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Computational simulation for cylinder vibration under interference of a stationary cylinder

International Journal of Simulation: Systems, science and technology 2016年第30期17卷 16.1-16.7页

作者： Cui, Zhendong Chen, Zhengshou School of Mathematics Physics & Information Science Zhejiang Ocean University ZhoushanZhejiang316022 China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan316022 China Key Laboratory of Offshore Engineering Technology of Zhejiang Province ZhoushanZhejiang316022 China Zhejiang Ouhua Shipbuilding Co. Ltd ZhoushanZhejiang316000 China

One degree of freedom vibration in cross-flow direction of an elastically mounted cylinder was simulated numerically in the wake of a stationary cylinder. The mass ratio was 1.9, and the spacing ratios of 5. The reduced velocities range from 2 to 16.5 with an interval of 0.5. Four regimes are found in the response of the cylinder under the interference of the upstream stationary cylinder. The numerical results indicate the vortexes from the upstream cylinder will take significant impact on cross-flow vibration of the downstream cylinder in the regime 2 of 5≤Vr≤8.5. Outside this range, the effect from the upstream stationary cylinder is week. The vibration of the downstream cylinder is something like the isolated cylinder. © 2016, UK Simulation Society. All rights reserved.

关键词： Vortex shedding

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A new PolSAR ship detection metric fused by polarimetric similarity and the third eigenvalue of the coherency matrix

A new PolSAR ship detection metric fused by polarimetric sim...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yuyang Xi Xi Zhang Quan Lai Wei Li Haitao Lang Beijing University of Chemical Technology Beijing China State Oceanic Administration The First Institute of Oceanography Qingdao China Inner Mongolian Key Laboratory of Remote Sensing & Geography Information System Inner Mongolian Normal University Hohhot China College of Information Science & Technology Beijing University of Chemical Technology Beijing China Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science & Technology Nanjing China

In this paper, we address the problem of ship detection in PolSAR image. We firstly investigate the differences of scattering mechanism between ship targets and the sea surface based on the polarimetric similarity analysis. It is shown that, the sea surface scattering is dominated by the odd bounce (denoted as r 1 ), while the scattering of ship targets are both dominated by the even bounce scattering (r 2 ) which has been widely accepted, and the line bounce scattering (r 4 ) which is a new find from the experiments. Based on those differences, a metric (r 2 +r 4 )/r 1 can be applied to distinguish ship targets from the sea surface. To further suppress the effects of sidelobes and imaging artifacts which have the same/similar scattering behaviors with ship targets, we introduce in the third eigenvalue λ 3 ) of the coherency matrix and obtain the metric (r 2 + r 4 )λ 3 /r 1 . The preliminary results show that a constant false alarm rate (CFAR) ship detector based on the proposed metric can obtain promising ship detection performance.

关键词： Marine vehicles Scattering Sea surface Measurement Eigenvalues and eigenfunctions Visualization Synthetic aperture radar

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Measurement of the Bc− meson production fraction and asymmetry in 7 and 13 TeV pp collisions

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Physical Review D 2019年第11期100卷 112006-112006页

