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2017 IEEE Conference on Energy Internet and Energy System Integration, EI2 2017

作者： Wang, Di Guo, Qinglai Song, Yan Gao, Kunlun Zhou, Aihua State Key Laboratory of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China Electric Power Research Institute State Grid Shanghai Municipal Electric Power Company Shanghai200437 China Advanced Computing and Big Data Laboratory of SGCC Global Energy Interconnection Research Institute Beijing102209 China

ISBN: (纸本)9781538614273

With the development of information technology, the operation of power grid increasingly depends upon the support of strong communication network. The power system consisting of grids infrastructure and communication equipment is a typical cyber-physical system(CPS). In recent years, power accidents that information-side failure impacts on physical operation happen at times. In order to simulate the impacts of information failure on grid operation, we develop a cyber-physical simulation tool for the secondary-voltage control of power system to implement the reasonable analysis and evaluation of the influence of different types of information failure on voltage control effect. In this paper, the graph database is introduced to construct the basic cyber-physical data model as an important part of the simulation tool, whose graphical modeling and management is friendly for users. In addition, the importance of different types of information is analyzed with the tool. It can be seen that the voltage information is more fragile and critical than the non-independent generator reactive information. © 2017 IEEE.

关键词： Cyber Physical System

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Euclid preparation. XXXIX. The effect of baryons on the Halo Mass Function

arXiv

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arXiv 2023年

作者： Castro, T. Borgani, S. Costanzi, M. Dakin, J. Dolag, K. Fumagalli, A. Ragagnin, A. Saro, A. Le Brun, A.M.C. Aghanim, N. Amara, A. Andreon, S. Auricchio, N. Baldi, M. Bardelli, S. Bodendorf, C. Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. Camera, S. Capobianco, V. Carbone, C. Carretero, J. Casas, S. Castellano, M. Cavuoti, S. Cimatti, A. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Corcione, L. Courbin, F. Courtois, H.M. Cropper, M. Da Silva, A. Degaudenzi, H. Di Giorgio, A.M. Dinis, J. Dubath, F. Duncan, C.A.J. Dupac, X. Farina, M. Farrens, S. Ferriol, S. Frailis, M. Franceschi, E. Fumana, M. Galeotta, S. Gillis, B. Giocoli, C. Grazian, A. Grupp, F. Haugan, S.V.H. Holmes, W. Hormuth, F. Hornstrup, A. Jahnke, K. Keihänen, E. Kermiche, S. Kiessling, A. Kilbinger, M. Kubik, B. Kunz, M. Kurki-Suonio, H. Ligori, S. Lilje, P.B. Lindholm, V. Lloro, I. Maiorano, E. Mansutti, O. Marggraf, O. Markovic, K. Martinet, N. Marulli, F. Massey, R. Maurogordato, S. Medinaceli, E. Meneghetti, M. Merlin, E. Meylan, G. Moresco, M. Moscardini, L. Munari, E. Niemi, S.-M. Padilla, C. Paltani, S. Pasian, F. Pettorino, V. Pires, S. Polenta, G. Poncet, M. Popa, L.A. Pozzetti, L. Raison, F. Rebolo, R. Renzi, A. Rhodes, J. Riccio, G. Romelli, E. Roncarelli, M. Saglia, R. Sapone, D. Sartoris, B. Schneider, P. Schrabback, T. Secroun, A. Seidel, G. Serrano, S. Sirignano, C. Sirri, G. Stanco, L. Starck, J.-L. Tallada-Crespí, P. Taylor, A.N. Tereno, I. Toledo-Moreo, R. Torradeflot, F. Tutusaus, I. Valentijn, E.A. Valenziano, L. Vassallo, T. Veropalumbo, A. Wang, Y. Weller, J. Zacchei, A. Zamorani, G. Zoubian, J. Zucca, E. Biviano, A. Bozzo, E. Cerna, C. Colodro-Conde, C. Di Ferdinando, D. Mauri, N. Neissner, C. Sakr, Z. Scottez, V. Tenti, M. Viel, M. Wiesmann, M. Akrami, Y. Anselmi, S. Baccigalupi, C. Ballardini, M. Borlaff, A.S. Bruton, S. Burigana, C. Cabanac, R. Cappi, A. Carvalho, C.S. INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy INFN Sezione di Trieste Via Valerio 2 TS Trieste34127 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy Dipartimento di Fisica - Sezione di Astronomia Università di Trieste Via Tiepolo 11 Trieste34131 Italy Institute for Computational Science University of Zurich Winterthurerstrasse 190 Zurich8057 Switzerland Universitäts-Sternwarte München Fakultät für Physik Ludwig-Maximilians-Universität München Scheinerstrasse 1 München81679 Germany INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy Dipartimento di Fisica e Astronomia "Augusto Righi" Alma Mater Studiorum Università di Bologna via Piero Gobetti 93/2 Bologna40129 Italy Laboratoire Univers et Théorie Observatoire de Paris Université PSL Université Paris Cité CNRS Meudon92190 France Université Paris-Saclay CNRS Institut d'astrophysique spatiale Orsay91405 France Institute of Cosmology and Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom INAF-Osservatorio Astronomico di Brera Via Brera 28 Milano20122 Italy Dipartimento di Fisica e Astronomia Università di Bologna Via Gobetti 93/2 Bologna40129 Italy INFN-Sezione di Bologna Viale Berti Pichat 6/2 Bologna40127 Italy Max Planck Institute for Extraterrestrial Physics Giessenbachstr. 1 Garching85748 Germany Pino Torinese10025 Italy Dipartimento di Fisica Università di Genova Via Dodecaneso 33 Genova16146 Italy INFN-Sezione di Genova Via Dodecaneso 33 Genova16146 Italy Department of Physics "E. Pancini" University Federico II Via Cinthia 6 Napoli80126 Italy INAF-Osservatorio Astronomico di Capodimonte Via Moiariello 16 Napoli80131 Italy INFN section of Naples Via Cinthia 6 Napoli80126 Italy Instit

