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38th International Cosmic Ray Conference, ICRC 2023

作者： Yuzurihara, Hirotaka Abe, H. Akutsu, T. Ando, M. Aoumi, M. Araya, A. Aritomi, N. Aso, Y. Bae, S. Bajpai, R. Cannon, K. Cao, Z. Chang, R.-J. Chen, A.H.-Y. Chen, D. Chen, H. Chen, Y. Chiba, A. Chiba, R. Chou, C. Eisenmann, M. Fujii, S. Fukunaga, I. Haba, D. Haino, S. Han, W.-B. Hayakawa, H. Hayama, K. Himemoto, Y. Hirata, N. Hirose, C. Hoshino, S. Hsieh, H.-F. Hsiung, C. Hsu, S.-C. Hui, D.C.Y. Inayoshi, K. Itoh, Y. Iwaya, M. Jin, H.B. Jung, K. Kajita, T. Kamiizumi, M. Kanda, N. Kato, J. Kato, T. Kim, S. Kimura, N. Kiyota, T. Kohri, K. Kokeyama, K. Komori, K. Kong, A.K.H. Koyama, N. Kume, J. Kuroyanagi, S. Kuwahara, S. Kwak, K. Lai, S. Lee, H.W. Lee, R. Lee, S. Leonardi, M. Li, K.L. Lin, L.C.-C. Lin, C.-Y. Lin, E.T. Liu, G.C. Ma, L.-T. Maeda, K. Matsuyama, M. Meyer-Conde, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, S. Miyoki, S. Morisaki, S. Moriwaki, Y. Murakoshi, M. Nakamura, K. Nakano, H. Narikawa, T. Nguyen Quynh, L. Nishino, Y. Nishizawa, A. Obayashi, K. Oh, J.J. Oh, K. Ohashi, M. Ohkawa, M. Oohara, K. Oshima, Y. Oshino, S. Page, M.A. Pan, K.-C. Park, J. Peña Arellano, F.E. Saha, S. Sakai, K. Sako, T. Sato, R. Sato, S. Sato, Y. Sawada, T. Sekiguchi, Y. Shao, L. Shikano, Y. Shimode, K. Shinkai, H. Shiota, J. Somiya, K. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takamori, A. Takatani, K. Takeda, H. Takeda, M. Tamaki, M. Tanaka, K. Tanaka, S.J. Tanaka, T. Taruya, A. Tomaru, T. Tomita, K. Tomura, T. Toriyama, A. Trani, A.A. Tsuchida, S. Uchikata, N. Uchiyama, T. Uehara, T. Ueno, K. Ushiba, T. van Putten, M.H.P.M. Wang, H. Washimi, T. Wu, C. Wu, H. Yamamoto, K. Yamamoto, M. Yamamoto, T. Yamamoto, T.S. Yamamura, S. Yamazaki, R. Yang, L.-C. Yang, Y. Yeh, S.-W. Yokoyama, J. Yokozawa, T. Yuzurihara, H. Zhao, Y. Zhu, Z.-H. KAGRA Observatory The University of Tokyo 238 Higashi-Mozumi Kamioka-cho Hida City Gifu506-1205 Japan Graduate School of Science Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo152-8551 Japan Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 238 Higashi-Mozumi Kamioka-cho Hida City Gifu506-1205 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo113-0032 Japan LIGO Hanford Observatory RichlandWA99352 United States 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan 245 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing100875 China Department of Physics National Cheng Kung University No.1 University Road Tainan City701 Taiwan Institute of Physics National Yang Ming Chiao Tung University 101 Univ. Street Hsinchu Taiwan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu506-1205 Japan Department of Physics National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu30013 Taiwan Faculty of Science University of Toyama 3190 Gofuku Toyama City Toyama930-8555 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Chiba Kashiwa City277-8582 Japan Department of Electrophysics National Yang Ming Chiao Tung University 101 Univ. Street Hsinchu Taiwan Department of Physics Graduate School of Science Osaka Metropolitan University 3-3-138 Sugimoto-cho Sumi

KAGRA is a ground-based gravitational wave detector located in the Kamioka mine, Gifu, Japan. The fourth international joint observing run (Observation-4;O4) by LIGO, Virgo, and KAGRA started from May 2023. The KAGRA detector characterization group aims to enhance the reliability of data analysis and GW detection by supporting detector commissioning and improving our understanding of detector instruments and acquired data. We provide various tools for monitoring, understanding, and mitigating noise sources, which help enhance the performance of the KAGRA detector and data analysis. Here, we report the recent activities and status of the detector characterization of KAGRA for O4. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gravitational effects

