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Model-independent Approach of the JUNO 8B Solar Neutrino Program

The Astrophysical Journal 2024年第2期965卷 122-122页

作者： Jie Zhao Baobiao Yue Haoqi Lu Yufeng Li Jiajie Ling Zeyuan Yu Angel Abusleme Thomas Adam Shakeel Ahmad Rizwan Ahmed Sebastiano Aiello Muhammad Akram Abid Aleem Tsagkarakis Alexandros Fengpeng An Qi An Giuseppe Andronico Nikolay Anfimov Vito Antonelli Tatiana Antoshkina Burin Asavapibhop João Pedro Athayde Marcondes de André Didier Auguste Weidong Bai Nikita Balashov Wander Baldini Andrea Barresi Davide Basilico Eric Baussan Marco Bellato Antonio Bergnoli Thilo Birkenfeld Sylvie Blin David Blum Simon Blyth Anastasia Bolshakova Mathieu Bongrand Clément Bordereau Dominique Breton Augusto Brigatti Riccardo Brugnera Riccardo Bruno Antonio Budano Jose Busto Ilya Butorov Anatael Cabrera Barbara Caccianiga Hao Cai Xiao Cai Yanke Cai Zhiyan Cai Riccardo Callegari Antonio Cammi Agustin Campeny Chuanya Cao Guofu Cao Jun Cao Rossella Caruso Cédric Cerna Chi Chan Jinfan Chang Yun Chang Guoming Chen Pingping Chen Po-An Chen Shaomin Chen Xurong Chen Yixue Chen Yu Chen Zhiyuan Chen Zikang Chen Jie Cheng Yaping Cheng Alexander Chepurnov Alexey Chetverikov Davide Chiesa Pietro Chimenti Artem Chukanov Gérard Claverie Catia Clementi Barbara Clerbaux Marta Colomer Molla Selma Conforti Di Lorenzo Daniele Corti Flavio Dal Corso Olivia Dalager Christophe De La Taille Zhi Deng Ziyan Deng Wilfried Depnering Marco Diaz Xuefeng Ding Yayun Ding Bayu Dirgantara Sergey Dmitrievsky Tadeas Dohnal Dmitry Dolzhikov Georgy Donchenko Jianmeng Dong Evgeny Doroshkevich Marcos Dracos Frédéric Druillole Ran Du Shuxian Du Stefano Dusini Martin Dvorak Timo Enqvist Heike Enzmann Andrea Fabbri Donghua Fan Lei Fan Jian Fang Wenxing Fang Marco Fargetta Dmitry Fedoseev Zhengyong Fei Li-Cheng Feng Qichun Feng Richard Ford Amélie Fournier Haonan Gan Feng Gao Alberto Garfagnini Arsenii Gavrikov Marco Giammarchi Nunzio Giudice Maxim Gonchar Guanghua Gong Hui Gong Yuri Gornushkin Alexandre Göttel Marco Grassi Maxim Gromov Vasily Gromov Minghao Gu Xiaofei Gu Yu Gu Mengyun Guan Yuduo Guan Nunzio Guardone Cong Guo Jingyuan Guo Wanlei Guo Xinheng Guo Yuhang Guo Paul Ha Institute of High Energy Physics Beijing People's Republic of China liyufeng@*** Sun Yat-Sen University Guangzhou People's Republic of China lingjj5@*** Pontificia Universidad Católica de Chile Santiago Chile Millennium Institute for SubAtomic Physics at the High-energy Frontier (SAPHIR) ANID Chile IPHC Université de Strasbourg CNRS/IN2P3 F-67037 Strasbourg France Pakistan Institute of Nuclear Science and Technology Islamabad Pakistan INFN Catania and Dipartimento di Fisica e Astronomia dell Università di Catania Catania Italy III. Physikalisches Institut B RWTH Aachen University Aachen Germany East China University of Science and Technology Shanghai People's Republic of China University of Science and Technology of China Hefei People's Republic of China Joint Institute for Nuclear Research Dubna Russia INFN Sezione di Milano and Dipartimento di Fisica dell Università di Milano Milano Italy Department of Physics Faculty of Science Chulalongkorn University Bangkok Thailand IJCLab Université Paris-Saclay CNRS/IN2P3 91405 Orsay France Department of Physics and Earth Science University of Ferrara and INFN Sezione di Ferrara Ferrara Italy INFN Milano Bicocca and University of Milano Bicocca Milano Italy INFN Sezione di Padova Padova Italy Eberhard Karls Universität Tübingen Physikalisches Institut Tübingen Germany SUBATECH Nantes Université IMT Atlantique CNRS-IN2P3 Nantes France Université Bordeaux CNRS LP2i Bordeaux UMR 5797 F-33170 Gradignan France Department of Physics National Taiwan University Taipei Dipartimento di Fisica e Astronomia dell’Università di Padova and INFN Sezione di Padova Padova Italy University of Roma Tre and INFN Sezione Roma Tre Roma Italy Centre de Physique des Particules de Marseille Marseille France Wuhan University Wuhan People's Republic of China INFN Milano Bicocca and Politecnico of Milano Milano Italy Institute of Physics National Yang Ming Chiao Tung University Hsinchu National United University Miao-Li Guangxi Uni

