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IEEE Vehicular Technology Conference

作者： Jaquinei de Oliveira Ana Cristina B. Kochem Vendramin Anelise Munaretto Myriam Delgado Graduate Program in Applied Computer Science (PPGCA) Federal University of Technology - Parana (UTFPR) Curitiba Brazil Graduate Program in Electrical and Computer Engineering (CPGEI) Federal University of Technology - Parana (UTFPR) Curitiba Brazil

Designed to Delay Tolerant Networks, the Cultural Greedy GrAnt (CGrAnt) routing protocol uses Ant Colony Optimization to represent the population space of a Cultural Algorithm. CGrAnt aims to improve the message forwarding by analyzing the network characteristics based on three distinct knowledge: Domain, History, and Situational. The Domain knowledge plays a central role in the CGrAnt operation as it provides a good balance between search space exploration (through the selection of new solutions) and exploitation (through the selection of previously found solutions). This work proposes alternative metrics to be used by the Domain knowledge of CGrAnt. Results show that the new proposed metrics increase the CGrAnt performance.

关键词： Measurement Routing protocols Sociology Statistics History Social network services

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A Hyper-Heuristic for the Environmental/Economic Dispatch Optimization Problem

A Hyper-Heuristic for the Environmental/Economic Dispatch Op...

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Brazilian Conference on Intelligent Systems (BRACIS)

作者： Richard A. Gonçalves Carolina P. Almeida Sandra M. Venske Josiel N. Kuk Lucas M. Pavelski Myriam R. Delgado Computer Science Department UNICENTRO Guarapuava PR Brazil Graduate Program in Electrical and Computer Engineering Federal University of Paraná Curitiba PR Brazil

Hyper-Heuristics are high-level methodologies developed to select or generate heuristics for solving complex problems. Despite their success, there is a lack of multi-objective hyper-heuristics. In the multi-objective optimization context, MOEA/D decomposes a problem into a number of sub problems handled by individuals in a collaborative manner. Our approach, named MOEA/D-HH SW , expands the MOEA/D framework with a multi-objective selection hyper-heuristic. It uses the proposed adaptive choice function with sliding window to determine which low-level heuristic (differential evolution operators) should be applied by each individual during MOEA/D execution. The proposed approach is tested in three known instances of the multi-objective environmental/economic dispatch problem, formulated as a non-linear constrained optimization problem with competing and non-commensurable objectives. MOEA/D-HH SW outperforms state-of-the-art algorithms reported in the literature for all considered instances.

关键词： Optimization Fuels Generators Sociology Statistics Propagation losses Mathematical model

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Data-driven analysis of kinaesthetic and tactile information for shape classification

Data-driven analysis of kinaesthetic and tactile information...

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Thiago Eustaquio Alves de Oliveira Vinicius Prado da Fonseca Emanuil Huluta Paulo F. F. Rosa Emil M. Petriu School of Electrical Engineering and Computer Science University of Ottawa Faculty of Medicine Ottawa ON CA School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada Defense Engineering Graduate Program The Military Institute of Engineering Rio de Janeiro Brazil

Humans sense of touch consists in a complexity of sensors and nervous system. The information inferred by this system enables the daily dexterous manipulation tasks. In biological systems, there is no conscious prioritization of sensors while performing tactile exploration and the selection of exploratory movements is driven by learning instincts and data gathered by previous movements. The development of artificial systems tries to mimic such systems with engineered sensors and strategies for movement selection. This paper presents a data-driven analysis to the problem of sensor selection in the contour following for shape discrimination task. This task consists of a 4-DOF robotic finger exploring a set of 7 synthetic shapes. The data collected from the motors, inertial measurement unit, and magnetometer was analyzed applying principal component analysis and a multilayer perceptron neural network. Results show the variation of classification rate depending on the fingertip material and sensor considered. It is worth to observe that the magnetometer was the most robust in both cases.

关键词： Shape Magnetometers Robot sensing systems Principal component analysis Magnetic sensors

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A Kinect-Aided Method to Estimate the Fundus Camera Extrinsic Parameters for Three-Dimensional Reconstruction of Retinal Blood Vessels

A Kinect-Aided Method to Estimate the Fundus Camera Extrinsi...

