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arXiv 2025年

作者： Afzal, F. Akondi, C.S. Albrecht, M. Amaryan, M. Arrigo, S. Arroyave, V. Asaturyan, A. Austregesilo, A. Baldwin, Z. Barbosa, F. Barlow, J. Barriga, E. Barsotti, R. Barton, D. Baturin, V. Berdnikov, V.V. Black, T. Boeglin, W. Boer, M. Briscoe, W.J. Britton, T. Cao, S. Chudakov, E. Chung, G. Cole, P.L. Cortes, O. Crede, V. Dalton, M.M. Darulis, D. Deur, A. Dobbs, S. Dolgolenko, A. Dugger, M. Dzhygadlo, R. Ebersole, D. Edo, M. Egiyan, H. Erbora, T. Eugenio, P. Fabrizi, A. Fanelli, C. Fang, S. Fitches, J. Foda, A.M. Furletov, S. Gan, L. Gao, H. Gardner, A. Gasparian, A. Glazier, D.I. Gleason, C. Goryachev, V.S. Grube, B. Guo, J. Guo, L. Hernandez, J. Hernandez, K. Hoffman, N.D. Hornidge, D. Hou, G. Hurck, P. Hurley, A. Imoehl, W. Ireland, D.G. Ito, M.M. Jaegle, I. Jarvis, N.S. Jeske, T. Jing, M. Jones, R.T. Kakoyan, V. Kalicy, G. Khachatryan, V. Kourkoumelis, C. LaDuke, A. Larin, I. Lawrence, D. Lersch, D.I. Li, H. Liu, B. Livingston, K. Lolos, G.J. Lorenti, L. Lyubovitskij, V. Ma, R. Mahmood, A. Marukyan, H. Matveev, V. McCaughan, M. McCracken, M. Meyer, C.A. Miskimen, R. Mitchell, R.E. Mizutani, K. Moran, P. Neelamana, V. Ng, L. Nissen, E. Orešić, S. Ostrovidov, A.I. Papandreou, Z. Paudel, C. Pedroni, R. Pentchev, L. Peters, K.J. Prather, E. Veetil, L. Puthiya Rakshit, S. Reinhold, J. Remington, A. Ritchie, B.G. Ritman, J. Rodriguez, G. Romanov, D. Saldana, K. Salgado, C. Schadmand, S. Schertz, A.M. Scheuer, K. Schick, A. Schmidt, A. Schumacher, R.A. Schwiening, J. Scott, M. Septian, N. Sharp, P. Shen, X. Shepherd, M.R. Sikes, J. Smith, A. Smith, E.S. Sober, D.I. Somov, A. Somov, S. Stevens, J.R. Strakovsky, I.I. Sumner, B. Suresh, K. Tarasov, V.V. Taylor, S. Teymurazyan, A. Thiel, A. Viducic, T. Whitlatch, T. Wickramaarachchi, N. Wunderlich, Y. Yu, B. Zarling, J. Zhang, Z. Zhou, X. Zihlmann, B. Polytechnic Sciences and Mathematics School of Applied Sciences and Arts Arizona State University TempeAZ85287 United States Department of Physics National and Kapodistrian University of Athens Athens15771 Greece Ruhr-Universität-Bochum Institut für Experimentalphysik BochumD-44801 Germany Helmholtz-Institut für Strahlen- und Kernphysik Universität Bonn BonnD-53115 Germany Department of Physics Carnegie Mellon University PittsburghPA15213 United States Department of Physics The Catholic University of America WashingtonDC20064 United States Department of Physics University of Connecticut StorrsCT06269 United States Department of Physics Duke University DurhamNC27708 United States Department of Physics Florida International University MiamiFL33199 United States Department of Physics Florida State University TallahasseeFL32306 United States Department of Physics The George Washington University WashingtonDC20052 United States School of Physics and Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom GSI Helmholtzzentrum für Schwerionenforschung GmbH DarmstadtD-64291 Germany Institute of High Energy Physics Beijing100049 China Department of Physics Indiana University BloomingtonIN47405 United States National Research Centre Kurchatov Institute Moscow123182 Russia Department of Physics Lamar University BeaumontTX77710 United States Department of Physics University of Massachusetts AmherstMA01003 United States National Research Nuclear University Moscow Engineering Physics Institute Moscow115409 Russia Department of Physics Mount Allison University SackvilleNBE4L 1E6 Canada Department of Physics Norfolk State University NorfolkVA23504 United States Department of Physics North Carolina A&T State University GreensboroNC27411 United States Department of Physics and Physical Oceanography University of North Carolina at Wilmington WilmingtonNC28403 United States Department of Physics Old Dominion University Norf

