检索结果-内蒙古大学图书馆

International Symposium on Intelligent Signal Processing and Communication Systems

作者： Yan-Xing Wang Yi-Gang Cen Lie-Quan Liang Ming Zeng Vladimir Mladenovic Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing School of Computer and Information Technology Beijing Jiaotong University Beijing Guangdong key laboratories on e-commerce market application technology Guangdong university of finance and economics Guangzhou China School of Automation Science and Engineering South China University of Technology Guangzhou China Faculty of Technical Sciences Cacak University of Kragujevac Cacak Serbia

In this paper, we propose an automated surface inspection approach based on generalized low-rank approximations of matrices(GLRAM). The GLRAM uses a series of low-rank matrices to approximate the structural texture backgrounds of the original defective images. Then the difference images obtained by the original defective images and the low-rank approximations will retain the defect areas. Finally, we can easily segment the defect area from the original image using a simple threshold segmentation method. Experimental results show that our proposed method can extract defect areas well from the structurally textured image.

关键词： Defect inspection Structural textures Machine vision Generalized low-rank approximations of matrices

来源：评论

学校读者我要写书评

暂无评论

Author Correction: A portrait of the Higgs boson by the CMS experiment ten years after the discovery

引用

Nature 2023年第7985期623卷 E4页

作者： A. Tumasyan W. Adam J. W. Andrejkovic T. Bergauer S. Chatterjee K. Damanakis M. Dragicevic A. Escalante Del Valle P. S. Hussain M. Jeitler N. Krammer L. Lechner D. Liko I. Mikulec P. Paulitsch F. M. Pitters J. Schieck R. Schöfbeck D. Schwarz S. Templ W. Waltenberger C.-E. Wulz M. R. Darwish T. Janssen T. Kello H. Rejeb Sfar P. Van Mechelen E. S. Bols J. D’Hondt A. De Moor M. Delcourt H. El Faham S. Lowette S. Moortgat A. Morton D. Müller A. R. Sahasransu S. Tavernier W. Van Doninck D. Vannerom B. Clerbaux G. De Lentdecker L. Favart J. Jaramillo K. Lee M. Mahdavikhorrami I. Makarenko A. Malara S. Paredes L. Pétré N. Postiau E. Starling L. Thomas M. Vanden Bemden C. Vander Velde P. Vanlaer D. Dobur J. Knolle L. Lambrecht G. Mestdach M. Niedziela C. Rendón C. Roskas A. Samalan K. Skovpen M. Tytgat N. Van Den Bossche B. Vermassen L. Wezenbeek A. Benecke G. Bruno F. Bury C. Caputo P. David C. Delaere I. S. Donertas A. Giammanco K. Jaffel Sa. Jain V. Lemaitre K. Mondal J. Prisciandaro A. Taliercio T. T. Tran P. Vischia S. Wertz G. A. Alves E. Coelho C. Hensel A. Moraes P. Rebello Teles W. L. Aldá Júnior M. Alves Gallo Pereira M. Barroso Ferreira Filho H. Brandao Malbouisson W. Carvalho J. Chinellato E. M. Da Costa G. G. Da Silveira D. De Jesus Damiao V. Dos Santos Sousa S. Fonseca De Souza J. Martins C. Mora Herrera K. Mota Amarilo L. Mundim H. Nogima A. Santoro S. M. Silva Do Amaral A. Sznajder M. Thiel F. Torres Da Silva De Araujo A. Vilela Pereira C. A. Bernardes L. Calligaris E. M. Gregores P. G. Mercadante S. F. Novaes Sandra S. Padula T. R. Fernandez Perez Tomei A. Aleksandrov G. Antchev R. Hadjiiska P. Iaydjiev M. Misheva M. Rodozov M. Shopova G. Sultanov A. Dimitrov T. Ivanov L. Litov B. Pavlov P. Petkov A. Petrov E. Shumka T. Cheng T. Javaid M. Mittal L. Yuan M. Ahmad G. Bauer Z. Hu S. Lezki K. Yi G. M. Chen H. S. Chen M. Chen F. Iemmi C. H. Jiang A. Kapoor H. Liao Z.-A. Liu V. Milosevic F. Monti R. Sharma J. Tao J. Thomas-Wilsker J. Wang H. Zhang J. Zhao A. Agapitos Y. An Y. Ban C. Chen A. Levin C. Li Q. Li X Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria TU Wien Vienna Austria Universiteit Antwerpen Antwerpen Belgium Institute of Basic and Applied Sciences Faculty of Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Université Libre de Bruxelles Bruxelles Belgium Vrije Universiteit Brussel Brussel Belgium Korea University Seoul Korea Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina Brazil The University of the State of Amazonas Manaus Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Beihang University Beijing China University of Chinese Academy of Sciences Beijing China Department of Physics Tsinghua University Beijing China Nanjing Normal University Department of Physics Nanjing China Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences. Beijing China University of Florida Gainesville Florida USA Massachusetts Institute of Technology Cambridge Massachusetts USA State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China Northeastern University Boston Massachusetts USA Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) -Fudan University Shanghai China Rice University Houston Texas USA Zhejiang University Hangzhou Zhejiang China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering