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Potential of core-collapse supernova neutrino detection at JUNO

Potential of core-collapse supernova neutrino detection at J...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Huang, Xin Abusleme, Angel Ahmad, Shakeel Ahmed, Rizwan Aiello, Sebastiano Akram, Muhammad An, Fengpeng An, Qi Andronico, Giuseppe Anfimov, Nikolay Antonelli, Vito Antoshkina, Tatiana Asavapibhop, Burin de André, João Pedro Athayde Marcondes Auguste, Didier Babic, Andrej Balashov, Nikita Baldini, Wander Barresi, Andrea Basilico, Davide Baussan, Eric Bellato, Marco Bergnoli, Antonio Birkenfeld, Thilo Blin, Sylvie Blum, David Blyth, Simon Bolshakova, Anastasia Bongrand, Mathieu Bordereau, Clément Breton, Dominique Brigatti, Augusto Brugnera, Riccardo Bruno, Riccardo Budano, Antonio Buscemi, Mario Busto, Jose Butorov, Ilya Cabrera, Anatael Cai, Hao Cai, Xiao Cai, Yanke Cai, Zhiyan Callegari, Riccardo Cammi, Antonio Campeny, Agustin Cao, Chuanya Cao, Guofu Cao, Jun Caruso, Rossella Cerna, Cédric Chang, Jinfan Chang, Yun Chen, Pingping Chen, Po-An Chen, Shaomin Chen, Xurong Chen, Yi-Wen Chen, Yixue Chen, Yu Chen, Zhang Cheng, Jie Cheng, Yaping Chetverikov, Alexey Chiesa, Davide Chimenti, Pietro Chukanov, Artem Claverie, Gérard Clementi, Catia Clerbaux, Barbara Di Lorenzo, Selma Conforti Corti, Daniele Dal Corso, Flavio Dalager, Olivia De La Taille, Christophe Deng, Jiawei Deng, Zhi Deng, Ziyan Depnering, Wilfried Diaz, Marco Ding, Xuefeng Ding, Yayun Dirgantara, Bayu Dmitrievsky, Sergey Dohnal, Tadeas Dolzhikov, Dmitry Donchenko, Georgy Dong, Jianmeng Doroshkevich, Evgeny Dracos, Marcos Druillole, Frédéric Du, Ran Du, Shuxian Dusini, Stefano Dvorak, Martin Enqvist, Timo Enzmann, Heike Fabbri, Andrea Fajt, Lukas Fan, Donghua Fan, Lei Fang, Jian Fang, Wenxing Fargetta, Marco Fedoseev, Dmitry Fekete, Vladko Feng, Li-Cheng Feng, Qichun Ford, Richard Fournier, Amélie Gan, Haonan Gao, Feng Garfagnini, Alberto Gavrikov, Arsenii Giammarchi, Marco Giaz, Agnese Giudice, Nunzio Gonchar, Maxim Gong, Guanghua Gong, Hui Gornushkin, Yuri Göttel, Alexandre Grassi, Marco Grewing, Christian Gromov, Vasily Gu, Minghao Gu, Xiaofei Gu, Yu Guan, Mengyun Guardone, Nunzio Gul, Maria Guo, Cong Guo, Jingyuan Guo, Wanlei Guo, Xinheng Guo, Yuhan Institute of High Energy Physics Chinese Academy of Science Beijing China Yerevan Physics Institute Yerevan Armenia Université Libre de Bruxelles Brussels Belgium Universidade Estadual de Londrina Londrina Brazil Pontificia Universidade Catolica do Rio de Janeiro Brazil Pontificia Universidad Católica de Chile Santiago Chile Universidad Tecnica Federico Santa Maria Valparaiso Chile Beijing Institute of Spacecraft Environment Engineering Beijing China Beijing Normal University Beijing China China Institute of Atomic Energy Beijing China Institute of High Energy Physics Beijing China North China Electric Power University Beijing China School of Physics Peking University Beijing China Tsinghua University Beijing China University of Chinese Academy of Sciences Beijing China Jilin University Changchun China College of Electronic Science and Engineering National University of Defense Technology Changsha China Chongqing University Chongqing China Dongguan University of Technology Dongguan China Jinan University Guangzhou China Sun Yat-Sen University Guangzhou China Harbin Institute of Technology Harbin China University of Science and Technology of China Hefei China The Radiochemistry and Nuclear Chemistry Group University of South China Hengyang China Wuyi University Jiangmen China Shandong University Jinan China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Qingdao China Institute of Modern Physics Chinese Academy of Sciences Lanzhou China Nanjing University Nanjing China Guangxi University Nanning China East China University of Science and Technology Shanghai China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai China Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological Sciences Shijiazhuang China Nankai University Tianjin China Wuhan University Wuhan China Xi’an

