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排序方式: 共有94条查询结果,搜索用时 15 毫秒
1.
对现行钢结构设计规范,包括中国规范(GB50017—2003)、美国规范(LRFD)、欧洲规范(EC3)和香港标准(HKC),进行了较详尽的比较,讨论了梁柱设计的各种原理和流程,总结了各规范设计方法的相似性与不同点。LRFD只利用一个验算方程来设计框架柱,而其他三种规范均需进行截面承载力、平面内稳定和平面外稳定承载力验算。比较发现,四种规范或标准各有优缺点,没有哪一种规范在各个方面均比其它规范更为优越。 相似文献
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结合老挝南塔河1#水电站移民规划工作经验,分析老挝水库移民的实施办法,进而浅谈老挝水库移民安置工作与国内的异同,指出从事国外水库移民工作应注意的问题。 相似文献
4.
Comments on the article by J. S. Hyde (see record 2005-11115-001), in which Hyde reviewed meta-analytic evidence on gender differences and concluded that most psychological gender differences are in the close-to-zero or small range. The current author notes some omissions from Hyde's review, including the findings through other research large gender differences are reflected in some kinds of interests and occupational preferences, in males' and females placement on the people-things dimension of interests, and in many kinds of mental illness and behavior problems. The current author's position is that that many psychological gender differences are small-to-nonexistent, some are moderate, and some are large. The task that confronts gender researchers is to explain the complex profile of psychological gender differences and to untangle the myriad social and biological factors that generate both gender differences and gender similarities. (PsycINFO Database Record (c) 2010 APA, all rights reserved) 相似文献
5.
Bors Douglas A.; Vigneau Fran?ois; Kronlund Antonia 《Canadian Metallurgical Quarterly》2006,38(2):176
Scores were analyzed from two samples of undergraduate university students in the Toronto area who answered a questionnaire on exam anxiety (the "Test Anxiety Inventory": TAI). The samples (n = 645 and n = 462), which were both made up of women and men, some with English as their mother tongue and others having a mother tongue other than English, showed very comparable result patterns. Women reported a higher exam anxiety level than men on both dimensions of the questionnaire: Worry (TAIW) and Emotionality (TAIE). Students with a mother tongue other than English, an indicator of recent immigration--reported a higher level of anxiety than students whose mother tongue is English, for both the worry component and the emotionality component. Also, and moreover generally, the worry component of TAI (but not the emotionality component) appeared to have a negative correlation with the results obtained by the students in the final exam for the psychology introductory course in which they were registered. This correlation was not attenuated when a general intelligence test was taken into account. Finally, the TAI factorial structure was similar for both samples, with a model using two correlated factors showing better adjustment to the data than a unifactorial model. Given the strong correlation between the two factors, however, (r = 0,80), this hierarchical design of the TAI was supported. (PsycINFO Database Record (c) 2010 APA, all rights reserved) 相似文献
6.
Comments on the article by J. S. Hyde (see record 2005-11115-001), which reviewed the results of 46 meta-analyses of studies investigating gender differences and produced results that supported the gender similarities hypothesis that men and women are similar along most psychological traits. The current authors agree with the gender similarities hypothesis but argue here that Hyde's review has limitations that caused it to underestimate the true extent of gender differences. They also outline the benefits of adopting an evolutionary psychological perspective on gender differences. (PsycINFO Database Record (c) 2010 APA, all rights reserved) 相似文献
7.
Julien Ah-Pine Marco Bressan Stephane Clinchant Gabriela Csurka Yves Hoppenot Jean-Michel Renders 《Multimedia Tools and Applications》2009,42(1):31-56
This paper deals with multimedia information access. We propose two new approaches for hybrid text-image information processing
that can be straightforwardly generalized to the more general multimodal scenario. Both approaches fall in the trans-media
pseudo-relevance feedback category. Our first method proposes using a mixture model of the aggregate components, considering
them as a single relevance concept. In our second approach, we define trans-media similarities as an aggregation of monomodal
similarities between the elements of the aggregate and the new multimodal object. We also introduce the monomodal similarity
measures for text and images that serve as basic components for both proposed trans-media similarities. We show how one can
frame a large variety of problem in order to address them with the proposed techniques: image annotation or captioning, text
illustration and multimedia retrieval and clustering. Finally, we present how these methods can be integrated in two applications:
a travel blog assistant system and a tool for browsing the Wikipedia taking into account the multimedia nature of its content.