来源：评论

学校读者我要写书评

暂无评论

Research on high-dimensional data reduction

International Journal of Database Theory and Application

引用

International Journal of Database Theory and application 2016年第1期9卷 87-96页

作者： Tian, Cuihua Wang, Yan Lin, Xueqin Lin, Jing Hong, Jiangshui School of Computer and Information Engineering Xiamen University of Technology Xiamen Fujian China Universities in Fujian Province Key Laboratory of Things Application Technology Xiamen Fujian China School of International Languages Xiamen University of Technology Xiamen China

In this paper, features of high-dimensional data are analyzed, and existing problems of the Canonical Correlation Analysis (CCA) are analyzed for a single view of a full supervised view data. In order to improve CCA, we introduce the method of classifier and present a Classifying to Reduce Correlation Dimensionality (CRCD). Meanwhile, combining big interval learning method, we propose the big correlation analysis (BCA). At last, experiments are respectively conducted by using artificial data set and UCI standard data set. The result shows that methods are feasible and effective. © 2016 SERSC.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Fast string matching algorithms for very long patterns based on BOMq 4th

Fast string matching algorithms for very long patterns based...

引用

4th International Conference on Electronics, Communications and Networks, CECNet2014

作者： Fan, Hongbo Shi, Shupeng Ma, GuoRui Zhang, Jing Department of Computer Science Kunming University of Science and Technology Kunming China Computer Technology Application Key Laboratory of Yunnan Province Kunming China

ISBN: (纸本)9781138028302

String matching is a fundamental problem in computer science, and has been widely used in many important fields. This article improved the BOMq algorithm (BOM with q-grams). Two parts q-grams test method was used to increase the jump probability after the factor automaton accepted the first input string of q characters. And an unaligned read method was presented to simplify the 2-Dimention table lookup of EBOM to 1-Dimention table lookup, which can reduce the cost of the first two transitions furthermore. Thus, some improved algorithms were obtained called EBOM_sb, BOMq_sb and BOMq1_q2sb. It is shown in the experimental results that BOMq_sb and BOMq1_q2sb are faster than other known algorithms on our platform for very long patterns on middle-level size alphabet such as pure English text, natural language, and random text of alphabet size 16. © 2015 Taylor & Francis Group, London.

关键词： Table lookup

来源：评论

学校读者我要写书评

暂无评论

Tuning quick search string matching algorithms with multi-windows and integer comparison 4th

Tuning quick search string matching algorithms with multi-wi...

引用

4th International Conference on Electronics, Communications and Networks, CECNet2014

作者： Fan, Hongbo Shi, Shupeng Dong, Li Zhang, Jing Department of Computer Science Kunming University of Science and Technology Kunming China Computer Technology Application Key Laboratory of Yunnan Province Kunming China

ISBN: (纸本)9781138028302

String matching is a fundamental problem in computer science, and has been widely used in many important fields. This paper improves the classic BM type algorithm, Quick Search Using multiple sliding window method, the comparison unit from character to integer is increased in the first comparison of the current window. These two improved point decreases the average cost of the comparison obviously. Thus, a new series of algorithms are presented named QSMI_k_x (Quick Search with multi-sliding windows and integer comparison for k sliding windows and x type integer comparison). It is shown in the experiment that the QSMI algorithm is faster than other known algorithms in many cases on our platform, such as the pattern length of 4 and 8 on middle-level or large alphabet. © 2015 Taylor & Francis Group, London.

