of Ionic Liquids Clean Process CAS Key Laboratory of Green Process and Engineering State Key Laboratory of Multiphase Complex Systems Innovation Academy for Green Manufacture Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China Longzihu New Energy Laboratory Zhengzhou Institute of Emerging Industrial Technology Henan University Zhengzhou450000 China Beijing Key Laboratory of Emission Surveillance and Control for Thermal Power Generation North China Electric Power University Beijing102206 China

Photocatalysis has the potential for lignin valorisation to generate functionalised aromatic monomers, but its large-scale application has been limited by the slow conversion rate and the low selectivity to desirable products. In this work, record-high lignin model compound Cβ-O bonds cleavage rate and excellent selectivity to aromatic monomers has been achieved over the development of new hexagonal-cubic phase junction CdS nanoparticles, enhanced external hydrogen donor supply and hydrogen transfer, and improved separation efficiency of photo-excited electron-hole pairs. Water is employed as external hydrogen supplier, resulting in significant increase in the conversion rate of 2-Phenoxy-1-phenylethanol (PP-ol) as lignin model from 21.8% to 100% and the selectivity of aromatic monomers from ~45% to ~94% under 1h of visible light irradiation. 2-phenoxyacetophenone (PP-one) as by-product from PP-ol conversion can be further completely cleaved to aromatic monomers with assistance of TEA and reaction time is reduced from 2-12 hrs to10 min under visible-light irradiation. © 2023, The Authors. All rights reserved.

关键词： Lignin

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Corrigendum to “Optimization control for the coordinated system of an ultra-supercritical unit based on stair-like predictive control algorithm” [control Eng. Pract. 82 (2019) 185–200]

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control Engineering Practice 2019年 83卷 213-213页

作者： Deliang Zeng Yaokui Gao Yong Hu Jizhen Liu State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Changping District 102206 Beijing China Key Laboratory of Measurement and Control New Technology and System for Industrial Process North China Electric Power University Changping District 102206 Beijing China

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Energy Consumption Data Prediction Analysis based on EEMD-ARMA Model

Energy Consumption Data Prediction Analysis based on EEMD-AR...

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IEEE International Conference on Mechatronics and Automation

作者： Ji Li Xudong Jiang Lei Shao Hongli Liu Chao Chen Gang Wang Dongdong Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Tianjin University of Technology Binshui Xidao 391 Tianjin China Tianjin Economic and Technological Development Zone Binhai New Area Continental Automotive (System) Tianjin Co. Ltd. Continental Automotive (System) Tianjin Co. Ltd No.2 Bohai Road Tianjin China China Academy of Industrial Internet Institute of Data management and application Beijing Block B East Lake International Center No. 6 Lize West Street Chaoyang District Beijing

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Energy consumption data prediction model based on EEMD-ARMA prediction model is used to predict the nonlinear and non-stationary characteristics of industrial energy consumption data, which leads to the difficulty of data prediction and low prediction accuracy. This paper improves empirical mode decomposition (empirical mode decomposition, EMD) by ensemble empirical mode decomposition (ensemble empirical mode decomposition, EEMD) and predicts energy consumption by combining auto regressive sliding average model (auto regressive and moving average model, ARMA). Finally, the historical data of a certain system is selected as a sample, and the comparative analysis of EMD-ARMA and EEMD-ARMA prediction results is used to verify that the EEMD-ARMA algorithm used is more accurate than the prediction algorithm, and its prediction results have certain reference value for the control of energy consumption data in enterprises.

