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41.
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
42.
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
43.
Alexandre Benoit Laurent Bonnaud Alice Caplier Phillipe Ngo Lionel Lawson Daniela G. Trevisan Vjekoslav Levacic Céline Mancas Guillaume Chanel 《Personal and Ubiquitous Computing》2009,13(1):33-41
This paper presents a driver simulator, which takes into account the information about the user’s state of mind (level of
attention, fatigue state, stress state). The user’s state of mind analysis is based on video data and biological signals.
Facial movements such as eyes blinking, yawning, head rotations, etc., are detected on video data: they are used in order
to evaluate the fatigue and the attention level of the driver. The user’s electrocardiogram and galvanic skin response are
recorded and analyzed in order to evaluate the stress level of the driver. A driver simulator software is modified so that
the system is able to appropriately react to these critical situations of fatigue and stress: some audio and visual messages
are sent to the driver, wheel vibrations are generated and the driver is supposed to react to the alert messages. A multi-threaded
system is proposed to support multi-messages sent by the different modalities. Strategies for data fusion and fission are
also provided. Some of these components are integrated within the first prototype of OpenInterface: the multimodal similar
platform. 相似文献
44.
J. J. Ben⩼ez M. A. Centeno J. A. Odriozola R. Conanec R. Marchand Y. Laurent 《Catalysis Letters》1995,34(3-4):379-388
XPS and DRIFTS (diffuse reflectance infrared spectroscopy) spectra of AlPO systems, formally AlPO4-Al2O3, obtained by the sol-gel method have been studied in order to understand their geometric and electronic structure. Both DRIFTS and XPS demonstrate that the acidbase character of these samples depends on a structural modification. For low phosphorus content an amorphous spinel-like solid is proposed. This geometric arrangement alters the electronic density of oxide ions and phosphorus cations and hence their Lewis acid-base properties with respect to the amorphous solid having aluminium and phosphorus only in tetrahedral arrangement. 相似文献
45.
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
46.
Camilo Castro López Xavier Lefebvre Nadège Brusselle-Dupend Marie-Hélène Klopffer Laurent Cangémi Sylvie Castagnet Jean-Claude Grandidier 《Journal of Materials Science》2016,51(8):3750-3761
This paper discusses the effect of porosity and hydrostatic pressure on diffusion kinetics and equilibrium water uptake in a semicrystalline fluoropolymer. Water sorption experiments at atmospheric pressure and under water pressures up to 250 MPa were carried out during 18 months at 40 °C on reference and porous samples. Porosity of samples was induced due to a cavitation process occurring at the highest triaxiality area of waisted and notched specimens during tensile tests. Water uptake was found to be very sensitive to porosity, showing an increase in samples with a high void fraction. On the other hand, water content decreased with increasing pressure suggesting a compaction of the porous space in which water can be stored. Two models describing this water uptake behaviour were considered. The first is a classical model which assumes that sorption occurs only by diffusion following Fick’s law. Fick’s model was found to be in agreement with the experimental results. A “Langmuir-type” sorption model was also proposed to describe water uptake in porous samples, considering a two-phase water transport mechanism: one portion of the absorbed water diffuses through the polymer matrix and the other portion is stored in voids. This model was implemented in a user subroutine using ABAQUS? software and simulations were confronted to experimental sorption curves showing satisfactory agreements. The potential of the Langmuir-type sorption model resides on its availability to be coupled to a poro-mechanical model, in order to improve the understanding of coupling between the mechanical behaviour and water sorption mechanism in a porous polymer. 相似文献
47.
Mohamed Rabhi Anis Ben Abdessalem Laurent Saintis Bruno Castanier Rodrigue Sohoin 《Quality and Reliability Engineering International》2023,39(3):1058-1082
Accelerated life testing (ALT) is widely used in high-reliability product estimation to get relevant information about an item's performance and its failure mechanisms. To analyse the observed ALT data, reliability practitioners need to select a suitable accelerated life model based on the nature of the stress and the physics involved. A statistical model consists of (i) a lifetime distribution that represents the scatter in product life and (ii) a relationship between life and stress. In practice, several accelerated life models could be used for the same failure mode and the choice of the best model is far from trivial. For this reason, an efficient selection procedure to discriminate between a set of competing accelerated life models is of great importance for practitioners. In this paper, accelerated life model selection is approached by using the Approximate Bayesian Computation (ABC) method and a likelihood-based approach for comparison purposes. To demonstrate the efficiency of the ABC method in calibrating and selecting accelerated life model, an extensive Monte Carlo simulation study is carried out using different distances to measure the discrepancy between the empirical and simulated times of failure data. Then, the ABC algorithm is applied to real accelerated fatigue life data in order to select the most likely model among five plausible models. It has been demonstrated that the ABC method outperforms the likelihood-based approach in terms of reliability predictions mainly at lower percentiles particularly useful in reliability engineering and risk assessment applications. Moreover, it has shown that ABC could mitigate the effects of model misspecification through an appropriate choice of the distance function. 相似文献
48.
Dorit Baras Shai Fine Laurent Fournier Dan Geiger Avi Ziv 《International Journal on Software Tools for Technology Transfer (STTT)》2011,13(3):247-261
Closing the feedback loop from coverage data to the stimuli generator is one of the main challenges in the verification process.
Typically, verification engineers with deep domain knowledge manually prepare a set of stimuli generation directives for that
purpose. Bayesian networks based CDG (coverage directed generation) systems have been successfully used to assist the process
by automatically closing this feedback loop. However, constructing these CDG systems requires manual effort and a certain
amount of domain knowledge from a machine learning specialist. We propose a new method that boosts coverage in the early stages
of the verification process with minimal effort, namely a fully automatic construction of a CDG system that requires no domain
knowledge. Experimental results on a real-life cross-product coverage model demonstrate the efficiency of the proposed method. 相似文献
49.
Designing and evaluating an energy efficient Cloud 总被引:1,自引:1,他引:0
Cloud infrastructures have recently become a center of attention. They can support dynamic operational infrastructures adapted
to the requirements of distributed applications. As large-scale distributed systems reach enormous sizes in terms of equipment,
the energy consumption issue becomes one of the main challenges for large-scale integration. Like any other large-scale distributed
system, Clouds face an increasing demand in energy. In this paper, we explore the energy issue by analyzing how much energy
virtualized environments cost. We provide an energy-efficient framework dedicated to Cloud architectures and we validate it
through different experimentations on a modern multicore platform. We show on a realistic example that our infrastructure
could save 25% of the Cloud nodes’ electrical consumption. 相似文献
50.