We measure for the first time the differential photoproduction cross section dσ/dt of the a2(1320) meson at an average photon beam energy of 8.5 GeV, using data with an integrated luminosity of 104 pb−1 collected by the GlueX experiment. We fully reconstruct the γp → ηπ0p reaction and perform a partial-wave analysis in the a2(1320) mass region with amplitudes that incorporate the linear polarization of the beam. This allows us to separate for the first time the contributions of natural- and unnatural-parity exchanges. These measurements provide novel information about the photoproduction mechanism, which is critical for the search for spin-exotic states. Copyright © 2025, The Authors. All rights reserved.

关键词： Polarization

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Study on stability of roll over control of aircraft

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Journal of Physics: Conference Series 2019年第5期1176卷

作者： Liu, Jun Zhu, Chunyan Institute of Applied Mathematics Qujing Normal University Qujing China College of Computer Science and Engineering Qujing Normal University Qujing China

In this paper, studies the problem of rolling out of flight in air flight that may be out of control. First, the corresponding mathematical model is established through dynamic analysis. Secondly, the mathematical model is studied by using Lyapunov function method. Finally, the stability of the model is demonstrated in theory. The effectiveness and safety of the controller are designed.

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

引用

IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Impossible meet-in-the-middle fault analysis on the LED lightweight cipher in VANETs

引用

Science China(Information Sciences) 2018年第3期61卷 118-130页

作者： Wei LI Vincent RIJMEN Zhi TAO Qingju WANG Hua CHEN Yunwen LIU Chaoyun LI Ya LIU School of Computer Science and Technology Donghua University imec-COSIC KU Leuven Shanghai Key Laboratory of Integrate Administration Technologies for Information Security Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Applied Mathematics and Computer Science Technical University of Denmark Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences College of Science National University of Defense Technology Department of Computer Science and Engineering University of Shanghai for Science and Technology

With the expansion of wireless technology, vehicular ad-hoc networks(VANETs) are emerging as a promising approach for realizing smart cities and addressing many serious traffic problems, such as road safety, convenience, and efficiency. To avoid any possible rancorous attacks, employing lightweight ciphers is most effective for implementing encryption/decryption, message authentication, and digital signatures for the security of the VANETs. Light encryption device(LED) is a lightweight block cipher with two basic keysize variants: LED-64 and LED-128. Since its inception, many fault analysis techniques have focused on provoking faults in the last four rounds to derive the 64-bit and 128-bit secret keys. It is vital to investigate whether injecting faults into a prior round enables breakage of the LED. This study presents a novel impossible meet-in-the-middle fault analysis on a prior round. A detailed analysis of the expected number of faults is used to uniquely determine the secret key. It is based on the propagation of truncated differentials and is surprisingly reminiscent of the computation of the complexity of a rectangle attack. It shows that the impossible meet-in-the-middle fault analysis could successfully break the LED by fault injections.

关键词： VANETs LED lightweight cipher impossible meet-in-the-middle fault analysis

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Big-data clustering: K-Means or k-indicators?

arXiv

引用

arXiv 2019年

作者： Chen, Feiyu Yang, Yuchen Xu, Liwei Zhang, Taiping Zhang, Yin School of Big Data and Software Engineering Chongqing University Chongqing China Department of Computational and Applied Mathematics Rice University HoustonTX United States School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu Sichuan China College of Computer Science Chongqing University Chongqing China Institute for Data and Decision Analytics Chinese University of Hong Kong Shenzhen China

The K-means algorithm is arguably the most popular data clustering method, commonly applied to processed datasets in some "feature spaces", as is in spectral clustering. Highly sensitive to initializations, however, K-means encounters a scalability bottleneck with respect to the number of clusters K as this number grows in big data applications. In this work, we promote a closely related model called K-indicators model and construct an efficient, semi-convex-relaxation algorithm that requires no randomized initializations. We present extensive empirical results to show advantages of the new algorithm when K is large. In particular, using the new algorithm to start the K-means algorithm, without any replication, can significantly outperform the standard K-means with a large number of currently state-ofthe-A rt random replications. Copyright © 2019, The Authors. All rights reserved.