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Future Technologies Conference

作者： E. Hernandez-Sanchez M. Elena Martinez-Perez A. Gastelum-Strozzi Engineering Graduate Program Universidad Nacional Autónoma de México. Ciudad de México Computer Science Department IIMAS Universidad Nacional Autónoma de México. Apartado Postal 20-126 Ciudad de México C.P. 01000 CCADET-HGM. Universidad Nacional Autónoma de México. Ciudad de México

ISBN: (纸本)9781509041725

This paper deals with the estimation of intrinsic and extrinsic parameters in a fundus camera system. These parameters are fundamental to retrieve three-dimensional reconstruction of eye's structure, in our case, retinal blood vessels. In this paper we used the Zhang's algorithm to estimate the intrinsic parameters of the optical system. Besides, an alternative method to estimate the extrinsic parameters is proposed based on the kinect one sensor. These modifications provide an easy way to characterise the whole fundus camera system. Experimental validation was carried out to verify the accuracy of the proposed method. A cylindrical known object was reconstructed having and average error of 3.96%. A real eye's structure was also reconstructed from a fundus image pair.

关键词： Fundus images 3D reconstruction Camera calibration Retinal blood vessels

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The cluster heritage project with XMM-Newton: Mass assembly and thermodynamics at the endpoint of structure formation I. programme overview

arXiv

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arXiv 2020年

作者： Arnaud, M. Ettori, S. Pratt, Gabriel W. Rossetti, M. Eckert, D. Gastaldello, F. Gavazzi, R. Kay, S.T. Lovisari, L. Maughan, B.J. Pointecouteau, E. Sereno, M. Bartalucci, I. Bonafede, A. Bourdin, H. Cassano, R. Duffy, R.T. Iqbal, A. Maurogordato, S. Rasia, E. Sayers, J. Andrade-Santos, F. Aussel, H. Barnes, D.J. Barrena, R. Borgani, S. Burkutean, S. Clerc, N. Corasaniti, P.-S. Cuillandre, J.-C. de Grandi, S. de Petris, M. Dolag, K. Donahue, M. Ferragamo, A. Gaspari, M. Ghizzardi, S. Gitti, M. Haines, C.P. Jauzac, M. Johnston-Hollitt, M. Jones, C. Kéruzoré, F. LeBrun, A.M.C. Mayet, F. Mazzotta, P. Melin, J.-B. Molendi, S. Nonino, M. Okabe, N. Paltani, S. Perotto, L. Pires, S. Radovich, M. Rubino-Martin, J.-A. Salvati, L. Saro, A. Sartoris, B. Schellenberger, G. Streblyanska, A. Tarrío, P. Tozzi, P. Umetsu, K. van der Burg, R.F.J. Vazza, F. Venturi, T. Yepes, G. Zarattini, S. AIM CEA CNRS Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité Gif-sur-YvetteF-91191 France INAF Osservatorio di Astrofisica e Scienza dello Spazio via Pietro Gobetti 93/3 Bologna40129 Italy INFN Sezione di Bologna viale Berti Pichat 6/2 BolognaI-40127 Italy INAF IASF-Milano via A. Corti 12 MilanoI-20133 Italy Department of Astronomy University of Geneva ch. d'Écogia 16 VersoixCH-1290 Switzerland CNRS Sorbonne Université UMR 7095 Institut d'Astrophysique de Paris 98 bis Boulevard Arago Paris75014 France Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy School of Natural Sciences The University of Manchester ManchesterM13 9PL United Kingdom Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States HH Wills Physics Laboratory University of Bristol Tyndall Ave BristolBS8 1TL United Kingdom IRAP Université de Toulouse CNRS CNES UPS 9 av du colonel Roche BP44346 Toulouse Cedex 431028 France Dipartimento di Fisica e Astronomia Universitá di Bologna Via Gobetti 92/3 Bologna40121 Italy INAF Istituto di Radio Astronomia Via Gobetti 101 Bologna40129 Italy Hamburger Sternwarte Gojenbergsweg 112 Hamburg21029 Germany Università degli studi di Roma Tor Vergata via della ricerca scientifica 1 Roma00133 Italy Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Laboratoire Lagrange Nice France INAF - Osservatorio Astronomico di Trieste via Tiepolo 11 TriesteI-34131 Italy IFPU - Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy California Institute of Technology PasadenaCA91125 United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States C/ Vía Láctea s/n La Laguna TenerifeE-38205 Spain Universidad de La Laguna Departamento de Astrofísica C/ Astrofísico Francisco Sánchez s/n La Laguna TenerifeE-38206 Spain D

