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排序方式: 共有289条查询结果,搜索用时 46 毫秒
1.
Christophe Lombard Stephane Le Doze Eric Marencak Paul-Marie Marquaire David Le Noc Grégory Bertrand François Lapicque 《International Journal of Hydrogen Energy》2006
The paper reports the results of on-site regeneration catalytic bed of the natural gas reformer in a 5 kW PEM fuel cell system. The Ni catalyst previously poisoned by sulphur from the available natural gas, could be re-activated by injection of pure water steam, following the method developed for industrial reformers using the same metal catalyst: this method was shown to be perfectly efficient, provided no natural gas was fed during the operation. Results of the tests conducted are presented and discussed in relation to published data on S-sorption on Ni surfaces. 相似文献
2.
Preliminary results of ENVISAT RA-2-derived water levels validation over the Amazon basin 总被引:1,自引:0,他引:1
Frédéric Frappart Stéphane Calmant Frédérique Seyler 《Remote sensing of environment》2006,100(2):252-264
Since the launch of the ENVISAT satellite in 2002, the Radar Altimetry Mission provides systematic observations of the Earth topography. Among the different goals of the ENVISAT Mission, one directly concerns land hydrology: the monitoring of the water levels of lakes, wetlands, and rivers. The ENVISAT Geophysical Data Records products contain, over different type of surfaces, altimeter ranges derived from four specialized algorithms or retrackers. However, none of the retrackers are intended to the processing of the radar echoes over continental waters. A validation study is necessary to assess the performances of the different ENVISAT-derived water levels to monitor inland waters. We have selected four test-zones over the Amazon basin to achieve this validation study. We compare first the performances of these retracking algorithms to deliver reliable water levels for land hydrology. Comparisons with in-situ gauge stations showed that Ice-1 algorithm, based on the Offset Centre of Gravity technique, provides the more accurate water stages. Second, we examine the potentiality to combine water levels derived from different sensors (Topex/Poseidon, ERS-1 and -2, GFO). 相似文献
3.
Nadia Plata Iris Hofer Stephane Roudier Jean-Pierre Schaller 《Journal of aerosol science》2006,37(12):1871-1875
A new cryogenic instrument was designed for the trapping of aerosols such as cigarette mainstream smoke at low temperature. The technique enabled the trapping of the mainstream smoke of a single cigarette and the particulate and vapor phases were trapped simultaneously. 2R4F reference cigarettes were smoked under International Standard Organization (ISO) regime and trapped at low temperature using the cryogenic instrument. After trapping, the mainstream smoke of the 2R4F reference cigarette was diluted with a solvent and selected smoke components could be quantified using gas-mass spectrometry (GCMS) and high-performance liquid chromatography (HPLC). The capability of the instrument for trapping the mainstream smoke was demonstrated. The feasibility of the procedure for the detection and the quantification of a large range of smoke components including carbonyls, alkaloids and organic volatile compounds (VOC) in the mainstream of a single cigarette was also shown. 相似文献
4.
We perform continuous collision detection (CCD) for articulated bodies where motion is governed by an adaptive dynamics simulation.
Our algorithm is based on a novel hierarchical set of transforms that represent the kinematics of an articulated body recursively,
as described by an assembly tree. The performance of our CCD algorithm significantly improves as the number of active degrees
of freedom in the simulation decreases. 相似文献
5.
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
6.
Bahoumina Prince Hallil Hamida Lachaud Jean-Luc Rebière Dominique Dejous Corinne Abdelghani Aymen Frigui Kamel Bila Stephane Baillargeat Dominique Zhang Qing Coquet Phillipe Paragua Carlos Pichonat Emmanuelle Happy Henri 《Microsystem Technologies》2022,28(6):1365-1378
Microsystem Technologies - This study presents the results on the feasibility of a resonant planar chemical capacitive sensor in the microwave frequency range suitable for gas detection and... 相似文献
7.
Lélia BlinAuthor Vitae Maria Gradinariu Potop-ButucaruAuthor Vitae Stephane RovedakisAuthor Vitae 《Journal of Parallel and Distributed Computing》2011,71(3):438-449
We propose a self-stabilizing algorithm for constructing a Minimum Degree Spanning Tree (MDST) in undirected networks. Starting from an arbitrary state, our algorithm is guaranteed to converge to a legitimate state describing a spanning tree whose maximum node degree is at most Δ∗+1, where Δ∗ is the minimum possible maximum degree of a spanning tree of the network.To the best of our knowledge, our algorithm is the first self-stabilizing solution for the construction of a minimum degree spanning tree in undirected graphs. The algorithm uses only local communications (nodes interact only with the neighbors at one hop distance). Moreover, the algorithm is designed to work in any asynchronous message passing network with reliable FIFO channels. Additionally, we use a fine grained atomicity model (i.e., the send/receive atomicity). The time complexity of our solution is O(mn2logn) where m is the number of edges and n is the number of nodes. The memory complexity is O(δlogn) in the send-receive atomicity model (δ is the maximal degree of the network). 相似文献
8.
Ducasse Stephane Pollet Damien 《IEEE transactions on pattern analysis and machine intelligence》2009,35(4):573-591
To maintain and understand large applications, it is important to know their architecture. The first problem is that unlike classes and packages, architecture is not explicitly represented in the code. The second problem is that successful applications evolve over time, so their architecture inevitably drifts. Reconstructing the architecture and checking whether it is still valid is therefore an important aid. While there is a plethora of approaches and techniques supporting architecture reconstruction, there is no comprehensive software architecture reconstruction state of the art and it is often difficult to compare the approaches. This paper presents a state of the art in software architecture reconstruction approaches. 相似文献
9.
Hyung Seok Kim Chris Joslin Thomas Di Giacomo Stephane Garchery Nadia Magnenat-Thalmann 《The Visual computer》2006,22(5):332-345
The goal of this research was the creation of an adaptation mechanism for the delivery of three-dimensional content. The adaptation of content, for various network and terminal capabilities – as well as for different user preferences, is a key feature that needs to be investigated. Current state-of-the art research of the adaptation shows promising results for specific tasks and limited types of content, but is still not well-suited for massive heterogeneous environments. In this research, we present a method for transmitting adapted three-dimensional content to multiple target devices. This paper presents some theoretical and practical methods for adapting three-dimensional content, which includes shapes and animation. We also discuss practical details of the integration of our methods into MPEG-21 and MPEG-4 architectures. 相似文献
10.