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on 12C nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve. This talk gives an overview of physics potential of CCSN neutrino detection in JUNO. © Copyright owned by the author(s).

关键词： Supernovae

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A Lane-Changing Trajectory Planning and Assistant Decision-Making Method for Autonomous Vehicle 18

A Lane-Changing Trajectory Planning and Assistant Decision-M...

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18th COTA International Conference of Transportation Professionals: Intelligence, Connectivity, and Mobility, CICTP 2018

作者： Cui, Zheyu Hu, Jianming Guan, Hongfei School of Computer Science Beijing Univ. of Posts and Telecommunications Beijing100876 China Dept. of Automation Tsinghua Univ. TNList Beijing100084 China Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing100094 China

ISBN: (数字)9780784481523

ISBN: (纸本)9780784481523

Aiming at shortcomings of the traditional decision-making models for lane changing, such as only considering the nearest time gaps, and using static gap models, overlook the dynamic characteristic of vehicles' gap. A new lane-changing decision-making model is put forward based on dynamic gap. By adopting an approach of depth-first search and the designed satisfactory function, the gap that meets the most satisfaction is selected for lanes changing. The second model uses the constraint optimization model on the basis of the gap obtained by the previous model. It then uses the primal dual interior point method based on the second order cone programming (SOCP) to obtain the optimal trajectory, after considering the comfort and traffic flow. Finally, the correctness, robustness, and linkage of the two models are verified through the design of typical lane changing scenarios and simulation experiments. © 2018 American Society of civil Engineers.

关键词： Second-order cone programming

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Spectral superresolution of multispectral imagery with joint sparse and low-rank learning

arXiv

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arXiv 2020年

作者： Gao, Lianru Hong, Danfeng Yao, Jing Zhang, Bing Gamba, Paolo Chanussot, Jocelyn Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-lab Grenoble38000 France School of Mathematics and Statistics Xian Jiaotong University Xian710049 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia27100 Italy Univ. Grenoble Alpes INRIA CNRS Grenoble INP LJK Grenoble38000 France

Extensive attention has been widely paid to enhance the spatial resolution of hyperspectral (HS) images with the aid of multispectral (MS) images in remote sensing. However, the ability in the fusion of HS and MS images remains to be improved, particularly in large-scale scenes, due to the limited acquisition of HS images. Alternatively, we super-resolve MS images in the spectral domain by the means of partially overlapped HS images, yielding a novel and promising topic: spectral superresolution (SSR) of MS imagery. This is challenging and less investigated task due to its high ill-posedness in inverse imaging. To this end, we develop a simple but effective method, called joint sparse and low-rank learning (J-SLoL), to spectrally enhance MS images by jointly learning low-rank HS-MS dictionary pairs from overlapped regions. J-SLoL infers and recovers the unknown hyperspectral signals over a larger coverage by sparse coding on the learned dictionary pair. Furthermore, we validate the SSR performance on three HS-MS datasets (two for classification and one for unmixing) in terms of reconstruction, classification, and unmixing by comparing with several existing state-of-the-art baselines, showing the effectiveness and superiority of the proposed J-SLoL algorithm. Furthermore, the codes and datasets will be available at: https://***/danfenghong/ IEEE_TGRS_J-SLoL, contributing to the RS community. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