关键词： K-means clustering

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Stochastic LQ Problem with Delayed Control 36

Stochastic LQ Problem with Delayed Control

引用

第36届中国控制会议

作者： Yuan-Hua Ni Cedric Ka-Fai Yiu Huanshui Zhang Ji-Feng Zhang College of Computer and Control Engineering Nankai University Department of Applied Mathematics The Hong Kong Polytechnic University School of Control Science and Engineering Shandong University Academy of Mathematics and Systems Science Chinese Academy of Sciences

ISBN: (纸本)9781538629185

A discrete-time stochastic LQ problem with multiplicative noises and state transmission delay is studied in this paper,which does not require any definiteness constraint on the cost weighting matrices. Necessary and sufficient conditions are derived for the case with a fixed time-state initial pair. A set of coupled discrete-time Riccati-like equations can be derived to characterize the existence and the form of the delayed optimal control. Furthermore, the convexity of the cost functional is fully characterized via certain properties of the solution of the Riccati-like equations.

关键词： Indefinite stochastic linear-quadratic optimal control input delay forward-backward stochastic difference equation convexity

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Upper Limit on the Photoproduction Cross Section of the Spin-Exotic π1(1600)

arXiv

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arXiv 2024年

作者： Afzal, F. Akondi, C.S. Albrecht, M. Amaryan, M. Arrigo, S. Arroyave, V. Asaturyan, A. Austregesilo, A. Baldwin, Z. Barbosa, F. Barlow, J. Barriga, E. Barsotti, R. Barton, D. Baturin, V. Berdnikov, V.V. Black, T. Boeglin, W. Boer, M. Briscoe, W.J. Britton, T. Cao, S. Chudakov, E. Chung, G. Cole, P.L. Cortes, O. Crede, V. Dalton, M.M. Darulis, D. Deur, A. Dobbs, S. Dolgolenko, A. Dugger, M. Dzhygadlo, R. Ebersole, D. Edo, M. Egiyan, H. Erbora, T. Eugenio, P. Fabrizi, A. Fanelli, C. Fang, S. Fitches, J. Foda, A.M. Furletov, S. Gan, L. Gao, H. Gardner, A. Gasparian, A. Glazier, D. Gleason, C. Goryachev, V.S. Grube, B. Guo, J. Guo, L. Hernandez, J. Hernandez, K. Hoffman, N.D. Hornidge, D. Hou, G. Hurck, P. Hurley, A. Imoehl, W. Ireland, D.G. Ito, M.M. Jaegle, I. Jarvis, N.S. Jeske, T. Jing, M. Jones, R.T. Kakoyan, V. Kalicy, G. Khachatryan, V. Kourkoumelis, C. LaDuke, A. Larin, I. Lawrence, D. Lersch, D.I. Li, H. Liu, B. Livingston, K. Lolos, G.J. Lorenti, L. Lyubovitskij, V. Ma, R. Mack, D. Mahmood, A. Marukyan, H. Matveev, V. McCaughan, M. McCracken, M. Meyer, C.A. Miskimen, R. Mitchell, R.E. Mizutani, K. Neelamana, V. Ng, L. Nissen, E. Orešić, S. Ostrovidov, A.I. Papandreou, Z. Paudel, C. Pedroni, R. Pentchev, L. Peters, K.J. Prather, E. Rakshit, S. Reinhold, J. Remington, A. Ritchie, B.G. Ritman, J. Rodriguez, G. Romanov, D. Saldana, K. Salgado, C. Schadmand, S. Schertz, A.M. Scheuer, K. Schick, A. Schmidt, A. Schumacher, R.A. Schwiening, J. Septian, N. Sharp, P. Shen, X. Shepherd, M.R. Sikes, J. Smith, A. Smith, E.S. Sober, D.I. Somov, A. Somov, S. Stevens, J.R. Strakovsky, I.I. Sumner, B. Suresh, K. Tarasov, V.V. Taylor, S. Teymurazyan, A. Thiel, A. Viducic, T. Whitlatch, T. Wickramaarachchi, N. Wunderlich, Y. Yu, B. Zarling, J. Zhang, Z. Zhou, X. Zihlmann, B. Polytechnic Sciences and Mathematics School of Applied Sciences and Arts Arizona State University TempeAZ85287 United States Department of Physics National and Kapodistrian University of Athens Athens15771 Greece Ruhr-Universität-Bochum Institut für Experimentalphysik BochumD-44801 Germany Helmholtz-Institut für Strahlen- und Kernphysik Universität Bonn BonnD-53115 Germany Department of Physics Carnegie Mellon University PittsburghPA15213 United States Department of Physics The Catholic University of America WashingtonDC20064 United States Department of Physics University of Connecticut StorrsCT06269 United States Department of Physics Duke University DurhamNC27708 United States Department of Physics Florida International University MiamiFL33199 United States Department of Physics Florida State University TallahasseeFL32306 United States Department of Physics The George Washington University WashingtonDC20052 United States School of Physics and Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom GSI Helmholtzzentrum für Schwerionenforschung GmbH DarmstadtD-64291 Germany Institute of High Energy Physics Beijing100049 China Department of Physics Indiana University BloomingtonIN47405 United States National Research Centre Kurchatov Institute Moscow123182 Russia Department of Physics Lamar University BeaumontTX77710 United States Department of Physics University of Massachusetts AmherstMA01003 United States National Research Nuclear University Moscow Engineering Physics Institute Moscow115409 Russia Department of Physics Mount Allison University SackvilleNBE4L 1E6 Canada Department of Physics Norfolk State University NorfolkVA23504 United States Department of Physics North Carolina A&T State University GreensboroNC27411 United States Department of Physics and Physical Oceanography University of North Carolina at Wilmington WilmingtonNC28403 United States Department of Physics Old Dominion University No

