The Experts below are selected from a list of 1881552 Experts worldwide ranked by ideXlab platform
O A Molchanov - One of the best experts on this subject based on the ideXlab platform.
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thermal ir satellite Data Application for earthquake research in japan and china
Journal of Geodynamics, 2002Co-Authors: A A Tronin, M Hayakawa, O A MolchanovAbstract:Abstract NOAA/AVHRR satellite thermal images indicated the presence of positive thermal anomalies that are associated with the large linear structures and fault systems of the Earth's crust. The relation between thermal anomalies and seismic activity was established for Middle Asia on the basis of a 7-year series of thermal images. In China the thermal anomaly has been located near Beijing, at the interface between the mountains and plain. The size of this anomaly is about 700 km in length and 50 km in width. The anomaly appeared about 6–24 days before and continued about a week after an earthquake. The anomaly was sensitive to crustal earthquakes with a magnitude more than 4.7 and for distance of up to 1000 km. The amplitude of this anomaly was about 3 °C. Thermal anomalies in Japan have more complex shape. The analysis of digital images for Japan shows the following: (a) the anomaly appears 7–10 days before shock; (b) the anomalies in Japan have small size; (c) probably, thermal anomaly is located in Kanto area and the magnitude of anomaly run up to 6 °C; (d) the tectonic position of anomalies is not clear now.
A A Tronin - One of the best experts on this subject based on the ideXlab platform.
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thermal ir satellite Data Application for earthquake research in japan and china
Journal of Geodynamics, 2002Co-Authors: A A Tronin, M Hayakawa, O A MolchanovAbstract:Abstract NOAA/AVHRR satellite thermal images indicated the presence of positive thermal anomalies that are associated with the large linear structures and fault systems of the Earth's crust. The relation between thermal anomalies and seismic activity was established for Middle Asia on the basis of a 7-year series of thermal images. In China the thermal anomaly has been located near Beijing, at the interface between the mountains and plain. The size of this anomaly is about 700 km in length and 50 km in width. The anomaly appeared about 6–24 days before and continued about a week after an earthquake. The anomaly was sensitive to crustal earthquakes with a magnitude more than 4.7 and for distance of up to 1000 km. The amplitude of this anomaly was about 3 °C. Thermal anomalies in Japan have more complex shape. The analysis of digital images for Japan shows the following: (a) the anomaly appears 7–10 days before shock; (b) the anomalies in Japan have small size; (c) probably, thermal anomaly is located in Kanto area and the magnitude of anomaly run up to 6 °C; (d) the tectonic position of anomalies is not clear now.
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thermal ir satellite sensor Data Application for earthquake research in china
International Journal of Remote Sensing, 2000Co-Authors: A A TroninAbstract:NOAA/AVHRR thermal images indicated the presence of positive thermal anomalies that are associated with the large linear structures and fault systems of the Earth's crust. The relation between thermal anomalies and seismic activity was established for Middle Asia on the basis of a 7-year series of thermal images. Thermal anomaly has been located near Beijing, on the border between the mountains and plain. The size of this anomaly is about 700 km long and 50 km wide. The anomaly appeared about 6-24 days before and continued about a week after an earthquake. The anomaly was sensitive to crust earthquakes with a magnitude more than 4.7 and for a distance of up to 500 km. The amplitude of this anomaly was about 3 C.
Tristan Richard - One of the best experts on this subject based on the ideXlab platform.
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Wine Analysis and Authenticity Using ^1H-NMR Metabolomics Data: Application to Chinese Wines
Food Analytical Methods, 2018Co-Authors: Louis Gougeon, Gregory Costa, Wen Ma, Pierre-louis Teissedre, François Guyon, Tristan RichardAbstract:A NMR-based metabolomics method was developed to semiautomatically quantify the main components of wine. The method was applied to discriminate wines from two regions of China, Shanxi and Ningxia, which were produced by 6 wineries and for 6 vintages. Two different cultivars, Cabernet Sauvignon and Beihong, were used for winemaking. The method allowed the quantification of 33 metabolites including sugars, amino acids, organic acids, alcohols, and phenolic compounds. Depending on the compounds, the LOD values were in the range of 0.6 to 116 mg/L. The results showed that NMR-based metabolomics combined with multivariate statistical analysis allowed wine separation as a function of terroir and cultivar. Nevertheless, wine differentiation as a function of wineries and ageing would need to be examined more carefully.
Nicholas Ayache - One of the best experts on this subject based on the ideXlab platform.
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spectral forests learning of surface Data Application to cortical parcellation
Medical Image Computing and Computer-Assisted Intervention, 2015Co-Authors: Herve Lombaert, Antonio Criminisi, Nicholas AyacheAbstract:This paper presents a new method for classifying surface Data via spectral representations of shapes. Our approach benefits classification problems that involve Data living on surfaces, such as in cortical parcellation. For instance, current methods for labeling cortical points into surface parcels often involve a slow mesh deformation toward pre-labeled atlases, requiring as much as 4 hours with the established FreeSurfer. This may burden neuroscience studies involving region-specific measurements. Learning techniques offer an attractive computational advantage, however, their representation of spatial information, typically defined in a Euclidean domain, may be inadequate for cortical parcellation. Indeed, cortical Data resides on surfaces that are highly variable in space and shape. Consequently, Euclidean representations of surface Data may be inconsistent across individuals. We propose to fundamentally change the spatial representation of surface Data, by exploiting spectral coordinates derived from the Laplacian eigenfunctions of shapes. They have the advantage over Euclidean coordinates, to be geometry aware and to parameterize surfaces explicitly. This change of paradigm, from Euclidean to spectral representations, enables a classifier to be applied directly on surface Data via spectral coordinates. In this paper, we decide to build upon the successful Random Decision Forests algorithm and improve its spatial representation with spectral features. Our method, Spectral Forests, is shown to significantly improve the accuracy of cortical parcellations over standard Random Decision Forests 74% versus 28% Dice overlaps, and produce accuracy equivalent to FreeSurfer in a fraction of its time 23 seconds versus 3 to 4 hours.
M. José Polo Martín - One of the best experts on this subject based on the ideXlab platform.
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HAIS (1) - Multivariate discretization for associative classification in a sparse Data Application domain
Lecture Notes in Computer Science, 2010Co-Authors: María N. Moreno García, Joel Pinho Lucas, Vivian Félix López Batista, M. José Polo MartínAbstract:Associative classification is becoming a promising alternative to classical machine learning algorithms It is a hybrid technique that combines supervised and unsupervised Data mining algorithms and builds classifiers from association rules' models The aim of this work is to apply these associative classifiers to improve estimation precision in the project management area where Data sparsity involves a major drawback Moreover, in this Application domain, most of the attributes are continuous; therefore, they must be discretized before generating the rules The discretization procedure has a significant effect on the quality of the induced rules as well as on the precision of the classifiers built from them In this paper, a multivariate supervised discretization method is proposed, which takes into account the predictive purpose of the association rules.