The Cluster HEritage project with XMM-Newton - Mass Assembly and Thermodynamics at the Endpoint of structure formation (CHEX-MATE) is a three-mega-second Multi-Year Heritage programme to obtain X-ray observations of a minimally-biased, signal-to-noise-limited sample of 118 galaxy clusters detected by Planck through the Sunyaev-Zeldovich effect. The programme, described in detail in this paper, aims to study the ultimate products of structure formation in time and mass. It is composed of a census of the most recent objects to have formed (Tier-1: 0.05 14 M 500 14 M), together with a sample of the highest mass objects in the Universe (Tier-2: z 500 > 7.25 × 1014 M). The programme will yield an accurate vision of the statistical properties of the underlying population, measure how the gas properties are shaped by collapse into the dark matter halo, uncover the provenance of non-gravitational heating, and resolve the major uncertainties in mass determination that limit the use of clusters for cosmological parameter estimation. We will acquire X-ray exposures of uniform depth, designed to obtain individual mass measurements accurate to 15 − 20% under the hydrostatic assumption. We present the project motivations, describe the programme definition, and detail the ongoing multi-wavelength observational (lensing, SZ, radio) and theoretical effort that is being deployed in support of the project. Copyright © 2020, The Authors. All rights reserved.

关键词： Thermodynamics

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Intrinsic Raman signatures of pristine hybrid perovskite CH3NH3PbI3 and its multiple stages of structure transformation

Intrinsic Raman signatures of pristine hybrid perovskite CH3...

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2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC)

作者： Qiong Chen Henan Liu Hui-Seon Kim Yucheng Liu Mengjin Yang Naili Yue Gang Ren Kai Zhu Shengzhong Frank Liu Nam-Gyu Park Yong Zhang Department of Electrical and Computer Engineering The University of North Carolina at Charlotte Charlotte NC USA Optical Science and Engineering graduate program The University of North Carolina at Charlotte Charlotte NC USA Department of Energy Science Sungkyunkwan University Suwon South Korea National Ministry of Education Shaanxi Normal University Xi'an China Chemical and Materials Science Center National Renewable Energy Laboratory CO USA Molecular Foundry Lawrence Berkeley National Laboratory Berkeley CA USA Key Laboratory of Applied Surface and Colloid Chemistry National Ministry of Education Shaanxi Normal University Xi'an Shaanxi CN School of Chemical Engineering Sungkyunkwan University Suwon South Korea University of North Carolina at Charlotte Charlotte NC US

By performing spatially resolved Raman and photoluminescence spectroscopy with different illumination conditions, we have achieved a unified understanding towards the spectroscopy signatures of the organic-inorganic hybrid perovskite, transforming from the pristine state (CH 3 NH 3 PbI 3 or MAPbI 3 ) to fully degraded state (i.e., PbI 2 ), for samples with varying crystalline domain size from mesoscopic scale to macroscopic size, synthesized by three different techniques.

关键词： Measurement by laser beam Density measurement Chemistry Lighting Optical films Degradation Spectroscopy

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TODIM Based Method to Process Heterogeneous Information

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Procedia computer science 2015年 55卷 318-327页

作者： Rodolfo Lourenzutti Renato A. Krohling Graduate Program in Computer Science PPGI UFES Federal University of Espirito Santo Av. Fernando Ferrari 514 Vitoria CEP 29075-910 Brazil Department of Production Engineering & Graduate Program in Computer Science PPGI UFES - Federal University of Espirito Santo Av. Fernando Ferrari 514 Vitoria CEP 29075-910 Esprito Santo ES Brazil

Multicriteria decision making (MCDM) problems are getting increasingly difficult over the years. Nowadays, it is very common to have a problem where heterogeneous types of information must be processed before making a decision. From a wide variety of MCDM methods, just a few of them are able to process mixed types of information at the same time and most of these methods relies on transformations that may cause problems and/or be unjustified. In this paper we bring a modular interpretation of the TODIM (an acronym in Portuguese for Interative Multi-criteria Decision Making) method to handle heterogeneous data types simultaneously, in a systematic way. We argue that the method have a modular capacity which is the novelty of our approach. Using this interpretation, the whole problem with heterogeneous information is broken into modules and processed in a straightforward way. Two examples are used to illustrate the approach showing the effectiveness and practicability.

关键词： Multi-criteria decision making (MCDM) TODIM heterogeneous information interval numbers fuzzy numbers intuitionistic fuzzy numbers.

