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arXiv 2019年

作者： Wang, Xueying Liu, Lei Li, Guangli Dong, Xiao Zhao, Peng Feng, Xiaobing State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100049 China College of Computer Science and Technology Jilin University ChangchunJilin130012 China

Background subtraction is a significant component of computer vision systems. It is widely used in video surveillance, object tracking, anomaly detection, etc. A new data source for background subtraction appeared as the emergence of low-cost depth sensors like Microsoft Kinect, Asus Xtion PRO, etc. In this paper, we propose a background subtraction approach on depth videos, which is based on convolutional neural networks (CNNs), called BGSNet-D (BackGround Subtraction neural Networks for Depth video). The method can be used in color unavailable scenarios like poor lighting situations, and can also be applied to combine with existing RGB background subtraction methods. A preprocessing strategy is designed to reduce the influences incurred by noise from depth sensors. The experimental results on the SBM-RGBD dataset show that the proposed method outperforms existing methods on depth data, and even reaches the performance of the methods that use RGB-D data. Copyright © 2019, The Authors. All rights reserved.

关键词： Anomaly detection

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Sub-percent precision measurement of neutrino oscillation parameters with JUNO

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Chinese Physics C 2022年第12期46卷 28-49页

作者： Angel Abusleme Thomas Adam Shakeel Ahmad Rizwan Ahmed Sebastiano Aiello Muhammad Akram Abid Aleem 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 Yu Chin Cheng Alexey Chetverikov Davide Chiesa Pietro Chimenti Artem Chukanov Gérard Claverie Catia Clementi Barbara Clerbaux 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 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 Hackspacher Caren Hagner Ran Han Yang Han Miao He Wei He Tobias Heinz Patrick Hellmuth Yuekun Heng Rafael Herrera YuenKeung Ho Yerevan Physics Institute YerevanArmenia UniversiéLibre de Bruxelles BrusselsBelgium Universidade Estadual de Londrina LondrinaBrazil Pontificia Universidade Catolica do Rio de Janeiro Rio de JaneiroBrazil Pontificia Universidad Católica de Chile SantiagoChile Universidad Tecnica Federico Santa Maria ValparaisoChile Beijing Institute of Spacecraft Environment Engineering BeijingChina Beijing Normal University BeijingChina China Institute of Atomic Energy BeijingChina Institute of High Energy Physics BeijingChina North China Electric Power University BeijingChina School of Physics Peking UniversityBeijingChina Tsinghua University BeijingChina University of Chinese Academy of Sciences BeijingChina Jilin University ChangchunChina College of Electronic Science and Engineering National University of Defense TechnologyChangshaChina Chongqing University ChongqingChina Dongguan University of Technology DongguanChina Jinan University GuangzhouChina Sun Yat-Sen University GuangzhouChina Harbin Institute of Technology HarbinChina University of Science and Technology of China HefeiChina The Radiochemistry and Nuclear Chemistry Group in University of South China HengyangChina Wuyi University JiangmenChina Shandong University JinanChinaand Key Laboratory of Particle Physics and Particle Irradiation of Ministry of EducationShandong UniversityQingdaoChina Institute of Modern Physics Chinese Academy of SciencesLanzhouChina Nanjing University NanjingChina Guangxi University NanningChina East China University of Science and Technology ShanghaiChina School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghaiChina Tsung-Dao Lee Institute Shanghai Jiao Tong UniversityShanghaiChina Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological SciencesShijiazhuangChina Nankai University TianjinChina Wuhan University WuhanChina Xi'an Jiaotong University Xi'anChina Xiamen University XiamenChina School of Physics and Microelectronics Zhengzhou UniversityZhengzhou

JUNO is a multi-purpose neutrino observatory under construction in the south of *** publication presents new sensitivity estimates for the measurement of the △m_(31)^(2),△m_(21)^(2),sin^(2)θ_(12),and sin^(2)θ_(13) oscillation parameters using reactor antineutrinos,which is one of the primary physics goals of the *** sensitivities are obtained using the best knowledge available to date on the location and overburden of the experimental site,the nuclear reactors in the surrounding area and beyond,the detector response uncertainties,and the reactor antineutrino spectral shape constraints expected from the TAO satellite *** is found that the △m_(21)^(2) and sin^(2)θ_(12) oscillation parameters will be determined to 0.5%precision or better in six years of data *** the same period,the △m_(31)^(2) parameter will be determined to about 0.2%precision for each mass ordering *** new precision represents approximately an order of magnitude improvement over existing constraints for these three parameters.