The Euclid photometric survey of galaxy clusters stands as a powerful cosmological tool, with the capacity to significantly propel our understanding of the Universe. Despite being subdominant to dark matter and dark energy, the baryonic component of our Universe holds substantial influence over the structure and mass of galaxy clusters. This paper presents a novel model that can be used to precisely quantify the impact of baryons on the virial halo masses of galaxy clusters using the baryon fraction within a cluster as a proxy for their effect. Constructed on the premise of quasi-adiabaticity, the model includes two parameters, which are calibrated using non-radiative cosmological hydrodynamical simulations, and a single large-scale simulation from the Magneticum set, which includes the physical processes driving galaxy formation. As a main result of our analysis, we demonstrate that this model delivers a remarkable 1% relative accuracy in determining the virial dark matter-only equivalent mass of galaxy clusters starting from the corresponding total cluster mass and baryon fraction measured in hydrodynamical simulations. Furthermore, we demonstrate that this result is robust against changes in cosmological parameters and against variation of the numerical implementation of the subresolution physical processes included in the simulations. Our work substantiates previous claims regarding the impact of baryons on cluster cosmology studies. In particular, we show how neglecting these effects would lead to biased cosmological constraints for a Euclid-like cluster abundance analysis. Importantly, we demonstrate that uncertainties associated with our model arising from baryonic corrections to cluster masses are subdominant when compared to the precision with which mass-observable (i.e. richness) relations will be calibrated using Euclid and to our current understanding of the baryon fraction within galaxy clusters. Copyright © 2023, The Authors. All rights reserved.