Dr. Julien Ah-Pine joined the XRCE Grenoble as Research Engineer in 2007. He is part of the Textual and Visual Pattern Analysis group and his current research activities are related to multi-modal information retrieval and machine learning. He received his PhD degree in mathematics from Pierre and Marie Curie University (University of Paris 6). From 2003 to 2007, he was with Thales Communications, working on relational analysis, data and text mining methods and social choice theory. Dr. Marco Bressan is Area Manager of the Textual and Visual Pattern Analysis area at Xerox Research Centre Europe. His main research interests are statistical learning and classification; image and video semantic scene understanding; image enhancement and aesthetics; object detection and recognition, particularly when dealing with uncontrolled environments. Prior to Xerox, several of his contributions in these fields were applied to a variety of scenarios including biometric solutions, data mining, CBIR and industrial vision. Dr. Bressan holds a BA in Applied Mathematics from the University of Buenos Aires, a M.Sc. in Computer Vision from the Computer Vision Centre in Spain and a Ph.D. in Computer Science and Artificial Intelligence from the Autonomous University of Barcelona. He is an active member of the network of Argentinean researchers abroad and one of the founders of the network of computer vision and cognitive science researchers. Stephane Clinchant is Ph.D. Student at University Joseph Fourier (Grenoble, France) and at the Xerox Research Centre Europe, that he joined in 2005. Before joining XRCE, Stephane obtained a Master Degree in Computer Sciences in 2005 from the Ecole Nationale Superieure d’Electrotechnique, d’Informatique, d’Hydraulique et des Telecommunications (France). His current research interests mainly focus on Machine Learning for Natural Language Processing and Multimedia Information Access. Dr. Gabriela Csurka is a research scientist in the Textual and Visual Pattern Analysis team at Xerox Research Centre Europe (XRCE). She obtained her Ph.D. degree (1996) in Computer Science from University of Nice Sophia - Antipolis. Before joining XRCE in 2002, she worked in fields such as stereo vision and projective reconstruction at INRIA (Sophia Antipolis, Rhone Alpes and IRISA) and image and video watermarking at University of Geneva and Institute Eurécom, Sophia Antipolis. Author of several publications in main journals and international conferences, she is also an active reviewer both for journals and conferences. Her current research interest concerns the exploration of new technologies for image content and aesthetic analysis, cross-modal image categorization and semantic based image segmentation. Yves Hoppenot is in charge of the development and integration of new technologies in our European research Technology Showroom. He is a software expert for the production, office and services sectors. Yves joined the Xerox Research Centre Europe in 2001. He graduated from the Ecole National Superieure des Telecommunications, Brest in France, and received a Master of Science degree from the Tampere University of Technology in Finland. Dr. Jean-Michel Renders joined the XRCE Grenoble as Research Engineer in 2001. His current research interests mainly focus on Machine Learning techniques applied to Statistical Natural Language Processing and Text Mining. Before joining XRCE, Jean-Michel obtained a PhD in Applied Sciences from the University of Brussels in 1993. He started his research activities in 1988, in the field of Robotics Dynamics and Control. Then, he joined the Joint Research Center of the European Communities to work on biologial metaphors (Genetic Algorithms, Neural Networks and Immune Networks) applied to process control. After spending one year as Visiting Scientist at York University (England), he spent 4 years applying Artificial Intelligence and Machine Learning Techniques in Industry (Tractebel - Suez). Then, he worked as Data Mining Senior Consultant and led projects in most major Belgian banks and utilities. 相似文献
Gabriela CsurkaEmail: |
