International audienceThis paper deals with global sensitivity analysis of computer model output. Given a set of independent input sample and associated model output vector with possibly the vector of output derivatives with respect to the input variables , we show that it is possible to evaluate the following global sensitivity measures: (i) the Sobol' indices, (ii) the Borgonovo's density-based sensitivity measure, and (iii) the derivative-based global sensitivity measure of Sobol' and Kucherenko. We compare the efficiency of the different methods to address factors fixing setting, an important issue in global sensitivity analysis. First, global sensitivity analysis of the Ishigami function is performed with the different methods. Then, they are applied to two different responses of a soil drainage model. The results show that the polynomial chaos expansion for estimating Sobol' indices is the most efficient approach
SUMMARYMixed finite element (MFE) and multipoint flux approximation (MPFA) methods have similar properties and are well suited for the resolution of Darcy's flow on anisotropic and heterogeneous domains.In this work, the link between hybrid and MPFA formulations is shown algebraically for the lowest order mixed methods of Raviart-Thomas (RT0) and Brezzi-Douglas-Marini (BDM1) on triangles. The efficiency of the four mixed formulations (Hybrid RT0, MPFA RT0, Hybrid BDM1 and MPFA BDM1) is investigated on high anisotropic and heterogeneous media and for unstructured triangular discretizations.Numerical experiments show that the MPFA BDM1 formulation outperforms both Hybrid RT0 and Hybrid BDM1 in the case of anisotropic domains and highly unstructured meshes.
The automatic classification of environmental noise sources from their acoustic signatures recorded at the microphone of a noise monitoring system (NMS) is an active subject of research nowadays. This paper shows how hidden Markov models (HMM's) can be used to build an environmental noise recognition system based on a timefrequency analysis of the noise signal. The performance of the proposed HMM-based approach is evaluated experimentally for the classification of five types of noise events (car, truck, moped, aircraft, train). The HMM-based approach is found to outperform previously proposed classifiers based on the average spectrum of noise event with more than 95% of correct classifications. For comparison, a classification test is performed with human listeners for the same data which shows that the best HMM-based classifier outperforms the "average" human listener who achieves only 91.8% of correct classification for the same task.
The automatic classification of environmental noise sources from their acoustic signatures recorded at the microphone of a noise monitoring system (NMS) is an active subject of research nowadays. This paper shows how hidden Markov models (HMM's) can be used to build an environmental noise recognition system based on a timefrequency analysis of the noise signal. The performance of the proposed HMM-based approach is evaluated experimentally for the classification of five types of noise events (car, truck, moped, aircraft, train). The HMM-based approach is found to outperform previously proposed classifiers based on the average spectrum of noise event with more than 95% of correct classifications. For comparison, a classification test is performed with human listeners for the same data which shows that the best HMM-based classifier outperforms the "average" human listener who achieves only 91.8% of correct classification for the same task.
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