关键词： Galaxies

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Euclid preparation. 3-dimensional galaxy clustering in configuration space Part I: 2-point correlation function estimation

arXiv

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arXiv 2025年

作者： de la Torre, S. Marulli, F. Keihänen, E. Viitanen, A. Viel, M. Veropalumbo, A. Branchini, E. Tavagnacco, D. Rizzo, F. Valiviita, J. Lindholm, V. Allevato, V. Parimbelli, G. Sarpa, E. Z., Ghaffari A., Amara S., Andreon N., Auricchio C., Baccigalupi M., Baldi S., Bardelli A., Basset D., Bonino M., Brescia J., Brinchmann A., Caillat S., Camera V., Capobianco C., Carbone J., Carretero S., Casas F.J., Castander M., Castellano G., Castignani S., Cavuoti A., Cimatti C., Colodro-Conde G., Congedo C.J., Conselice L., Conversi Y., Copin F., Courbin H.M., Courtois M., Crocce A., Da Silva H., Degaudenzi G., De Lucia A.M., Di Giorgio J., Dinis F., Dubath C.A.J., Duncan X., Dupac S., Dusini M., Farina S., Farrens F., Faustini S., Ferriol N., Fourmanoit M., Frailis E., Franceschi P., Franzetti M., Fumana S., Galeotta K., George W., Gillard B., Gillis C., Giocoli P., Gómez-Alvarez B.R., Granett A., Grazian F., Grupp L., Guzzo S.V.H., Haugan W., Holmes F., Hormuth A., Hornstrup S., Ilić K., Jahnke M., Jhabvala B., Joachimi S., Kermiche A., Kiessling M., Kilbinger B., Kubik M., Kunz H., Kurki-Suonio S., Ligori P.B., Lilje I., Lloro G., Mainetti D., Maino E., Maiorano O., Mansutti O., Marggraf K., Markovic M., Martinelli N., Martinet R., Massey S., Maurogordato E., Medinaceli S., Mei M., MELMhior Y., Mellier M., Meneghetti E., Merlin G., Meylan M., Moresco B., Morin L., Moscardini E., Munari C., Neissner S.-M., Niemi C., Padilla S., Paltani F., Pasian K., Pedersen W.J., Percival V., Pettorino S., Pires G., Polenta M., Poncet L., Pozzetti F., Raison A., Renzi J., Rhodes G., Riccio E., Romelli M., Roncarelli E., Rossetti R., Saglia Z., Sakr A.G., Sánchez D., Sapone B., Sartoris P., Schneider T., Schrabback M., Scodeggio A., Secroun E., Sefusatti G., Seidel M., Seiffert S., Serrano C., Sirignano G., Sirri L., Stanco J., Steinwagner C., Surace P., Tallada-Crespí A.N., Taylor I., Tereno R., Toledo-Moreo Aix-Marseille Université CNRS CNES LAM Marseille France Dipartimento di Fisica e Astronomia Augusto Righi Alma Mater Studiorum Università di Bologna via Piero Gobetti 93/2 Bologna40129 Italy INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy INFN-Sezione di Bologna Viale Berti Pichat 6/2 Bologna40127 Italy Department of Physics Helsinki Institute of Physics University of Helsinki Gustaf Hällströmin katu 2 00014 Finland INAF-Osservatorio Astronomico di Roma Via Frascati 33 Monteporzio Catone00078 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy SISSA International School for Advanced Studies Via Bonomea 265 TS Trieste34136 Italy INFN Sezione di Trieste Via Valerio 2 TS Trieste34127 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy INAF-Osservatorio Astronomico di Brera Via Brera 28 Milano20122 Italy INFN-Sezione di Genova Via Dodecaneso 33 Genova16146 Italy Dipartimento di Fisica Università degli studi di Genova INFN-Sezione di Genova via Dodecaneso 33 Genova16146 Italy Dipartimento di Fisica Università di Genova Via Dodecaneso 33 Genova16146 Italy Department of Physics University of Helsinki P.O. Box 64 00014 Finland Helsinki Institute of Physics University of Helsinki Gustaf Hällströmin katu 2 Helsinki Finland INAF-Osservatorio Astronomico di Capodimonte Via Moiariello 16 Napoli80131 Italy Campus UAB Carrer de Can Magrans s/n Barcelona08193 Spain School of Mathematics and Physics University of Surrey Guild-ford SurreyGU2 7XH United Kingdom Dipartimento di Fisica e Astronomia Università di Bologna Via Gobetti 93/2 Bologna40129 Italy Centre National d’Etudes Spatiales Centre spatial de Toulouse 18 avenue Edouard Belin Toulouse31401Cedex 9 France Pino

