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Hybrid Symbiotic Organisms Search for Optimal Fuzzified Joint Reconfiguration and Capacitor Placement in Electric Distribution systems

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INAE Letters 2017年第3期2卷 107-121页

作者： Mostafa Sedighizadeh Masoud Esmaili Amir Eisapour-Moarref Faculty of Electrical and Computer Engineering Shahid Beheshti University Tehran Iran Department of Electrical Engineering West Tehran Branch Islamic Azad University Tehran Iran Centre of Excellence for Power System Automation and Operation Iran University of Science and Technology Tehran Iran

Network reconfiguration and capacitor allocation are two valuable tools to optimally manage distribution systems. In the present work, an efficient algorithm is proposed for the joint problem of reconfiguration and capacitor placement in distribution systems. The multi-objective problem is formulated to minimize objective functions of total network power losses, bus voltage violation, and branch loading deviations subject to different technical and operational constraints. To enhance exploration ability, the Symbiotic Organisms Search algorithm is strengthened with Particle Swarm Optimization capabilities to develop the Hybrid Symbiotic Organisms Search algorithm, which is applied to solve the proposed problem. Objective functions are fuzzified and aggregated by a geometric mean operator to have the same scales. The proposed joint optimization problem is solved with the introduced Hybrid Symbiotic Organisms Search algorithm in two different case studies of balanced and unbalanced test systems. According to obtained results that are compared with some well-known evolutionary algorithms, the superiority of the Hybrid Symbiotic Organisms Search algorithm is confirmed in terms of solution optimality and convergence speed.

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HDLTex: Hierarchical Deep Learning for Text Classification

HDLTex: Hierarchical Deep Learning for Text Classification

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International Conference on Machine Learning and Applications (ICMLA)

作者： Kamran Kowsari Donald E. Brown Mojtaba Heidarysafa Kiana Jafari Meimandi Matthew S. Gerber Laura E. Barnes Department of Computer Science University of Virginia Charlottesville VA USA Data Science Institute University of Virginia Charlottesville VA USA Department of System and Information Engineering University of Virginia Charlottesville VA USA

Increasingly large document collections require improved information processing methods for searching, retrieving, and organizing text. Central to these information processing methods is document classification, which has become an important application for supervised learning. Recently the performance of traditional supervised classifiers has degraded as the number of documents has increased. This is because along with growth in the number of documents has come an increase in the number of categories. This paper approaches this problem differently from current document classification methods that view the problem as multi-class classification. Instead we perform hierarchical classification using an approach we call Hierarchical Deep Learning for Text classification (HDLTex). HDLTex employs stacks of deep learning architectures to provide specialized understanding at each level of the document hierarchy.

关键词： Machine learning Support vector machines computer architecture Kernel Mathematical model Recurrent neural networks

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Application of modified extended method in CREAM for safety inspector in coal mines

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IOP Conference Series: Earth and Environmental science 2018年第3期108卷

作者： Jinhe Wang Xiaohong Zhang Jianchao Zeng Division of Industrial and System Engineering Taiyuan University of Science & Technology Taiyuan 030024 China Economics & Management School Taiyuan University of Science & Technology Taiyuan 030024 China School of Computer Science and Control Engineering North University of China Taiyuan 030051 China

Safety inspector often performs duties in circumstances contributes to the oc currence of human failures. Therefore, the paper aims at quantifying human failure pro bability (HFP) of safety inspector during the coal mine operation with cognitive reliabi lity and error analysis method (CREAM). Whereas, some shortcomings of this approa ch that lacking considering the applicability of the common performance condition (C PC), and the subjective of evaluating CPC level which weaken the accuracy of the qua ntitative prediction results. A modified extended method in CREAM which is able to a ddress these difficulties with a CPC framework table is proposed, and the proposed me thodology is demonstrated by the virtue of a coal-mine accident example. The results a re expected to be useful in predicting HFP of safety inspector and contribute to the enh ancement of coal mine safety.