The physics potential of detecting 8B solar neutrinos will be exploited at the Jiangmen Underground Neutrino Observatory (JUNO), in a model-independent manner by using three distinct channels of the charged current (C...

The physics potential of detecting ⁸B solar neutrinos will be exploited at the Jiangmen Underground Neutrino Observatory (JUNO), in a model-independent manner by using three distinct channels of the charged current (CC), neutral current (NC), and elastic scattering (ES) interactions. Due to the largest-ever mass of ¹³C nuclei in the liquid scintillator detectors and the expected low background level, ⁸B solar neutrinos are observable in the CC and NC interactions on ¹³C for the first time. By virtue of optimized event selections and muon veto strategies, backgrounds from the accidental coincidence, muon-induced isotopes, and external backgrounds can be greatly suppressed. Excellent signal-to-background ratios can be achieved in the CC, NC, and ES channels to guarantee the observation of the ⁸B solar neutrinos. From the sensitivity studies performed in this work, we show that JUNO, with 10 yr of data, can reach the 1σ precision levels of 5%, 8%, and 20% for the ⁸B neutrino flux, ${\sin }^{2}{\theta }_{12}$

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Statistical inference for community detection in signed networks

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Physical Review E 2017年第4期95卷 042313-042313页

作者： Xuehua Zhao Bo Yang Xueyan Liu Huiling Chen School of Digital Media Shenzhen Institute of Information Technology Shenzhen 518172 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China College of Computer Science and Technology Jilin University Changchun 130012 China College of Physics and Electronic Information Wenzhou University Wenzhou 325035 China

The problem of community detection in networks has received wide attention and proves to be computationally challenging. In recent years, with the surge of signed networks with positive links and negative links, to find community structure in such signed networks has become a research focus in the area of network science. Although many methods have been proposed to address the problem, their performance seriously depends on the predefined optimization objectives or heuristics which are usually difficult to accurately describe the intrinsic structure of community. In this study, we present a statistical inference method for community detection in signed networks, in which a probabilistic model is proposed to model signed networks and the expectation-maximization–based parameter estimation method is deduced to find communities in signed networks. In addition, to efficiently analyze signed networks without any a priori information, a model selection criterion is also proposed to automatically determine the number of communities. In our experiments, the proposed method is tested in the synthetic and real-word signed networks and compared with current methods. The experimental results show the proposed method can more efficiently and accurately find the communities in signed networks than current methods. Notably, the proposed method is a mathematically principled method.