The 2-point correlation function of the galaxy spatial distribution is a major cosmological observable that enables constraints on the dynamics and geometry of the Universe. The Euclid mission aims at performing an extensive spectroscopic survey of approximately 20–30 million Hα-emitting galaxies up to about redshift two. This ambitious project seeks to elucidate the nature of dark energy by mapping the 3-dimensional clustering of galaxies over a significant portion of the sky. This paper presents the methodology and software developed for estimating the 3-dimensional 2-point correlation function within the Euclid Science Ground Segment. The software is designed to overcome the significant challenges posed by the large and complex Euclid data set, which involves millions of galaxies. Key challenges include efficient pair counting, managing computational resources, and ensuring the accuracy of the correlation function estimation. The software leverages advanced algorithms, including kd-tree, octree, and linked-list data partitioning strategies, to optimise the pair-counting process. These methods are crucial for handling the massive volume of data efficiently. The implementation also includes parallel processing capabilities using shared-memory open multi-processing to further enhance performance and reduce computation times. Extensive validation and performance testing of the software are presented. Those have been performed by using various mock galaxy catalogues to ensure that it meets the stringent accuracy requirement of the Euclid mission. The results indicate that the software is robust and can reliably estimate the 2-point correlation function, which is essential for deriving cosmological parameters with high precision. Furthermore, the paper discusses the expected performance of the software during different stages of the Euclid Wide Survey observations and forecasts how the precision of the correlation function measurements will improve over the mission’s t

关键词： Dark energy

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Fast 3D Map Reconstruction Using Dense Visual Simultaneous Localization and Mapping Based on Unmanned Aerial Vehicle

Fast 3D Map Reconstruction Using Dense Visual Simultaneous L...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Siyuan Peng Fang Huang Jian Tao Bo Tie Jun Lu Xiaodong Zhang School of Resources & Environment University of Electronic Science and Technology of China Chengdu P.R. China Institute of Remote Sensing Big Data University of Electronic Science and Technology of China Chengdu P.R. China Texas A&M Engineering Experiment Station and High Performance Research Computing Texas A&M University College Station TX USA

Traditional 3D map reconstruction methods based on unmanned aerial (UA) always relies on multi-camera optical equipment or additional space positioning equipment. All of these restrict the UA's application scenarios to some extent. Visual simultaneous localization and mapping (VSLAM), using the camera as the only external sensor, can construct a 3D map of its spatial environment while recording its own localization. This paper proposes a technique of fast 3D map reconstruction based on UAs by using VSLAM, with parallel computing based on CUDA, to achieve fast dense reconstruction based on a UA platform. This research belongs to one of the emerging but very exciting application areas using UAs. It helps to expand the application scope of UAs to some extent. From the experiments, it is demonstrated that the methodology presented in this paper works well, and could be applied to real world applications.

关键词： Unmanned aerial Visual SLAM 3D map High performance computing Dense reconstruction

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Unveiling latent behaviors of video viewers with cross-platform information 27

Unveiling latent behaviors of video viewers with cross-platf...

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27th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2017

作者： Gu, Xuhong Zhou, Yipeng Wu, Di Chan, Terence H. Chen, Min School of Data and Computer Science Sun Yat-sen University China Institute for Telecommunications Research University of South Australia Australia Guangdong Province Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University China Collaborative Innovation Center of High Performance Computing National University of Defense Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781450350037

The online video streaming service is of huge market values with billions of worldwide users. For online video providers, e.g., Net-flix, Youku, the crucial question is how to understand users' view behaviors and preferences because this knowledge is important for their business operation. Existing solutions mainly rely on analyzing users' historical view records, which however are not always available, especially for new videos and unprovided videos. Different from existing solutions, we propose to infer user behaviors and preferences by jointly analyzing data collected from multiple platforms (e.g., video streaming systems, video databases, etc.). In particular, we use the movie data crawled from a leading video streaming system (i.e., Youku), and a well-known video database in China (i.e, Douban) for this study. Our investigation points out that movie quality (evaluated in terms of Douban scores) and release date jointly influence viewers' preferences. In addition, we reveal a series of user behaviors, e.g., users are reluctant to post comments or ratings for movies they do not like, and user eyeballs are heavily captured by new movies. Understanding of these user behaviors covered by this study is essential for video recommendation and video popularity prediction which can benefit video procurement and advertisement campaign. © 2017 ACM.

