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CEUR Workshop Proceedings 2022年 3091卷

作者： Kovalev, Igor Semenkin, Eugene Schwenker, Friedhelm Shaidurov, Vladimir Ganchev, Todor Škraba, Andrej Stanimirović, Predrag Kolehmainen, Mikko Kronberger, Gabriel Affenzeller, Michael Brester, Christina Ryzhikov, Ivan Kazakovtsev, Lev Sopov, Evgenii Semenkina, Maria Akhmedova, Shakhnaz Stanovov, Vladimir Krasnoyarsk Regional Science and Technology City Hall Department of Computer Science of Siberian Federal University Krasnoyarsk Russia Siberian Institute of Applied System Analysis named after A.N. Antamoshkin The Department ofsystem analysis and operations research of Reshetnev University Krasnoyarsk Russia Institute of Neural Information Processing Ulm University Ulm Germany The Institute of Computational Modeling SB RAS Krasnoyarsk Russia Technical University of Varna Varna Bulgaria University of Maribor UM Faculty of Organizational Sciences Cybernetics and Decision Support Systems Laboratory Slovenia Department of Computer Science Niš Serbia Department of Environmental and Biological Sciences Environmental Informatics University of Eastern Finland Kuopio Finland University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria University of Eastern Finland UEF · Department of Environmental Science Kuopio Finland The Department of Systems Analysis Reshetnev University of Science and Technology Krasnoyarsk Russia Siberian Institute of Applied System Analysis Krasnoyarsk Russia University of Applied Sciences Upper Austria and Softwarepark Hagenberg Austria Reshetnev Siberian States University of Science and Technology Siberian Institute of Applied System Analysis Krasnoyarsk Russia SibSAU · Institute of computer science and telecommunications Krasnoyarsk Russia

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The MR neuroimaging protocol for the Accelerating Medicines Partnership® Schizophrenia Program

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Schizophrenia 2025年第1期11卷 1-14页

作者： Harms, Michael P. Cho, Kang-Ik K. Anticevic, Alan Bolo, Nicolas R. Bouix, Sylvain Campbell, Dylan Cannon, Tyrone D. Cecchi, Guillermo Goncalves, Mathias Haidar, Anastasia Hughes, Dylan E. Izyurov, Igor John, Omar Kapur, Tina Kim, Nicholas Kotler, Elana Kubicki, Marek Kuperman, Joshua M. Laulette, Kristen Lindberg, Ulrich Markiewicz, Christopher Ning, Lipeng Poldrack, Russell A. Rathi, Yogesh Romo, Paul A. Tamayo, Zailyn Wannan, Cassandra Wickham, Alana Yassin, Walid Zhou, Juan Helen Addington, Jean Alameda, Luis Arango, Celso Breitborde, Nicholas J. K. Broome, Matthew R. Cadenhead, Kristin S. Calkins, Monica E. Chen, Eric Yu Hai Choi, Jimmy Conus, Philippe Corcoran, Cheryl M. Cornblatt, Barbara A. Diaz-Caneja, Covadonga M. Ellman, Lauren M. Fusar-Poli, Paolo Gaspar, Pablo A. Gerber, Carla Glenthøj, Louise Birkedal Horton, Leslie E. Hui, Christy Lai Ming Kambeitz, Joseph Kambeitz-Ilankovic, Lana Keshavan, Matcheri S. Kim, Sung-Wan Koutsouleris, Nikolaos Kwon, Jun Soo Langbein, Kerstin Mamah, Daniel Mathalon, Daniel H. Mittal, Vijay A. Nordentoft, Merete Pearlson, Godfrey D. Perez, Jesus Perkins, Diana O. Powers, Albert R. Rogers, Jack Sabb, Fred W. Schiffman, Jason Shah, Jai L. Silverstein, Steven M. Smesny, Stefan Stone, William S. Strauss, Gregory P. Thompson, Judy L. Upthegrove, Rachel Verma, Swapna K. Wang, Jijun Wolf, Daniel H. Kahn, Rene S. Kane, John M. McGorry, Patrick D. Nelson, Barnaby Woods, Scott W. Shenton, Martha E. Wood, Stephen J. Bearden, Carrie E. Pasternak, Ofer Department of Psychiatry Washington University School of Medicine St. Louis MO United States Department of Psychiatry Brigham and Women’s Hospital Harvard Medical School Boston MA United States Department of Psychiatry Yale University School of Medicine New Haven CT United States Department of Psychiatry Beth Israel Deaconess Medical Center Harvard Medical School Boston MA United States Department of Software Engineering and Information Technology École de technologie supérieure Montréal QC Canada Department of Psychology Yale University New Haven CT United States T.J. Watson Research Laboratory IBM Research Yorktown Heights NY United States Department of Psychology Stanford University Stanford CA United States Department of Psychology University of California Los Angeles CA United States Department of Psychiatry and Psychotherapy Jena University Hospital Jena Germany Massachusetts General Hospital Harvard Medical School Boston MA United States Department of Radiology Brigham and Women’s Hospital Harvard Medical School Boston MA United States Department of Radiology University of California San Diego CA United States Department of Clinical Physiology and Nuclear Medicine Copenhagen University Hospital - Rigshospitalet Glostrup Denmark Seaman Family MR Research Centre Calgary AB Canada Orygen Parkville VIC Australia Centre for Sleep and Cognition and Centre for Translational MR Research Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Department of Psychiatry Hotchkiss Brain Institute University of Calgary Calgary AB Canada General Psychiatry Service Treatment and Early Intervention in Psychosis Program Lausanne University Hospital and University of Lausanne Lausanne Switzerland Department of Child and Adolescent Psychiatry Institute of Psychiatry and Mental Health Hospital General Universitario Gregorio Marañón Instituto de Salud Carlos III School of Medicine Universidad Complutense Madrid Spain

