• The Bulletin of The Korean Astronomical Society
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• v.42 no.1
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• pp.53.1-53.1
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• 2017

The YONSEI (YOnsei Nearby Supernova Evolution Investigation) project is to investigate the luminosity evolution of Type Ia supernovae (SNe Ia) by using their early-type host galaxies. As a part of this project, we have constructed our own SN catalogue. SALT2 and MLCS2k2 light-curve fitters implemented in SNANA package are employed to analyze the light-curve data. The catalogue provides a rest-frame peak magnitude in B-band or distance modulus, a light-curve shape parameter, and a color or an extinction value of each SN in the redshift range from 0.01 to 1.4. In this poster, we will present our progress in the detailed systematic tests for this catalogue.

• Korean Journal of Mathematics
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• v.26 no.3
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• pp.425-437
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• 2018

In this paper, we linked the motion of spherical images with the motion of their curves. Surfaces generated by the evolution of spherical image of a space curve are constructed. Also geometric proprieties of these surfaces are obtained.

• The KIPS Transactions:PartB
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• v.10B no.5
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• pp.521-526
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• 2003

In this paper, we propose a geometric active contour model based on intensity information and curve evolution for detecting region boundaries. We put boundary extraction problem as the minimization of the difference between the average intensity of the region and the intensity of the expanding closed curves. We used level set theory to implement the curve evolution for optimal solution. It offered much more freedom in the initial curve position than a general active contour model. Our methods could detect regions whose boundaries are not necessarily defiened by gradient compared to general edge based methods and detect multiple boundaries at the same time. We could improve the result by using anisotropic diffusion filter in image preprocessing. The performance of our model was demonstrated on several data sets like CT and MRI medical images.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.56.1-56.1
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• 2013

Supernovae type Ia (SNe Ia) cosmology is providing the only direct evidence for the presence of dark energy. This result is based on the assumption that the look-back time evolution of SNe Ia luminosity, after light-curve shape correction, would be negligible. However, the most recent compilation of SNe Ia data shows systematic difference in the Hubble residual (HR) between the E and Sd/Irr galaxies, indicating that the light-curve fitters used by the SNe Ia community cannot quite correct for a large portion of the population age effect. In order to investigate this possibility more directly, we have obtained low-resolution spectra for 30 nearby early-type host galaxies. This data set is used to estimate the luminosity-weighted mean ages and metallicities of host galaxies by employing the population synthesis models. We found an interesting trend between the host galaxy age and HR, in the sense that younger galaxies have positive residuals (i.e., light-curve corrected SNe Ia luminosity is fainter). This result is rather independent of the choice of the population synthesis models employed. Taken at face value, this age (evolution) effect can mimic a large fraction of the HR used in the discovery of the dark energy. This result is significant at 1.4 - 3 sigma levels, depending on the light curve fitters adopted, and further observations and analyses are certainly required to confirm the trend reported here.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.69.2-69.2
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• 2011

The discovery of dark energy from Type Ia supernovae (SN Ia) is based on the implicit assumption that the look-back time evolution of SN Ia luminosity, after light-curve corrections, would be negligible. A strong support for this assumption was the apparent insensitivity of SN Ia distances across the host galaxy morphologies. However, Hicken et al. 2009 (H09) shows a systematic difference in the Hubble residual (HR) of $0.144{\pm}0.070$ mag between the E-S0 and Scd/Sd/Irr galaxies, after light-curve corrections. If true, this indicates that the light-curve fitters used by the SN Ia community can not correct for the population age (and therefore the evolution) effect. In order to confirm this, we have combined nearby SN Ia samples and the first-year SDSS-II SN Survey. The SNANA package was used for analyzing SN Ia light-curve, both for the MLCS2k2 and SALT2 fitters. We find a systematic difference in the HR of $0.10-0.13{\pm}0.030$ mag between E-S0 and Scd/Sd/Irr galaxies, which is in agreement with the result of H09, but now at the 3-5 ${\sigma}$ level. Considering the significant difference in the mean age of stellar population between these morphological types, the difference in the HR reported here suggests that the evolution effect of SN Ia luminosity should be considered in the cosmological application of SN Ia data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.39-49
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• 2013