关键词： neutrino oscillation reactor antineutrino precision measurement JUNO

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Maximum Lifetime Optimized Collaboration of Data and Energy Routing in VANET

Maximum Lifetime Optimized Collaboration of Data and Energy ...

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2019虚拟现实与智能系统国际会议

作者： Ming Hu Hanlin Chen Jiejun Hu Hui Yan Ping Yu Platform of Jilin Province Science and Technology (S&T) Department "Jilin Province S&T Innovation Center for Physical Simulation and Security of Water resources and Electric Power Engineering" Changchun Institute of Technology College of Computer Science and Technology Changchun University of Technology School of Computer Sciences and Electrical Engineering University of Essex

The roadside units(RSUs) based on traditional grid has high carbon footprint and high deployment cost in vehicle dedicated *** ideal green alternative has become an important issue in Vehicular Ad-Hoc Networks(VANETs).In this paper,we propose a novel framework of energy harvesting and cooperation to maximize the life cycle in *** this VANET scenario,the RSU node can harvest energy from the nature and also receive down steam vehicle energy dispatch to maintain the energy needed for data transmission,may transfers a portion of obtained energies by RSU nodes to the neighboring RSU nodes through energy *** vehicle can access the RSU in two ways:1) direct delivery when the vehicle is within the transmission range of the RSU;2) multihop relay when the vehicle is not within the transmission range of the *** analysing the data rate,transmission power and energy transmission,we solve the problem of maximum network life cycle in the conservation law of energy and *** consider involving energy constraints and data transmission joint problem,we optimize each RUS nodes energy management over vehicular outgoing data links and its energy transfers to neighboring RSU *** this case,with no energy cooperation,we show that each RSU node should allocate more power to links with more noise and/or more data *** addition,when there is energy cooperation,our numerical results indicate that the energy is routed from nodes with higher energy to nodes with lower *** results show that energy and data routing cooperation in VANET strategy can effectively improve the network life cycle.

关键词： Road side units Energy harvesting data and energy routing cooperation life cycle

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Maximum Lifetime Optimized Collaboration of Data and Energy Routing in VANET

Maximum Lifetime Optimized Collaboration of Data and Energy ...

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International Conference on Virtual Reality and Intelligent Systems (ICVRIS)

作者： Ming Hu Hanlin Chen Jiejun Hu Hui Yan Ping Yu Platform of Jilin Province Science and Technology (S&T) Department "Jilin Province S&T Innovation Center for Physical Simulation and Security of Water resources and Electric Power Engineering" Changchun Institute of Technology Changchun China College of Computer Science and Technology Changchun University of Technology Changchun China School of Computer Sciences and Electrical Engineering University of Essex Colchester U.K

The roadside units (RSUs) based on traditional grid has high carbon footprint and high deployment cost in vehicle dedicated network. An ideal green alternative has become an important issue in Vehicular Ad-Hoc Networks (VANETs). In this paper, we propose a novel framework of energy harvesting and cooperation to maximize the life cycle in VENETs. In this VANET scenario, the RSU node can harvest energy from the nature and also receive down steam vehicle energy dispatch to maintain the energy needed for data transmission, may transfers a portion of obtained energies by RSU nodes to the neighboring RSU nodes through energy cooperation. Each vehicle can access the RSU in two ways: 1) direct delivery when the vehicle is within the transmission range of the RSU; 2) multi-hop relay when the vehicle is not within the transmission range of the RSU. By analysing the data rate, transmission power and energy transmission, we solve the problem of maximum network life cycle in the conservation law of energy and traffic. We consider involving energy constraints and data transmission joint problem, we optimize each RUS nodes energy management over vehicular outgoing data links and its energy transfers to neighboring RSU nodes. For this case, with no energy cooperation, we show that each RSU node should allocate more power to links with more noise and/or more data flow. In addition, when there is energy cooperation, our numerical results indicate that the energy is routed from nodes with higher energy to nodes with lower energy. Simulation results show that energy and data routing cooperation in VANET strategy can effectively improve the network life cycle.

