• Journal of The Korean Astronomical Society
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• v.50 no.1
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• pp.7-20
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• 2017

We carry out the study of $850{\mu}m$ sources in a part of the XMM-LSS field. The $850{\mu}m$ imaging data were obtained by the SCUBA-2 on the James Clerk Maxwell Telescope (JCMT) for three days in July 2015 with an integration time of 6.1 hours, covering a circular area with a radius of 15'. We choose the central area up to a radius of 9'.15 for the study, where the noise distribution is relatively uniform. The root mean square (rms) noise at the center is 2.7 mJy. We identify 17 sources with S/N > 3.5. Differential number count is estimated in flux range between 3.5 and 9.0 mJy after applying various corrections derived by imaging simulations, which is consistent with previous studies. For detailed study on the individual sources, we select three sources with more reliable measurements (S/N > 4.5), and construct their spectral energy distributions (SEDs) from optical to far-infrared band. Redshift distribution of the sources ranges from 0.36 to 3.28, and their physical parameters are extracted using MAGPHYS model, which yield infrared luminosity $L_{IR}=10^{11.3}-10^{13.4}L_{\odot}$, star formation rate $SFR=10^{1.3}-10^{3.2}M_{\odot}yr^{-1}$ and dust temperature $T_D=30-53K$. We investigate the correlation between $L_{IR}$ and $T_D$, which appears to be consistent with previous studies.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.1-6
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• 2014

In this study, we propose optimized RBFNNs based on night vision face recognition simulator with the aid of $(2D)^2$ PCA algorithm. It is difficult to obtain the night image for performing face recognition due to low brightness in case of image acquired through CCD camera at night. For this reason, a night vision camera is used to get images at night. Ada-Boost algorithm is also used for the detection of face images on both face and non-face image area. And the minimization of distortion phenomenon of the images is carried out by using the histogram equalization. These high-dimensional images are reduced to low-dimensional images by using $(2D)^2$ PCA algorithm. Face recognition is performed through polynomial-based RBFNNs classifier, and the essential design parameters of the classifiers are optimized by means of Differential Evolution(DE). The performance evaluation of the optimized RBFNNs based on $(2D)^2$ PCA is carried out with the aid of night vision face recognition system and IC&CI Lab data.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.195-201
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• 2014

In this study, face recognition system was designed based on polynomial Radial Basis Function Neural Networks(pRBFNNs) pattern classifier using 2-directional 2-dimensional principal component analysis algorithm. Existing one dimensional PCA leads to the reduction of dimension of image expressed by the multiplication of rows and columns. However $(2D)^2PCA$(2-Directional 2-Dimensional Principal Components Analysis) is conducted to reduce dimension to each row and column of image. and then the proposed intelligent pattern classifier evaluates performance using reduced images. The proposed pRBFNNs consist of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with the aid of fuzzy c-means clustering. In the conclusion part of rules. the connection weight of RBFNNs is represented as the linear type of polynomial. The essential design parameters (including the number of inputs and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. Using Yale and AT&T dataset widely used in face recognition, the recognition rate is obtained and evaluated. Additionally IC&CI Lab dataset is experimented with for performance evaluation.