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Mediterranean Conference on Embedded Computing (MECO)

作者： Andrej Škraba Andrej Koložvari Davorin Kofjač Radovan Stojanović Vladimir Stanovov Eugene Semenkin Faculty of Organizational Sciences Cybernetics & Decision Support System Laboratory Kranj Slovenia Faculty of Electrical Engineering University of Montenegro Podgorica Montenegro Siberian State Aerospace University Computer Science and Telecommunications Institute Krasnoyarsk Russian Federation

The paper describes the development of the three prototypes which enable monitoring of heart pulse sensor data on the cloud. The prototypes are based on a) Bluetooth module, b) Low Energy Bluetooth module and c) ESP8266 Wi-Fi module. The client side implementation is developed by the application of JavaScript. *** was used on the server side with application of node-serialport library with Bluetooth modules. In the case where ESP8266 Wi-Fi module was used, the data was transmitted directly to the cloud. The three different prototypes were compared according to the power consumption and complexity of design. The code example is provided illustrating the approach to develop cloud based graphing interface for biomedical mobile monitoring.

关键词： Bluetooth Cloud computing Servers Hardware Monitoring Browsers Real-time systems

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Generalized interface automata with multicast synchronization

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Frontiers of computer science 2015年第1期9卷 1-14页

作者： Fei HE Xiaoyu SONG Ming GU Jiaguang SUN Tsinghua National Laboratory for Information Science and Technology (TNList) Tsinghua University Beijing 100084 China Key Laboratory for Information System Security Ministry of Education Beijing 100084 China School of Software Tsinghua University Beijing 100084 China Department of Electrical & Computer Engineering Portland State University Oregon 97207 USA

Interface automata are one of the prominent formalisms for specifying interface behaviors of componentbased systems. However, only one-to-one communication is allowed in the composition of interface automata. This paper presents multicast interface automata which generalize the classic interface automata and accommodate multicast communication mechanism. The multicast interface automata endorse both bottom-up and top-down design methodologies. Theoretical results on compatibility and refinement are established for incremental design and independent implementability.

关键词： interface automata multicast communication,component interaction verification

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

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Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin De Leener Maxime Descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René Labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp Deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van De Ville Marios C Yiannakas Kenneth A Weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

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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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Joint modeling of user check-in behaviors for point-of-interest recommendation 15

Joint modeling of user check-in behaviors for point-of-inter...

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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

作者： Yin, Hongzhi Zhou, Xiaofang Shao, Yingxia Wang, Hao Sadiq, Shazia University of Queensland School of Information Technology and Electrical Engineering QLD4072 Australia School of Computer Science and Technology Soochow University China School of EECS Peking University China Science and Technology on Integrated Information System Laboratory State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781450337946

Point-of-Interest (POI) recommendation has become an important means to help people discover attractive and interesting locations, especially when users travel out of town. However, extreme sparsity of user-POI matrix creates a severe challenge. To cope with this challenge, a growing line of research has exploited the temporal effect, geographical-social influence, content effect and word-of-mouth effect. However, current research lacks an integrated analysis of the joint effect of the above factors to deal with the issue of data-sparsity, especially in the out-of-town recommendation scenario which has been ignored by most existing work. In light of the above, we propose a joint probabilistic generative model to mimic user check-in behaviors in a process of decision making, which strategically integrates the above factors to effectively overcome the data sparsity, especially for out-of-town users. To demonstrate the applicability and flexibility of our model, we investigate how it supports two recommendation scenarios in a unified way, i.e., home-town recommendation and out-of-town recommendation. We conduct extensive experiments to evaluate the performance of our model on two real large-scale datasets in terms of both recommendation effectiveness and efficiency, and the experimental results show its superiority over other competitors. © 2015 ACM.

关键词： Location based services

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The 5th International Conference on Biomedical engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Gene expression imputation across multiple brain regions provides insights into schizophrenia risk (vol 51, pg 659, 2019)

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NATURE GENETICS 2019年第6期51卷 1068-1068页

作者： Huckins, Laura M. Dobbyn, Amanda Ruderfer, Douglas M. Hoffman, Gabriel Wang, Weiqing Pardinas, Antonio F. Rajagopal, Veera M. Als, Thomas D. Nguyen, Hoang T. Girdhar, Kiran Boocock, James Roussos, Panos Fromer, Menachem Kramer, Robin Domenici, Enrico Gamazon, Eric R. Purcell, Shaun Demontis, Ditte Borglum, Anders D. Walters, James T. R. O'Donovan, Michael C. Sullivan, Patrick Owen, Michael J. Devlin, Bernie Sieberts, Solveig K. Cox, Nancy J. Im, Hae Kyung Sklar, Pamela Stahl, Eli A. Pamela Sklar Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai New York NY USA Vanderbilt University Medical Center Nashville TN USA MRC Centre for Neuropsychiatric Genetics and Genomics Cardiff University Cardiff UK Department of Biomedicine Aarhus University Aarhus Denmark The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Denmark Center for Integrative Sequencing Aarhus University Aarhus Denmark Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Human Brain Collection Core National Institute of Mental Health Bethesda MD USA Laboratory of Neurogenomic Biomarkers Centre for Integrative Biology (CIBIO) University of Trento Trento Italy Clare Hall University of Cambridge Cambridge UK University of North Carolina at Chapel Hill Chapel Hill NC USA Karolinska Institutet Stockholm Sweden Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Systems Biology Sage Bionetworks Seattle WA USA Section of Genetic Medicine Department of Medicine University of Chicago Chicago IL USA Integrated Technology Research Laboratories Pharmaceutical Research Division Takeda Pharmaceutical Company Limited Fujisawa Japan Neuropsychiatry Section Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Neuropsychiatric Signaling Program Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Psychiatry JJ Peters Virginia Medical Center Bronx NY USA Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York NY USA Friedman Brain Institute

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

关键词： Gene expression Genome-wide association studies Schizophrenia Transcriptomics

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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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RETRACTED ARTICLE: Potential of support vector regression for solar radiation prediction in Nigeria

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Natural Hazards 2015年第2期77卷 1055-1068页

作者： Olatomiwa, Lanre Mekhilef, Saad Shamshirband, Shahaboddin Petkovic, Dalibor Power Electronics and Renewable Energy Research Laboratory (PEARL) Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur Malaysia Department of Electrical and Electronic Engineering Federal University of Technology Minna Nigeria Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department for Mechatronics and Control Faculty of Mechanical Engineering University of Niš Nis Serbia

In this paper, the accuracy of soft computing technique in solar radiation prediction based on series of measured meteorological data (monthly mean sunshine duration, monthly mean maximum and minimum temperature) taking from Iseyin meteorological station in Nigeria was examined. The process, which simulates the solar radiation with support vector regression (SVR), was constructed. The inputs were monthly mean maximum temperature (Tmax), monthly mean minimum temperature (Tmin) and monthly mean sunshine duration ( $$ \bar{n} $$ ). Polynomial and radial basis functions (RBF) are applied as the SVR kernel function to estimate solar radiation. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR with polynomial basis function compared to RBF. The SVR coefficient of determination R 2 with the polynomial function was 0.7395 and with the radial basis function, the R 2 was 0.5877.

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