作者： R. Aaij C. Abellán Beteta T. Ackernley B. Adeva M. Adinolfi H. Afsharnia C. A. Aidala S. Aiola Z. Ajaltouni S. Akar P. Albicocco J. Albrecht F. Alessio M. Alexander A. Alfonso Albero G. Alkhazov P. Alvarez Cartelle A. A. Alves, Jr. S. Amato Y. Amhis L. An L. Anderlini G. Andreassi M. Andreotti F. Archilli J. Arnau Romeu A. Artamonov M. Artuso K. Arzymatov E. Aslanides M. Atzeni B. Audurier S. Bachmann J. J. Back S. Baker V. Balagura W. Baldini A. Baranov R. J. Barlow S. Barsuk W. Barter M. Bartolini F. Baryshnikov G. Bassi V. Batozskaya B. Batsukh A. Battig A. Bay M. Becker F. Bedeschi I. Bediaga A. Beiter L. J. Bel V. Belavin S. Belin N. Beliy V. Bellee K. Belous I. Belyaev G. Bencivenni E. Ben-Haim S. Benson S. Beranek A. Berezhnoy R. Bernet D. Berninghoff H. C. Bernstein E. Bertholet A. Bertolin C. Betancourt F. Betti M. O. Bettler Ia. Bezshyiko S. Bhasin J. Bhom M. S. Bieker S. Bifani P. Billoir A. Bizzeti M. Bjørn M. P. Blago T. Blake F. Blanc S. Blusk D. Bobulska V. Bocci O. Boente Garcia T. Boettcher A. Boldyrev A. Bondar N. Bondar S. Borghi M. Borisyak M. Borsato J. T. Borsuk T. J. V. Bowcock C. Bozzi M. J. Bradley S. Braun A. Brea Rodriguez M. Brodski J. Brodzicka A. Brossa Gonzalo D. Brundu E. Buchanan A. Buonaura C. Burr A. Bursche J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai R. Calabrese L. Calero Diaz S. Cali R. Calladine M. Calvi M. Calvo Gomez A. Camboni P. Campana D. H. Campora Perez L. Capriotti A. Carbone G. Carboni R. Cardinale A. Cardini P. Carniti K. Carvalho Akiba A. Casais Vidal G. Casse M. Cattaneo G. Cavallero R. Cenci J. Cerasoli M. G. Chapman M. Charles Ph. Charpentier G. Chatzikonstantinidis M. Chefdeville V. Chekalina C. Chen S. Chen A. Chernov S.-G. Chitic V. Chobanova M. Chrzaszcz A. Chubykin P. Ciambrone M. F. Cicala X. Cid Vidal G. Ciezarek F. Cindolo P. E. L. Clarke M. Clemencic H. V. Cliff J. Closier J. L. Cobbledick V. Coco J. A. B. Coelho J. Cogan E. Cogneras L. Cojocariu P. Collins T. Colombo A. Comerma-Montells A. Contu N. Cooke G. Coombs S. Coquereau G. Corti C. Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Physik-Institut Universität Zürich Zürich Switzerland Oliver Lodge Laboratory University of Liverpool Liverpool United Kingdom Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela Santiago de Compostela Spain H.H. Wills Physics Laboratory University of Bristol Bristol United Kingdom Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France University of Michigan Ann Arbor USA (associated with Syracuse University) INFN Sezione di Milano Milano Italy University of Cincinnati Cincinnati Ohio USA INFN Laboratori Nazionali di Frascati Frascati Italy Fakultät Physik Technische Universität Dortmund Dortmund Germany European Organization for Nuclear Research (CERN) Geneva Switzerland School of Physics and Astronomy University of Glasgow Glasgow United Kingdom ICCUB Universitat de Barcelona Barcelona Spain Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI) Gatchina Russia Imperial College London London United Kingdom Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil LAL Univ. Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France INFN Sezione di Firenze Firenze Italy Institute of Physics Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland INFN Sezione di Ferrara Ferrara Italy Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France Institute for High Energy Physics NRC Kurchatov Institute (IHEP NRC KI) Protvino Russia Protvino Russia Syracuse University Syracuse New York USA Yandex School of Data Analysis Moscow Russia INFN Sezione di Cagliari Monserrato Italy Department of Physics University of Warwick Coventry United Kingdom Department of Physics and Astronomy University of Manchester Manchester United Kingdom INFN Sezione di Genova Genova Italy INFN Sezione di Pisa Pisa Italy National

The production fraction of the Bc− meson with respect to the sum of B− and B¯0 mesons is measured in both 7 and 13 TeV center-of-mass (c.m.) energy pp collisions produced by the Large Hadron Collider (LHC), using the LHCb detector. The rate, approximately 3.7 per mille, does not change with energy, but shows a transverse momentum dependence. The Bc−−Bc+ production asymmetry is also measured and is consistent with zero within the determined statistical and systematic uncertainties of a few percent.

关键词： Particle interactions Particle production Quantum chromodynamics Strong interaction

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Flickr group recommendation via heterogeneous information networks 15

Flickr group recommendation via heterogeneous information ne...

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7th International Conference on Internet Multimedia Computing and Service, ICIMCS 2015

作者： Wang, Yueyang Xia, Yuanfang Tang, Siliang Wu, Fei Zhuang, Yueting College of Computer Science and Technology Zhejiang University Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Innovation Joint Research Center for ICPS China

ISBN: (纸本)9781450335287

As an important characteristic of social media (i.e. Flickr or Facebook), user communities or groups are beginning to attract increasing attention. Most of the previous studies on group recommendation only consider a homogeneous relationship, such as the rating of users to groups in social network. In real world, each group has its textual descriptions (e.g. the "Sky & Cloud" group in Flickr), and the describing words about a given group may mention particular entities as well as their belonging categories (e.g. "Nature"). In fact, the group recommendation can be conducted in a heterogeneous information network, where informative cues are in general multi-typed. Motivated by the assumption that different types of relationships in heterogeneous information network can be potentially used to boost the performance of the group recommendation. We propose to combine heterogeneous relationship information for users with implicit user feedback. In general, the group recommendation in this paper is formulated as a non-negative matrix factorization (NMF) method regularized with user-user similarity via heterogeneous information networks. Experiments show that our proposed approach outperforms other counterpart recommendation techniques. © 2015 ACM.

关键词： Non-negative matrix factorization

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Time-Series Analysis on the Impact Level upon Taiwan Stock Market with Events of Signing International Agreements

Time-Series Analysis on the Impact Level upon Taiwan Stock M...

