检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

10,083 篇 会议
268 篇 期刊文献
37 册 图书

馆藏范围

10,388 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

7,284 篇 工学
- 5,754 篇 计算机科学与技术...
- 3,640 篇 软件工程
- 1,365 篇 信息与通信工程
- 1,287 篇 电气工程
- 877 篇 控制科学与工程
- 825 篇 电子科学与技术（可...
- 465 篇 机械工程
- 462 篇 生物工程
- 392 篇 光学工程
- 370 篇 动力工程及工程热...
- 365 篇 生物医学工程（可授...
- 288 篇 仪器科学与技术
- 258 篇 化学工程与技术
- 239 篇 交通运输工程
- 230 篇 网络空间安全
- 206 篇 材料科学与工程（可...
- 193 篇 土木工程
- 188 篇 建筑学
- 184 篇 安全科学与工程
2,754 篇 理学
- 1,410 篇 数学
- 868 篇 物理学
- 503 篇 生物学
- 362 篇 统计学（可授理学、...
- 342 篇 化学
- 240 篇 系统科学
1,333 篇 管理学
- 819 篇 管理科学与工程(可...
- 629 篇 图书情报与档案管...
- 366 篇 工商管理
444 篇 医学
- 346 篇 临床医学
- 192 篇 基础医学(可授医学...
186 篇 教育学
143 篇 法学
126 篇 经济学
48 篇 农学
41 篇 军事学
31 篇 文学
11 篇 艺术学
1 篇 哲学
1 篇 历史学

主题

1,028 篇 program processo...
729 篇 computer archite...
423 篇 computational mo...
386 篇 hardware
341 篇 high performance...
258 篇 parallel process...
237 篇 computer science
193 篇 optimization
172 篇 benchmark testin...
164 篇 concurrent compu...
163 篇 application soft...
159 篇 runtime
149 篇 bandwidth
146 篇 computer vision
132 篇 deep learning
129 篇 registers
129 篇 libraries
122 篇 scalability
122 篇 parallel program...
121 篇 throughput

机构

70 篇 college of compu...
57 篇 school of comput...
56 篇 university of ch...
45 篇 school of comput...
34 篇 department of co...
32 篇 college of compu...
31 篇 univ chinese aca...
30 篇 school of comput...
30 篇 tsinghua univers...
27 篇 school of comput...
25 篇 school of data a...
23 篇 college of compu...
23 篇 computer network...
23 篇 high energy astr...
22 篇 graduate school ...
21 篇 graduate school ...
20 篇 center for astro...
20 篇 department of ph...
20 篇 department of ph...
20 篇 the graduate sch...

作者

24 篇 ikeda d.
23 篇 belz j.w.
22 篇 jui c.c.h.
22 篇 oshima a.
22 篇 tsuda r.
22 篇 kim j.h.
22 篇 bergman d.r.
22 篇 allen m.
22 篇 matsuyama t.
22 篇 wong t.
22 篇 ishii t.
22 篇 kim s.w.
22 篇 takamura m.
22 篇 yashiro k.
22 篇 kadota k.
22 篇 mayta r.
22 篇 ozawa s.
22 篇 kasami s.
22 篇 rodriguez d.c.
22 篇 fujisue k.

语言

9,497 篇 英文
851 篇 其他
67 篇 中文
3 篇 西班牙文
2 篇 土耳其文

检索条件"任意字段=Conference on High Performance Computer Modernization Program"

共 10388 条记录，以下是4201-4210 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Telescope Array search for EeV photons 37

Telescope Array search for EeV photons

引用

37th International Cosmic Ray conference, ICRC 2021

作者： Kalashev, Oleg E. Kharuk, Ivan V. Kuznetsov, Mikhail Yu. Rubtsov, Grigory I. Abbasi, R.U. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalshev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Zundel, Z. Kido, E. Kim, H.B. Kim, J.H. Kim, J.A. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Zhezher, Y. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamora, T. Nakamura, Y. Nakazawa, A. Nishio, E. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Yoshioka, Y. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Institute for Nuclear Research of the Russian Academy of Sciences 60th October Anniversary st. 7A Moscow Russia Department of Physics Loyola University Chicago ChicagoIL United States High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research of the Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawashi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique Théorique Université Libre de Bruxelles Brussels Belgium Department of Physics Yonsei University Seodaemun-gu Seoul Korea Republi

