• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.203-207
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• 2006

This paper deals with a problem of detecting BCC using confocal raman spectrum. Specially, we propose Fisher measure based filtering for rejection of frequency components being noisy or non-discriminative. we use PCA (principal component analysis) for reduction of feature space dimension. Also, we apply MAP detector for classification of BCC raman spectrum. The experimental results shows that our proposed method can reduce the feature dimension and also raise the detection ratio.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.522-538
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• 2013

Content based image retrieval has become an increasingly important research topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The retrieval systems rely on a key component, the predefined or learned similarity measures over images. We note that, the similarity measures can be potential improved if the data distribution information is exploited using a more sophisticated way. In this paper, we propose a similarity measure learning approach for image retrieval. The similarity measure, so called Fisher kernel, is derived from the probabilistic distribution of images and is the function over observed data, hidden variable and model parameters, where the hidden variables encode high level information which are powerful in discrimination and are failed to be exploited in previous methods. We further propose a discriminative learning method for the similarity measure, i.e., encouraging the learned similarity to take a large value for a pair of images with the same label and to take a small value for a pair of images with distinct labels. The learned similarity measure, fully exploiting the data distribution, is well adapted to dataset and would improve the retrieval system. We evaluate the proposed method on Corel-1000, Corel5k, Caltech101 and MIRFlickr 25,000 databases. The results show the competitive performance of the proposed method.

• The KIPS Transactions:PartB
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• v.16B no.3
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• pp.239-246
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• 2009

In this paper, we propose a Korean character recognition method from outboard signboard images. We have chosen 808 classes of Korean characters by an analysis of frequencies of appearance in a dictionary of signboard names. The proposed method mainly consists of three steps: feature extraction, rough classification, and coarse classification. The first step is to extract a nonlinear directional segments feature, which is immune to the distortion of character shapes. The second step computes an ordered set of 10 recognition candidates using a minimum distance classifier. The last step reorders the recognition candidates using a Fisher discriminant measure. As experimental results, the recognition accuracy is 80.45% for the first choice, and 93.51% for the top five choices.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.89-97
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• 2004

An object to reflect or emit light is captured by imaging system as distorted image due to various distortion. It is called image restoration that estimates original object by removing distortion. There are two categories in image restoration method. One is a deterministic method and the other is a stochastic method. In this paper, image restoration using Minimum Fisher Information(MFI), derived from B. Roy Frieden is proposed. In MFI restoration, experimental results to be made according to noise control parameter were investigated. And cross entropy(Kullback-Leibler entropy) was used as a standard measure of restoration accuracy, It is confirmed that restoration results using MFI have various roughness according to noise control parameter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1424-1440
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• 2015

It is a challenging problem to search the intended images from a large number of candidates. Content based image retrieval (CBIR) is the most promising way to tackle this problem, where the most important topic is to measure the similarity of images so as to cover the variance of shape, color, pose, illumination etc. While previous works made significant progresses, their adaption ability to dataset is not fully explored. In this paper, we propose a similarity learning method on the basis of probabilistic generative model, i.e., probabilistic latent semantic analysis (PLSA). It first derives Fisher kernel, a function over the parameters and variables, based on PLSA. Then, the parameters are determined through simultaneously maximizing the log likelihood function of PLSA and the retrieval performance over the training dataset. The main advantages of this work are twofold: (1) deriving similarity measure based on PLSA which fully exploits the data distribution and Bayes inference; (2) learning model parameters by maximizing the fitting of model to data and the retrieval performance simultaneously. The proposed method (PLSA-FK) is empirically evaluated over three datasets, and the results exhibit promising performance.

• Journal of The Korean Astronomical Society
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• v.29 no.spc1
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• pp.7-10
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• 1996

With an interim calibration based upon half the Key Project's galaxies, the Tully-Fisher relation, the (Dn, $\sigma$) relation and type II supernovae yield $H_0$= 73$\pm$10 km/sec/Mpc.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.43.4-43.4
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• 2021

One of the most promising secondary target programmes of DESI is the peculiar velocity survey, which will notably improve the measurements of cosmology parameters in the low-redshift universe. We use the Fundamental plane and Tully-Fisher relation as distance indicators to calculate peculiar velocities for DESI. This required additional observations to obtain spectra with sufficient quality to measure the velocity dispersions in the case of the fundamental plane, and to get off-centre redshift measurements to reconstruct the rotation curve in the case of the Tully-Fisher relation. However, we devised a clever strategy for suitable target galaxies, that takes advantage of the spare fibres of DESI to gather the required additional data without causing conflicts with the main survey programmes. We provide a brief overview of the preliminary results and success rate based on the first measurements obtained during survey validation as well as an outlook on expected improvements in the fσ₈ measurements once the survey has been completed.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1223-1229
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• 2012

In this paper, we propose a generalized Kullback-Leibler (KL) information for measuring the distance between two distribution functions where the extension to the censored case is immediate. The generalized KL information has the nonnegativity and characterization properties, and its censored version has the additional property of monotonic increase. We also extend the discussion to the discrete case and propose a generalized censored measure which is comparable to Pearson's chi-square statistic.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.67-77
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• 1999

For a orientation-shift model supported on the unit sphere, Euler angles are the conventional measure to parametrize orientation-shifts. The essential role which is played by rotationally symmetry of an underlying distribution is reviewed. In this paper we propose the inference procedure based on Euler angles for the rotationally symmetric spherical distribution. The likelihood ratio test(LRT) based on the Euler angles is worked out. The asymptotic distribution of the test under the null hypotheses and certain contiguous alternatives is obtained.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.419-431
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• 2022

This study was conducted to develop and test the effects of an emotional intelligence program for new nurses. The study design was a mixed method research. Participants were 29(intervention group: 15, control group: 14) new nurse. The emotional intelligence improvement program was conducted once a week for a total of 8 weeks (20 hours).The emotional intelligence program was provided for 8 weeks (20 hours). Quantitative data were analyzed using Chi-square, Fisher's exact test, t-test, repeated measure ANOVA with SPSS/WIN 18.0. Qualitative data were analyzed using content analysis. Quantitative results showed that emotional intelligence, Job stress were no significantly better in the experimental group compared to the control group. According to the qualitative results, the new nurse experienced improvement in emotional intelligence, empowerment, a reduction in stress, Changed from 'alone' to 'us', and as well as I dream of my future self after participation in the emotional intelligence program. A study to apply the emotional intelligence improvement program to nurses at various times and to verify its effectivene.