• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.141-147
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• 2000

In the restoration of degraded noisy motion blurred image, we have trade-off problem between smoothing the noise and restoration of the edge region. While the noise is smoothed, die edge or details will be corrupted. On the other hand, restoring the edge will amplify the noise. To solve this problem we propose an adaptive algorithm which uses I- H regularization operator for flat region and Laplacian regularization operator for edge region. Through the experiments, we verify that the proposed method shows better results in the suppression of the noise amplification in flat region, introducing less ringing artifacts in edge region and better ISNR than those of the conventional ones.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.5
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• pp.2291-2297
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• 2012

X-ray fluorescence sprectrum signal include the continuum. XRF analysis the components of material by the amplitude of peaks. XRF remove the noise and background. To remove the noise, we apply the smoothing filter. And background removal methods applied such as SNIP, Morphology, Threshold methods. In this paper, we applied Threshold using DWT. Interval threshold method divide the some blocks in particular levels. We propose the method that is divided the particular level.

• Genomics & Informatics
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• v.6 no.3
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• pp.126-129
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• 2008

The robust identification and comprehensive profiling of copy number alterations (CNAs) is highly challenging. The amount of data obtained from high-throughput technologies such as array-based comparative genomic hybridization is often too large and it is required to develop a comprehensive and versatile tool for the detection and visualization of CNAs in a genome-wide scale. With this respective, we introduce a software framework, CGHscape that was originally developed to explore the CNAs for the study of copy number variation (CNV) or tumor biology. As a standalone program, CGHscape can be easily installed and run in Microsoft Windows platform. With a user-friendly interface, CGHscape provides a method for data smoothing to cope with the intrinsic noise of array data and CNA detection based on SW-ARRAY algorithm. The analysis results can be demonstrated as log2 plots for individual chromosomes or genomic distribution of identified CNAs. With extended applicability, CGHscape can be used for the initial screening and visualization of CNAs facilitating the cataloguing and characterizing chromosomal alterations of a cohort of samples.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.3
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• pp.256-261
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• 2010

In this paper, a new nonlinear predictor using general regression neural network(GRNN) is proposed for the subband coding of moving pictures. The performance of a proposed nonlinear predictor is compared with BMA(Block Match Algorithm), the most conventional motion estimation technique. As a result, the nonlinear predictor using GRNN can predict well more 2-3dB than BMA. Specially, because of having a clustering process and smoothing noise signals, this predictor well preserves edges in frames after predicting the subband signal. This result is important with respect of human visual system and is excellent performance for the subband coding of moving pictures.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.358-360
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• 2005

스무딩은 가변 비트율로 저장된 비디오 데이터를 클라이언트로 전송할 때 일련의 고정 비트율로 전송할 수 있도록 전송 계획을 세우는 것이다. 이러한 스무딩 알고리즘들 중에서 e-PCRTT 알고리즘은 전송률 변화 횟수가 주어지고 구간의 크기가 고정이어서 전송률 변화 횟수, 첨두 전송률, 버퍼 이용률 등의 평가 요소들이 증가될 수 있다. 이러한 문제점을 개선하기 위해 전송률 변화 횟수의 제한이 없고 구간의 크기가 가변적인 EBA(Enhanced Bandwidth Allocation)[9,10] 스무딩 알고리즘을 제안한 바 있다. 그러나 이 방법에서는 전송률의 증가 또는 감소를 고려하지 않고 전송률을 변화시키기 때문에 이전 구간의 전송률에 비해 급격하게 높은 전송률이 요구될 수 있으며 증가되는 전송률로 보내야 하는 프레임 개수가 많아질 수 있다. 이는 네트워크 자원의 효율적인 사용을 어렵게 할 수 있다. 따라서, 본 논문에서는 EBA 알고리즘을 개선하여 첨두 전송률과 증가되는 전송률로 보내야 되는 프레임 개수를 감소시키는 알고리즘을 제안한다. 제안된 알고리즘의 성능은 E.T 90의 비디오 소스를 가지고 EBA 알고리즘과 전송률 변화 횟수, 첨두 전송률, 증가되는 전송률로 보내야 하는 프레임 개수 등을 비교 분석하여 평가하였다.