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Implementing urban agriculture as nature-based solutions in China: Challenges and global lessons

Soil and Environmental Health

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Soil and Environmental Health 2024年第1期2卷

作者： Zhu, Zhe Chan, Faith Ka Shun Li, Gang Xu, Mengxia Feng, Meili Zhu, Yong-Guan Key Laboratory of Urban Environment and Health Institute of Urban Environment Chinese Academy of Sciences Xiamen 361021 China Department of Chemical and Environmental Engineering University of Nottingham Ningbo China Ningbo 315100 China Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control CAS Haixi Industrial Technology Innovation Center in Beilun Ningbo 315830 China School of Geographical Sciences University of Nottingham Ningbo China Ningbo 315100 China Water@Leeds School of Geography University of Leeds Leeds LS2 9JT United Kingdom Research Base for Shenzhen Municipal Policy & Development Southern University of Science and Technology Shenzhen 518000 China New Materials Institute University of Nottingham Ningbo China Ningbo 315100 China Key Laboratory for Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province University of Nottingham Ningbo China Ningbo 315100 China State Key Lab of Urban and Regional Ecology Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing 100085 China

Urban agriculture, the cultivation, processing and distribution of plants and animals within urban or suburban boundaries, is becoming an increasingly relevant strategy to enhance food safety for urban residents. With the rapid urbanization in the past decades, three forms of urban agriculture have emerged in China: small-scale, capital-intensive, and tourism-type. Each form presents unique benefits and challenges, and their development has been largely shaped by the local socio-economic context and government policies. The benefits of urban agriculture are numerous including the improvement of community cohesion, promotion of physical health, enhancement of environmental sustainability, and creation of new economic opportunities. However, it also faces many challenges such as land availability, capital shortage, market access, and governmental support. This review summarized the benefits of urban agriculture and its current limitations and challenges, and proposed strategies to overcome these hurdles in China based on successful examples in the Global North. Four specific recommendations were provided to promote urban agriculture in China: integration of urban agriculture into cities, solutions for land and resource use, government support and reactions, as well as planning and regulations. Adoption these recommendations is expected to result in healthier cities and a more robust agricultural system in China. © 2024 The Author(s)

关键词： Environmental health Environmental sustainability Food security Global North Green city One health Urban farming Urban planning

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Front Cover: Revealing the Roles of Guanidine Hydrochloride Ionic Liquid in Ion Inhibition and Defects Passivation for Efficient and Stable Perovskite Solar Cells (ChemSusChem 14/2024)

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ChemSusChem 2024年第14期17卷

作者： Aamir Saeed Dr. Liang Wang Zhaoyang Chen Junhui Fang Iqbal Hussain Lin Yuan Shuai Wang Jianwei Zhao Prof. Dr. Haitao Zhang Prof. Dr. Qingqing Miao CAS Key Laboratory of Green Process and Engineering Institute of Process Engineering Chinese Academy of Sciences Beijing 100190 P.R. China University of Chinese Academy of Sciences Beijing 100049 China Info-Powered Energy System Research Center (i-PERC) The University of Electro-Communications Tokyo 182-8585 Longzihu New Energy Laboratory Zhengzhou Institute of Emerging Industrial Technology Henan University Zhengzhou 450000 P. R. China Shenzhen Huasuan Technology Co. Ltd Shenzhen 518055 P.R. China Langfang Technological Centre of Green Industry Langfang 065001 P.R. China

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Correction to: 2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales

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Archives of virology 2021年第12期166卷 3567-3579页