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Small P values may not yield robust findings:an example using REST-meta-PD

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science Bulletin 2021年第21期66卷 2148-2152,M0003页

作者： Xi-Ze Jia Na Zhao Hao-Ming Dong Jia-Wei Sun Marek Barton Roxana Burciu Nicolas Carrière Antonio Cerasa Bo-Yu Chen Jun Chen Stephen Coombes Luc Defebvre Christine Delmaire Kathy Dujardin Fabrizio Esposito Guo-Guang Fan Federica Di Nardo Yi-Xuan Feng Brett W.Fling Saurabh Garg Moran Gilat Martin Gorges Shu-Leong Ho Fay BHorak Xiao Hu Xiao-Fei Hu Biao Huang Pei-Yu Huang Ze-Juan Jia Christina Jones Jan Kassubek Lenka Krajcovicova Ajay Kurani Jing Li Qing Li Ai-Ping Liu Bo Liu Hu Liu Wei-Guo Liu Renaud Lopes Yu-Ting Lou Wei Luo Tara Madhyastha Ni-Ni Mao Grainne McAlonan Martin J.McKeown Shirley Pang Andrea Quattrone Irena Rektorova Alessia Sarica Hui-Fang Shang James M.Shine Priyank Shukla Tomas Slavicek Xiao-Peng Song Gioacchino Tedeschi Alessandro Tessitore David Vaillancourt Jian Wang Jue Wang Z.Jane Wang Lu-Qing Wei Xia Wu Xiao-Jun Xu Lei Yan Jing Yang Wan-Qun Yang Nai-Lin Yao De-Long Zhang Jiu-Quan Zhang Min-Ming Zhang Yan-Ling Zhang Cai-Hong Zhou Chao-Gan Yan Xi-Nian Zuo Mark Hallett Tao Wu Yu-Feng Zang Center for Cognition and Brain Disorders the Affiliated HospitalHangzhou Normal UniversityHangzhou 310015China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121China National Basic Science Data Center Beijing 100190China State Key Laboratory of Cognitive Neuroscience and Learning&McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China School of Information and Electronics Technology Jiamusi UniversityJiamusi 154007China Neuroscience Program Central European Institute of TechnologyCEITECMasaryk UniversityBrno 62500Czech Republic Department of Applied Physiology and Kinesiology University of FloridaGainesville FL 32611USA Univ.Lille InsermCHU LilleU1172-LilNCog-Lille Neuroscience&CognitionLille F-59000France The Institute for Biomedical Research and Innovation National Research CouncilMangone CS 87050Italy Department of Radiology The First Affiliated Hospital of China Medical UniversityShenyang 110001China Department of Radiology Guangdong Provincial Hospital of Chinese MedicineGuangzhou 510120China Department of Medicine Surgery and DentistryScuola Medica SalernitanaUniversity of SalernoFiscianoSA 132-84084Italy Department of Advanced Medical and Surgery Sciences University of Campania“Luigi Vanvitelli”Caserta 81100Italy Eye Center of the 2nd Affiliated Hospital Medical College of Zhejiang UniversityHangzhou 310020China Zhejiang Provincial Key Lab of Ophthalmology Hangzhou 310020China Department of Health and Exercise Science Colorado State UniversityFort Collins CO 80523USA Pacific Parkinson’s Research Centre University of British ColumbiaVancouver BC V6E 2M6Canada Department of Medicine(Neurology)University of British Columbia Vancouver BC V6T 1B7Canada Brain and Mind Center The University of SydneySydney NSW 2006Australia Department of Neurology Ulm UniversityUlm 89081Germany Division of Neurology Department of MedicineQueen Mary HospitalUniversity of Hong KongHong Kong 999077China Department of

Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].

关键词：帕金森病 precise correction

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BHEL Smart Wall Blowing system: New Product Development in Manufacturing Industry

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Vikalpa 2021年第4期45卷 240 - 249页