关键词： String searching algorithms

来源：评论

学校读者我要写书评

暂无评论

Publisher Correction: Observation of triple J/ψ meson production in proton-proton collisions

引用

Nature Physics 2023年 461页

作者： A. Tumasyan W. Adam J. W. Andrejkovic T. Bergauer S. Chatterjee K. Damanakis M. Dragicevic A. Escalante Del Valle R. Frühwirth M. Jeitler N. Krammer L. Lechner D. Liko I. Mikulec P. Paulitsch F. M. Pitters J. Schieck R. Schöfbeck D. Schwarz S. Templ W. Waltenberger C.-E. Wulz V. Chekhovsky A. Litomin V. Makarenko M. R. Darwish E. A. De Wolf T. Janssen T. Kello A. Lelek H. Rejeb Sfar P. Van Mechelen S. Van Putte N. Van Remortel F. Blekman E. S. Bols J. D’Hondt M. Delcourt H. El Faham S. Lowette S. Moortgat A. Morton D. Müller A. R. Sahasransu S. Tavernier W. Van Doninck D. Beghin B. Bilin B. Clerbaux G. De Lentdecker L. Favart A. K. Kalsi K. Lee M. Mahdavikhorrami I. Makarenko L. Moureaux L. Pétré A. Popov N. Postiau E. Starling L. Thomas M. Vanden Bemden C. Vander Velde P. Vanlaer T. Cornelis D. Dobur J. Knolle L. Lambrecht G. Mestdach M. Niedziela C. Rendón C. Roskas A. Samalan K. Skovpen M. Tytgat B. Vermassen L. Wezenbeek A. Benecke A. Bethani G. Bruno F. Bury C. Caputo P. David C. Delaere I. S. Donertas A. Giammanco K. Jaffel Sa. Jain V. Lemaitre K. Mondal J. Prisciandaro A. Taliercio M. Teklishyn T. T. Tran P. Vischia S. Wertz G. A. Alves C. Hensel A. Moraes P. Rebello Teles W. L. Aldá Júnior M. Alves Gallo Pereira M. Barroso Ferreira Filho H. Brandao Malbouisson W. Carvalho J. Chinellato E. M. Da Costa G. G. Da Silveira D. De Jesus Damiao V. Dos Santos Sousa S. Fonseca De Souza C. Mora Herrera K. Mota Amarilo L. Mundim H. Nogima A. Santoro S. M. Silva Do Amaral A. Sznajder M. Thiel F. Torres Da Silva De Araujo A. Vilela Pereira C. A. Bernardes L. Calligaris T. R. Fernandez Perez Tomei E. M. Gregores D. S. Lemos P. G. Mercadante S. F. Novaes Sandra S. Padula A. Aleksandrov G. Antchev R. Hadjiiska P. Iaydjiev M. Misheva M. Rodozov M. Shopova G. Sultanov A. Dimitrov T. Ivanov L. Litov B. Pavlov P. Petkov A. Petrov T. Cheng T. Javaid M. Mittal L. Yuan M. Ahmad G. Bauer C. Dozen Z. Hu J. Martins Y. Wang K. Yi E. Chapon G. M. Chen H. S. Chen M. Chen F. Iemmi A. Kapoor D. Leggat H. Liao Z.-A. Liu V. Milosevic F. M Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Institute for Nuclear Problems Minsk Belarus Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Korea University Seoul Korea Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista São Paulo Brazil Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China University of Florida Gainesville Florida USA Massachusetts Institute of Technology Cambridge Massachusetts USA State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan University Shanghai China University of California Riverside Riverside California USA Rice University Houston Texas USA Zhejiang University Hangzhou China Zhejiang China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo

来源：评论

学校读者我要写书评

暂无评论

An object-oriented description method of EPMM process

引用

IOP Conference Series: Earth and Environmental Science 2017年第1期69卷

作者： Zuo Jiang Fan Yang School of Mathematics and Computer Science Yunnan Minzu University Kunming Yunnan 650504 China School of Software Yunnan University Kunming Yunnan 650504 China Key Laboratory of IOT Application Technology of Universities in Yunnan Province China