关键词： Clustering Community structure Stochastic inference

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Calibration strategy of the JUNO experiment

arXiv

引用

arXiv 2020年

作者： Abusleme, Angel Adam, Thomas Ahmad, Shakeel Ahmed, Rizwan Aiello, Sebastiano Akram, Muhammad An, Fengpeng An, Guangpeng An, Qi Andronico, Giuseppe Anfimov, Nikolay Antonelli, Vito Antoshkina, Tatiana Asavapibhop, Burin de André, João Pedro Athayde Marcondes Auguste, Didier Babic, Andrej 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 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 Costa, Salvatore Corso, Flavio Dal 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, Shuxian Dusini, Stefano Dvorak, Martin Enqvist, Timo Enzmann, Heike Fabbri, Andrea Fajt, Lukas Fan, Donghua Fan, Lei Fang, Can Fang, Jian Fang, Wenxing Fargetta, Marco Fedoseev, Dmitry Fekete, Vladko Feng, Li-Cheng Feng, Qichun Ford, Richard Formozov, Andrey Fournier, Amélie Gan, Haonan Gao, Feng Garfagnini, Alberto Genster, Christoph 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, Xi 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 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 ofEducation 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 Jiaotong University Xi’an China Xiamen University Xiamen

We present the calibration strategy for the 20 kton liquid scintillator central detector of the Jiangmen Underground Neutrino Observatory (JUNO). By utilizing a comprehensive multiple-source and multiple-positional calibration program, in combination with a novel dual calorimetry technique exploiting two independent photosensors and readout systems, we demonstrate that the JUNO central detector can achieve a better than 1% energy linearity and a 3% effective energy resolution, required by the neutrino mass ordering determination. Copyright © 2020, The Authors. All rights reserved.

关键词： Calibration

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TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy Resolution

arXiv

引用

arXiv 2020年

作者： Abusleme, Angel Adam, Thomas Ahmad, Shakeel Aiello, Sebastiano Akram, Muhammad Ali, Nawab An, Fengpeng An, Guangpeng An, Qi Andronico, Giuseppe Anfimov, Nikolay Antonelli, Vito Antoshkina, Tatiana Asavapibhop, Burin de André, João Pedro Athayde Marcondes Auguste, Didier Babic, Andrej Baldini, Wander Barresi, Andrea Baussan, Eric Bellato, Marco Bergnoli, Antonio Bernieri, Enrico Biare, David Birkenfeld, Thilo Blin, Sylvie Blum, David Blyth, Simon Bolshakova, Anastasia Bongrand, Mathieu Bordereau, Clément Breton, Dominique Brigatti, Augusto Brugnera, Riccardo Budano, Antonio Buscemi, Mario Busto, Jose Butorov, Ilya Cabrera, Anatael Cai, Hao Cai, Xiao Cai, Yanke Cai, Zhiyan Cammi, Antonio Campeny, Agustin Cao, Chuanya Cao, Guofu Cao, Jun Caruso, Rossella Cerna, Cédric Chakaberia, Irakli Chang, Jinfan Chang, Yun Chen, Pingping Chen, Po-An Chen, Shaomin Chen, Shenjian Chen, Xurong Chen, Yi-Wen Chen, Yixue Chen, Yu Chen, Zhang Cheng, Jie Cheng, Yaping Chepurnov, Alexander Chiesa, Davide Chimenti, Pietro Chukanov, Artem Chuvashova, Anna Claverie, Gérard Clementi, Catia Clerbaux, Barbara Lorenzo, Selma Conforti Di Corti, Daniele Costa, Salvatore Dal Corso, Flavio de la Taille, Christophe Deng, Jiawei Deng, Zhi Deng, Ziyan Depnering, Wilfried Diaz, Marco Ding, Xuefeng Ding, Yayun Dirgantara, Bayu Dmitrievsky, Sergey Dohnal, Tadeas Donchenko, Georgy Dong, Jianmeng Dornic, Damien Doroshkevich, Evgeny Dracos, Marcos Druillole, Frédéric Du, Shuxian Dusini, Stefano Dvorak, Martin Enqvist, Timo Enzmann, Heike Fabbri, Andrea Fajt, Lukas Fan, Donghua Fan, Lei Fang, Can Fang, Jian Fatkina, Anna Fedoseev, Dmitry Fekete, Vladko Feng, Li-Cheng Feng, Qichun Ford, Richard Formozov, Andrey Fournier, Amélie Gan, Haonan Gao, Feng Garfagnini, Alberto Göttel, Alexandre Genster, Christoph Giammarchi, Marco Giaz, Agnese Giudice, Nunzio Giuliani, Franco Gonchar, Maxim Gong, Guanghua Gong, Hui Gorchakov, Oleg Gornushkin, Yuri Grassi, Marco Grewing, Christian Gromov, Maxim Gromov, Vasily Gu, Minghao Gu, Xiaofei Gu, Yu Guan, Mengyun Guardone, Nun 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 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 Radiochemistry and Nuclear Chemistry Group in University of South China Hengyang China Wuyi University Jiangmen China Shandong University Jinan 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 Jiaotong University Xi’an China Xiamen University Xiamen China School of Physics and Microelectronics Zhengzhou University Zhengzhou China Institute of Physics National Chiao-Tung Universi