The spin-exotic hybrid meson π1(1600) is predicted to have a large decay rate to the ωππ final state. Using 76.6 pb−1 of data collected with the GlueX detector, we measure the cross sections for the reactions γp → ωπ+π−p, γp → ωπ0π0p, and γp → ωπ−π0∆++ in the range Eγ = 8 − 10 GeV. Using isospin conservation, we set the first upper limits on the photoproduction cross sections of the π10(1600) and π1−(1600). We combine these limits with lattice calculations of decay widths and find that photoproduction of η′π is the most sensitive two-body system to search for the π1(1600). Copyright © 2024, The Authors. All rights reserved.

关键词： Bosons

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Measurement of Spin-Density Matrix Elements in ∆++(1232) photoproduction

arXiv

引用

arXiv 2024年

作者： Afzal, F. Akondi, C.S. Albrecht, M. Amaryan, M. Arrigo, S. Arroyave, V. Asaturyan, A. Austregesilo, A. Baldwin, Z. Barbosa, F. Barlow, J. Barriga, E. Barsotti, R. Barton, D. Baturin, V. Berdnikov, V.V. Black, T. Boeglin, W. Boer, M. Briscoe, W.J. Britton, T. Cao, S. Chudakov, E. Chung, G. Cole, P.L. Cortes, O. Crede, V. Dalton, M.M. Darulis, D. Deur, A. Dobbs, S. Dolgolenko, A. Dugger, M. Dzhygadlo, R. Ebersole, D. Edo, M. Egiyan, H. Erbora, T. Eugenio, P. Fabrizi, A. Fanelli, C. Fang, S. Fitches, J. Foda, A.M. Furletov, S. Gan, L. Gao, H. Gardner, A. Gasparian, A. Glazier, D.I. Gleason, C. Goryachev, V.S. Grube, B. Guo, J. Guo, L. Hernandez, J. Hernandez, K. Hoffman, N.D. Hornidge, D. Hou, G. Hurck, P. Hurley, A. Imoehl, W. Ireland, D.G. Ito, M.M. Jaegle, I. Jarvis, N.S. Jeske, T. Jing, M. Jones, R.T. Kakoyan, V. Kalicy, G. Khachatryan, V. Kourkoumelis, C. LaDuke, A. Larin, I. Lawrence, D. Lersch, D.I. Li, H. Liu, B. Livingston, K. Lolos, G.J. Lorenti, L. Lyubovitskij, V. Ma, R. Mack, D. Mahmood, A. Marukyan, H. Matveev, V. McCaughan, M. McCracken, M. Meyer, C.A. Miskimen, R. Mitchell, R.E. Mizutani, K. Neelamana, V. Ng, L. Nissen, E. Orešić, S. Ostrovidov, A.I. Papandreou, Z. Paudel, C. Pedroni, R. Pentchev, L. Peters, K.J. Prather, E. Rakshit, S. Reinhold, J. Remington, A. Ritchie, B.G. Ritman, J. Rodriguez, G. Romanov, D. Saldana, K. Salgado, C. Schadmand, S. Schertz, A.M. Scheuer, K. Schick, A. Schmidt, A. Schumacher, R.A. Schwiening, J. Septian, N. Sharp, P. Shen, X. Shepherd, M.R. Sikes, J. Smith, A. Smith, E.S. Sober, D.I. Somov, A. Somov, S. Stevens, J.R. Strakovsky, I.I. Sumner, B. Suresh, K. Tarasov, V.V. Taylor, S. Teymurazyan, A. Thiel, A. Viducic, T. Whitlatch, T. Wickramaarachchi, N. Wunderlich, Y. Yu, B. Zarling, J. Zhang, Z. Zhou, X. Zihlmann, B. Polytechnic Sciences and Mathematics School of Applied Sciences and Arts Arizona State University TempeAZ85287 United States Department of Physics National and Kapodistrian University of Athens Athens15771 Greece Ruhr-Universität-Bochum Institut für Experimentalphysik BochumD-44801 Germany Helmholtz-Institut für Strahlen- und Kernphysik Universität Bonn