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Fourier-based rotation-invariant feature boosting: An efficient framework for geospatial object detection

arXiv

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arXiv 2019年

作者： Wu, Xin Hong, Danfeng Chanussot, Jocelyn Xu, Yang Tao, Ran Wang, Yue School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Fractional Signals and Systems School of Information and Electronics Beijing Institute of Technology Beijing100081 China Wessling82234 Germany Munich80333 Germany Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-lab GrenobleF-38000 France Faculty of Electrical and Computer Engineering University of Iceland Reykjavik101 Iceland School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Letters on IEEE Xplore. Geospatial object detection of remote sensing imagery has been attracting an increasing interest in recent years, due to the rapid development in spaceborne imaging. Most of previously proposed object detectors are very sensitive to object deformations, such as scaling and rotation. To this end, we propose a novel and efficient framework for geospatial object detection in this letter, called Fourier-based rotation-invariant feature boosting (FRIFB). A Fourier-based rotation-invariant feature is first generated in polar coordinate. Then, the extracted features can be further structurally refined using aggregate channel features. This leads to a faster feature computation and more robust feature representation, which is good fitting for the coming boosting learning. Finally, in the test phase, we achieve a fast pyramid feature extraction by estimating a scale factor instead of directly collecting all features from image pyramid. Extensive experiments are conducted on two subsets of NWPU VHR-10 dataset, demonstrating the superiority and effectiveness of the FRIFB compared to previous state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Aggregates

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Potential to identify neutrino mass ordering with reactor antineutrinos at JUNO

arXiv

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arXiv 2024年

作者： Abusleme, Angel Adam, Thomas Ahmad, Shakeel Ahmed, Rizwan Aiello, Sebastiano Akram, Muhammad Aleem, Abid An, Fengpeng An, Qi Andronico, Giuseppe Anfimov, Nikolay Antonelli, Vito Antoshkina, Tatiana Asavapibhop, Burin de André, João Pedro Athayde Marcondes Auguste, Didier Bai, Weidong Balashov, Nikita Baldini, Wander Barresi, Andrea Basilico, Davide Baussan, Eric Bellato, Marco Beretta, Marco Bergnoli, Antonio Bick, Daniel Bieger, Lukas Biktemerova, Svetlana Birkenfeld, Thilo Morton-Blake, Iwan Blum, David Blyth, Simon Bolshakova, Anastasia Bongrand, Mathieu Bordereau, Clément Breton, Dominique Brigatti, Augusto Brugnera, Riccardo Bruno, Riccardo Budano, Antonio Busto, Jose Cabrera, Anatael Caccianiga, Barbara Cai, Hao Cai, Xiao Cai, Yanke Cai, Zhiyan Callier, Stéphane Cammi, Antonio Campeny, Agustin Cao, Chuanya Cao, Guofu Cao, Jun Caruso, Rossella Cerna, Cédric Cerrone, Vanessa Chan, Chi Chang, Jinfan Chang, Yun Chatrabhuti, Auttakit Chen, Chao Chen, Guoming Chen, Pingping Chen, Shaomin Chen, Yixue Chen, Yu Chen, Zhangming Chen, Zhiyuan Chen, Zikang Cheng, Jie Cheng, Yaping Cheng, Yu Chin Chepurnov, Alexander Chetverikov, Alexey Chiesa, Davide Chimenti, Pietro Chin, Yen-Ting Chu, Ziliang Chukanov, Artem Claverie, Gérard Clementi, Catia Clerbaux, Barbara Molla, MartaColomer Di Lorenzo, Selma Conforti Coppi, Alberto Corti, Daniele Csakli, Simon Corso, Flavio Dal Dalager, Olivia Datta, Jaydeep De LaTaille, Christophe Deng, Zhi Deng, Ziyan Ding, Xiaoyu Ding, Xuefeng Ding, Yayun Dirgantara, Bayu Dittrich, Carsten Dmitrievsky, Sergey Dohnal, Tadeas Dolzhikov, Dmitry Donchenko, Georgy Dong, Jianmeng Doroshkevich, Evgeny Dou, Wei Dracos, Marcos Druillole, Frédéric Du, Ran Du, Shuxian Dugas, Katherine Dusini, Stefano Duyang, Hongyue Eck, Jessica Enqvist, Timo Fabbri, Andrea Fahrendholz, Ulrike Fan, Lei Fang, Jian Fang, Wenxing Fargetta, Marco Fedoseev, Dmitry Fei, Zhengyong Feng, Li-Cheng Feng, Qichun Ferraro, Federico Fournier, Amélie Gan, Haonan Gao, Feng Garfagnini, Alberto Gavrikov, Arsenii Giammarchi, Marco Giudice, Yerevan Physics Institute Yerevan Armenia Université Libre de Bruxelles Brussels Belgium Universidade Estadual de Londrina Londrina Brazil Pontificia Universidade Catolica do Rio de Janeiro Rio de Janeiro Brazil ANID Chile Pontificia Universidad Católica de Chile Santiago Chile Beijing Institute of Spacecraft Environment Engineering Beijing China Beijing Normal University Beijing China China Institute of Atomic Energy Beijing China Institute of High Energy Physics Beijing China North China Electric Power University Beijing China School of Physics Peking University Beijing China Tsinghua University Beijing China University of Chinese Academy of Sciences Beijing China Jilin University Changchun China College of Electronic Science and Engineering National University of Defense Technology Changsha China Chongqing University Chongqing China Dongguan University of Technology Dongguan China Jinan University Guangzhou China Sun Yat-Sen University Guangzhou China Harbin Institute of Technology Harbin China University of Science and Technology of China Hefei China The Radiochemistry and Nuclear Chemistry Group University of South China Hengyang China Wuyi University Jiangmen China Shandong University Jinan China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Qingdao China Nanjing University Nanjing China Guangxi University Nanning China East China University of Science and Technology Shanghai China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai China Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological Sciences Shijiazhuang China Nankai University Tianjin China Wuhan University Wuhan China Xi’an Jiaotong University Xi’an China Xiamen University Xiamen China School of Physics and Microelectronics Zhengzhou University Zhengzhou China Institute of Physics National Ya

