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IEEE Power and Energy Society General Meeting

作者： Matthew J. Reno Jeremiah Deboever Barry Mather Electric Power Systems Research Sandia National Laboratories School of Electrical and Computer Engineering Georgia Institute of Technology Power System Engineering Center National Renewable Energy Laboratory

ISBN: (纸本)9781538622131;9781538622124

Distribution system analysis with ever increasing numbers of distributed energy resources (DER) requires quasistatic time-series (QSTS) analysis to capture the time-varying and time-dependent aspects of the system. Previous literature has demonstrated the benefits of QSTS, but there is limited information available for the requirements and standards for performing QSTS simulations. This paper provides a novel analysis of the QSTS requirements for the input data time-resolution, the simulation time-step resolution, and the length of the simulation. Detailed simulations quantify the specific errors introduced by not performing yearlong high-resolution QSTS simulations.

关键词： Photovoltaic systems Switches Time series analysis Indexes Distributed power generation

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A bilinear identification-modeling framework from time domain data

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PAMM 2019年第1期19卷

作者： Dimitrios S. Karachalios Ion Victor Gosea Athanasios C. Antoulas Max Planck Institute for Dynamics of Complex Technical Systems Data-Driven System Reduction and Identification (DRI) group Sandtorstraße 1 Magdeburg 39106 Rice University Houston Electrical and Computer Engineering Department 6100 Main St. Houston TX 77005 Baylor College of Medicine 1 Baylor Plaza Houston TX 77030

An ever-increasing need for improving the accuracy includes more involved and detailed features, thus inevitably leading to larger-scale dynamical systems [1]. To overcome this problem, efficient finite methods heavily rely on model reduction. One of the main approaches to model reduction of both linear and nonlinear systems is by means of interpolation. The Loewner framework is a direct data-driven method able to identify and reduce models derived directly from measurements. For measured data in the frequency domain, the Loewner framework is well established in linear case [2] while it has already extended to nonlinear [6]. On the other hand in the case of time domain data, the Loewner framework was already applied for approximating linear models [3]. In this study, an algorithm which uses time domain data for nonlinear (bilinear) system reduction and identification is presented.

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Hybrid cryptography and steganography method to embed encrypted text message within image

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Journal of Physics: Conference Series 2019年第1期1339卷

作者： Khider Nassif Jassim Ahmed Khudhur Nsaif Asama Kuder Nseaf Al Hamidy Hazidar Bagus Priambodo Emil Naf'an Mardhiah Masril Inge Handriani Zico Pratama Putra Department of Statistics. Faculty of Management and Economics Wasit University Al-Kut Iraq Department of Electrical Electronic & Systems Engineering Faculty of Engineering & Built Environment Universiti Kebangsaan Malaysia Malaysia Institute of Visual Informatics (IVI) Universiti Kebangsaan Malaysia Malaysia Information System Faculty of Computer Science Universitas Mercu Buana Jl. Raya Meruya Selatan Kembangan Jakarta 11650 Universitas Putra Indonesia YPTK Padang 25221 Indonesia School of Electronic Engineering and Computer Science Queen Mary University of London

The businesses in various fields use the online communication application to gather their data and information with local and global sources. The gathered data may sensitive such as the financial and businesses development information. The hackers or online thief try to stole the valuable data i.e. credit card numbers. The organizations looking for secure online channels in order to transfer their data efficiently and avoid the data thieving. One of the most applicable methods that developed to secure the online transferred data is the cryptography which transfers the original data or information to encrypted formulation. Cryptography still has many drawbacks such as stole and decrypts the original texts using automatic decryption counter. The main aim of this research is to improve the cryptography securing level using supportive method which is Steganography. The Steganography is the processes of hide the data or information in media files such as video, images and audio files. There are four stages represent the methodology of this paper; (1) encrypt the original texts using RSA algorithm, (2) hide the encrypted texts in Image files, (3) extract the encrypted texts from Image files, and (4) decrypt the original texts using decryption key of RSA algorithm. It is expected to improve the security level of the online transferred textual data. The performance of the final results will be evaluated through compare the Image files quality before and after hide the data in these files. The quality of the original and stego Image files need to be same or near in order to maximize the difficulty of detect that there data hide in these files.