Dr. Julien Ah-Pine joined the XRCE Grenoble as Research Engineer in 2007. He is part of the Textual and Visual Pattern Analysis group and his current research activities are related to multi-modal information retrieval and machine learning. He received his PhD degree in mathematics from Pierre and Marie Curie University (University of Paris 6). From 2003 to 2007, he was with Thales Communications, working on relational analysis, data and text mining methods and social choice theory. Dr. Marco Bressan is Area Manager of the Textual and Visual Pattern Analysis area at Xerox Research Centre Europe. His main research interests are statistical learning and classification; image and video semantic scene understanding; image enhancement and aesthetics; object detection and recognition, particularly when dealing with uncontrolled environments. Prior to Xerox, several of his contributions in these fields were applied to a variety of scenarios including biometric solutions, data mining, CBIR and industrial vision. Dr. Bressan holds a BA in Applied Mathematics from the University of Buenos Aires, a M.Sc. in Computer Vision from the Computer Vision Centre in Spain and a Ph.D. in Computer Science and Artificial Intelligence from the Autonomous University of Barcelona. He is an active member of the network of Argentinean researchers abroad and one of the founders of the network of computer vision and cognitive science researchers. Stephane Clinchant is Ph.D. Student at University Joseph Fourier (Grenoble, France) and at the Xerox Research Centre Europe, that he joined in 2005. Before joining XRCE, Stephane obtained a Master Degree in Computer Sciences in 2005 from the Ecole Nationale Superieure d’Electrotechnique, d’Informatique, d’Hydraulique et des Telecommunications (France). His current research interests mainly focus on Machine Learning for Natural Language Processing and Multimedia Information Access. Dr. Gabriela Csurka is a research scientist in the Textual and Visual Pattern Analysis team at Xerox Research Centre Europe (XRCE). She obtained her Ph.D. degree (1996) in Computer Science from University of Nice Sophia - Antipolis. Before joining XRCE in 2002, she worked in fields such as stereo vision and projective reconstruction at INRIA (Sophia Antipolis, Rhone Alpes and IRISA) and image and video watermarking at University of Geneva and Institute Eurécom, Sophia Antipolis. Author of several publications in main journals and international conferences, she is also an active reviewer both for journals and conferences. Her current research interest concerns the exploration of new technologies for image content and aesthetic analysis, cross-modal image categorization and semantic based image segmentation. Yves Hoppenot is in charge of the development and integration of new technologies in our European research Technology Showroom. He is a software expert for the production, office and services sectors. Yves joined the Xerox Research Centre Europe in 2001. He graduated from the Ecole National Superieure des Telecommunications, Brest in France, and received a Master of Science degree from the Tampere University of Technology in Finland. Dr. Jean-Michel Renders joined the XRCE Grenoble as Research Engineer in 2001. His current research interests mainly focus on Machine Learning techniques applied to Statistical Natural Language Processing and Text Mining. Before joining XRCE, Jean-Michel obtained a PhD in Applied Sciences from the University of Brussels in 1993. He started his research activities in 1988, in the field of Robotics Dynamics and Control. Then, he joined the Joint Research Center of the European Communities to work on biologial metaphors (Genetic Algorithms, Neural Networks and Immune Networks) applied to process control. After spending one year as Visiting Scientist at York University (England), he spent 4 years applying Artificial Intelligence and Machine Learning Techniques in Industry (Tractebel - Suez). Then, he worked as Data Mining Senior Consultant and led projects in most major Belgian banks and utilities. 相似文献
8.
在新型城镇化大潮的冲击下,古村落的保护与发展面临巨大的威胁,尤其是以传统农耕为主的聚族而居、精耕细作的文化形态面临着被淡忘甚至湮灭的危险。首先明确了我国的传统乡建还在不断进步当中,农耕文化作为传统乡村建设的重要组成部分,其作用与意义不容忽视,而后综合分析了新型城镇化与新农村建设之间的矛盾冲突和两极分化,思考在此种现实背景下传统乡建到底该以何种方式推进,之后探讨了如何将作为物质和精神载体的农耕文化通过设计策略的同异介入与传统乡建融会贯通,从产业、空间、文化、技术四个方面以及设计载体和传播方式上的多样性来寻求传统乡村建设的新模式,试图以农耕文化为底色,为新时代我国的传统乡村建设提供新的思路和有效路径。 相似文献
9.
A Technique for Extracting Sub-source Similarities from Information Sources Having Different Formats
In this paper we propose a semi-automatic technique for deriving the similarity degree between two portions of heterogeneous information sources (hereafter, sub-sources). The proposed technique consists in two phases: the first one selects the most promising pairs of sub-sources, whereas the second one computes the similarity degree relative to each promising pair. We show that the detection of sub-source similarities is a special case (and a very interesting one, for semi-structured information sources) of the more general problem of Scheme Match. In addition, we present a real example case to clarify the proposed technique, a set of experiments we have conducted to verify the quality of its results, a discussion about its computational complexity and its classification in the context of related literature. Finally, we discuss some possible applications which can benefit by derived similarities. 相似文献
10.