关键词： Video streaming

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Context-aware video recommendation by mining users' view preferences based on access points 27

Context-aware video recommendation by mining users' view pre...

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27th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2017

作者： Zhang, Jiaming Zhou, Yipeng Wu, Di Yang, Chunfeng School of Data and Computer Science Sun Yat-sen University Guangzhou Guangdong China Institute for Telecommunications Research University of South Australia Australia Guangdong Province Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University Guangzhou Guangdong China Collaborative Innovation Center of High Performance Computing National University of Defense Technology China Tencent Inc. China

ISBN: (纸本)9781450350037

With the astronomical growth of online video population, the video recommendation system is crucial for users to locate videos fitting their interests. The collaborative filtering (CF) realizing personalized recommendation by analyzing users' historical view records is currently the most prevalent algorithm adopted by existing systems. Nevertheless, video recommendation performance can be improved for most cases, particularly the one with cold-start problem, if additional information is involved. In this article, we propose an AP-based Context-Aware (APCA) recommendation scheme on top of the traditional factor-based CF algorithm by utilizing the information of access points which has not been explored yet by existing works. The underlying principle is that users' view preferences are expected to be different with different contexts, e.g., hotel, home, public areas, which can be inferred by mining the information of access points via which users launch video requests. With the data collected from Tencent Video, a leading online video provider in China, we present a measurement study to show user view preferences in different contexts before our APCA algorithm is introduced. Experiments are executed driven by the trace data collected from Tencent Video to validate the effectiveness of our scheme in improving recommendation performance. © 2017 ACM.

关键词： Collaborative filtering

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Euclid preparation TBD. Galaxy power spectrum modelling in real space