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Coarse-to-Fine Learning for Single-Image Super-Resolution

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IEEE Transactions on Neural Networks and Learning systems 2017年第5期28卷 1109-1122页

作者： Zhang, Kaibing Tao, Dacheng Gao, Xinbo Li, Xuelong Li, Jie School of Computer and Information Science Hubei Engineering University Xiaogan Hubei432000 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway Street UltimoNSW2007 Australia State Key Laboratory of Integrated Services Networks School of Electronic Engineering Xidian University Xi'an Shaanxi710071 China State Key Laboratory of Transient Optics and Photonics Xi'An Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an Shaanxi710119 China Video and Image Processing System Laboratory School of Electronic Engineering Xidian University Xi'an Shaanxi710071 China

This paper develops a coarse-to-fine framework for single-image super-resolution (SR) reconstruction. The coarse-to-fine approach achieves high-quality SR recovery based on the complementary properties of both example learning-and reconstruction-based algorithms: example learning-based SR approaches are useful for generating plausible details from external exemplars but poor at suppressing aliasing artifacts, while reconstruction-based SR methods are propitious for preserving sharp edges yet fail to generate fine details. In the coarse stage of the method, we use a set of simple yet effective mapping functions, learned via correlative neighbor regression of grouped low-resolution (LR) to high-resolution (HR) dictionary atoms, to synthesize an initial SR estimate with particularly low computational cost. In the fine stage, we devise an effective regularization term that seamlessly integrates the properties of local structural regularity, nonlocal self-similarity, and collaborative representation over relevant atoms in a learned HR dictionary, to further improve the visual quality of the initial SR estimation obtained in the coarse stage. The experimental results indicate that our method outperforms other state-of-the-art methods for producing high-quality images despite that both the initial SR estimation and the followed enhancement are cheap to implement. © 2012 IEEE.

关键词： Optical resolving power

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LifeTime and improving European healthcare through cell-based interceptive medicine (vol 587, pg 377, 2020)

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NATURE 2021年第7852期592卷 E8-E8页

作者： Rajewsky, Nikolaus Almouzni, Genevieve Gorski, Stanislaw A. Aerts, Stein Amit, Ido Bertero, Michela G. Bock, Christoph Bredenoord, Annelien L. Cavalli, Giacomo Chiocca, Susanna Clevers, Hans De Strooper, Bart Eggert, Angelika Ellenberg, Jan Fernandez, Xose M. Figlerowicz, Marek Gasser, Susan M. Hubner, Norbert Kjems, Jorgen Knoblich, Jurgen A. Krabbe, Grietje Lichter, Peter Linnarsson, Sten Marine, Jean-Christophe Marioni, John C. Marti-Renom, Marc A. Netea, Mihai G. Nickel, Dorthe Nollmann, Marcelo Novak, Halina R. Parkinson, Helen Piccolo, Stefano Pinheiro, Ines Pombo, Ana Popp, Christian Reik, Wolf Roman-Roman, Sergio Rosenstiel, Philip Schultze, Joachim L. Stegle, Oliver Tanay, Amos Testa, Giuseppe Thanos, Dimitris Theis, Fabian J. Torres-Padilla, Maria-Elena Valencia, Alfonso Vallot, Celine van Oudenaarden, Alexander Vidal, Marie Voet, Thierry Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine in the Helmholtz Association Berlin Germany Charité-Universitätsmedizin Berlin Germany Berlin Institute of Health (BIH) Berlin Germany German Center for Cardiovascular Research (DZHK) Partner Site Berlin Berlin Germany Institute for Biology Humboldt University of Berlin Berlin Germany Cardiovascular and Metabolic Sciences Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany Department of Pediatric Oncology/Hematology Charité-Universitätsmedizin Berlin Berlin Germany Institut Curie CNRS PSL Research University Sorbonne Université Nuclear Dynamics Unit Equipe Labellisée Ligue contre le cancer Paris France VIB Center for Brain and Disease Research Leuven Belgium Department of Human Genetics KU Leuven Leuven Belgium Department of Neurosciences KU Leuven Leuven Belgium UK Dementia Research Institute at UCL University College London London UK Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK Department of Immunology Weizmann Institute of Science Rehovot Israel Centre for Genomic Regulation (CRG) Barcelona Institute of Science and Technology Barcelona Spain CNAG-CRG Centre for Genomic Regulation Barcelona Institute of Science and Technology Barcelona Spain Universitat Pompeu Fabra Barcelona Spain ICREA Barcelona Spain CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Department of Laboratory Medicine Medical University of Vienna Vienna Austria Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases Vienna Austria Department of Medical Humanities Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht The Netherlands Institute of Human Genetics UMR 9002 CNRS and University of Montpellier Montpellier France Department of Experimental Oncology IEO European Institute of Oncology IRCCS Milan Italy Department of Oncology and Hemato

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International partnership for governing generative artificial intelligence models in medicine