BonnD-53115 Germany Department of Physics Carnegie Mellon University PittsburghPA15213 United States Department of Physics The Catholic University of America WashingtonDC20064 United States Department of Physics University of Connecticut StorrsCT06269 United States Department of Physics Duke University DurhamNC27708 United States Department of Physics Florida International University MiamiFL33199 United States Department of Physics Florida State University TallahasseeFL32306 United States Department of Physics The George Washington University WashingtonDC20052 United States School of Physics and Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom GSI Helmholtzzentrum für Schwerionenforschung GmbH DarmstadtD-64291 Germany Institute of High Energy Physics Beijing100049 China Department of Physics Indiana University BloomingtonIN47405 United States National Research Centre Kurchatov Institute Moscow123182 Russia Department of Physics Lamar University BeaumontTX77710 United States Department of Physics University of Massachusetts AmherstMA01003 United States National Research Nuclear University Moscow Engineering Physics Institute Moscow115409 Russia Department of Physics Mount Allison University SackvilleNBE4L 1E6 Canada Department of Physics Norfolk State University NorfolkVA23504 United States Department of Physics North Carolina A&T State University GreensboroNC27411 United States Department of Physics and Physical Oceanography University of North Carolina at Wilmington Wilmington28403 United States Department of Physics Old Dominion University Norfo

We measure the spin-density matrix elements (SDMEs) of the ∆++(1232) in the photoproduction reaction γp → π−∆++(1232) with the GlueX experiment in Hall D at Jefferson Lab. The measurement uses a linearly-polarized photon beam with energies from 8.2 to 8.8 GeV and the statistical precision of the SDMEs exceeds the previous measurement by three orders of magnitude for the momentum transfer squared region below 1.4 GeV2. The data are sensitive to the previously undetermined relative sign between couplings in existing Regge-exchange models. Linear combinations of the extracted SDMEs allow for a decomposition into natural and unnatural-exchange amplitudes. We find that the unnatural exchange plays an important role in the low momentum transfer region. Copyright © 2024, The Authors. All rights reserved.

关键词： Momentum transfer

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Study on fault tolerance method in cloud platform based on workload consolidation model of virtual machine

引用

Journal of engineering Science and Technology Review 2017年第5期10卷 41-49页

作者： Li, Zhixin Liu, Lei Tong, Zeyu College of Computer Science and Technology Jilin University Changchun130012 China School of Computer Technology and Engineering Changchun Institute Of Technology Changchun130012 China Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore21218 United States