We present updated results of the search for the ultra-high energy photons with primary energies greater than 10 EeV. The data of the Telescope Array Surface Detector collected over 11 years are used in this work. The method is based on the machine learning classifier, which is trained on both the reconstructed composition-sensitive parameters of the event and the calibrated waveform signals at each triggered station of the Surface Detector. © Copyright owned by the author(s).

关键词： Photons

来源：评论

学校读者我要写书评

暂无评论

Analysis of TAx4 hybrid trigger and events 37

Analysis of TAx4 hybrid trigger and events

引用

37th International Cosmic Ray conference, ICRC 2021

作者： Kim, Sang Woo Abbasi, R.U. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuzmin, V. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nishio, E. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. Department of Physics Sungkyunkwan University Jang-an-gu Suwon Korea Republic of Department of Physics Loyola University Chicago ChicagoIL United States High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan Astrophysical Big Bang Laboratory RIKEN Wako Saitama Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawa-shi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique Théorique Université Libre de Bruxelles Brussels Belgium Department of Physics Yonsei University Seodaemun-gu Seoul Korea Republic of Center for Astrophysics

The Telescope Array is the largest ultra-high energy cosmic ray hybrid detector in the Northern hemisphere. While the TAx4 Surface Detector (TAx4 SD) has a duty cycle of 100%, it should be noted that the TAx4 Fluorescence Detector (TAx4 FD) observes the full longitudinal profile of the cosmic ray air showers and therefore is able to determine their energies more accurately than TAx4 SD. In addition, observing cosmic rays in hybrid mode ("hybrid events") has several advantages. Events seen in hybrid mode by the TAx4 FD and SD are used to establish the energy scale of the TAx4 SD. Moreover, the FD longitudinal profile is used to determine the mass composition of the primary comic ray particles, when the event geometries are well constrained by FD and SD measurements simultaneously. Despite large differences in the TAx4 SD/FD standalone performances, both detector types complement each other in measuring important physical quantities. Since August 2019, direction, energy, and Xmax can be obtained from reconstructing hybrid events. In this poster, preliminary analysis of TAx4 hybrid trigger and TAx4 hybrid events will be presented with focus on energy and Xmax observations. © Copyright owned by the author(s).

关键词： Cosmic rays

来源：评论

学校读者我要写书评

暂无评论

A Fast Classification Approach to Upper-Limb Posture Recognition 13

A Fast Classification Approach to Upper-Limb Posture Recogni...

引用

2020 IEEE Congress on Cybermatics: 13th IEEE International conferences on Internet of Things, iThings 2020, 16th IEEE International conference on Green Computing and Communications, GreenCom 2020, 13th IEEE International conference on Cyber, Physical and Social Computing, CPSCom 2020 and 6th IEEE International conference on Smart Data, SmartData 2020

作者： Wu, Xueyan Jiang, Yinghang Liu, Qi Wu, Hao Liu, Xiaodong Nanjing University of Information Science Technology Nanjing China School of Computer and Software Nanjing University of Information Science Technology Nanjing China School of Computing Edinburgh Napier University Edinburgh Edinburgh United Kingdom

ISBN: (纸本)9781728176475

At present, the study of upper limb posture recognition is still in the primary stage, due to the diversity of the objective environment and the complexity of the human body posture, the upper limb posture has no Public dataset. In this paper, an upper extremity data acquisition system is designed, with a three-channel Data acquisition mode, collect acceleration signal and gyroscope signal as sample data. The data sets were pre-processed with de-weighting, interpolation, and feature extraction. With the goal of recognizing human posture, experiments with KNN, logistic regression, and random gradient descent algorithms were conducted. In order to verify the superiority of each algorithm, the data window was adjusted to compare the recognition speed, computation time and accuracy of each classifier. For the problem of improving the accuracy of human posture recognition, a neural network model based on full connectivity is developed. In the process of constructing the network model, the effects of different hidden layers, activation functions, and optimizers on the recognition rate were experimentally for the comparative analysis, the softplus activation function with better recognition performance and the adagrad optimizer are selected. Finally, by comparing the comprehensive recognition accuracy and time efficiency with other classification models, the fully connected neural network is verified in the human posture Superiority in Identification. © 2020 IEEE.