作者： Jens H Kuhn Scott Adkins Bernard R Agwanda Rim Al Kubrusli Sergey V Alkhovsky Gaya K Amarasinghe Tatjana Avšič-Županc María A Ayllón Justin Bahl Anne Balkema-Buschmann Matthew J Ballinger Christopher F Basler Sina Bavari Martin Beer Nicolas Bejerman Andrew J Bennett Dennis A Bente Éric Bergeron Brian H Bird Carol D Blair Kim R Blasdell Dag-Ragnar Blystad Jamie Bojko Wayne B Borth Steven Bradfute Rachel Breyta Thomas Briese Paul A Brown Judith K Brown Ursula J Buchholz Michael J Buchmeier Alexander Bukreyev Felicity Burt Carmen Büttner Charles H Calisher Mengji Cao Inmaculada Casas Kartik Chandran Rémi N Charrel Qi Cheng Yuya Chiaki Marco Chiapello Il-Ryong Choi Marina Ciuffo J Christopher S Clegg Ian Crozier Elena Dal Bó Juan Carlos de la Torre Xavier de Lamballerie Rik L de Swart Humberto Debat Nolwenn M Dheilly Emiliano Di Cicco Nicholas Di Paola Francesco Di Serio Ralf G Dietzgen Michele Digiaro Olga Dolnik Michael A Drebot J Felix Drexler William G Dundon W Paul Duprex Ralf Dürrwald John M Dye Andrew J Easton Hideki Ebihara Toufic Elbeaino Koray Ergünay Hugh W Ferguson Anthony R Fooks Marco Forgia Pierre B H Formenty Jana Fránová Juliana Freitas-Astúa Jingjing Fu Stephanie Fürl Selma Gago-Zachert George Fú Gāo María Laura García Adolfo García-Sastre Aura R Garrison Thomas Gaskin Jean-Paul J Gonzalez Anthony Griffiths Tony L Goldberg Martin H Groschup Stephan Günther Roy A Hall John Hammond Tong Han Jussi Hepojoki Roger Hewson Jiang Hong Ni Hong Seiji Hongo Masayuki Horie John S Hu Tao Hu Holly R Hughes Florian Hüttner Timothy H Hyndman M Ilyas Risto Jalkanen Dàohóng Jiāng Gilda B Jonson Sandra Junglen Fujio Kadono Karia H Kaukinen Michael Kawate Boris Klempa Jonas Klingström Gary Kobinger Igor Koloniuk Hideki Kondō Eugene V Koonin Mart Krupovic Kenji Kubota Gael Kurath Lies Laenen Amy J Lambert Stanley L Langevin Benhur Lee Elliot J Lefkowitz Eric M Leroy Shaorong Li Longhui Li Jiànróng Lǐ Huazhen Liu Igor S Lukashevich Piet Maes William Marciel de Souza Marco Marklewitz Sergio H Marshall Shin-Yi L Marzano Se Integrated Research Facility at Fort Detrick National Institute of Allergy and Infectious Diseases National Institutes of Health Frederick MD USA. kuhnjens@mail.nih.gov. United States Department of Agriculture Agricultural Research Service US Horticultural Research Laboratory Fort Pierce FL USA. Zoology Department National Museums of Kenya Nairobi Kenya. Jomo Kenyatta University of Agriculture & Technology Nairobi Kenya. Division Phytomedicine Faculty of Life Sciences Humboldt-Universität zu Berlin Berlin Germany. D.I. Ivanovsky Institute of Virology of N.F. Gamaleya National Center on Epidemiology and Microbiology of Ministry of Health of Russian Federation Moscow Russia. Department of Pathology and Immunology Washington University School of Medicine St. Louis MO USA. Faculty of Medicine University of Ljubljana Ljubljana Slovenia. Centro de Biotecnología y Genómica de Plantas Universidad Politécnica de Madrid-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria Campus de Montegancedo Pozuelo de Alarcón Madrid Spain. Departamento de Biotecnología-Biología Vegetal Escuela Técnica Superior de Ingeniería Agronómica Alimentaria y de Biosistemas Universidad Politécnica de Madrid Madrid Spain. Center for Ecology of Infectious Diseases Department of Infectious Diseases Department of Epidemiology and Biostatistics Insitute of Bioinformatics University of Georgia Athens GA USA. Institute of Novel and Emerging Infectious Diseases Friedrich-Loeffler-Institut Federal Research Institute for Animal Health Greifswald-Insel Riems Germany. Department of Biological Sciences Mississippi State University Mississippi State MS USA. Center for Microbial Pathogenesis Institute for Biomedical Sciences Georgia State University Atlanta GA USA. Edge BioInnovation Consulting and Mgt Frederick MD USA. Institute of Diagnostic Virology Friedrich-Loeffler-Institut Greifswald-Insel Riems Germany. UFyMA INTA-CONICET Cordoba Argentina. Genomics and Bioinformatics D

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