This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters. To improve the forecasting accuracy, the load curve forecasted using the RBF network models is corrected by the weighted sum of both the error of the current prediction and the change in the errors between the current and the previous prediction. The optimal weights (called "gains" in the error correction) are identified by differential evolution. The peak load forecasted by the RBF network models is also corrected by combining the load curve outputs of the RBF models by linear addition with 24 coefficients. The optimal coefficients for reducing both the forecasting mean absolute percent error (MAPE) and the sum of errors are also identified using differential evolution. The proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange. Simulation results reveal satisfactory forecasts: 1.230% MAPE for daily peak load and 1.128% MAPE for daily load curve.

• The Journal of the Korea Contents Association
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• v.5 no.6
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• pp.138-145
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• 2005

This paper describes a technique for detecting the boundary of the optic disk in digital image of the retina using inward and outward curve evolution. This paper offers medical information about glaucoma progresses. For accurate boundary detection, image inpainting based on texture synthesis removes blood vessels crossing the optic disk. For removing noises and preserving boundary of optic disk in image inpainting process, the anisotropic diffusion filtering is necessary. After pre-processing, the optic disk boundary is determined using inward and outward curve evolution. The experimental results show that the algorithm is effective for detection of optic disk boundary.

• Journal of the Institute of Convergence Signal Processing
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• v.1 no.1
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• pp.23-31
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• 2000

Iterative curve evolution techniques are powerful methods for image segmentation. Classical methods proposed curve evolutions which guarantee close contours at convergence and, combined with the level set method, they easily handled curve topology changes. In this paper, we present a new geometric active contour model based on level set methods introduced by Osher & Sethian for detection of object boundaries or shape and we adopt anisotropic diffusion filtering method for removing noise from original image. Classical methods allow only one-way curve evolutions : shrinking or expanding of the curve. Thus, the initial curve must encircle all the objects to be segmented or several curves must be used, each one totally inside one object. But our method allows a two-way curve evolution : parts of the curve evolve in the outward direction while others evolve in the inward direction. It offers much more freedom in the initial curve position than with a classical geodesic search method. Our algorithm performs accurate and precise segmentations from noisy images with complex objects(jncluding sharp angles, deep concavities or holes), Besides it easily handled curve topology changes. In order to minimize the processing time, we use the narrow band method which allows us to perform calculations in the neighborhood of the contour and not in the whole image.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.485-486
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• 2015

We use light-curve fitting models (MLCS2k2, SALT2, and SNooPy) as implemented in SNANA to make the YOnsei Nearby Supernova Evolution Investigation (YONSEI) Supernova Catalogue. The catalogue consists of several hundred Type Ia supernovae (SNe Ia) in the redshift range from 0.01 to 1.35, and provides distance moduli, light-curve shape parameters, and color or extinction values for each supernova. This data set will be used to study the dependence of SNe Ia luminosities on the host galaxy morphologies. In this paper, we present the YONSEI Supernova Catalogue and preliminary systematic tests for the catalogue.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.76.1-76.1
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• 2012

We have employed SNANA supernova analysis package to make YONSEI Supernova Catalogue 1, which contains distance modulus, light-curve shape parameters, and color or extinction values of each supernova. This database is used to study the dependence of Type Ia supernovae (SNe Ia) luminosities on the host-galaxy morphologies. The redshift range of this catalogue is 0.010 < z < 1.555, and we use three light-curve fitters: SALT2, MLCS2k2 (Rv = 3.1), and MLCS2k2 (Rv = 1.7). We find a systematic difference in the Hubble residual (HR) of $0.1{\pm}0.031$ mag between E-S0 and Scd/Sd/Irr host-galaxies, and of $0.16{\pm}0.044$ mag between passive and star-burst host-galaxies. This difference is significant over the $3{\sigma}$ level. Considering the significant difference in the mean age of stellar population between these morphological types, the difference in the HR reported here suggests that the evolution effect of SNe Ia luminosity should be considered in the cosmological application of SNe Ia data.