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Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

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Scientific data 2021年第1期8卷 72页

作者： Gilberto Pastorello Carlo Trotta Eleonora Canfora Housen Chu Danielle Christianson You-Wei Cheah Cristina Poindexter Jiquan Chen Abdelrahman Elbashandy Marty Humphrey Peter Isaac Diego Polidori Markus Reichstein Alessio Ribeca Catharine van Ingen Nicolas Vuichard Leiming Zhang Brian Amiro Christof Ammann M Altaf Arain Jonas Ardö Timothy Arkebauer Stefan K Arndt Nicola Arriga Marc Aubinet Mika Aurela Dennis Baldocchi Alan Barr Eric Beamesderfer Luca Belelli Marchesini Onil Bergeron Jason Beringer Christian Bernhofer Daniel Berveiller Dave Billesbach Thomas Andrew Black Peter D Blanken Gil Bohrer Julia Boike Paul V Bolstad Damien Bonal Jean-Marc Bonnefond David R Bowling Rosvel Bracho Jason Brodeur Christian Brümmer Nina Buchmann Benoit Burban Sean P Burns Pauline Buysse Peter Cale Mauro Cavagna Pierre Cellier Shiping Chen Isaac Chini Torben R Christensen James Cleverly Alessio Collalti Claudia Consalvo Bruce D Cook David Cook Carole Coursolle Edoardo Cremonese Peter S Curtis Ettore D'Andrea Humberto da Rocha Xiaoqin Dai Kenneth J Davis Bruno De Cinti Agnes de Grandcourt Anne De Ligne Raimundo C De Oliveira Nicolas Delpierre Ankur R Desai Carlos Marcelo Di Bella Paul di Tommasi Han Dolman Francisco Domingo Gang Dong Sabina Dore Pierpaolo Duce Eric Dufrêne Allison Dunn Jiří Dušek Derek Eamus Uwe Eichelmann Hatim Abdalla M ElKhidir Werner Eugster Cacilia M Ewenz Brent Ewers Daniela Famulari Silvano Fares Iris Feigenwinter Andrew Feitz Rasmus Fensholt Gianluca Filippa Marc Fischer John Frank Marta Galvagno Mana Gharun Damiano Gianelle Bert Gielen Beniamino Gioli Anatoly Gitelson Ignacio Goded Mathias Goeckede Allen H Goldstein Christopher M Gough Michael L Goulden Alexander Graf Anne Griebel Carsten Gruening Thomas Grünwald Albin Hammerle Shijie Han Xingguo Han Birger Ulf Hansen Chad Hanson Juha Hatakka Yongtao He Markus Hehn Bernard Heinesch Nina Hinko-Najera Lukas Hörtnagl Lindsay Hutley Andreas Ibrom Hiroki Ikawa Marcin Jackowicz-Korczynski Dalibor Janouš Wilma Jans Rachhpal Jassal Shicheng Jiang Tomomichi Kato Myr Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. gzpastorello@lbl.gov. DIBAF University of Tuscia Viterbo 01100 Italy. Euro-Mediterranean Centre on Climate Change Foundation (CMCC) Lecce 73100 Italy. Climate & Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Department of Civil Engineering California State University Sacramento CA 95819 USA. Department of Geography Environment and Spatial Sciences Michigan State University East Lansing MI 48823 USA. Department of Computer Science University of Virginia Charlottesville VA 22904 USA. TERN Ecosystem Processes Menzies Creek VIC3159 Australia. Max Planck Institute for Biogeochemistry Jena 07701 Germany. Laboratoire des Sciences du Climat et de l'Environnement (LSCE) CEA CNRS UVSQ UPSACLAY Gif sur Yvette 91190 France. Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China. Department of Soil Science University of Manitoba Winnipeg MB R3T2N2 Canada. Department of Agroecology and Environment Agroscope Research Institute Zürich 8046 Switzerland. School of Geography and Earth Sciences McMaster University L8S4K1 Hamilton ON Canada. Department of Physical Geography and Ecosystem Science Lund University Lund 22362 Sweden. Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln NE 68583 USA. School of Ecosystem and Forest Sciences The University of Melbourne Richmond VIC3121 Australia. Department of Biology Research Group PLECO University of Antwerp Antwerp 2610 Belgium. Joint Research Centre European Commission Ispra 21027 Italy. TERRA Teaching and Research Center University of Liege Gembloux B-5030 Belgium. Finnish Meteorological Institute Helsinki 00560 Finland. ES

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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