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Nature medicine 2025年 1-4页

作者： Jasmine Chiat Ling Ong Yilin Ning Gary S Collins Danielle S Bitterman Ashley N Beecy Robert T Chang Alastair K Denniston Oscar Freyer Stephen Gilbert Anne de Hond Artuur M Leeuwenberg Liang Zhao John C W Lim Mingxuan Liu Xiaoxuan Liu Christopher A Longhurst Yian Ma Yue Qiu Rupa Sarkar Bin Sheng Kuldev Singh Iris Siu Kwan Tan Yih Chung Tham Arun J Thirunavukarasu Daniel Shu Wei Ting Silke Vogel Rui Zhang Jianfei Zhao Wendy W Chapman Nigam H Shah Karel G M Moons Tien Yin Wong Nan Liu Division of Pharmacy Singapore General Hospital Singapore Singapore. Duke-NUS AI + Medical Sciences Initiative Duke-NUS Medical School Singapore Singapore. Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore. UK EQUATOR Centre Centre for Statistics in Medicine University of Oxford Oxford UK. Artificial Intelligence in Medicine (AIM) Program Mass General Brigham Harvard Medical School Boston MA USA. Department of Radiation Oncology Brigham and Women's Hospital/Dana-Farber Cancer Institute Boston MA USA. Department of Medicine Division of Cardiology Weill Cornell Medicine New York NY USA. Information Technology Data Science New York-Presbyterian Hospital New York NY USA. Stanford University School of Medicine Palo Alto CA USA. Byers Eye Institute Stanford University Palo Alto CA USA. College of Medicine and Health University of Birmingham Birmingham UK. University Hospitals Birmingham NHS Foundation Trust Birmingham UK. Else Kröner Fresenius Center for Digital Health TUD Dresden University of Technology Dresden Germany. Julius Centre for Health Sciences and Primary Care University Medical Centre Utrecht Utrecht University Utrecht Netherlands. Department of Pharmacy University of California San Francisco San Francisco CA USA. Centre of Regulatory Excellence Duke-NUS Medical School Singapore Singapore. SingHealth Duke-NUS Global Health Institute Duke-NUS Medical School Singapore Singapore. Department of Medicine University of California San Diego La Jolla CA USA. Department of Pediatrics University of California San Diego La Jolla CA USA. Tsinghua Medicine Tsinghua University Beijing China. The Lancet Group London UK. Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China. MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China. Artificial

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Understanding and Predicting Trends in Urban Freight Transport

Understanding and Predicting Trends in Urban Freight Transpo...

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International Conference on Mobile Data Management

作者： Petar Mrazovic Bahaeddin Eravci Josep Lluis Larriba-Pey Hakan Ferhatosmanoglu Mihhail Matskin Dept. of Software and Computer Systems Royal Institute of Technology Stockholm Sweden Dept. of Computer Engineering Bilkent University Ankara Turkey Dept. of Computer Architecture Polytechnic University of Catalonia Barcelona Spain Max Planck Institute for Informatics Saarbruecken Germany

ISBN: (纸本)9781538639337

Among different components of urban mobility, urban freight transport is usually considered as the least sustainable. Limited traffic infrastructures and increasing demands in dense urban regions lead to frequent delivery runs with smaller freight vehicles. This increases the traffic in urban areas and has negative impacts upon the quality of life in urban populations. Data driven optimizations are essential to better utilize existing urban transport infrastructures and to reduce the negative effects of freight deliveries for the cities. However, there is limited work and data driven research on urban delivery areas and freight transportation networks. In this paper, we collect and analyse data on urban freight deliveries and parking areas towards an optimized urban freight transportation system. Using a new check-in based mobile parking system for freight vehicles, we aim to understand and optimize freight distribution processes. We explore the relationship between areas' availability patterns and underlying traffic behaviour in order to understand the trends in urban freight transport. By applying the detected patterns we predict the availabilities of loading/unloading areas, and thus open up new possibilities for delivery route planning and better managing of freight transport infrastructures.

关键词： Loading Urban areas Mobile communication Market research Mobile applications Sociology Statistics

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting system (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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Erratum to: Search for dark matter produced in association with a leptonically decaying Z boson in proton–proton collisions at s = 13 Te

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The European Physical Journal C 2021年第4期81卷 1-16页