Neuroimaging with MRI has been a frequent component of studies of individuals at clinical high risk (CHR) for developing psychosis, with goals of understanding potential brain regions and systems impacted in the CHR state and identifying prognostic or predictive biomarkers that can enhance our ability to forecast clinical outcomes. To date, most studies involving MRI in CHR are likely not sufficiently powered to generate robust and generalizable neuroimaging results. Here, we describe the prospective, advanced, and modern neuroimaging protocol that was implemented in a complex multi-site, multi-vendor environment, as part of the large-scale Accelerating Medicines Partnership® Schizophrenia Program (AMP® SCZ), including the rationale for various choices. This protocol includes T1- and T2-weighted structural scans, resting-state fMRI, and diffusion-weighted imaging collected at two time points, approximately 2 months apart. We also present preliminary variance component analyses of several measures, such as signal- and contrast-to-noise ratio (SNR/CNR) and spatial smoothness, to provide quantitative data on the relative percentages of participant, site, and platform (i.e., scanner model) variance. Site-related variance is generally small (typically <10%). For the SNR/CNR measures from the structural and fMRI scans, participant variance is the largest component (as desired;40–76%). However, for SNR/CNR in the diffusion scans, there is substantial platform-related variance (>55%) due to differences in the diffusion imaging hardware capabilities of the different scanners. Also, spatial smoothness generally has a large platform-related variance due to inherent, difficult to control, differences between vendors in their acquisitions and reconstructions. These results illustrate some of the factors that will need to be considered in analyses of the AMP SCZ neuroimaging data, which will be the largest CHR cohort to date. Watch Dr. Harms discuss this article at https://vimeo.

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Incorporating AEGIS autonomous science into mars science laboratory rover mission operations 15th

Incorporating AEGIS autonomous science into mars science lab...

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15th International Conference on Space Operations, SpaceOps 2018

作者： Francis, Raymond Estlin, Tara Doran, Gary Gaines, Daniel Johnstone, Stephen Montaño, Suzanne Peret, Laurent Mousset, Valérie Gasnault, Olivier Frydenvang, Jens Wiens, Roger C. Schaffer, Steven Pavri, Betina Verma, Vandana Chattopadhyay, Debarati Bornstein, Benjamin Mittal, Nimisha Deflores, Lauren Jet Propulsion Laboratory Machine Learning and Instrument Autonomy Research Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Artificial Intelligence Research Group California Institute of Technology PasadenaCA91109 United States Intelligence and Space Research Division Los Alamos National Laboratory Los AlamosNM87545 United States Telespazio France FIMOC Toulouse France FIMOC Centre National d’Études Spatiales Toulouse France FIMOC Institut de Recherche en Astrophysique et Planétologie Université de Toulouse CNRS CNES UPS France Natural History Museum of Denmark University of Copenhagen Copenhagen Denmark Los Alamos National Laboratory Intelligence and Space Research Division Los AlamosNM87545 United States Jet Propulsion Laboratory Artificial Intelligence Research Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Instrument Operations Engineering Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Robot Operations Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Planning and Sequencing Systems Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Electronics and Software Section California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Science Planning Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Instrument System Engineering Group California Institute of Technology PasadenaCA91109 United States

