检索结果-内蒙古大学图书馆

Simulation of electromagnetic radiation and scattering using a finite difference-time domain technique

computer applications in Engineering Education 1992年第1期1卷 45-45页

作者： Li, K. Tassoudji, M.A. Shin, R.T. Kong, J.A. Department of Electrical Engineering and Computer Science and Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 Kevin Li:received his BS and SM degrees (1990) in Electrical Engineering and EE (Electrical Engineer's) degree (1991) from the Massachusetts Institute of Technology where he is currently pursuing his PhD in Electrical Engineering. Since 1990 he has been the recipient of a United States Air Force Laboratory Graduate Fellowship. His main research interests include electromagnetic scattering radar cross section prediction and numerical techniques. Mr. Li is a member of Tau Beta Pi and a student member of IEEE. M. Ali Tassoudji:received his BS (1987) and MS (1989) degrees in Electrical Engineering from the University of Michigan Ann Arbor. Since 1989 he has been a research assistant at the Massachusetts Institute of Technology where he is currently working towards his PhD in Electrical Engineering. His research interests include electromagnetic scattering and propagation modeling of microwave circuits and numerical techniques. Mr. Tassoudji is a member of IEEE and Eta Kappa Nu. Robert T. Shin:received his SB (1977) SM (1980) and PhD (1984) in Electrical Engineering from the Massachusetts Institute of Technology. Since 1984 he has been a member of the Air Defense Techniques Group at MIT Lincoln Laboratory as a Research Staff member and as a Senior Staff member from 1989. His research interests are in the areas of electromagnetic wave scattering and propagation theoretical model development and data interpretation for microwave remote sensing. He is the coauthor ofTheory of Microwave Remote Sensing(Wiley 1985). Dr. Shin is a member of The Electromagnetics Academy IEEE American Geophysical Union Tau Beta Pi Eta Kappa Nu and Commission F of the International Union of Radio Science. Since 1987 he has served on the Editorial Board of theJournal of Electromagnetic Waves and Applications(JEWA). Jin Au Kong:is Professor of Electrical Enginee

The finite difference-time domain (FD-TD) technique is applied to the solution of Maxwell's equations. A computer program, which can be used to simulate and study numerous electromagnetic phenomena, is developed and implemented on an IBM 386 compatible personal computer. The FD-TD technique is a useful tool for students in electromagnetics. The technique is flexible and can be applied to many basic EM scattering and radiation problems. Because field solutions are found as a function of time, visualization of the propagation of the EM fields is possible. The FD-TD technique is implemented for a two-dimensional rectangular grid in conjunction with a second-order absorbing boundary condition. Both E- and H-field polarizations are analyzed. Finite objects consisting of dielectric, magnetic and conducting materials, and perfectly conducting infinite ground planes are modeled. Plane wave and line current sources are implemented. In addition to the capability of animating the propagation of the EM fields, radiation and scattering patterns can be generated.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Applying multimedia technology to undergraduate engineering laboratories

引用

computer applications in Engineering Education 1995年第3期3卷 157-157页

作者： Elsherbeni, A. Tew, M. Mokaddem, A. Electrical Engineering Department University of Mississippi University Mississippi 38677 Atef Z. Elsherbeni:received an honorary BS degree in Electronics and Communications an honorary BS degree in Applied Physics and a Meng degree in Electrical Engineering all from Cairo University Egypt in 1976 1979 and 1982 respectively and a PhD degree in Electrical Engineering from Manitoba University Canada in 1987. From January to August 1987 he was a postdoctoral fellow at the Electrical Engineering Department University of Manitoba. He joined the faculty at the University of Mississippi in 1987 as an assistant professor and advanced to the rank of associate professor in 1991. His professional interests include microstrip transmission lines and antennas scattering and diffraction numerical techniques and computer applications for electromagnetic education. Dr. Elsherbeni is a senior member of IEEE and a member of Commission B of the International Union of Radio Science (URSI). His honorary memberships include the Electromagnetics Academy and the Scientific Sigma Xi Society. Mark D. Tew:obtained his BS in Electrical Engineering with highest honors in 1971 and an MS in Electrical Engineering in 1973 from the University of Mississippi. He attended the University of Illinois at Urbana-Champaign as a TRW Doctoral Fellow and received the PhD in 1979. He joined the University of Mississippi in 1979 and was appointed associate professor in 1986. His interests lie in improving undergraduate laboratories through the incorporation of computer-aided data acquisition and introduction of multimedia. He is a senior member of IEEE and member of several honorary societies.