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment under construction in South China. This paper presents an updated estimate of JUNO’s sensitivity to neutrino mass ordering using the reactor antineutrinos emitted from eight nuclear reactor cores in the Taishan and Yangjiang nuclear power plants. This measurement is planned by studying the fine interference pattern caused by quasi-vacuum oscillations in the oscillated antineutrino spectrum at a baseline of 52.5 km and is completely independent of the CP violating phase and neutrino mixing angle θ23. The sensitivity is obtained through a joint analysis of JUNO and Taishan Antineutrino Observatory (TAO) detectors utilizing the best available knowledge to date about the location and overburden of the JUNO experimental site, local and global nuclear reactors, JUNO and TAO detector responses, expected event rates and spectra of signals and backgrounds, and systematic uncertainties of analysis inputs. We find that a 3σ median sensitivity to reject the wrong mass ordering hypothesis can be reached with an exposure to approximately 6.5 years × 26.6 GW thermal power. © 2024, CC BY.

关键词： Nuclear power plants

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Prediction of Energy Resolution in the JUNO experiment

arXiv

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arXiv 2024年

作者： Abusleme, Angel Adam, Thomas Adamowicz, Kai Ahmad, Shakeel Ahmed, Rizwan Aiello, Sebastiano An, Fengpeng An, Qi Andronico, Giuseppe Anfimov, Nikolay Antonelli, Vito Antoshkina, Tatiana de André, João Pedro Athayde Marcondes Auguste, Didier Bai, Weidong Balashov, Nikita Baldini, Wander Barresi, Andrea Basilico, Davide Baussan, Eric Bellato, Marco Beretta, Marco Bergnoli, Antonio Bick, Daniel Bieger, Lukas Biktemerova, Svetlana Birkenfeld, Thilo Blake, Iwan Blum, David Blyth, Simon Bolshakova, Anastasia Bongrand, Mathieu Bordereau, Clément Breton, Dominique Brigatti, Augusto Brugnera, Riccardo Bruno, Riccardo Budano, Antonio Busto, Jose Cabrera, Anatael Caccianiga, Barbara Cai, Hao Cai, Xiao Cai, Yanke Cai, Zhiyan Callier, Stéphane Calvez, Steven Cammi, Antonio Campeny, Agustin Cao, Chuanya Cao, Guofu Cao, Jun Caruso, Rossella Cerna, Cédric Cerrone, Vanessa Chang, Jinfan Chang, Yun Chatrabhuti, Auttakit Chen, Chao Chen, Guoming Chen, Pingping Chen, Shaomin Chen, Xin Chen, Yiming Chen, Yixue Chen, Yu Chen, Zelin Chen, Zhangming Chen, Zhiyuan Chen, Zikang Cheng, Jie Cheng, Yaping Cheng, Yu Chin Chepurnov, Alexander Chetverikov, Alexey Chiesa, Davide Chimenti, Pietro Chin, Yen-Ting Chou, Po-Lin Chu, Ziliang Chukanov, Artem Claverie, Gérard Clementi, Catia Clerbaux, Barbara Molla, Marta Colomer Di Lorenzo, Selma Conforti Coppi, Alberto Corti, Daniele Csakli, Simon Cui, Chenyang Corso, Flavio Dal Dalager, Olivia Datta, Jaydeep De La Taille, Christophe Deng, Zhi Deng, Ziyan Ding, Xiaoyu Ding, Xuefeng Ding, Yayun Dirgantara, Bayu Dittrich, Carsten Dmitrievsky, Sergey Dohnal, Tadeas Dolzhikov, Dmitry Donchenko, Georgy Dong, Jianmeng Doroshkevich, Evgeny Dou, Wei Dracos, Marcos Druillole, Frédéric Du, Ran Du, Shuxian Dugas, Katherine Dusini, Stefano Duyang, Hongyue Eck, Jessica Enqvist, Timo Fabbri, Andrea Fahrendholz, Ulrike Fan, Lei Fang, Jian Fang, Wenxing Fedoseev, Dmitry Feng, Li-Cheng Feng, Qichun Ferraro, Federico Fournier, Amélie Fritsch, Fritsch Gan, Haonan Gao, Feng Gao, Feng Garfagnini, Alberto Gavrikov, Arsenii Yerevan Physics Institute Yerevan Armenia Université Libre de Bruxelles Brussels Belgium Universidade Estadual de Londrina Londrina