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International Conference on Computing, Measurement, Control and Sensor Network (CMCSN)

作者： Pei-Wei Tsai You-Xin Lin Jing Zhang Jui-Fang Chang Jhih-Sheng Chen Jilin Zhang College of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of International Business National Kaohsiung University of Applied Sciences Kaohsiung Taiwan Department of Mathematics and Physics Fujian University of Technology Fuzhou China

ISBN: (纸本)9781509010943

Stock investment is one of the available conventional financial commodities in the investment market. The individual price of stocks including stocks in the field of industrial, medical, financial, overall, etc. Is subject to many different aspects. The relationship between aspects, the international economy, and the stock price is complex and is not easy to be categorized. Moreover, the trend of the world increases the cooperation and links between countries and different nations. Many agreements in tourism, trade, etc. Are signed between countries. In this paper, we focus on the effect on the stock prices caused by the agreements signed between Taiwan and other countries. The time series method is utilized to analyze the effect on the stock price forecasting caused by 5 of the incidents including WTO, Taiwan-Nicaragua FTA, Taiwan-Honduras FTA, and Taiwan-Japan BIA. Both the pre-event and the post-event periods are included for observing the fluctuation behavior of the stock market price. The MAPE value is used as the criterion to verify the correctness of the forecasting result. The MAPE values obtained in all experiments are all less than 10%. It implies that the time series method presents good forecasting ability in the analyzed cases.

关键词： Stock markets Forecasting Time series analysis Companies Industries Predictive models

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A novel method for road centerline extraction from high spatial resolution satellite images

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Journal of Information Hiding and Multimedia Signal Processing 2016年第5期7卷 1069-1080页

作者： Xu, Rui Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education Spatial Information Research Center of Fujian Province Fuzhou University No.523 Gongye Road Fuzhou350002 China College of Information Science and Engineering Fujian University of Technology No.3 Xueyuan Road University Town Fuzhou350108 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Fuzhou350108 China

An integrated method for road centerline extraction has been proposed in this paper which consists of four main steps. First, the Graph Cut algorithm using proposed similarity function is employed to segment the image, and the segmented image is thresholded to initial binary skeleton. Second, the road skeleton is refined by using a series of morphology operations and shape features. Third, the curvilinear feature of road skeleton is detected by a set of multiple filtering detectors. Finally,a regression method is performed to extract smooth and accurate road centerlines. Validation tests show that the proposed method is able to achieve a comparatively good performance in road centerline extraction from high spatial resolution satellite images. © 2016, Ubiquitous International. All Rights Reserved.

关键词： Regression analysis

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A hierarchical object-based method for suburban road extraction from remote sensing image

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ICIC Express Letters, Part B: Applications 2016年第12期7卷 2671-2676页

作者： Xu, Rui College of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No. 3 Xueyuan Road University Town Fuzhou350118 China Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education Spatial Information Research Center of Fujian Province Fuzhou University No. 523 Gongye Road Fuzhou350002 China

Road extraction from remote sensing image is a very popular topic. There are many methods dealing with this problem, but few of them focus on suburban roads. This paper presents a hierarchical object-based method for suburban road extraction. First, the image is preprocessed by the Normalized Difference Vegetation Index (NDVI) and the Hue-Saturation-Intensity (HSI) Index. Vegetation regions, shadows and water areas are then removed. Second, texture features are applied to classifying the preprocessed image into two categories: road and non-road. Homogeneous property is combined to remove false classification so as to generate initial road skeleton. Third, the road skeleton is refined by using a series of morphology operations and shape features. Fourth, the curvilinear feature of road skeleton is detected by a set of multiple filtering detectors. Finally, a regression method is performed to extract smooth and accurate road centerlines. The experimental results indicate that the proposed method is suitable for suburban road extraction. © 2016 ISSN 2185-2766.

关键词： Vegetation

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Application of an improved clustering algorithm in customer risk preference recognition model

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ICIC Express Letters, Part B: Applications 2016年第5期7卷 1077-1082页

作者： Xiao, Lin Xu, Shenghua Pan, Jeng-Shyang Yang, Tonghua College of Information Management Jiangxi University of Finance and Economics No. 665 West Yuping Road Nanchang330013 China College of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou350108 China Key Laboratory of Big Data Mining and Applications of Fujian Province Fuzhou350108 China

The main problem of this paper is to establish a recognition model for the risk preference of brokerage individual customer. It is based on transaction data. Its core algorithm is the use of improved K-means algorithm. Its process includes the principal component analysis and clustering operation of the data. In this paper, we improve the design of automatic merging approximation class in clustering algorithm. The main basis of customer risk is the investment subject of the customer. The contribution of this research is to promote the accuracy and success rate of marketing and provide a good support for the company to make reasonable marketing decision. © 2016, ICIC International. All rights reserved.