关键词： Data acquisition

来源：评论

学校读者我要写书评

暂无评论

A study of analysis method for the identification of UHECR source type 37

A study of analysis method for the identification of UHECR s...

引用

37th International Cosmic Ray conference, ICRC 2021

作者： Yoshida, Fugo Tameda, Yuichiro Abbasi, R.U. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nishio, E. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University 18-8 Hatsucho Neyagawa-shi Osaka Japan Department of Physics Loyola University Chicago ChicagoIL United States High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawa-shi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique Théorique Université Libre de Bruxelles Brussels Belgium Department of Physics Yonsei University Seodaemu

The autocorrelation analysis using the arrival direction of Ultra high Energy Cosmic Rays (UHECRs) has been previously reported by the Telescope Array (TA) experiment. It is expected that the autocorrelation function reflects the source distribution. We simulate the expected arrival direction distribution of the cosmic rays using the catalogs of candidate sources. We take into account random deflection in the magnetic fields, with the magnitude of deflection determined by the charge and energy of the cosmic rays, coherence length and magnitude of the extragalactic magnetic field (EGMF), and by distance to source. In addition, in order to compare with the results of TA, we consider the TA exposure. We compare the autocorrelation of the arrival directions corresponding to different source catalogs with the isotropic distribution. We calculate the autocorrelation function for each type of source candidates using this procedure. We will discuss the ability of this method to identify the source type of UHECRs. © Copyright owned by the author(s).

关键词： Cosmic rays

来源：评论

学校读者我要写书评

暂无评论

A source code analysis method with parallel acceleration for mining MPI application communication counts 21

A source code analysis method with parallel acceleration for...

引用

21st IEEE International conference on high performance Computing and Communications, 17th IEEE International conference on Smart City and 5th IEEE International conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Zhang, Rui Xiao, Limin Yan, Baicheng Wei, Bing Zhou, Yi Wang, Dong School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781728120584

MPI(Message Passing Interface) applications are widely used to solve complex problems in the field of science and engineering. In addition to calculation information concerns, communication information is also an important focal point for MPI application analysis. The communication information is generally obtained through manual reviewing the source code, which is impractical for complex applications. Another way is to develop an instrumentation library and compile the target application with the library. Despite the communication information can be accurately obtained by this type of methods, the target application must be executed. To address these issues, in this paper, a source code analysis method is proposed to get the MPI applications communication information, which does not need to execute the target application and can work with general computers. Furthermore, a parallel strategy is presented to accelerate the source code analysis process. Two frequently-used communication patterns are used to evaluate the proposed method. The experimental results show that the method proposed in this paper not only can be ran on general computers, but also reduce the analysis overhead, and can also achieve efficiency advantages in the MPI applications with large message size. © 2019 IEEE.

关键词： Message passing

来源：评论

学校读者我要写书评

暂无评论

Hybrid Naming Scheme Based PURSUIT Architecture for Smart City 4

Hybrid Naming Scheme Based PURSUIT Architecture for Smart Ci...

