The role of La2O3 loading in Pd/Al2O3-La2O3 prepared by sol–gel on the catalytic properties in the NO reduction with H2 was studied. The catalysts were characterized by N2 physisorption, temperature-programmed reduction, differential thermal analysis, temperature-programmed oxidation and temperature-programmed desorption of NO.
The physicochemical properties of Pd catalysts as well as the catalytic activity and selectivity are modified by La2O3 inclusion. The selectivity depends on the NO/H2 molar ratio (GHSV = 72,000 h−1) and the extent of interaction between Pd and La2O3. At NO/H2 = 0.5, the catalysts show high N2 selectivity (60–75%) at temperatures lower than 250 °C. For NO/H2 = 1, the N2 selectivity is almost 100% mainly for high temperatures, and even in the presence of 10% H2O vapor. The high N2 selectivity indicates a high capability of the catalysts to dissociate NO upon adsorption. This property is attributed to the creation of new adsorption sites through the formation of a surface PdOx phase interacting with La2O3. The formation of this phase is favored by the spreading of PdO promoted by La2O3. DTA shows that the phase transformation takes place at temperatures of 280–350 °C, while TPO indicates that this phase transformation is related to the oxidation process of PdO: in the case of Pd/Al2O3 the O2 uptake is consistent with the oxidation of PdO to PdO2, and when La2O3 is present the O2 uptake exceeds that amount (1.5 times). La2O3 in Pd catalysts promotes also the oxidation of Pd and dissociative adsorption of NO mainly at low temperatures (<250 °C) favoring the formation of N2. 相似文献
This article reports an aramid pulp (AP) treated with two ionic liquids (IL), namely 1-n-butyl-3-methylimidazolium chloride (C4.Cl) and 1-carboxymethyl-3-methylimidazolium chloride (HO2C), and its use as a filler in reinforced rigid polyurethane foams (RPUF). The RPUF were incorporated with the treated AP at three weight fractions (c.a. 0.1, 0.5, and 1.0 wt%) and were produced by the free rising method. The results showed that the studied IL promoted a better interaction between the AP and the RPUF system, which increased the overall reactivity, imparting a higher cell anisotropy. This also yielded a positive effect in mechanical properties and thermal stability of the RPUF. Compared to the neat RPUF, outstanding increases of approximately 50 and 20% were achieved in compressive modulus and strength, respectively. In all, the use of IL promoted increased compatibility between matrix and reinforcement, especially that HO2C IL. 相似文献
The production of polymer fibers from the combination of zein and PEO might have great potential in the field of biomaterial. Zein/PEO fibers were obtained in this work through solution electrospinning. An experimental design, 24-1, was used for evaluating the influences of PEO content in the blend, distance from the needle tip to the collector, applied electric voltage and solution flow for average fiber diameter and relative-yield process. Beyond this, the relationship between PEO content in the blend and the fiber properties were evaluated through FTIR, DSC, TG, tensile tests, and cytotoxic tests. The factor that exerts the greatest effect on the average fiber diameter response was the electrical voltage. The increase in PEO content in the blend decreased the thermal stability and increased the degree of the fibers' crystallinity. The mechanical tests showed that fibers with higher elongation were obtained at richer PEO blends. The fibers presented cytocompatible characteristics. 相似文献
A community within a graph can be broadly defined as a set of vertices that exhibit high cohesiveness (relatively high number of edges within the set) and low conductance (relatively low number of edges leaving the set). Community detection is a fundamental graph processing analytic that can be applied to several application domains, including social networks. In this context, communities are often overlapping, as a person can be involved in more than one community (e.g., friends, and family); and evolving, since the structure of the network changes. We address the problem of streaming overlapping community detection, where the goal is to maintain communities in the presence of streaming updates. This way, the communities can be updated more efficiently. To this end, we introduce SONIC—a find-and-merge type of community detection algorithm that can efficiently handle streaming updates. SONIC first detects when graph updates yield significant community changes. Upon the detection, it updates the communities via an incremental merge procedure. The SONIC algorithm incorporates two additional techniques to speed-up the incremental merge; min-hashing and inverted indexes. Results show that SONIC can provide high quality overlapping communities, while handling streaming updates several orders of magnitude faster than the alternatives performing from-scratch computation. 相似文献