Brazil Pontificia Universidade Catolica do Rio de Janeiro Rio de Janeiro Brazil Anid Chile Pontificia Universidad Católica de Chile Santiago Chile Beijing Institute of Spacecraft Environment Engineering Beijing China Beijing Normal University Beijing China China Institute of Atomic Energy Beijing China Institute of High Energy Physics Beijing China North China Electric Power University Beijing China School of Physics Peking University Beijing China Tsinghua University Beijing China University of Chinese Academy of Sciences Beijing China Jilin University Changchun China College of Electronic Science and Engineering National University of Defense Technology Changsha China Chongqing University Chongqing China Dongguan University of Technology Dongguan China Jinan University Guangzhou China Sun Yat-sen University Guangzhou China Harbin Institute of Technology Harbin China University of Science and Technology of China Hefei China The Radiochemistry and Nuclear Chemistry Group University of South China Hengyang China Wuyi University Jiangmen China Shandong University Jinan China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Qingdao China Nanjing University Nanjing China Guangxi University Nanning China East China University of Science and Technology Shanghai China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai China Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological Sciences Shijiazhuang China Nankai University Tianjin China Wuhan University Wuhan China Xi’an Jiaotong University Xi’an China Xiamen University Xiamen China School of Physics and Microelectronics Zhengzhou University Zhengzhou China Institute of Physics National Ya

This paper presents an energy resolution study of the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3% at 1 MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components of the JUNO detector. Various factors affecting the detection of inverse beta decay signals have an impact on the energy resolution, extending beyond the statistical fluctuations of the detected number of photons, such as the properties of the liquid scintillator, performance of photomultiplier tubes, and the energy reconstruction algorithm. To account for these effects, a full JUNO simulation and reconstruction approach is employed. This enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy resolution. The results of study reveal an energy resolution of 2.95% at 1 MeV. Furthermore, this study assesses the contribution of major effects to the overall energy resolution budget. This analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data collection. Moreover, it provides a guideline for comprehending the energy resolution characteristics of liquid scintillator-based detectors. © 2024, CC BY.

关键词： Budget control

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More diverse means better: Multimodal deep learning meets remote sensing imagery classification

arXiv

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arXiv 2020年

作者： Hong, Danfeng Gao, Lianru Yokoya, Naoto Yao, Jing Chanussot, Jocelyn Du, Qian Zhang, Bing Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-lab Grenoble38000 France Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Graduate School of Frontier Sciences University of Tokyo Chiba277-8561 Japan RIKEN Tokyo103-0027 Japan School of Mathematics and Statistics Xian Jiaotong University Xian710049 China Univ. Grenoble Alpes INRIA CNRS Grenoble INP LJK Grenoble38000 France Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China