This article presents efforts to improve the presentation of material in high frequency, microwave, and electronics laboratories using multimedia technology. A crowded undergraduate curriculum leaves little time to dwell on difficult concepts in these areas of study, yet the laboratory experience is enhanced by greater understanding. A moderate or small-sized electrical engineering department is well suited for a one-on-one approach to teaching where close faculty—student interaction serves to fill many student needs. Even in this environment, better visualization tools may increase learning by reinforcing material introduced in the classroom. This project uses multimedia to enhance instruction and presentation in the high frequency, microwave, and electronics laboratories.

关键词：

来源：评论

学校读者我要写书评

暂无评论

First machine learning gravitational-wave search mock data challenge

引用

Physical Review D 2023年第2期107卷 023021-023021页

作者： Marlin B. Schäfer Ondřej Zelenka Alexander H. Nitz He Wang Shichao Wu Zong-Kuan Guo Zhoujian Cao Zhixiang Ren Paraskevi Nousi Nikolaos Stergioulas Panagiotis Iosif Alexandra E. Koloniari Anastasios Tefas Nikolaos Passalis Francesco Salemi Gabriele Vedovato Sergey Klimenko Tanmaya Mishra Bernd Brügmann Elena Cuoco E. A. Huerta Chris Messenger Frank Ohme Max-Planck-Institut für Gravitationsphysik Albert-Einstein-Institut D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany Friedrich-Schiller-Universität Jena D-07743 Jena Germany Michael Stifel Center Jena D-07743 Jena Germany CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China Department of Astronomy Beijing Normal University Beijing 100875 China Peng Cheng Laboratory Shenzhen 518055 China Department of Informatics Aristotle University of Thessaloniki GR-54124 Thessaloniki Greece Department of Physics Aristotle University of Thessaloniki GR-54124 Thessaloniki Greece GSI Helmholtz Center for Heavy Ion Research Planckstraße 1 64291 Darmstadt Germany Università di Trento Dipartimento di Fisica I-38123 Povo Trento Italy INFN Trento Institute for Fundamental Physics and Applications I-38123 Povo Trento Italy INFN Sezione di Padova I-35131 Padova Italy Department of Physics University of Florida PO Box 118440 Gainesville Florida 32611-8440 USA European Gravitational Observatory (EGO) I-56021 Cascina Pisa Italy Scuola Normale Superiore Piazza dei Cavalieri 7 I-56126 Pisa Italy INFN Sezione di Pisa Largo Bruno Pontecorvo 3 I-56127 Pisa Italy Data Science and Learning Division Argonne National Laboratory Lemont Illinois 60439 USA Department of Computer Science University of Chicago Chicago Illinois 60637 USA SUPA School of Physics and Astronomy University of Glasgow Glasgow G12 8QQ United Kingdom

We present the results of the first Machine Learning Gravitational-Wave Search Mock data Challenge. For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 s with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and run-time for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs ≥200 per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection.

关键词： Gravitational wave detection Gravitational waves Machine learning

来源：评论

学校读者我要写书评

暂无评论

Why Are There Six Degrees of Separation in a Social Network?