The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A ton-level liquid scintillator detector will be placed at ∼ 30 m from a core of the Taishan Nuclear Power Plant. The reactor antineutrino spectrum will be measured with sub-percent energy resolution, to provide a reference spectrum for future reactor neutrino experiments, and to provide a benchmark measurement to test nuclear databases. A spherical acrylic vessel containing 2.8 ton gadolinium-doped liquid scintillator will be viewed by 10 m2 Silicon Photomultipliers (SiPMs) of > 50% photon detection efficiency with almost full coverage. The photoelectron yield is about 4500 per MeV, an order higher than any existing large-scale liquid scintillator detectors. The detector operates at -50◦C to lower the dark noise of SiPMs to an acceptable level. The detector will measure about 2000 reactor antineutrinos per day, and is designed to be well shielded from cosmogenic backgrounds and ambient radioactivities to have a ∼ 10% background-to-signal ratio. The experiment is expected to start operation in 2022. Copyright © 2020, The Authors. All rights reserved.

关键词： Nuclear power plants

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Efficient Actor-critic Algorithm with Dual Piecewise Model Learning

Efficient Actor-critic Algorithm with Dual Piecewise Model L...

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IEEE Symposium Series on Computational Intelligence

作者： Shan Zhong Quan Liu Shengrong Gong Qiming Fu Jin Xu Ministry of Education Jilin University Changchun Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and Technology Suzhou Jiangsu School of Computer Science and Technology Soochow University Suzhou Jiangsu School of Computer Science and Engineering Changshu Institute of Technology Changshu College of Electronic & Information Engineering Suzhou University of Science and Technology Suzhou Jiangsu

The actor-critic (AC) algorithm is a class of important reinforcement learning (RL) methods commonly used in continuous MDPs. However, few of its variants concern in sample efficiency. This paper proposed a AC variant, called AC-DPML, aiming at handling RL problems by combining AC with dual piecewise model learning and planning. Dual piecewise model learning stands for learning two models, the state-based piecewise model and the action-based piecewise model. The state-based piecewise model is established according to the division of the state space, while the action-based piecewise model is built depending on the division of the action space. Both models are linearly approximated and learned by the samples attributed to. The planning processes of the two models are launched only if the prediction errors do not exceed the error threshold. The value function and the policy are further updated by the planning of the state-based piecewise model, but only the value function is updated in that of the action-based piecewise model. Experimentally, AC-DPML is implemented in two classic RL benchmarks with continuous MDPs. The results demonstrate that AC-DPML coordinates the two models perfectly and it outperforms the representative methods in terms of convergence rate and sample efficiency.

关键词： Computational modeling Planning Predictive models Prediction algorithms Estimation Indexes Data models

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Blind decovolution algorithm of turbulence images using Gaussian model 7

Blind decovolution algorithm of turbulence images using Gaus...