During the last decades the Web has become the greatest repository of digital information. In order to organize all this information, several text categorization methods have been developed, achieving accurate results in most cases and in very different domains. Due to the recent usage of Internet as communication media, short texts such as news, tweets, blogs, and product reviews are more common every day. In this context, there are two main challenges; on the one hand, the length of these documents is short, and therefore, the word frequencies are not informative enough, making text categorization even more difficult than usual. On the other hand, topics are changing constantly at a fast rate, causing the lack of adequate amounts of training data. In order to deal with these two problems we consider a text classification method that is supported on the idea that similar documents may belong to the same category. Mainly, we propose a neighborhood consensus classification method that classifies documents by considering their own information as well as information about the category assigned to other similar documents from the same target collection. In particular, the short texts we used in our evaluation are news titles with an average of 8 words. Experimental results are encouraging; they indicate that leveraging information from similar documents helped to improve classification accuracy and that the proposed method is especially useful when labeled training resources are limited. 相似文献
Cyber physical systems (CPSs) can be found nowadays in various fields of activity. The increased interest for these systems as evidenced by the large number of applications led to complex research regarding the most suitable methods for design and development. A promising solution for specification, visualization, and documentation of CPSs uses the Object Management Group (OMG) unified modeling language (UML). UML models allow an intuitive approach for embedded systems design, helping end-users to specify the requirements. However, the UML models are represented in an informal language. Therefore, it is difficult to verify the correctness and completeness of a system design. The object constraint language (OCL) was defined to add constraints to UML, but it is deficient in strict notations of mathematics and logic that permits rigorous analysis and reasoning about the specifications. In this paper, we investigated how CPS applications modeled using UML deployment diagrams could be formally expressed and verified. We used Z language constructs and prototype verification system (PVS) as formal verification tools. Considering some relevant case studies presented in the literature, we investigated the opportunity of using this approach for validation of static properties in CPS UML models. 相似文献
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.
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
Machine Learning - Machine Learning studies often involve a series of computational experiments in which the predictive performance of multiple models are compared across one or more datasets. The... 相似文献
ZrSe2 is a band semiconductor studied long time ago. It has interesting electronic properties, and because its layer structure can be intercalated with different atoms to change some of the physical properties. In this investigation, we found that Zr deficiencies alter the semiconducting behavior and the compound can be turned into a superconductor. In this paper, we report our studies related to this discovery. The decreasing of the number of Zr atoms in small proportion according to the formula ZrxSe2, where x is varied from about 8.1 to 8.6 K, changing the semiconducting behavior to a superconductor with transition temperatures ranging between 7.8 and 8.5 K, is depending on the deficiencies. Outside of those ranges, the compound behaves as semiconducting with the properties already known. In our experiments, we found that this new superconductor has only a very small fraction of superconducting material determined by magnetic measurements with applied magnetic field of 10 Oe. Our conclusions is that superconductivity is filamentary. However, in one studied sample, the fraction was about 10.2 %, whereas in others is only about 1% or less. We determined the superconducting characteristics; the critical fieldsthat indicate a type 2 superonductor with Ginzburg-Landau κ parameter of the order about 2.7. The synthesis procedure is quite normal following the conventional solid state reaction. In this paper, included are the electronic characteristics, transition temperature, and evolution with temperature of the critical fields. 相似文献
Three-dimensional (3D) representations of complex geometric shapes, especially when they are reconstructed from magnetic resonance imaging (MRI) and computed tomography (CT) data, often result in large polygon meshes which require substantial storage for their handling, and normally have only one fixed level of detail (LOD). This can often be an obstacle for efficient data exchange and interactive work with such objects. We propose to replace such large polygon meshes with a relatively small set of coefficients of the patchwise partial differential equation (PDE) function representation. With this model, the approximations of the original shapes can be rendered with any desired resolution at interactive rates. Our approach can directly work with any common 3D reconstruction pipeline, which we demonstrate by applying it to a large reconstructed medical data set with irregular geometry. 相似文献