引用

Physical Review X 2023年第2期13卷 021032-021032页

作者： I. Samoylenko D. Aleja E. Primo K. Alfaro-Bittner E. Vasilyeva K. Kovalenko D. Musatov A. M. Raigorodskii R. Criado M. Romance D. Papo M. Perc B. Barzel S. Boccaletti Moscow Institute of Physics and Technology 9 Institutskiy Pereulok Dolgoprudny Moscow Region 141701 Russia National Research University Higher School of Economics 6 Ulitsa Usacheva Moscow 119048 Russia Universidad Rey Juan Carlos Calle Tulipán s/n 28933 Móstoles Madrid Spain Department of Internal Medicine University of Michigan Ann Arbor Michigan 48109 USA Laboratory of Mathematical Computation on Complex Networks and their Applications Universidad Rey Juan Carlos Calle Tulipán s/n Móstoles 28933 Madrid Spain P.N. Lebedev Physical Institute of the Russian Academy of Sciences 53 Leninsky Prospekt Moscow 119991 Russia Scuola Superiore Meridionale Largo S. Marcellino 10 80138 Napoli NA Italy Russian Academy of National Economy and Public Administration 84 Prospekt Vernadskogo Moscow 119606 Russia Caucasus Mathematical Center at Adyghe State University 208 Pervomayskaya Ulitsa Maykop Adygea 385000 Russia Moscow State University 1 Leninskie Gory Moscow 119991 Russia Institute of Mathematics and Computer Science Buryat State University 5 Ulitsa Ranzhurova Ulan-Ude Buryatia 670000 Russia Data Complex Networks and Cybersecurity Research Institute Universidad Rey Juan Carlos Plaza Manuel Becerra 14 28028 Madrid Spain Department of Neuroscience and Rehabilitation University of Ferrara Ferrara Italy Center for Translational Neurophysiology of Speech and Communication Fondazione Istituto Italiano di Tecnologia Ferrara Italy Faculty of Natural Sciences and Mathematics University of Maribor Koroška cesta 160 2000 Maribor Slovenia Department of Medical Research China Medical University Hospital China Medical University Taichung 404332 Taiwan Complexity Science Hub Vienna Josefstädterstraße 39 1080 Vienna Austria Alma Mater Europaea Slovenska ulica 17 2000 Maribor Slovenia Department of Physics Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul Republic of Korea Department of Mathematics Bar-Ilan University Ramat-Gan 52900 Israel The Gonda Multi

A wealth of evidence shows that real-world networks are endowed with the small-world property, i.e., that the maximal distance between any two of their nodes scales logarithmically rather than linearly with their size. In addition, most social networks are organized so that no individual is more than six connections apart from any other, an empirical regularity known as the six degrees of separation. Why social networks have this ultrasmall-world organization, whereby the graph’s diameter is independent of the network size over several orders of magnitude, is still unknown. We show that the “six degrees of separation” is the property featured by the equilibrium state of any network where individuals weigh between their aspiration to improve their centrality and the costs incurred in forming and maintaining connections. We show, moreover, that the emergence of such a regularity is compatible with all other features, such as clustering and scale-freeness, that normally characterize the structure of social networks. Thus, our results show how simple evolutionary rules of the kind traditionally associated with human cooperation and altruism can also account for the emergence of one of the most intriguing attributes of social networks.

关键词： Degree distributions Network evolution Real world networks Small-world networks

来源：评论

学校读者我要写书评

暂无评论

MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock data Challenge

arXiv

引用

arXiv 2022年

作者： Schäfer, Marlin Benedikt Zelenka, Ondřej Nitz, Alexander H. Wang, He Wu, Shichao Guo, Zong-Kuan Cao, Zhoujian Ren, Zhixiang Nousi, Paraskevi Stergioulas, Nikolaos Iosif, Panagiotis Koloniari, Alexandra E. Tefas, Anastasios Passalis, Nikolaos Salemi, Francesco Vedovato, Gabriele Klimenko, Sergey Mishra, Tanmaya Brügmann, Bernd Cuoco, Elena Huerta, E.A. Messenger, Chris Ohme, Frank Max-Planck-Institut für Gravitationsphysik Albert-Einstein-Institut HannoverD-30167 Germany Leibniz Universität Hannover HannoverD-30167 Germany Friedrich-Schiller-Universität Jena JenaD-07743 Germany Michael Stifel Center Jena JenaD-07743 Germany CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing100190 China Department of Astronomy Beijing Normal University Beijing100875 China Peng Cheng Laboratory Shenzhen518055 China Department of Informatics Aristotle University of Thessaloniki ThessalonikiGR-54124 Greece Department of Physics Aristotle University of Thessaloniki ThessalonikiGR-54124 Greece GSI Helmholtz Center for Heavy Ion Research Planckstraße 1 Darmstadt64291 Germany Università di Trento Dipartimento di Fisica Povo TrentoI-38123 Italy INFN Trento Institute for Fundamental Physics and Applications Povo TrentoI-38123 Italy INFN Sezione di Padova PadovaI-35131 Italy Department of Physics University of Florida PO Box 118440 GainesvilleFL32611-8440 United States Cascina PisaI-56021 Italy Scuola Normale Superiore Piazza dei Cavalieri 7 PisaI-56126 Italy INFN Sezione di Pisa Largo Bruno Pontecorvo 3 PisaI-56127 Italy Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States SUPA School of Physics and Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom

We present the results of the first Machine Learning Gravitational-Wave Search Mock data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 seconds with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and runtime for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs ≥ 200 per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Perspectives for self-driving labs in synthetic biology

arXiv

引用

arXiv 2022年

作者： Martin, Hector Garcia Radivojevic, Tijana Zucker, Jeremy Bouchard, Kristofer Sustarich, Jess Peisert, Sean Arnold, Dan Hillson, Nathan Babnigg, Gyorgy Marti, Jose Manuel Mungall, Christopher J. Beckham, Gregg T. Waldburger, Lucas Carothers, James Sundaram, ShivShankar Agarwal, Deb Simmons, Blake A. Backman, Tyler Banerjee, Deepanwita Tanjore, Deepti Ramakrishnan, Lavanya Singh, Anup Lawrence Berkeley National Laboratory Biological Systems and Engineering Division BerkeleyCA United States Department of Energy Agile BioFoundry EmeryvilleCA United States Joint BioEnergy Institute EmeryvilleCA United States BCAM Basque Center for Applied Mathematics Bilbao Spain Lawrence Berkeley National Laboratory Scientific Data Division BerkeleyCA United States Helen Wills Neuroscience Institute Redwood Center for Theoretical Neuroscience BerkeleyCA United States Lawrence Berkeley National Laboratory Energy Storage and Distributed Resources Division BerkeleyCA United States University of California Davis Department of Computer Science DavisCA United States Global Security Computing Applications Division Lawrence Livermore National Laboratory LivermoreCA United States Engineering Directorate Lawrence Livermore National Laboratory LivermoreCA United States Center for Bioengineering Lawrence Livermore National Laboratory LivermoreCA United States Department of Chemical Engineering Molecular Engineering & Sciences Institute Center for Synthetic Biology University of Washington SeattleWA United States Biosciences Division Argonne National Laboratory ArgonneIL United States Advanced Biofuels and Bioproducts Process Development Unit Lawrence Berkeley National Laboratory BerkeleyCA United States Biomaterials and Biomanufacturing Division Sandia National Laboratories LivermoreCA United States Earth and Biological Sciences Division Pacific Northwest National Laboratories RichlandWA United States Resources and Enabling Sciences Center National Renewable Energy Laboratory GoldenCO80401 United States Department of Bioengineering University of California BerkeleyCA United States

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology. © 2022, CC BY.

关键词： Synthetic biology

来源：评论

学校读者我要写书评

暂无评论

Machine Learning methods to estimate observational properties of galaxy clusters in large volume cosmological N-body simulations

arXiv

引用

arXiv 2022年

作者： de Andres, Daniel Yepes, Gustavo Sembolini, Federico Martínez-Muñoz, Gonzalo Cui, Weiguang Robledo, Francisco Chuang, Chia-Hsun Rasia, Elena Departamento de Física Teórica Universidad Autónoma de Madrid M-8 Cantoblanco Madrid28049 Spain Universidad Autónoma de Madrid Cantoblanco Madrid28049 Spain Equifax Ibérica Data & Analytics Paseo de la Castellana Madrid259D Spain Computer Science Department Escuela Politécnica Superior Universidad Autónoma de Madrid Cantoblanco28049 Spain Institute for Astronomy University of Edinburgh Royal Observatory EdinburghEH9 3HJ United Kingdom Departamento de Fundamentos del Análisis Económico II Universidad del País Vasco Euskal Herriko Unibertsitatea Barrio Sarriena s/n Bizkaia Leioa48940 Spain Laboratoire de Mathématiques et de leurs Applications Université de Pau et des Pays de l’Adour Avenue de l’Université BP 576 Pau64012 France Department of Physics and Astronomy University of Utah Salt Lake CityUT84112 United States Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall StanfordCA94305 United States INAF - Osservatorio Astronomico Trieste via Tiepolo 11 34123 Trieste34123 Italy Institute of Fundamental Physics of the Universe via Beirut 2 Grignano Trieste34151 Italy