arXiv

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arXiv 2023年

作者： Pezzotta, A. Moretti, C. Zennaro, M. Dizgah, A. Moradinezhad Crocce, M. Sefusatti, E. Ferrero, I. Pardede, K. Eggemeier, A. Barreira, A. Angulo, R.E. Marinucci, M. Quevedo, B. Camacho de la Torre, S. Alkhanishvili, D. Biagetti, M. Breton, M.-A. Castorina, E. D’Amico, G. Desjacques, V. Guidi, M. Kärcher, M. Oddo, A. Ibanez, M. Pellejero Porciani, C. Pugno, A. Salvalaggio, J. Sarpa, E. Veropalumbo, A. Vlah, Z. Amara, A. Andreon, S. Auricchio, N. Baldi, M. Bardelli, S. Bender, R. Bodendorf, C. Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. Camera, S. Capobianco, V. Carbone, C. Cardone, V.F. Carretero, J. Casas, S. Castander, F.J. Castellano, M. Cavuoti, S. Cimatti, A. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Corcione, L. Courbin, F. Courtois, H.M. Da Silva, A. Degaudenzi, H. Di Giorgio, A.M. Dinis, J. Dupac, X. Dusini, S. Ealet, A. Farina, M. Farrens, S. Fosalba, P. Frailis, M. Franceschi, E. Galeotta, S. Gillis, B. Giocoli, C. Granett, B.R. Grazian, A. Grupp, F. Guzzo, L. Haugan, S.V.H. Hormuth, F. Hornstrup, A. Jahnke, K. Joachimi, B. Keihänen, E. Kermiche, S. Kiessling, A. Kilbinger, M. Kitching, T. Kubik, B. Kunz, M. Kurki-Suonio, H. Ligori, S. Lilje, P.B. Lindholm, V. Lloro, I. Maiorano, E. Mansutti, O. Marggraf, O. Markovic, K. Martinet, N. Marulli, F. Massey, R. Medinaceli, E. Mellier, Y. Meneghetti, M. Merlin, E. Meylan, G. Moresco, M. Moscardini, L. Munari, E. Niemi, S.-M. Padilla, C. Paltani, S. Pasian, F. Pedersen, K. Percival, W.J. Pettorino, V. Pires, S. Polenta, G. Pollack, J.E. Poncet, M. Popa, L.A. Pozzetti, L. Raison, F. Renzi, A. Rhodes, J. Riccio, G. Romelli, E. Roncarelli, M. Rossetti, E. Saglia, R. Sapone, D. Sartoris, B. Schneider, P. Schrabback, T. Secroun, A. Seidel, G. Seiffert, M. Serrano, S. Sirignano, C. Sirri, G. Stanco, L. Surace, C. Tallada-Crespí, P. Taylor, A.N. Tereno, I. Toledo-Moreo, R. Torradeflot, F. Tutusaus, I. Valentijn, E.A. Max Planck Institute for Extraterrestrial Physics Giessenbachstr. 1 Garching85748 Germany SISSA International School for Advanced Studies Via Bonomea 265 TS Trieste34136 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy Institute for Astronomy University of Edinburgh Royal Observatory Blackford Hill EdinburghEH9 3HJ United Kingdom INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy Department of Physics Oxford University Keble Road OxfordOX1 3RH United Kingdom Université de Genève Département de Physique Théorique Centre for Astroparticle Physics 24 quai Ernest-Ansermet Genève 4CH-1211 Switzerland Campus UAB Carrer de Can Magrans s/n Barcelona08193 Spain Campus UAB Carrer de Can Magrans s/n Cerdanyola del Vallés Barcelona08193 Spain INFN Sezione di Trieste Via Valerio 2 TS Trieste34127 Italy Institute of Theoretical Astrophysics University of Oslo P.O. Box 1029 Blindern Oslo0315 Norway Strada Costiera 11 Trieste34151 Italy Universität Bonn Argelander-Institut für Astronomie Auf dem Hügel 71 Bonn53121 Germany Excellence Cluster ORIGINS Boltzmannstrasse e 2 Garching85748 Germany Ludwig-Maximilians-University Schellingstrasse 4 Munich80799 Germany Paseo Manuel de Lardizabal 4 Donostia-San Sebastián Guipuzkoa20018 Spain IKERBASQUE Basque Foundation for Science Bilbao48013 Spain Dipartimento di Scienze Matematiche Fisiche e Informatiche Università di Parma Viale delle Scienze 7/A Parma43124 Italy Technion Israel Institute of Technology Israel Carrer Gran Capitá 2-4 Barcelona08034 Spain Aix-Marseille Université CNRS CNES LAM Marseille France Area Science Park Località Padriciano 99 Trieste34149 Italy Laboratoire Univers et Théorie Observatoire de Paris Université PSL Université Paris Cité CNRS Meudon92190

We investigate the accuracy of the perturbative galaxy bias expansion in view of the forthcoming analysis of the Euclid spectroscopic galaxy samples. We compare the performance of an Eulerian galaxy bias expansion, using state-of-art prescriptions from the effective field theory of large-scale structure (EFTofLSS), against a hybrid approach based on Lagrangian perturbation theory and high-resolution simulations. These models are benchmarked against comoving snapshots of the Flagship I N-body simulation at z = (0.9, 1.2, 1.5, 1.8), which have been populated with Hα galaxies leading to catalogues of millions of objects within a volume of about 58 h−3 Gpc3. Our analysis suggests that both models can be used to provide a robust inference of the parameters (h, ωc) in the redshift range under consideration, with comparable constraining power. We additionally determine the range of validity of the EFTofLSS model in terms of scale cuts and model degrees of freedom. From these tests, it emerges that the standard third-order Eulerian bias expansion can accurately describe the full shape of the real-space galaxy power spectrum up to the maximum wavenumber kmax = 0.45 hMpc−1, even with a measurement precision well below the percent level. In particular, this is true for a configuration with six free nuisance parameters, including local and non-local bias parameters, a matter counterterm, and a correction to the shot-noise contribution. Fixing either tidal bias parameters to physically-motivated relations still leads to unbiased cosmological constraints. We finally repeat our analysis assuming a volume that matches the expected footprint of Euclid, but without considering observational effects, as purity and completeness, showing that we can get consistent cosmological constraints over this range of scales and redshifts. © 2023, CC BY.