引用

4th International conference on Smart Cities, Internet of Things and Applications, SCIoT 2020

作者： Javaheri, Atiyeh Hashemi, Seyyed Naser Seyyed Bohlooli, Ali University of Isfahan Faculty of Computer Engineering Isfahan Iran

ISBN: (纸本)9781728196114

Information-Centric Networking (ICN) is focused on content itself as the key factor of communication instead of network addresses. As a successful nominee for future architecture on the Internet, ICN provides a networking paradigm shift from host-oriented to content-oriented communication. This means that a user can declare its desired content by the unique name of that content irrespective of the hosting location. ICN provides high performance content distribution framework, stronger security solutions, better mobility support and scalable network architecture. It supports different naming schemes encompassing flat, hierarchical, hybrid, and attribute-value names. These properties construct ICN as an appropriate networking infrastructure for IoT applications such as smart city. ICN can better handle large IoT name spaces with lower processing resource usage. It reduces energy consumption by in-network caching of contents. Considering an NDN-based smart city, the available naming schemes can be classified into hybrid and hierarchical names. The disadvantages of the proposed naming schemes can be summarized as the long length of the names in hierarchical approach, the difficulty of finding unique content in attributed-value naming scheme, not being user-friendly in flat naming method, and complexity in hybrid naming structures. Considering these drawbacks, we presented a hybrid name scheme for the smart city by PURSUIT architecture that provides faster name lookup in IoT communications. © 2020 IEEE.

关键词： Smart city

来源：评论

学校读者我要写书评

暂无评论

On performance Prediction of Big Data Transfer in high-performance Networks

On Performance Prediction of Big Data Transfer in High-perfo...

引用

IEEE International conference on Communications (ICC)

作者： Wuji Liu Daqing Yun Chase Q. Wu Nageswara S.V. Rao Aiqin Hou Wei Shen Department of Computer Science New Jersey Institute of Technology Newark NJ USA Computer and Information Sciences Program Harrisburg University Harrisburg PA USA Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge TN USA School of Information Science and Technology Northwest University Xi’an Shaanxi China School of Informatics Science and Technology Zhejiang Sci-Tech University Hangzhou Zhejiang China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Big data generated by large-scale scientific and industrial applications need to be transferred between different geographical locations for remote storage, processing, and analysis. high-speed dedicated connections provisioned in high-performance Networks (HPNs) are increasingly utilized to carry out such big data transfer. HPN management highly relies on an important capability of performance (mainly throughput) prediction to reserve sufficient bandwidth and meanwhile avoid over-provisioning that may result in unnecessary resource waste. This capability is critical to improving the resource (mainly bandwidth) utilization of dedicated connections and meeting various user requests for data transfer. Conventional methods conduct performance prediction by fitting prior observed transfer history with predefined loss functions, without considering unobservable latent factors such as competing loads on end hosts. Such latent factors also have a significant impact on the application-level data transfer performance, which may result in an inaccurate prediction model. In this paper, we first investigate the impact of latent factors and propose a clustering-based method to eliminate their negative impact on performance prediction. We then develop a robust machine learning-based performance predictor by: i) incorporating the proposed latent factor elimination method into data preprocessing, and ii) adopting a customized domain guided loss function. Extensive experimental results show that our predictor achieves significantly higher prediction accuracy than several other state-of-the-art methods.

关键词： Data transfer Big Data Throughput Bandwidth Measurement Predictive models Protocols

来源：评论

学校读者我要写书评

暂无评论

A study of multi-space search optimization 18th

A study of multi-space search optimization

引用

Joint conferences on 18th International conference on Intelligent Systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired Computing , NaBIC 2018

作者： Beckedahl, Derrick Nel, Andreas Pillay, Nelishia Department of Computer Science University of Pretoria Pretoria South Africa

ISBN: (纸本)9783030166564

Traditionally search algorithms have explored a solution space to solve optimization problems. However, as the field has advanced, in order to overcome the challenges posed by searching the solution space directly such as premature convergence, search has been applied to different spaces. These include genetic programming which explores the program space, cultural learning and semantic genetic programming which work in the belief and behavioural spaces respectively and hyper-heuristics which explores a heuristic space. In solving a problem one of these spaces is usually explored. The research presented in this paper forms part of an initiative aimed at deriving n-space search algorithms that explore more than one space to solve a problem. As a starting point this paper focuses on 2-space search algorithms. The paper presents two models for exploring search across spaces, namely, the concurrent model and the sequential model. The application of these models is then illustrated, the concurrent model is applied to solving the one-dimensional packing problem and the sequential model is applied to classification, namely, network instrusion detection and weather prediction. The models have produced good results for both problem domains, illustrating the potential of multi-space search. Future work will examine extending these algorithms to n-space search algorithms with n greater than 2 as well as additional models for combining the search across spaces. © Springer Nature Switzerland AG 2020.