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7th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2016

作者： Zhang, Lijuan Qiu, Huan Su, Zhengbo Yin, Tingting Li, Dongming Peng, Jiaqi Su, Wei College of Computer Science and Engineering Changchun University of Technology Changchun130012 China School of Computer Science and Technology Harbin Institute of Technology 150001 China School of Information Technology Jilin Agriculture University Changchun130118 China Informatization Center Changchun University of Science and Technology Changchun130022 China

ISBN: (纸本)9784990534349

In order to overcome the influence of atmospheric turbulence and obtain a high resolution target image, we present a blind decovolution algorithm for the restoration of atmospheric turbulence-degraded images basing on Gaussian model. This paper estimates the initial value of point spread function (PSF) using the wavefront reconstruction, and then apply to maximum likelihood (ML) estimation as the basic principle in order to estimate both the object image and the PSF jointly. The regularization method is used to protect the edge and detailed information. The restoration procedure involves the ML criterion to minimize the cost function as well as adding the regularize parameters. In order to verify the effectiveness of our proposed restoration method, a series of restoring experiments are performed on the simulated and real atmospheric turbulence-degraded images. The experimental results show that, compared with the ML-EM algorithm, our approach exhibits better performance in signal-to-noise ratio (ISNR), computation time, and visual quality, which opens up many perspectives for atmospheric turbulence degraded images restoring in the astronautics field. © 2016, Fuji Technology Press. All rights reserved.

关键词： Optical transfer function

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Research on selectional preferences algorithm on Chinese language 7

Research on selectional preferences algorithm on Chinese lan...

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7th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2016

作者： Li, Dongming Su, Zhengbo Wang, Ting Peng, Jiaqi Zheng, Junhao Zhang, Lijuan Su, Wei School of Information Technology Jilin Agriculture University Changchun130118 China School of Computer Science and Technology Harbin Institute of Technology 150001 China College of Computer Science and Engineering Changchun University of Technology Changchun130012 China Informatization Center Changchun University of Science and Technology Changchun130022 China

ISBN: (纸本)9784990534349

An algorithm of capturing selectional preferences on Chinese language is given, which is based on minimum description length (MDL) principle. We combine it with latent semantic clustering (LSC) for better results. Our proposed algorithm includes three steps. Firstly, MDL is used to calculate δSC of each verb-noun pair;Then, the probabilities of a verb preferring for a noun P(v,n) is computed based on LSC model and EM is used to evaluate the parameters;Lately, for the same verb-noun pair, the sum of δSC and P(v,n) is considered to represent the association between the verb and the noun. The experiments show MDL performs better than LSC, and the combining approach is the best. No ontology is involved, and just a segmentation and part of speech labeling of a Chinese corpora is needed. Both indicate that our proposed method is appropriate for Chinese language. © 2016, Fuji Technology Press. All rights reserved.

关键词： Semantics

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A Diverse View of science to Catalyse Change