关键词： Galaxies

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Multi-temporal MOD09A1-based detecting of major growth stages of paddy rice on a provincial scale 37

Multi-temporal MOD09A1-based detecting of major growth stage...

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37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017

作者： Huang, Linsheng Zhang, Shasha Zhao, Jinling Huang, Wenjiang Huang, Jinyang Qi, Xiaobo Anhui Engineering Laboratory Agro-Ecological Big Data Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing100094 China

ISBN: (纸本)9781509049516

Growth stage information is a very primary parameter for managing the grain crop. In this study, time series Enhanced Vegetation Index (EVI) data in 2015 were obtained in Anhui Province, China based on 8-day composite MOD09A1 data products, and the extraction method of rice phenology was specifically analyzed at a provincial scale. HANTS (Harmonic Analysis of Time Series) filtering algorithm was firstly used to smooth the time series EVI curves and identify every growth and development period of paddy rice. The results indicate that HANTS has a better performance in removing noise, restoring original information and retain the change information of multi-temporal EVI curves. The primary planting regions were located in the southern Huai-River areas, from early June to late June in 2015. It turned into the transplanting stage in succession from south to north and followed by the heading stage in succession in early August. From early September to late October, it turned into mature stage from south to north in succession. © 2017 IEEE.

关键词： Indexes

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A novel data quality assessment framework for vehicular network testbeds 12

A novel data quality assessment framework for vehicular netw...

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12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities, TRIDENTCOM 2017

作者： Tian, Daxin Zhu, Yukai Zhou, Jianshan Duan, Xuting Wang, Yunpeng Song, Jeungeun Rong, Hui Guo, Peng Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing XueYuan Road No.37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No.68 Tianjin300300 China Beihang University Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering XueYuan Road No.37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781631901577

big data technique is considered as a powerful tool to exploit all the potential of the Internet of Things and the smart cities. The development of internet of Vehicles (IoV) and wireless communication technologies have boosted diverse applications related to smart cities and Cyber-Physical Systems, but the data quality of vehicular sensors is an important issue due to the high-speed mobile wireless communication environment and physical sensor noise. This paper presents our experiences for big data analytics based on a vehicular network testbed, in terms of sensors data management, multi-dimension data fusion and data quality assessment for the vehicular sensor data. The proposed data quality assessment framework consist of feature extraction based on multi-sensor data fusion and multi-level wavelet transform, as well as a semi-supervised learning based classification algorithm. The comparison experiment shows that the proposed framework and approaches can extract feasible features and solve the unbalanced label problems, which achieve a better assessment effect. Copyright © 2018 EAI.

关键词： Smart city

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A Novel Intrusion Detection System Based on Advanced Naive Bayesian Classification 1st

A Novel Intrusion Detection System Based on Advanced Naive B...

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1st International Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Wang, Yunpeng Li, Yuzhou Tian, Daxin Wang, Congyu Wang, Wenyang Hui, Rong Guo, Peng Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No. 37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No. 68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No. 30 Beijing100083 China XueYuan Road No. 37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

Intrusion Detection System is a pattern recognition task whose aim is to detect and report the occurrence of abnormal or unknown network behaviors in a given network system being monitored. In this paper, we propose a machine learning model, advanced Naive Bayesian Classification (NBC-A) which is based on NBC and ReliefF algorithm, to be used in the novel IDS. We use ReliefF algorithm to give every attribute of network behavior in KDD’99 dataset a weight that reflects the relationship between attributes and final class for better classification results. The novel IDS has a higher True Positive (TP) rate and a lower False Positive (FP) rate in detection performance. © 2018, ICST institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Intrusion detection

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