关键词： Routing Energy harvesting Vehicular ad hoc networks Energy consumption Data communication Delays Optimization

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Hyperbolic node embedding for signed networks

arXiv

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arXiv 2019年

作者： Song, Wenzhuo Chen, Hongxu Liu, Xueyan Jiang, Hongzhe Wang, Shengsheng College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Advanced Analytics Institute School of Computer Science Faculty of Engineering and IT University of Technology Sydney NSW2007 Australia College of Mechanical and Electronic Engineering Nanjing Forestry University Nanjing210037 China

Signed network embedding methods aim to learn vector representations of nodes in signed networks. However, existing algorithms only managed to embed networks into low-dimensional Euclidean spaces whereas many intrinsic features of signed networks are reported more suitable for non-Euclidean spaces. For instance, previous works did not consider the hierarchical structures of networks, which is widely witnessed in real-world networks. In this work, we answer an open question that whether the hyperbolic space is a better choice to accommodate signed networks and learn embeddings that can preserve the corresponding special characteristics. We also propose a non-Euclidean signed network embedding method based on structural balance theory and Riemannian optimization, which embeds signed networks into a Poincar ball in a hyperbolic space. This space enables our approach to capture underlying hierarchy of nodes in signed networks because it can be seen as a continuous tree. We empirically compare our method against six Euclidean-based baselines in three tasks on seven real-world datasets, and the results show the effectiveness of our method. Copyright © 2019, The Authors. All rights reserved.

关键词： Geometry

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JUNO sensitivity on proton decay p→νK^(+)searches

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Chinese Physics C 2023年第11期47卷 7-22页

作者： Angel Abusleme Thomas Adam Shakeel Ahmad Rizwan Ahmed Sebastiano Aiello Muhammad Akram 安丰鹏安琪 Giuseppe Andronico Nikolay Anfimov Vito Antonelli Tatiana Antoshkina Burin Asavapibhop João Pedro Athayde Marcondes de André Didier Auguste 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 Mario Buscemi Jose Busto Ilya Butorov Anatael Cabrera Barbara Caccianiga 蔡浩蔡啸蔡严克蔡志岩 Riccardo Callegari Antonio Cammi Agustin Campeny 曹传亚曹国富曹俊 Rossella Caruso Cédric Cerna 常劲帆 Yun Chang 陈平平 Po-An Chen 陈少敏陈旭荣 Yi-Wen Chen 陈义学陈羽陈长程捷程雅苹 Alexey Chetverikov Davide Chiesa Pietro Chimenti Artem Chukanov Gérard Claverie Catia Clementi Barbara Clerbaux Selma Conforti Di Lorenzo Daniele Corti Flavio Dal Corso Olivia Dalager Christophe De La Taille 邓智邓子艳 Wilfried Depnering Marco Diaz Xuefeng Ding 丁雅韵 Bayu Dirgantara Sergey Dmitrievsky Tadeas Dohnal Dmitry Dolzhikov Georgy Donchenko 董建蒙 Evgeny Doroshkevich Marcos Dracos Frédéric Druillole 杜然杜书先 Stefano Dusini Martin Dvorak Timo Enqvist Heike Enzmann Andrea Fabbri Ulrike Fahrendholz 范东华樊磊方建方文兴 Marco Fargetta Dmitry Fedoseev Li-Cheng Feng 冯启春 Richard Ford Amélie Fournier 甘浩男 Feng Gao Alberto Garfagnini Arsenii Gavrikov Marco Giammarchi Agnese Giaz Nunzio Giudice Maxim Gonchar 龚光华宫辉 Yuri Gornushkin Alexandre Göttel Marco Grassi Christian Grewing Vasily Gromov 顾旻皓谷肖飞古宇关梦云 Nunzio Guardone Maria Gul 郭聪郭竞渊郭万磊郭新恒郭宇航 Paul Hackspacher Caren Hagner 韩然 Yang Han Muhammad Sohaib Hassan 何苗何伟 Tobias Heinz Patrick Hellmuth 衡月昆 Rafael Herrera 贺远强侯少静 Yee Hsiung Bei-Zhen Hu 胡航胡健润胡俊胡守扬胡涛胡宇翔胡焯钧黄春豪黄桂鸿黄翰雄黄文昊黄鑫黄性涛黄永波惠加琪霍雷霍文驹 Cédric Huss Safeer Hussain Ara Ioannisian Roberto Isocrate Beatrice Jelmini Kuo-Lun Jen Ignacio Jeria 季筱璐吉星曌贾慧慧贾俊基蹇司玉蒋荻蒋炜江晓山金如意荆小平 Cécile Jollet Jari Joutsenvaara Sirichok Jungthawan Leonidas Kalousis Philipp Ka Pontificia Universidad Católica de Chile SantiagoChile IPHC Universitéde StrasbourgCNRS/IN2P3F-67037 StrasbourgFrance Pakistan Institute of Nuclear Science and Technology IslamabadPakistan INFN Catania and Dipartimento di Fisica e Astronomia dell Universitàdi Catania CataniaItaly East China University of Science and Technology ShanghaiChina University of Science and Technology of China HefeiChina Joint Institute for Nuclear Research DubnaRussia INFN Sezione di Milano and Dipartimento di Fisica dell Universitàdi Milano MilanoItaly Department of Physics Faculty of ScienceChulalongkorn UniversityBangkokThailand IJCLab UniversitéParis-SaclayCNRS/IN2P391405 OrsayFrance Department of Physics and Earth Science University of Ferrara and INFN Sezione di FerraraFerraraItaly INFN Milano Bicocca and University of Milano Bicocca MilanoItaly INFN Sezione di Padova PadovaItaly III.Physikalisches Institut B RWTH Aachen UniversityAachenGermany Eberhard Karls Universität Tübingen Physikalisches InstitutTübingenGermany Institute of High Energy Physics BeijingChina SUBATECH Universitéde NantesIMT AtlantiqueCNRS-IN2P3NantesFrance Univ.Bordeaux CNRSLP2IUMR 5797F-33170 GradignanFrance Department of Physics National Taiwan UniversityTaipei Dipartimento di Fisica e Astronomia dell’Universitàdi Padova and INFN Sezione di Padova PadovaItaly University of Roma Tre and INFN Sezione Roma Tre RomaItaly Aix Marseille Univ CNRS/IN2P3CPPMMarseilleFrance Wuhan University WuhanChina INFN Milano Bicocca and Politecnico of Milano MilanoItaly National United University Miao-Li Dongguan University of Technology DongguanChina Tsinghua University BeijingChina Institute of Modern Physics Chinese Academy of SciencesLanzhouChina Institute of Physics National Yang Ming Chiao Tung UniversityHsinchu North China Electric Power University BeijingChina Sun Yat-Sen University GuangzhouChina Beijing Institute of Spacecraft Environment Engineering BeijingChina Universidade Estadual de Londrina LondrinaBrazil INFN Sez