In this paper we study the applicability of a set of supervised machine learning (ML) models specifically trained to infer observed related properties of the baryonic component (stars and gas) from a set of features of dark matter only cluster-size halos. The training set is built from The Three Hundred project which consists of a series of zoomed hydrodynamical simulations of cluster-size regions extracted from the 1 Gpc volume Multidark dark-matter only simulation (MDPL2). We use as target variables a set of baryonic properties for the intra cluster gas and stars derived from the hydrodynamical simulations and correlate them with the properties of the dark matter halos from the MDPL2 N-body simulation. The different ML models are trained from this database and subsequently used to infer the same baryonic properties for the whole range of cluster-size halos identified in the MDPL2. We also test the robustness of the predictions of the models against mass resolution of the dark matter halos and conclude that their inferred baryonic properties are rather insensitive to their DM properties which are resolved with almost an order of magnitude smaller number of particles. We conclude that the ML models presented in this paper can be used as an accurate and computationally efficient tool for populating cluster-size halos with observational related baryonic properties in large volume N-body simulations making them more valuable for comparison with full sky galaxy cluster surveys at different wavelengths. We make the best ML trained model publicly available. © 2022, CC BY.

关键词： Stars

来源：评论

学校读者我要写书评

暂无评论

Collaborative Experience between Scientific Software Projects using Agile Scrum Development

arXiv

引用

arXiv 2021年

作者： Baxter, A.L. BenZvi, S.Y. Bonivento, W. Brazier, A. Clark, M. Coleiro, A. Collom, D. Colomer-Molla, M. Cousins, Bryce Orellana, A. Delgado Dornic, D. Ekimtcov, V. ElSayed, S. Rosso, A. Gallo Godwin, P. Griswold, S. Habig, A. Hill, R. Horiuchi, S. Howell, D.A. Johnson, M.W.G. Jurić, M. Kneller, J.P. Kopec, A. Kopper, C. Kulikovskiy, V. Lamoureux, M. Lang, R.F. Li, S. Lincetto, M. Lindstrom, W. Linvill, M.W. McCully, C. Migenda, J. Milisavljevic, D. Nelson, S. Novoseltseva, R. O'Sullivan, E. Petravick, D. Pointon, B.W. Raj, N. Renshaw, A. Rumleskie, J. Sonley, T. Tapia, R. Tseng, J.C.L. Tunnell, C.D. Vannoye, G. Vigorito, C.F. Virtue, C.J. Weaver, C. Weil, K.E. Winslow, L. Wolski, R. Xu, X.J. Xu, Y. Department of Physics and Astronomy Purdue University West LafayetteIN47907 United States Department of Physics and Astronomy University of Rochester RochesterNY14627 United States INFN sezione di Cagliari Istituto Nazionale Complesso Universitario di Monserrato - S.P. per Sestu Km 0.700 Monserrato CagliariI-09042 Italy Center for Advanced Computing Cornell University IthacaNY14853 United States Université de Paris CNRS AstroParticule et Cosmologie ParisF-75013 France Las Cumbres Observatory GoletaCA93117 United States c/ Catedrático José Beltrán Paterna Valencia2 E-46980 Spain Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Department of Physics The Pennsylvania State University University ParkPA16802 United States Pontificia Universidad Catolica de Chile Región Metropolitana Santiago Chile Aix Marseille Univ CNRS IN2P3 CPPM Marseille France National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of California Santa BarbaraCA93106 United States Department of Physics Laurentian University SudburyONP3E 2C6 Canada Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Physics and Astronomy University of Minnesota Duluth DuluthMN55812 United States Center for Neutrino Physics Department of Physics Virginia Tech BlacksburgVA24061 United States University of California Santa BarbaraCA93106 United States DiRAC Institute and the Department of Astronomy University of Washington SeattleWA98195 United States Department of Physics NC State University RaleighNC27695 United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States INFN Sezione di Genova Via Dodecaneso 33 Genova16146 Italy INFN Sezione di Padova Università di Padova D