The fault tolerance method of virtual machines (VM) guarantees reliability to the service capability of cloud platforms. VM workloads are dynamic and uncertain, and thus, they affect the reliability and task processing capability of entire cloud platforms. In this study, a fault tolerance method based on the VM workload consolidation model was proposed to solve problems concerning the reliability and task processing capability of cloud platforms caused by VM workloads, thus improving the reliability of VMs and overall performance of cloud platforms. First, the method was analyzed on the basis of the distinct relationship of VM workload and VM reliability and task processing capability. Then, the workload state of VM was predicted and analyzed by linear regression using VM workload monitoring data, and the VM workload consolidation algorithm was constructed based on expected workload constraint and optimization of fault tolerance time. Finally, the fault tolerance method based on the VM workload consolidation model was compared with the Radom method and the Max method. Research results demonstrate the potential of the proposed method to improve VM reliability in cloud platforms by 20% and 47% compared with those for the Radom and Max methods, respectively. In the same workload phase, the task completion rate of the proposed method increased significantly (15% and 30%, and 22% and 30%), and the percentages were higher than those for the Radom and Max methods, respectively. Moreover, the proposed method shortened task response time. This study concludes that the workload consolidation of VMs can increase the reliability and task processing capability of VMs. This proposed method can provide technological support to the fault tolerance of VMs in cloud platforms. © 2017 Eastern Macedonia and Thrace Institute of Technology.

关键词： Virtual machine

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2022 Review of Data-Driven Plasma Science

arXiv

引用

arXiv 2022年

作者： Anirudh, Rushil Archibald, Rick Asif, M. Salman Becker, Markus M. Benkadda, Sadruddin Bremer, Peer-Timo Budé, Rick H.S. Chang, C.S. Chen, Lei Churchill, R.M. Citrin, Jonathan Gaffney, Jim A. Gainaru, Ana Gekelman, Walter Gibbs, Tom Hamaguchi, Satoshi Hill, Christian Humbird, Kelli Jalas, Sören Kawaguchi, Satoru Kim, Gon-Ho Kirchen, Manuel Klasky, Scott Kline, John L. Krushelnick, Karl Kustowski, Bogdan Lapenta, Giovanni Li, Wenting Ma, Tammy Mason, Nigel J. Mesbah, Ali Michoski, Craig Munson, Todd Murakami, Izumi Najm, Habib N. Erik J. Olofsson, K. Park, Seolhye Peterson, J. Luc Probst, Michael Pugmire, David Sammuli, Brian Sawlani, Kapil Scheinker, Alexander Schissel, David P. Shalloo, Rob J. Shinagawa, Jun Seong, Jaegu Spears, Brian K. Tennyson, Jonathan Thiagarajan, Jayaraman Ticoş, Catalin M. Trieschmann, Jan van Dijk, Jan Van Essen, Brian Ventzek, Peter Wang, Haimin Wang, Jason T.L. Wang, Zhehui Wende, Kristian Xu, Xueqiao Yamada, Hiroshi Yokoyama, Tatsuya Zhang, Xinhua Lawrence Livermore National Laboratory LivermoreCA94550 United States Oak Ridge National Laboratory Oak RidgeTN37830 United States University of California Riverside RiversideCA92521 United States Felix-Hausdorff-Str. 2 Greifswald17489 Germany Aix Marseille University CNRS PIIM UMR 7345 Marseille France Department of Applied Physics Eindhoven University of Technology Netherlands Princeton Plasma Physics Laboratory Princeton NJ08540 United States Institute of Ion Physics and Applied Physics University of Innsbruck Technikerstrasse 25 Innsbruck6020 Austria DIFFER - Dutch Institute for Fundamental Energy Research Eindhoven Netherlands Science and Technology of Nuclear Fusion Group Eindhoven University of Technology Eindhoven Netherlands Department of Physics and Astronomy University of California Los AngelesCA90095 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95051 United States Center for Atomic and Molecular Technologies Graduate School of Engineering Osaka University 2-1 Yamadaoka Suita Osaka Japan Department of Nuclear Sciences and Applications International Atomic Energy Agency Wagramer Strasse 5 PO Box 100 Vienna1400 Austria Center for Free-Electron Laser Science Department of Physics Universität Hamburg Luruper Chaussee 149 Hamburg22761 Germany Division of Information and Electronic Engineering Graduate School of Engineering Muroran Institute of Technology Hokkaido Muroran050-8585 Japan Seoul National University Seoul151-741 Korea Republic of Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Los Alamos National Laboratory Los AlamosNM87545 United States Center for Ultrafast Optical Science University of Michigan Ann ArborMI48109-2099 United States Department of Mathematics KULeuven University of Leuven Belgium Department of Physical Sciences The University of Kent CanterburyCT2 7NH United Kingdom Department of Chemical and Biomolecular Engineering University of California Berk

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma-processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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