The Jiangmen Underground Neutrino Observatory(JUNO)is a large liquid scintillator detector designed to explore many topics in fundamental *** this study,the potential of searching for proton decay in the p→νK^(+)mode with JUNO is *** kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for ***,the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed *** on these advantages,the detection efficiency for the proton decay via p→νK^(+)is 36.9%±4.9%with a background level of 0.2±0.05(syst)±0.2(stat)events after 10 years of data *** estimated sensitivity based on 200 kton-years of exposure is 9.6×1033 years,which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies.

关键词： proton decay grand unified theories JUNO liquid scintillator detector

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Research on Industrial Internet of Things Security architecture and Protection Strategy

Research on Industrial Internet of Things Security Architect...

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2019虚拟现实与智能系统国际会议

作者： Hanlin Chen Ming Hu Hui Yan Ping Yu Platform of Jilin Province Science and Technology （S＆T） Department "Jilin Province S＆T Innovation Center for PhysicalSimulation and Security of Water resources and Electric Power Engineering" Changchun Institute of Technology College of Computer Science and Technology Changchun University of Technology

Industrial Internet of Things（IIoT） is a fusion of industrial automation systems and IoT *** features comprehensive sensing,interconnected transmission,intelligent processing,self-organization and *** applications span intelligent transportation,smart factories,and *** areas such as power grid and intelligent environment *** the widespread application of IIoT technology,the cyber security threats to industrial IoT systems are increasing day by day,and information security issues have become a major challenge in the development *** order to protect the industrial IoT system jfrom network attacks,this paper aims to study the industrial IoT information security protection technology,and the typical architecture of industrial Internet of things system,and analyzes the network security threats faced by industrial Internet of things system according to the different levels of the architecture,and designs the security protection strategies applied to different levels of structures based on the specific means of network attack.