作者： A. M. Sirunyan A. Tumasyan W. Adam T. Bergauer M. Dragicevic J. Erö A. Escalante Del Valle R. Frühwirth M. Jeitler N. Krammer L. Lechner D. Liko T. Madlener I. Mikulec F. M. Pitters N. Rad J. Schieck R. Schöfbeck M. Spanring S. Templ W. Waltenberger C.-E. Wulz M. Zarucki V. Chekhovsky A. Litomin V. Makarenko J. Suarez Gonzalez M. R. Darwish E. A. De Wolf D. Di Croce X. Janssen T. Kello A. Lelek M. Pieters H. Rejeb Sfar H. Van Haevermaet P. Van Mechelen S. Van Putte N. Van Remortel F. Blekman E. S. Bols S. S. Chhibra J. D’Hondt J. De Clercq D. Lontkovskyi S. Lowette I. Marchesini S. Moortgat A. Morton Q. Python S. Tavernier W. Van Doninck P. Van Mulders D. Beghin B. Bilin B. Clerbaux G. De Lentdecker B. Dorney L. Favart A. Grebenyuk A. K. Kalsi I. Makarenko L. Moureaux L. Pétré A. Popov N. Postiau E. Starling L. Thomas C. Vander Velde P. Vanlaer D. Vannerom L. Wezenbeek T. Cornelis D. Dobur M. Gruchala I. Khvastunov M. Niedziela C. Roskas K. Skovpen M. Tytgat W. Verbeke B. Vermassen M. Vit G. Bruno F. Bury C. Caputo P. David C. Delaere M. Delcourt I. S. Donertas A. Giammanco V. Lemaitre K. Mondal J. Prisciandaro A. Taliercio M. Teklishyn P. Vischia S. Wuyckens J. Zobec G. A. Alves C. Hensel A. Moraes W. L. Aldá Júnior E. Belchior Batista Das Chagas H. BRANDAO MALBOUISSON W. Carvalho J. Chinellato E. Coelho E. M. Da Costa G. G. Da Silveira D. De Jesus Damiao S. Fonseca De Souza J. Martins D. Matos Figueiredo M. Medina Jaime C. Mora Herrera L. Mundim H. Nogima P. Rebello Teles L. J. Sanchez Rosas A. Santoro S. M. Silva Do Amaral A. Sznajder M. Thiel F. Torres Da Silva De Araujo A. Vilela Pereira C. A. Bernardes L. Calligaris T. R. Fernandez Perez Tomei E. M. Gregores D. S. Lemos P. G. Mercadante S. F. Novaes Sandra S. Padula A. Aleksandrov G. Antchev I. Atanasov R. Hadjiiska P. Iaydjiev M. Misheva M. Rodozov M. Shopova G. Sultanov M. Bonchev A. Dimitrov T. Ivanov L. Litov B. Pavlov P. Petkov A. Petrov W. Fang Q. Guo H. Wang L. Yuan M. Ahmad Z. Hu Y. Wang E. Chapon G. M. Chen H. S. Chen M. Chen A. Kapoor D. Leggat H. Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Vienna University of Technology Vienna Austria Institute for Nuclear Problems Minsk Belarus Universiteit Antwerpen Antwerpen Belgium Department of Basic and Applied Sciences Faculty of Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Université Libre de Bruxelles Bruxelles Belgium Vrije Universiteit Brussel Brussel Belgium Ghent University Ghent Belgium IRFU CEA Université Paris-Saclay Gif-sur-Yvette France Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina Brazil Universidade Federal de Pelotas Pelotas Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan University Shanghai China Zhejiang University Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of NRC ‘K

A Correction to this paper has been published: https://***/10.1140/epjc/s10052-020-08739-5

关键词： Elementary Particles Quantum Field Theory Nuclear Physics Heavy Ions Hadrons Quantum Field Theories String Theory Measurement Science and Instrumentation Astronomy Astrophysics and Cosmology Nuclear Energy

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Towards an Algorithm and Communication Cost Model for the Parallel Particle Swarm Optimization

Towards an Algorithm and Communication Cost Model for the Pa...

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ARCS 2016;29th International Conference on architecture of Computing systems

作者： Romeo Shuka Sebastian Niemann Juergen Brehm Christian Mueller-Schloer Institute of Systems Engineering System and Computer Architecture Leibniz Universitat Hannover

The adaptation of sequential algorithms for High Performance Computing (HPC) systems is determined by a tradeoff between algorithmic effectiveness (software) and communication frequency (hardware) of the parallel implementation (efficiency). To get a better understanding of the correlation, we define simple models for both, software and hardware, in order to dynamically find the best mapping parameters for the execution of the algorithm on the parallel system. For the evaluation of our method we look at population-based algorithms like the Particle Swarm Optimization Algorithm (PSO) for the solution of optimization problems. Different goals like best quality of the solution of the optimization problem for a given execution time or best execution time to find the optimum are defined by the user. Our method enables us to find the best parameters for the mapping which then results in an efficient and effective parallel implementation to achieve the user-defined goals on a High Performance Computing Cluster (HPCC).

关键词： High Performance Computing Optimization algorithms Particle Swarm Optimization Particle swarm optimization High Performance Computing Optimization algorithms algorithms execution time parallel system computer hardware Parallel Lines

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