Developing sustainable software for the scientific community requires expertise in software engineering and domain science. This can be challenging due to the unique needs of scientific software, the insufficient resources for software engineering practices in the scientific community, and the complexity of developing for evolving scientific contexts. While open-source software can partially address these concerns, it can introduce complicating dependencies and delay development. These issues can be reduced if scientists and software developers collaborate. We present a case study wherein scientists from the SuperNova Early Warning System collaborated with software developers from the Scalable Cyberinfrastructure for Multi-Messenger Astrophysics project. The collaboration addressed the difficulties of open-source software development, but presented additional risks to each team. For the scientists, there was a concern of relying on external systems and lacking control in the development process. For the developers, there was a risk in supporting a user-group while maintaining core development. These issues were mitigated by creating a second Agile Scrum framework in parallel with the developers’ ongoing Agile Scrum process. This Agile collaboration promoted communication, ensured that the scientists had an active role in development, and allowed the developers to evaluate and implement the scientists’ software requirements. The collaboration provided benefits for each group: the scientists actuated their development by using an existing platform, and the developers utilized the scientists’ use-case to improve their systems. This case study suggests that scientists and software developers can avoid scientific computing issues by collaborating and that Agile Scrum methods can address emergent concerns. Copyright © 2021, The Authors. All rights reserved.

关键词： Open source software

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling

引用

NPJ digital medicine 2024年第1期7卷 240页

作者： Gregory Holste Mingquan Lin Ruiwen Zhou Fei Wang Lei Liu Qi Yan Sarah H Van Tassel Kyle Kovacs Emily Y Chew Zhiyong Lu Zhangyang Wang Yifan Peng Department of Population Health Sciences Weill Cornell Medicine New York NY USA. Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX USA. Department of Surgery University of Minnesota Minneapolis MN USA. Center for Biostatistics and Data Science Washington University School of Medicine St. Louis MO USA. Department of Obstetrics & Gynecology Columbia University Irving Medical Center New York NY USA. Israel Englander Department of Ophthalmology Weill Cornell Medicine New York NY USA. Division of Epidemiology and Clinical Applications National Eye Institute National Institutes of Health (NIH) Bethesda MD USA. National Center for Biotechnology Information (NCBI) National Library of Medicine (NLM) National Institutes of Health (NIH) Bethesda MD USA. Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX USA. atlaswang@utexas.edu. Department of Population Health Sciences Weill Cornell Medicine New York NY USA. yip4002@med.cornell.edu.

来源：评论

学校读者我要写书评

暂无评论

CLEER - AN AI-SYSTEM DEVELOPED TO ASSIST EQUIPMENT ARRANGEMENTS ON WARSHIPS

引用

NAVAL ENGINEERS JOURNAL 1989年第3期101卷 127-137页

作者： ZHOU, HH SILVERMAN, BG SIMKOL, J Dr. H. Harry Zhou:is a research professor at the Institute for Artificial Intelligence of The George Washington University. Dr. Zhou received his master's degree and Ph.D. in computer science from Vanderbilt University in 1984 and 1987 respectively. He did his dissertation in the fields of artificial intelligence analogical reasoning and machine learning. His research interests include: classifier systems genetic algorithms learning by analogy inductive learning adaptive expert systems automated knowledge acquisition and adaption. He is also interested in data base design programming languages mental modeling and software engineering. Dr. Barry G. Silverman:is director of the Institute for Artificial Intelligence and a professor at the Engineering Administration Department of The George Washington University. He is also president of IntelliTek Inc. an AI consulting firm. Dr. Silverman received the B.S.E. M.S.E. and Ph.D. degrees from the University of Pennsylvania. He has been a principal developer of four generic AI products as well as eight AI applications. Since 1979 he has written over 100 papers and reports on these AI efforts. Joel Simkol:is a research scientist currently engaged in designing expert system architectures to support electronic warfare vulnerability analyses threat assessments shipboard topside antenna arrangements and C3countermeasures employment. Mr. Simkol's work in applying expert systems technology to electromagnetic interference and to spectrum management has generated increased interest and participation from all branches of government agencies.

This paper describes a modularized AI system being built to help improve electromagnetic compatibility (EMC) among shipboard topside equipment and their associated systems. CLEER is intended to act as an easy to use integrator of existing expert knowledge and pre-existing data bases and large scale analytical models. Due to these interfaces; to the need for portability of the software; and to artificial intelligence related design requirements (such as the need for spatial reasoning, expert data base management, model base management, track-based reasoning, and analogical (similar ship) reasoning) it was realized that traditional expert system shells would be inappropriate, although relatively off-the-shelf AI technology could be incorporated. In the same vein, the rapid prototyping approach to expert system design and knowledge engineering was not pursued in favor of a rigorous systems engineering methodology. The critical design decisions affecting CLEER's development are summarized in this paper along with lessons learned to date all in terms of “how,” “why,” and “when” specific features are being developed.

关键词： Artificial Intelligence

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：