ISBN: (数字)9781624105623

ISBN: (纸本)9781624105623

The AEGIS (Autonomous Exploration for Gathering Increased Science) intelligent targeting software system has been in use on the Mars Science laboratory (MSL) mission since 2016. The system allows on-board autonomous selection of targets for the ChemCam remote geochemistry instrument based on analysis of images taken by the rover. This paper describes the deployment of AEGIS to MSL and the operational use of the system since rollout to science operations in May of 2016. We describe how MSL Science Operations have adapted to include autonomous target selection, both in procedures and in exploration strategies, and how mission planners and scientists have used AEGIS autonomy to enhance their work on the MSL mission. © 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Rovers

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A three-way incremental-learning algorithm for radar emitter identification

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Frontiers of Computer Science 2016年第4期10卷 673-688页

作者： Xin XU Wei WANG Jianhong WANG Science and Technology on Information System Engineering Laboratory Nanjing Research Institute of Electronic Engineering (NRIEE) Nanjing 210007 China State Key Laboratory for Novel Software and Technology Nanjing University Nanjing 210093 China

Radar emitter identification has been recognized as an indispensable task for electronic intelligence system. With the increasingly accumulated radar emitter intelligence and information, one key issue is to rebuild the radar emitter classifier efficiently with the newly-arrived information. Although existing incremental learning algorithms are superior in saving significant computational cost by incremental learning on continuously increasing training samples, they are not adaptable enough yet when emitter types , features and sampies are increasing dramatically. For instance, the intra-pulse characters of emitter signals could be fitrther extracted and thus expand the feature dimension. The; same goes for the radar emitter type dimension when samples from new radar emitter types are gathered. In addition, existing incremental classifiers are still problematic in terms of computational cost, sensitivity to data input order, and difficulty in multiemitter type identification. To address the above problems, we bring forward a three-way incremental learning algorithm （TILA） for radar emitter identification which is adaptable for the increase in emitter features, types and. samples.

关键词： radar emitter identification incremental learning classification data mining

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A Test Language for Avionics system

A Test Language for Avionics System

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2017 IEEE 8th International Conference on software engineering and Service Science

作者： Yanfang Liu Jianghua Lv Wei Wang Tao Li Shilong Ma Department of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment System Engineering Research Institute of China State Shipbuilding Corporation

In this paper, we proposed an automated test framework, logical test device which makes complex avionics systems transparent and jump machines which ensure data security of SUT(system Under Test). Based on this test framework, we mainly proposed a universal test language for avionics systems. The key feature of our test language is to introduce device type data and device collaboration operations which support automatic collaboration between logical test device and jump machines, meanwhile make test language more universal. This test language has been applied to the actual avionics system testing in China Academy of Electronics and Information Technology. Relative to avionics system testing before automation, our test language changes work pattern of avionics system testing and improves the test efficiency.

关键词： Avionics system test framework test language automated testing device collaboration

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Educational modeling of a learning management system

Educational modeling of a learning management system

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International Conference on Electrical and Information Technologies (ICEIT)

作者： Mohammed Ouadoud Amel Nejjari Mohamed Yassin Chkouri Kamal Eddine El Kadiri UAE Research Operational and Statistic Applied (LIROSA) Tetouan Morocco UAE Laboratory of the Information System and Software Engineering (SIGL) Tetouan Morocco UAE Faculty of Sciences (FS) Laboratory of Informatics Research Operational and Statistic Applied (LIROSA) Tetouan Morocco

The problems of most Learning Management systems (LMS) are first of all of a pedagogical nature and then of a technical one. Studying these problems, which are interrelated, provides a useful conceptual reference that enables us to design a new model for a more relevant solution. In this paper, a new conceptual model of an LMS is presented. The model is based on the hybridization between four learning theories, namely the traditional pedagogy, the behaviorism, the cognitivism, and the social constructivism. We will present at first each of these learning theories by discussing both their advantages and limits. Then, together the main principles of these learning theories and the technical functionalities of the proposed LMS that result from the hybridization of these principles are outlined. We finally discuss the implications of the new conceptual model for how to design and develop new LMS that fit the needs of their final users, in particular learners.