引用

Angewandte Chemie 2020年第42期132卷

作者： Urbina-Blanco C.A. Jilani S.Z. Speight I.R. Bojdys M.J. Friščić T. Stoddart J.F. Nelson T.L. Mack J. Robinson R.A.S. Waddell E.A. Lutkenhaus J.L. Godfrey M. Abboud M.I. Aderinto S.O. Aderohunmu D. Bibič L. Borges J. Dong V.M. Ferrins L. Fung F.M. John T. Lim F.P.L. Masters S.L. Mambwe D. Thordarson P. Titirici M.-M. Tormet-González G.D. Unterlass M.M. Wadle A. Yam V.W.W. Yang Y.-W. Laboratory for Chemical Technology Ghent University Ghent B-9052 Belgium Department of Chemistry Georgetown University Washington DC 20057 United States Department of Chemistry Vanderbilt University Nashville TN 37235 United States Department of Chemistry King's College London (UK) and Department of Chemistry Humboldt-Universität zu Berlin Berlin 12489 Germany Department of Chemistry McGill University Montréal QC H3A 0B8 Canada Department of Chemistry Northwestern University Evanston IL 60208 United States Institute for Molecular Design and Synthesis Tianjin University Tianjin 300072 China School of Chemistry University of New South Wales Sydney NSW 2052 Australia Department of Chemistry Oklahoma State University Stillwater OK 74078 United States Department of Chemistry University of Cincinnati Cincinnati OH 45221 United States Department of Chemistry University of Alabama in Huntsville Huntsville AL 35899 United States Artie McFerrin Department of Chemical Engineering Texas A&M University College Station TX 77843 United States Department of Chemistry The University of Mississippi University MS 38677 United States Department of Chemistry University of Oxford Chemistry Research Laboratory Oxford OX1 3TA United Kingdom Department of Chemistry The University of Sheffield Sheffield S3 7HF United Kingdom Department of Chemistry Covenant University CST Canaanland Ota Ogun State Nigeria School of Pharmacy University of East Anglia Norwich NR4 7TJ United Kingdom Department of Chemistry CICECO—Aveiro Institute of Materials University of Aveiro Campus Universitário de Santiago Aveiro 3810-193 Portugal Department of Chemistry University of California Irvine CA 92697 United States Department of Chemistry and Chemical Biology Northeastern University Boston MA 02115 United States Department of Chemistry National University of Singapore Singapore 117543 Singapore Leibniz Institute of Surface Engineering (IOM) Leipzig 04318 G

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Restoration algorithm based on cepstrum for motion blur image 7

Restoration algorithm based on cepstrum for motion blur imag...

引用

7th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2016

作者： Li, Dongming Lv, Panfeng Su, Zhengbo Peng, Jiaqi Zheng, Junhao Zhang, Lijuan Su, Wei School of Information Technology Jilin Agriculture University Changchun130118 China School of Computer Science and Technology Harbin Institute of Technology 150001 China College of Computer Science and Engineering Changchun University of Technology Changchun130012 China Informatization Center Changchun University of Science and Technology Changchun130022 China

ISBN: (纸本)9784990534349

Image restoration algorithm based on cepstrum is proposed in this paper for restoring object image from the motion blur images with noise. The key of restoration for the degraded image is to build point spread function (PSF). PSF consists of two blur parameters, which are the blur angle and the blur length. Due to the bright band direction is the same direction as motion direction, the motion blur angle is obtained by this preprocessing cepstrum method. Then we can get the estimates of the blurred motion length due to the cepstrum's symmetry properties. Therefore, we estimate the PSF, and restore the observed image using the Lucy-Richardson iterative algorithm. In order to test the validity of the proposed algorithm, a series of restoration experiments on simulated and real degraded images are performed. The experimental result shows that the proposed algorithm can effectively reduce the noise as well as preserve the edge and detailed information. Moreover, it has lower NSME than other method. Our algorithm can provide a good basis for the subsequent feature extraction and target recognition. © 2016, Fuji Technology Press. All rights reserved.

关键词： Optical transfer function

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Group sparse Bayesian learning for active surveillance on epidemic dynamics

arXiv

引用

arXiv 2017年

作者： Pei, Hongbin Yang, Bo Liu, Jiming Dong, Lei College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education China Department of Computer Science Hong Kong Baptist University Hong Kong Institute of Remote Sensing and Geographical Information Systems Peking University Beijing China

Predicting epidemic dynamics is of great value in understanding and controlling diffusion processes, such as infectious disease spread and information propagation. This task is intractable, especially when surveillance resources are very limited. To address the challenge, we study the problem of active surveillance, i.e., how to identify a small portion of system components as sentinels to effect monitoring, such that the epidemic dynamics of an entire system can be readily predicted from the partial data collected by such sentinels. We propose a novel measure, the γ value, to identify the sentinels by modeling a sentinel network with row sparsity structure. We design a flexible group sparse Bayesian learning algorithm to mine the sentinel network suitable for handling both linear and non-linear dynamical systems by using the expectation maximization method and variational approximation. The efficacy of the proposed algorithm is theoretically analyzed and empirically validated using both synthetic and real-world data. Copyright © 2017, The Authors. All rights reserved.

关键词： Monitoring

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