作者： Abhishek Kumar R. Dhanuskodi R. Kaliappan K. Nandakumar Abhishek Kumar teaches design philosophies at Anant National University Ahmedabad. He earned his Ph.D in Management from Pondicherry University. He is an Economics graduate from Calcutta University and MBA from BIM Trichy. He has published more than 20 articles in reputed international journals has authored two books written articles and columns for newspapers and is quoted on issues related to leadership and marketing by various media platforms. His research work comprises construction of brand personality scale for media aesthetics and phenomenological design. His recent publications are on philosophy of a photograph hermeneutic reality of product and on philosophy of intimate spaces. R. Dhanuskodi has nearly 40 years of R&D experience at BHEL India in technical areas applicable for thermal power plants. He is a life member of The Institution of Engineers (India) and The Combustion Institute. He has won two BHEL’s Excel awards under the best author category for technical papers. He has visited France Netherlands and Germany under Indo-Europe Clean Coal Development Program. He has guided 42 UG PG and PhD project works. He holds 11 patents 40 copyrights and 2 design registrations. He has presented papers in 20 conferences and published in 10 national and international journals. R. Kaliappan completed his bachelors in electrical and electronics engineering and Masters in Computer Science. He has 36 years of research experience in different fields of power generation and power plant subsystems such as heat transfer studies on boiler circulation efficiency improvements of boiler subsystems product improvements/ enhancements and setting up test facilities for research studies. He has published a number of technical papers on MHD power generation and heat transfer studies in various national and international journals. He has more than 25 patents and copyrights on products development related to power boilers. He has won BHEL’S gold medal for product development for Smart Wall Blowing system. K. Nandakumar

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Modeling and Analyzing Web Application with Aspect-Oriented Hierarchical Coloured Petri Nets

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China Communications 2016年第5期13卷 89-102页

作者： Xiaoxing Sun Huiqun Yu Honghao Liang Nianhua Yang Department of Computer Science and Engineering East China University of Science and Technology Department of System Technology East China University of Science and Technology Library Shanghai No NDA Smart Technology Co Ltd. School of Business Information Shanghai University of International Business and Economics

Aspect-oriented modeling can uncover potential design faults, yet most existing work fails to achieve both separation and composition in a natural and succinct way. This study presents an aspect-oriented modeling and analysis approach with hierarchical Coloured Petri Nets(HCPN). HCPN has sub-models and well-defined semantics combining a set of submodels. These two characteristics of HCPN are nicely integrated into aspect oriented modeling. Submodels are used to model aspects while the combination mechanism contributes to aspects weaving. Furthermore, the woven aspect oriented HCPN model can be simulated and analyzed by the CPN Tools. A systematic web application case study is conducted. The results show the system original properties are satisfied after weaving aspects and all design flaws are revealed. As such, the approach can support web application design and analysis in an aspect-oriented fashion concisely and effectively.

关键词： aspect-oriented modeling hierarchical coloured Petri net CPN Tools ASKCTL

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Corrigendum to “Fine-Grained Control-Flow Integrity Based on Points-to Analysis for CPS”

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Security and Communication Networks 2018年第1期2018卷

作者： Weizhong Qiang Shizhen Wang Hai Jin Jiangying Zhong Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 ***

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Hierarchical modeling of seed variety yields and decision making for future planting plans

arXiv

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arXiv 2017年

作者： Zhong, Huaiyang Li, Xiaocheng Lobell, David Ermon, Stefano Brandeau, Margaret L. Department of Management Science and Engineering Stanford University StanfordCA United States Department of Earth System Science Stanford University StanfordCA United States Computer Science Department Stanford University StanfordCA United States

Eradicating hunger and malnutrition is a key development goal of the 21st century. We address the problem of optimally identifying seed varieties to reliably increase crop yield within a risk-sensitive decision making framework. Specifically, we introduce a novel hierarchical machine learning mechanism for predicting crop yield (the yield of different seed varieties of the same crop). We integrate this prediction mechanism with a weather forecasting model, and propose three different approaches for decision making under uncertainty to select seed varieties for planting so as to balance yield maximization and risk. We apply our model to the problem of soybean variety selection given in the 2016 Syngenta Crop Challenge. Our prediction model achieves a median absolute error of 3.74 bushels per acre and thus provides good estimates for input into the decision models. Our decision models identify the selection of soybean varieties that appropriately balance yield and risk as a function of the farmer's risk aversion level. More generally, our models support farmers in decision making about which seed varieties to plant. Copyright © 2017, The Authors. All rights reserved.

关键词： Crops

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Human performance measures for the evaluation of nuclear power plant control room interfaces: A systematic review 10

Human performance measures for the evaluation of nuclear pow...

