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AL-BITTAR T., SOUBRA A.H., 2013, Bearing capacity of strip footings on spatially random soils using sparse polynomial chaos expansion, International Journal for Numerical and Analytical Methods in Geomechanics, 37 (13), 2039-2060.
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The paper deals with the analysis of load-carrying capacity (LCC) of a thin-walled steel beam under compression the axis of which is randomly spatially curved. Open and close thin-walled crosssections are considered for the beam, respectively. The initial curvature is modelled by a random field. The Latin Hypercube Sampling Method was applied. The load carrying capacity is calculated by geometrically nonlinear solution using ANSYS software. The results are presented both in histograms and in a table. The LCC statistical characteristics of beams with open and closed crosssections have been compared. A comparison with the LCC according to the standards is carried out as well.
In this paper we study the cross-language speech emotion recognition using high-order Markov random fields, especially the application in Vietnamese speech emotion recognition. First, we extract the basic speech features including pitch frequency, formant frequency and short-term intensity. Based on the low level descriptor we further construct the statistic features including maximum, minimum, mean and standard deviation. Second, we adopt the high-order Markov random fields (MRF) to optimize the cross-language speech emotion model. The dimensional restrictions may be modeled by MRF. Third, based on the Vietnamese and Chinese database we analyze the efficiency of our emotion recognition system. We adopt the dimensional emotion model (arousal-valence) to verify the efficiency of MRF configuration method. The experimental results show that the high-order Markov random fields can improve the dimensional emotion recognition in the cross-language experiments, and the configuration method shows promising robustness over different languages.
Matthieu Constant, Olivier Blanc and Patrick Watrin
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 FENTON G.A., VANMARCKE E.H., Simulation of randomfields via local average subdivision, Journal of Engineering Mechanics
models in biomedical text retrieval task. Both Balaneshin-kordan et al. [ 8 ] and Xie et al. [ 9 ] used the Markov RandomField (MRF) model and got very high retrieval performance. Song et al. [ 10 ] proposed to retrieve relevant biomedical articles by combining three retrieval models, including BM25, PL2, and BB2, and their results performed the best in 2015 CDS task B.
In the query expansion procedure of all these previous studies, it is showed that the quality of the expanded terms is important. Since the diagnosis can better reflect users’ true information needs
Morphological characteristics of ripples are analyzed considering bed surfaces as two dimensional random fields of bed elevations. Two equilibrium phases are analyzed with respect to successive development of ripples based on digital elevation models. The key findings relate to the shape of the two dimensional second-order structure functions and multiscaling behavior revealed by higher-order structure functions. Our results suggest that (1) the two dimensional second-order structure functions can be used to differentiate the two equilibrium phases of ripples; and (2) in contrast to the elevational time series of ripples that exhibit significant multiscaling behavior, the DEMs of ripples at both equilibrium phases do not exhibit multiscaling behavior.