Identification of a multi-dimensional space-dependent heat source from boundary data
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Publication Date
Fri Jan 01 2016
Journal Name
Applied Numerical Mathematics
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Publication Date
Fri Oct 14 2016
Journal Name
International Journal For Computational Methods In Engineering Science And Mechanics
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Publication Date
Tue Apr 01 2014
Journal Name
International Communications In Heat And Mass Transfer
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Publication Date
Mon May 01 2017
Journal Name
Applied Mathematics And Computation
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Publication Date
Tue Mar 16 2021
Journal Name
International Journal For Computational Methods In Engineering Science And Mechanics
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Publication Date
Tue Oct 01 2013
Journal Name
Proceedings Of The International Astronomical Union
The infrared <i>K</i>-band identification of the DSO/G2 source from VLT and Keck data
Abstract<p>A fast moving infrared excess source (G2) which is widely interpreted as a core-less gas and dust cloud approaches Sagittarius A* (Sgr A*) on a presumably elliptical orbit. VLT <italic>K<sub>s</sub></italic>-band and Keck <italic>K</italic>′-band data result in clear continuum identifications and proper motions of this ∼19<sup><italic>m</italic></sup> Dusty S-cluster Object (DSO). In 2002-2007 it is confused with the star S63, but free of confusion again since 2007. Its near-infrared (NIR) colors and a comparison to other sources in the field speak in favor of the DSO being an IR excess star with photospheric continuum emission at 2 microns than a</p>
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Publication Date
Mon Jul 01 2019
Journal Name
Applied Mathematical Modelling
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Publication Date
Tue Jan 01 2019
Journal Name
Proceedings Of The 5th International Conference On Information Systems Security And Privacy
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Publication Date
Wed Sep 23 2020
Journal Name
Artificial Intelligence Research
Hybrid approaches to feature subset selection for data classification in high-dimensional feature space
This paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spe
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Publication Date
Tue May 01 2012
Journal Name
Engineering Analysis With Boundary Elements
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