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10th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2017

作者： Xu, Jie Anders, Shilo Pruttianan, Arisa France, Daniel J. Lau, Nathan Adams, Julie A. Weinger, Matthew B. Center for Research and Innovation in System Safety Vanderbilt University Medical Center 1211 21st Avenue S. MAB Suite 732 NashvilleTN37212 United States Department of Industrial and Systems Engineering Virginia Tech 250 Durham Hall 1145 Perry Street BlacksburgVA24061 United States School of Electrical Engineering and Computer Science Oregon State University 1148 Kelley Engineering Center CorvallisOR97331 United States

ISBN: (纸本)9781510851160

The purpose of this study was to review the literature related to measuring human performance in the design and evaluation of the human-system interface (HSI) of nuclear power plant (NPP) control rooms and synthesize lessons learned and future directions for research and practice in this field. The design and evaluation of digital or hybrid HSI requires comprehensive measurement of human performance to ensure quality and safety of the nuclear power process control. Human factors evaluation frameworks have been proposed to guide the measurement of human performance constructs. However, there are few guides for selecting appropriate measures for those constructs. A systematic review of empirical research literature was conducted. Studies were identified through academic publication databases and grey literature searches. Eligible studies were coded and information regarding human performance measures was extracted and analyzed. A total of 80 studies met the criteria. Human performance measures extracted from the reviewed studies included measures of: behavioral performance;workload;situation awareness;human-human or human-machine cooperation;other cognitive performance indicators;and system performance. Behavioral performance (speed and accuracy), workload, and situation awareness were the most frequently measured constructs with validated and domain-specific instruments. The results of the literature review provided an overview of the human performance constructs and their corresponding measures for NPP control room HSI evaluation. These results can be used as an initial guide to establish appropriate and comprehensive measurements of human performance for such evaluations. Future directions in measure development are also discussed. © 10th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2017. All rights reserved.

关键词： Nuclear power plants

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Methane emissions from the Marcellus Shale in southwestern Pennsylvania and northern West Virginia based on airborne measurements

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Journal of Geophysical Research 2017年第8期122卷 4639-4653页

作者： Ren, Xinrong Hall, Dolly L. Vinciguerra, Timothy Benish, Sarah E. Stratton, Phillip R. Ahn, Doyeon Hansford, Jonathan R. Cohen, Mark D. Sahu, Sayantan He, Hao Grimes, Courtney Salawitch, Ross J. Ehrman, Sheryl H. Dickerson, Russell R. Department of Atmospheric and Oceanic Science University of Maryland College Park MD United States Air Resources Laboratory National Oceanic and Atmospheric Administration College Park MD United States Department of Chemical and Biomolecular Engineering University of Maryland College Park MD United States Department of Chemistry and Biochemistry University of Maryland College Park MD United States Department of Computer Science University of Maryland College Park MD United States Earth System Science Interdisciplinary Center University of Maryland College Park MD United States

Natural gas production in the U.S. has increased rapidly over the past decade, along with concerns about methane (CH4) leakage (total fugitive emissions), and climate impacts. Quantification of CH4 emissions from oil ... 详细信息

Natural gas production in the U.S. has increased rapidly over the past decade, along with concerns about methane (CH₄) leakage (total fugitive emissions), and climate impacts. Quantification of CH₄ emissions from oil and natural gas (O&NG) operations is important for establishing scientifically sound, cost-effective policies for mitigating greenhouse gases. We use aircraft measurements and a mass balance approach for three flight experiments in August and September 2015 to estimate CH₄ emissions from O&NG operations in the southwestern Marcellus Shale region. We estimate the mean ± 1σ CH₄ emission rate as 36.7 ± 1.9 kg CH₄ s^-1 (or 1.16 ± 0.06 Tg CH₄ yr^-1) with 59% coming from O&NG operations. We estimate the mean ± 1σ CH₄ leak rate from O&NG operations as 3.9 ± 0.4% with a lower limit of 1.5% and an upper limit of 6.3%. This leak rate is broadly consistent with the results from several recent top-down studies but higher than the results from a few other observational studies as well as in the U.S. Environmental Protection Agency CH₄ emission inventory. However, a substantial source of CH₄ was found to contain little ethane (C₂H₆), possibly due to coalbed CH₄ emitted either directly from coalmines or from wells drilled through coalbed layers. Although recent regulations requiring capture of gas from the completion venting step of the hydraulic fracturing appear to have reduced losses, our study suggests that for a 20 year time scale, energy derived from the combustion of natural gas extracted from this region will require further controls before it can exert a net climate benefit compared to coal. © 2017. American Geophysical Union.

关键词： methane emission Marcellus climate impact atmospheric composition atmospheric chemistry

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