In order to use the object-oriented mature tools and language in software process model, make the software process model more accord with the industrial standard, it's necessary to study the object-oriented modelling of software process. Based on the formal process definition in EPMM, considering the characteristics that Petri net is mainly formal modelling tool and combining the Petri net modelling with the object-oriented modelling idea, this paper provides this implementation method to convert EPMM based on Petri net into object models based on object-oriented description.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Discovering admissible Web services with uncertain QoS

引用

Frontiers of computer Science 2015年第2期9卷 265-279页

作者： Xiaodong FU Kun YUE Li LIU Ping ZOU Yong FENG Yunnan Provincial Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming 650500 China School of Information Science and Engineering Yunnan University Kunming 650091 China Faculty of Management and Economics Kunming University of Science and Technology Kunming 650093 China

Open and dynamic environments lead to inher- ent uncertainty of Web service QoS （Quality of Service）, and the QoS-aware service selection problem can be looked upon as a decision problem under uncertainty. We use an empiri- cal distribution function to describe the uncertainty of scores obtained from historical transactions. We then propose an approach to discovering the admissible set of services in- cluding alternative services that are not dominated by any other alternatives according to the expected utility criterion. Stochastic dominance （SD） rules are used to compare two services with uncertain scores regardless of the distribution form of their uncertain scores. By using the properties of SD rules, an algorithm is developed to reduce the number of SD tests, by which the admissible services can be reported pro- gressively. We prove that the proposed algorithm can be run on partitioned or incremental alternative services. Moreover, we achieve some useful theoretical conclusions for correct pruning of unnecessary calculations and comparisons in each SD test, by which the efficiency of the SD tests can be im- proved. We make a comprehensive experimental study using real datasets to evaluate the effectiveness, efficiency, and scal- ability of the proposed algorithm.

关键词： Web services uncertain QoS partial preference,empirical distribution function stochastic dominance admis-sible set

来源：评论

学校读者我要写书评

暂无评论

Exploiting the locality information of dense trajectory feature for human action recognition 15

Exploiting the locality information of dense trajectory feat...

引用

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

作者： Fan, Baixiang Liu, Yanbin Han, Yahong School of Computer Science and Technology Tianjin University China School of Computer Science and Technology Tianjin University China Tianjin Key Laboratory of Cognitive Computing and Application China

ISBN: (纸本)9781450335287

In recent years, human action recognition has gained great progress with the help of effective local descriptors like improved trajectory and encoding approaches like fisher vector. However, the locality information used for preserving neighborhood relationship among descriptors is usually ignored. In this paper, we exploit and preserve the locality information among descriptors to achieve a better recognition rate. Exploiting the locality information has two-fold role. First, it preserves the neighborhood structure of descriptors which can alleviate the intra-class variation problem in action recognition. Second, intrinsic dimensionality of features can be found for dimensionality reduction and noise removal. We evaluate this approach on two most challenging datasets: HMDB51 and UCF101. The experimental results show the effectiveness of the framework after locality information is exploited and incorporated. © 2015 ACM.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Multi-layer supervised dictionary learning for visual classification 15

Multi-layer supervised dictionary learning for visual classi...

引用

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

作者： Guo, Qiang Zhang, Chengyue Han, Yahong School of Computer Science and Technology Tianjin University China School of Computer Science and Technology Tianjin University China Tianjin Key Laboratory of Cognitive Computing and Application China

ISBN: (纸本)9781450335287

For the task of visual categorization, the learning model is expected to be endowed with discriminative visual feature representation and flexibilities in processing multi-layer categories structure. Many existing approaches are designed based on a flat category structure, or rely on a restricted category structure, hence may not be appreciated for dealing with complex category structure and large numbers of categories. In this paper, we propose a novel dictionary learning method by taking advantage of the hierarchical category structure. A shared discriminative dictionary and a discriminative classification model are learnt for visual categorization. An optimization framework for learning all the components of the proposed model is presented. In the process of optimization, the hierarchical semantic structure among categories is preserved in the dictionary. Experiments on Caltech256 and ImageNet object data subset demonstrate that our approach achieves promising performance on data with large numbers of classes compared with some state-ofthe- art methods. © 2015 ACM.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：