关键词： Industrial IoT security threats Security strategy

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Lunar impact craters identification and age estimation with Chang'E data by deep and transfer learning

arXiv

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arXiv 2019年

作者： Yang, Chen Zhao, Haishi Bruzzone, Lorenzo Benediktsson, Jon Atli Liang, Yanchun Liu, Bin Zeng, Xingguo Guan, Renchu Li, Chunlai Ouyang, Ziyuan College of Earth Sciences Jilin University Changchun China Key Laboratory of Lunar and Deep Space Exploration National Astronomical Observatories Chinese Academy of Sciences Beijing China College of Computer Science and Technology Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy Electrical and Computer Engineering University of Iceland Reykjavik101 Iceland Institute of Geochemistry Chinese Academy of Sciences

Impact craters, as "lunar fossils", are the most dominant lunar surface features and occupy most of the Moon's surface. Their formation and evolution record the history of the Solar System. Sixty years of triumphs in the lunar exploration projects accumulated a large amount of lunar data. Currently, there are 9137 existing recognized craters. However, only 1675 of them have been determined age, which is obviously not satisfactory to reveal the evolution of the Moon. Identifying craters is a challenging task due to their enormous difference in size, large variations in shape and vast presence. Furthermore, estimating the age of craters is extraordinarily difficult due to their complex and different morphologies. Here, in order to effectively identify craters and estimate their age, we convert the crater identification problem into a target detection task and crater age estimation into a taxonomy structure. From an initial small number of available craters, we progressively identify craters and estimate their age from Chang'E data by transfer learning (TL) using deep neural networks. For comprehensive identification of multi-scale craters, a two-stage craters detection approach is developed. Thus 117,240 unrecognized lunar craters that range in diameter from 532 km to 1 km are identified. Then, a two-stage classification approach is developed to estimate the age of craters by simultaneously extracting their morphological features and stratigraphic information. The age of 79,243 craters larger than 3 km in diameter is estimated. These identified and aged craters throughout the mid and low-latitude regions of the Moon are crucial for reconstructing the dynamic evolution process of the Solar System. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Multi-view adaptive fusion network for 3D object detection

arXiv

引用

arXiv 2020年

作者： Wang, Guojun Tian, Bin Zhang, Yachen Chen, Long Cao, Dongpu Wu, Jian State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130022 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China School of Data and Computer Science Sun Yat-Sen University Guangzhou510275 China Lab University of Waterloo Canada College of Automotive Engineering Jilin University China

3D object detection based on LiDAR-camera fusion is becoming an emerging research theme for autonomous driving. However, it has been surprisingly difficult to effectively fuse both modalities without information loss and interference. To solve this issue, we propose a single-stage multi-view fusion framework that takes LiDAR Birds-Eye View, LiDAR Range View and Camera View images as inputs for 3D object detection. To effectively fuse multi-view features, we propose an Attentive Pointwise Fusion (APF) module to estimate the importance of the three sources with attention mechanisms which can achieve adaptive fusion of multi-view features in a pointwise manner. Besides, an Attentive Pointwise Weighting (APW) module is designed to help the network learn structure information and point feature importance with two extra tasks: foreground classification and center regression, and the predicted foreground probability will be used to reweight the point features. We design an end-to-end learnable network named MVAF-Net to integrate these two components. Our evaluations conducted on the KITTI 3D object detection datasets demonstrate that the proposed APF and APW module offer significant performance gain and that the proposed MVAF-Net achieves state-of-the-art performance in the KITTI benchmark. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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Numerical and Experimental Research on the Anti-sloshing Characteristic of Swash Bulkhead in LNG Independent Type C Tank

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IOP Conference Series: Materials science and engineering 2020年第1期887卷

作者： Ge Liu Qimu Wen Yi Yang Chong Wang System engineering research institute Beijing 100094 China. College of Computer Science and Technology Jilin University Changchun 130012 China.

The sloshing induced by partial loaded in LNG (Liquified Natural Gas) independent type C tank will threat the structure of tank. For sloshing suppression, installation of swash bulkhead is a good choice. With model experiment, the sloshing suppression mechanism of perforated swash bulkhead is investigated. Comparing to the tank without swash bulkhead, the suppression efficiency is obtained. Base on the CFD (Computational fluid dynamic) theory, a numerical method is used to sloshing simulation of tank with perforated swash bulkhead, which is verified by the model experiment. Thus, with the same permeation ratio, the effect of anti-sloshing is analysed numerically with comparison of ring baffle and perforated bulkhead. It is found that perforated swash bulkhead has a better performance in sloshing suppression at higher filling level than the ring baffle. And there is a small variation in anti-sloshing efficiency of perforated swash bulkhead

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