关键词： Clustering algorithms

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Cluster based hybrid key management scheme for ad hoc networks

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ICIC Express Letters, Part B: Applications 2016年第6期7卷 1329-1334页

作者： Zhang, Jing Xu, Li Tang, Rongmei Zhang, Shunmiao School of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou350118 China School of Mathematics and Computer Science Fujian Normal University Fuzhou University Town Minhou Fuzhou350118 China

Secure communication is part of the most important aspects for ad hoc networks. One cluster based Hybrid key Management Scheme is proposed in this paper for ad hoc networks. The clusters are constructed by the R-hop connected dominating sets based Partition Algorithm, which is only with the knowledge of local connectivity information. Furthermore, Hybrid key Management Scheme can be seen as an integration of both the asymmetric key and symmetric key cryptography. The asymmetric one is based on the elliptic curve cryptosystem, which is an asymmetric one. However, inter cluster key management is based on symmetric key encryption. Experimental results show this scheme is reliable and it is able to achieve higher success of landing ratio than other comparable algorithms. © 2016 ICIC International.

关键词： Ad hoc networks

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Correction to: Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial

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Critical care (London, England) 2022年第1期26卷 115页

作者： Lu Ke Jiajia Lin Gordon S Doig Arthur R H van Zanten Yang Wang Juan Xing Zhongheng Zhang Tao Chen Lixin Zhou Dongpo Jiang Qindong Shi Jiandong Lin Jun Liu Aibin Cheng Yafeng Liang Peiyang Gao Junli Sun Wenming Liu Zhenyu Yang Rumin Zhang Wei Xing An Zhang Zhigang Zhou Tingfa Zhou Yang Liu Fei Tong Qiuhui Wang Aijun Pan Xiaobo Huang Chuming Fan Weihua Lu Dongwu Shi Lei Wang Wei Li Liming Gu Yingguang Xie Rongqing Sun Feng Guo Lin Han Lihua Zhou Xiangde Zheng Feng Shan Jianbo Liu Yuhang Ai Yan Qu Liandi Li Hailing Li Zhiguo Pan Donglin Xu Zhiqiang Zou Yan Gao Chunli Yang Qiuye Kou Xijing Zhang Jinglan Wu Chuanyun Qian Weixing Zhang Minjie Zhang Yuan Zong Bingyu Qin Fusen Zhang Zhe Zhai Yun Sun Ping Chang Bo Yu Min Yu Shiying Yuan Yijun Deng Liyun Zhao Bin Zang Yuanfei Li Fachun Zhou Xiaomei Chen Min Shao Weidong Wu Ming Wu Zhaohui Zhang Yimin Li Qiang Guo Zhiyong Wang Yuanqi Gong Yunlin Song Kejian Qian Yongjian Feng Baocai Fu Xueyan Liu Zhiping Li Chuanyong Gong Cheng Sun Jian Yu Zhongzhi Tang Linxi Huang Biao Ma Zhijie He Qingshan Zhou Rongguo Yu Zhihui Tong Weiqin Li Department of Critical Care Medicine Jinling Hospital Medical School of Nanjing University No. 305 Zhongshan East Road Nanjing 210000 Jiangsu Province China. National Institute of Healthcare Data Science Nanjing University Nanjing China. Northern Clinical School Royal North Shore Hospital University of Sydney Sydney Australia. Department of Intensive Care Medicine Gelderse Vallei Hospital Willy Brandtlaan 10 6716 RP Ede The Netherlands. Department of Medical Research and Biometrics Center State Key Laboratory of Cardiovascular Disease Fuwai Hospital National Center for Cardiovascular Diseases Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China. Benq Medical Center Nanjing China. Department of Emergency Medicine Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China. Tropical Clinical Trials Unit Department of Clinical Sciences Liverpool School of Tropical Medicine Liverpool L3 5QA UK. First People's Hospital of Foshan Foshan China. Daping Hospital Army Medical University Chongqing China. First Affiliated Hospital of Xi'an Jiao Tong University Xi'an China. First Affiliated Hospital of Fujian Medical University Fuzhou China. Suzhou Municipal Hospital Suzhou China. North China University of Science and Technology Affiliated Hospital Tangshan China. Qindao University Medical College Affiliated Yantai Yuhuangding Hospital Yantai China. Chengdu University of Traditional Chinese Medicine Affiliated Hospital Chengdu China. Luoyang Central Hospital Affiliated to Zhengzhou University Luoyang China. Changzhou No. 2 People's Hospital Affiliated to Nanjing Medical University Changzhou China. The Second Affiliated Hospital of Harbin Medical University Harbin China. Zibo Central Hospital Zibo China. Department of Intensive Care Medicine The Third Xiangya Hospital of Central South University Changsha China. The Second Affiliated Hospital of Chongqing Medical University Chongqing China. First People's Hospita

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