• Journal of Pharmaceutical Investigation
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• v.37 no.6
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• pp.377-395
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• 2007

Near infrared(NIR) spectral data have been used for the noninvasive analysis of various biological samples. Nonetheless, absorption bands of NIR region are overlapped extensively. It is very difficult to select the proper wavelengths of spectral data, which give the best PCR(principal component regression) models for the analysis of constituents of biological samples. The NIR data were used after polynomial smoothing and differentiation of 1st order, using Savitzky-Golay filters. To find the best PCR models, all-possible combinations of available principal components from the given NIR spectral data were derived by in-house programs written in MATLAB codes. All of the extensively generated PCR models were compared in terms of SEC(standard error of calibration), $R^2$, SEP(standard error of prediction) and SECP(standard error of calibration and prediction) to find the best combination of principal components of the initial PCR models. The initial PCR models were found by SEC or Malinowski's indicator function and a priori selection of spectral points were examined in terms of correlation coefficients between NIR data at each wavelength and corresponding concentrations. For the test of the developed program, aqueous solutions of BSA(bovine serum albumin) and glucose were prepared and analyzed. As a result, the best PCR models were found using a priori selection of spectral points and the final model selection by SEP or SECP.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.155-161
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• 2015

Dike lines are important features for describing the detailed shapes of dikes and for detecting topographic changes on dike surfaces. Historically, dike lines have been generated using only the LiDAR data. This paper proposes a new methodology for selecting an appropriate dike line on various dike surfaces using the topographic LiDAR data and multispectral orthoimages taken in the Nakdong River basins. The fi rst baselines were generated from the given LiDAR data using the modified convex hull algorithm and smoothing spline function, and the second baselines were generated from the given orthoimages by the Canny operator. Next, one baseline was selected among the two baselines at 10m intervals by comparing their elevations, and the selected baseline at 10m interval was defined as the dike line segment. Finally, the selected dike line segments were connected to construct the 3D dike lines. The statistical results show that the dike lines generated using both the LiDAR data and multispectral orthoimages had the improved horizontal and vertical accuracies than the dike lines generated only using the LiDAR data on the various dike surfaces.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.517-529
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• 2009

Many satellite image fusion techniques have been developed in order to produce a high resolution multispectral (MS) image by combining a high resolution panchromatic (PAN) image and a low resolution MS image. Heretofore, most high resolution image fusion techniques have used IKONOS and QuickBird images. Recently, GeoEye-1, offering the highest resolution of any commercial imaging system, was launched. In this study, we have experimented with GeoEye-1 images in order to evaluate which fusion algorithms are suitable for these images. This paper presents compares and evaluates the efficiency of five image fusion techniques, the $\grave{a}$ trous algorithm based additive wavelet transformation (AWT) fusion techniques, the Principal Component analysis (PCA) fusion technique, Gram-Schmidt (GS) spectral sharpening, Pansharp, and the Smoothing Filter based Intensity Modulation (SFIM) fusion technique, for the fusion of a GeoEye-1 image. The results of the experiment show that the AWT fusion techniques maintain more spatial detail of the PAN image and spectral information of the MS image than other image fusion techniques. Also, the Pansharp technique maintains information of the original PAN and MS images as well as the AWT fusion technique.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.937-944
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• 2021

The problem of selecting variables is addressed in partial linear regression. Model selection for partial linear models is not easy since it involves nonparametric estimation such as smoothing parameter selection and estimation for linear explanatory variables. In this work, several approaches for variable selection are proposed using a fast forward selection algorithm, least angle regression (LARS). The proposed procedures use t-test, all possible regressions comparisons or stepwise selection process with variables selected by LARS. An example based on real data and a simulation study on the performance of the suggested procedures are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2381-2399
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• 2019

Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks. To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP). In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches. We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.