关键词： Tools Electronic learning Collaboration Information technology Learning management systems Training

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Studying and comparing the free e-learning platforms 4th

Studying and comparing the free e-learning platforms

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Colloquium in Information Science and Technology (CIST)

作者： Mohammed Ouadoud Mohamed Yassin Chkouri Amel Nejjari Kamal Eddine El Kadiri Faculty of Sciences (FS) Research Operational and Statistic Applied (LIROSA) Tetouan Morocco UAE Laboratory of the Information System and Software Engineering (SIGL) Tetouan Morocco

ISBN: (纸本)9781509007523

E-learning platforms are constantly changing; they have transformed the everyday life of teachers and students. An e-learning platforms study, from a methodical, systemic approach that identifies the dimensions, specifications and the essential criteria for the evaluation of these platforms is necessary. In this paper, we propose an analytical study of free e-learning platforms. Four platforms have been selected according to requested criteria filter among the 600 platforms listed in the THOT CURSUS directory. Subsequently, we make a comparative study of these platforms. On the one hand, we would like to measure the degree of adequacy between technological opportunities they offer and the pedagogical presuppositions of training. On the other hand, for clarifying and supporting the users in their choice of the most suitable platform for their needs so that they can benefit to the maximum of potential offered by technologies in pedagogy, and approach the problematic of the platform choice for a better pedagogy.

关键词： Electronic learning Training Usability Licenses Standards

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MIMO antennas for a terrestrial point-to-point wireless link: From the optimum antenna spacing to a compact array

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Progress In Electromagnetics research B 2016年第1期67卷 59-70页

作者： Wu, Xuan Hui Smith, Douglas Yang, Thomas Department of Electrical and Computer Engineering and Technology Minnesota State University MankatoMN United States US Air Force Research Laboratory RomeNY United States Department of Electrical Computer Software and System Engineering Embry-Riddle Aeronautical University Daytona BeachFL United States

Multiple-input-multiple-output antennas are investigated for terrestrial point-to-point wireless link. The lack of rich scatters in a terrestrial wireless channel results in an ill-conditioned channel matrix for a long range link with compact arrays, which can cause a high bit error rate. This paper demonstrates that the channel matrix can be improved by carefully selecting the antenna spacing. Unfortunately, an optimum antenna spacing that guarantees a good channel matrix is too large to implement for most long range terrestrial wireless links. On the other hand, a channel capacity study of an 8×8 link reveals that multiple antennas do provide more capacity even with small antenna spacing. Constellation multiplexing is then applied to the compact array configuration to solve the unreliable communication problem. In addition, a multilevel maximum ratio combining technique is introduced to improve detection efficiency.

关键词： MIMO systems

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Conducted EMI simulation for a high power Ultra-precision PMSM driven by PWM converter

Conducted EMI simulation for a high power Ultra-precision PM...

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IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Youpeng Huangfu Shuhong Wang Juanning Yang Hongxu Wang Luca Di Rienzo Djilali Hamza State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University Xi'an China Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Qinchuan Technology Research Institute Software & System Institute Qinchuan Machine Tool & Tool Group Corp. Baoji China Electrical and Computer Engineering Dept. Queen's University Kingston Ontario Canada

ISBN: (纸本)9781509014439

The conducted electromagnetic interference (EMI) and overvoltage characteristics for a spindle permanent magnet synchronous motor (PMSM) driven by pulse width modulation (PWM) converter are comprehensively presented. The high power, ultra-precision PMSM is applied to a computer numerical control (CNC) grinding machine. The conducted EMI prediction model mainly includes multi-core shielded cable, high frequency (HF) converter and PMSM models. The multi-transmission line (MTL) theory is employed to establish the multi-core shielded cable considering their coupling effect caused by the control pairs. The parasitic capacitance for the PWM converter is calculated by using the analytical formula. The HF equivalent circuit model of the PMSM is obtained combining the methods of impedance measurement and vector fitting method. The influence of the rotor position on the PMSM impedance measurement is considered. The common mode (CM) and differential mode (DM) interference voltage and current both for the output of inverter and the input of PMSM terminals are conducted by using the prediction model. The parametric influence of the long multi-core shielded cables on the overvoltage for the PMSM terminal is discussed.

关键词： Decision support systems

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

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

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

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