关键词： Genetic programming

来源：评论

学校读者我要写书评

暂无评论

Using Undersampling with Ensemble Learning to Identify Factors Contributing to Preterm Birth 19

Using Undersampling with Ensemble Learning to Identify Facto...

引用

19th IEEE International conference on Machine Learning and Applications, ICMLA 2020

作者： Dong, Shi Feric, Zlatan Li, Guangyu Wu, Chieh Gu, April Z. Dy, Jennifer Meeker, John Padilla, Ingrid Y. Cordero, Jose Velez Vega, Carmen Rosario, Zaira Alshawabkeh, Akram Kaeli, David Dept. of Electrical and Computer Engineering Northeastern University United States Dept. of Civil and Environmental Engineering Northeastern University United States Department of Civil and Environmental Engineering Cornell University United States University of Michigan United States University of Puerto Rico Mayaguez Puerto Rico University of Georgia Graduate School of Public Health University of Puerto Rico Medical Campus Puerto Rico

ISBN: (纸本)9781728184708

In this paper, we propose Ensemble Learning models to identify factors contributing to preterm birth. Our work leverages a rich dataset collected by a NIEHS P42 Center that is trying to identify the dominant factors responsible for the high rate of premature births in northern Puerto Rico. We investigate analytical models addressing two major challenges present in the dataset: 1) the significant amount of incomplete data in the dataset, and 2) class imbalance in the dataset. First, we leverage and compare two types of missing data imputation methods: 1) mean-based and 2) similarity-based, increasing the completeness of this dataset. Second, we propose a feature selection and evaluation model based on using undersampling with Ensemble Learning to address class imbalance present in the dataset. We leverage and compare multiple Ensemble Feature selection methods, including Complete Linear Aggregation (CLA), Weighted Mean Aggregation (WMA), Feature Occurrence Frequency (OFA) and Classification Accuracy Based Aggregation (CAA). To further address missing data present in each feature, we propose two novel methods: 1) Missing Data Rate and Accuracy Based Aggregation (MAA), and 2) Entropy and Accuracy Based Aggregation (EAA). Both proposed models balance the degree of data variance introduced by the missing data handling during the feature selection process, while maintaining model performance. Our results show a 42% improvement in sensitivity versus fallout over previous state-of-the-art methods. © 2020 IEEE.

关键词： Data handling

来源：评论

学校读者我要写书评

暂无评论

An adaptive flow table adjustment algorithm for SDN 21

An adaptive flow table adjustment algorithm for SDN

引用

作者： Xu, Xiaolong Hu, Liuyun Lin, Haowei Fan, Zexuan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728120584

SDN (Software-defined networking) are important for current network systems, such as cloud systems. The characteristics of flow in SDN and the impact of flow table entries and controllers on data packet transmission are analyzed. We propose an adaptive flow table adjustment algorithm for SDN called CFC (Combine flow table and controller). It is important for CFC to monitor the network traffic in real time. According to the proportion of active flow entries in the OpenFlow switch, the value of idle-timeout will be set dynamically based on the characteristics of different data flows. When the switch forwards packets, it will minimize the calculation load of controller, and allocate a reasonable value of idle-timeout to the idle flow table entries. So it can delete discarded flow table entries in time and improve the hit rate of data packets. The experiments show that the utilization rate of the flow table resource is enhanced. With the improvement of data packets' forwarding rate, the average throughput of the network has also increased. At the same time, it can also cope with the burst traffic in SDN and ensure them to be forwarded normally and effectively. © 2019 IEEE.

关键词： Controllers

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 417 418 419 420 421 422 423 424 425 426 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：