This is the pre-acceptance version, to read the final version please go to IEEE Transactions on Geoscience and Remote Sensing on IEEE Xplore. Classification and identification of the materials lying over or beneath the Earth’s surface have long been a fundamental but challenging research topic in geoscience and remote sensing (RS) and have garnered a growing concern owing to the recent advancements of deep learning techniques. Although deep networks have been successfully applied in single-modality-dominated classification tasks, yet their performance inevitably meets the bottleneck in complex scenes that need to be finely classified, due to the limitation of information diversity. In this work, we provide a baseline solution to the aforementioned difficulty by developing a general multimodal deep learning (MDL) framework. In particular, we also investigate a special case of multi-modality learning (MML) – cross-modality learning (CML) that exists widely in RS image classification applications. By focusing on "what", "where", and "how" to fuse, we show different fusion strategies as well as how to train deep networks and build the network architecture. Specifically, five fusion architectures are introduced and developed, further being unified in our MDL framework. More significantly, our framework is not only limited to pixel-wise classification tasks but also applicable to spatial information modeling with convolutional neural networks (CNNs). To validate the effectiveness and superiority of the MDL framework, extensive experiments related to the settings of MML and CML are conducted on two different multimodal RS datasets. Furthermore, the codes and datasets will be available at: https://***/danfenghong/IEEE_TGRS_MDL-RS, Copyright © 2020, The Authors. All rights reserved.

关键词： Network architecture

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Analysis of vehicle stop-and-go driving behaviors at signalized intersections 17

Analysis of vehicle stop-and-go driving behaviors at signali...

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17th COTA International Conference of Transportation Professionals: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and Innovation, CICTP 2017

作者： Zhu, Sicong Kim, Inhi Zhao, Qi Song, Guohua Yu, Lei Chen, Zhigui MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology Beijing Jiaotong Univ. Haidian District Beijing100044 China Clayton Campus School of Civil Engineering Monash Univ. Australia North China Univ. of Technology 5 Jinyuanzhuang Rd. Shijingshan District Beijing100144 China College of Science and Technology Texas Southern Univ. 3100 Cleburne Ave. HoustonTX77004 United States Beijing Jiaotong Univ. Haidian District Beijing100044 China

ISBN: (数字)9780784480915

ISBN: (纸本)9780784480915

Vehicle specific power (VSP) has been widely utilized to reveal the impact of vehicle operating conditions on estimations of emissions and energy consumption that are dependent upon speed and acceleration or deceleration based on second-by-second vehicle operation. The analysis of acceleration and deceleration stop-and-go behaviors at intersections is needed for queue simulation and emissions estimation. High-resolution data obtained by driving simulator sensors can provide useful insights into driving behavior at intersections. Hence, aggressive driving behaviors that account for a significant share of vehicle emissions can be identified by data-driven analysis of VSP distribution. Based on the analysis results, an aggregated VSP distribution represents the profile of driving behaviors at signalized intersections. © 2018 American Society of civil Engineers (ASCE). All rights reserved.

关键词： Energy utilization

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A prototype IOT based wireless sensor network for traffic information monitoring

引用

International Journal of Pavement Research and Technology 2018年第2期11卷 146-152页

作者： Huang, Yucheng Wang, Linbing Hou, Yue Zhang, Wei Zhang, Yinning Virginia Polytechnic Institute and State Univ. BlacksburgVA24061 United States USTB-Virginia Tech Joint Lab on Multifunctional Materials National Center for Materials Service Safety University of Science and Technology Beijing Beijing Virginia Tech BlacksburgVA24061 United States National Center for Materials Service Safety University of Science and Technology Beijing Beijing100083 China Graduate Business School Bentley University WalthamMA02452 United States Dept. of Civil and Environmental Engineering Virginia Polytechnic Institute and State Univ. BlacksburgVA24061 United States

An Internet of things (IOT) based wireless sensor system, solely using wireless accelerometers, is developed for traffic volume and vehicle classification monitoring in this paper. A series of laboratory tests, field tests as well as numerical simulation were performed to validate the feasibility and accuracy of the monitoring system. Besides, in order to eliminate the impacts of noises in the output signals, an advanced algorithm is developed to analyze the test data. The findings based on the test results indicate that the system is capable of reliably detecting axles and calculating axle spacing in both laboratory and field tests. In addition, compared with the actual measurements, the numerical simulation further validates the feasibility of the integrated wireless sensor system for traffic information monitoring. © 2018 Chinese Society of Pavement engineering

关键词： Accelerometers

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