• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.91-96
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• 2005

다차원층화에서 선형계획법을 이용한 표본배정 방법은 Winkler(1990, 2001), Sitter와 Skinner(1994, 2002)가 제안하였다. 이 방법들은 표본크기가 층 개수보다 크지 않는 경우에 공통적으로 선형계획법을 이용하여 표본배정을 실시하였다. 반복 비율 적합방법(IPF), 일반화 반복 비율 적합(GIFP), SS 방법을 통해 셀 값을 결정하고 선형계획법을 이용하여 표본의 배정확률을 통해 표본배정을 실시한다. 이 3가지 방법들로 표본을 배정하고 평균 및 분산추정량을 비교한다.

• The Korean Journal of Applied Statistics
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• v.13 no.1
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• pp.197-206
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• 2000

For estimating missing cells in contingency table, we suggest an iterative method which extends IPF (Iterative Proportional Fitting) method. The suggested m~thod is not restricted by the number and the location of missing cells, and does not distort the given quasi-independency.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.527-540
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• 2010

Rule-based classification analysis is widely used for massive datamining because it is easy to understand and its algorithm is uncomplicated. In this classification analysis, majority vote of rules or weighted combination of rules using their supports are frequently used in order to combine rules. We propose a method to combine rules by using the multinomial distribution in this paper. Iterative proportional fitting algorithm is used to estimate the multinomial distribution which maximizes entropy constrained on rules' support. Simulation experiments show that this method can compete with other well known classification models in the case of two similar populations.

• Korean Journal of Organic Agriculture
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• v.15 no.3
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• pp.319-330
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• 2007

This study analyzed physical and chemical characteristics of peat soil to use peat soil as the materials fur pot media and investigated seedling quality of horticultural plants in order to use peat soil as the raw materials fur pot media. The summary of the results is as follows; The chemical characteristics of peat soil, which is main ingredient of pot media are as follows; pH was 4.9, EC was less than $2.0ds{\cdot}m^{-1}$, which interferes the growth of the plant and organic ingredient was 33%. When looking into the germination characteristics of plants according to the mixture of pot media, red pepper showed 54.2% speed of germination and 97% germination rate in peat soil single treatment. Therefore the peatsoil was appropriate for the pot media for red pepper. In case of cucumbers, in the 50 : 50 treatment of main ingredient (peat soil) and auxiliary ingredients (vermiculite, peat moss and perlite) they showed 100% speed of germination and 100% germination rate. Therefore 50 : 50 treatment was appropriate fur the pot media for cucumbers. In case of chinese cabbage, peat soil, perlite and peat moss mixture (50 : 25 : 25) treatment showed the highest speed of germination (77.5%), while the germination rate was a little lower (92.15%) than comparative soil. However, it was appropriate for the pot media for chinese cabbage. In case of watermelon, germination was bad because of the influence of EC when the teat soil ingredient is over 80%. However, in the mixture of peat soil and vermiculite (50:50) treatment, they showed 91.6% speed of germination and 100% germination rate. Therefore it was appropriate for the pot media for watermelon. When looking into the growth of the plants according to the mixture of ingredients, peat soil and perlite (50:50) mixture showed excellent seedling quality for cucumbers, peat soil and perlite (50:50) mixture showed excellent seedling quality and it was proven to be appropriate for the pot media for cucumbers. In case of watermelon, peat soil, peat moss and perlite (80 : 10 : 10) mixture showed excellent seedling quality and it was proven to be appropriate for the pot media for watermelon.

• Journal of Korea Multimedia Society
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• v.11 no.5
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• pp.597-609
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• 2008

Due to the recursive updating nature of background model, previous background modeling methods are often perturbed by crowd scenes where foreground pixels occurs more frequently than background pixels. To resolve this problem, an adaptive background modeling method, which is based on the well-known Gaussian mixture background model, is proposed. In the proposed method, the learning rate of background model is adaptively adjusted with respect to the crowdedness of the scene. Consequently, the learning process is suppressed in crowded scene to maintain proper background model. Experiments on real dataset revealed that the proposed method could perform background subtraction effectively even in crowd situation while the performance is almost the same to the previous method in normal scenes. Also, the F-measure was increased by 5-10% compared to the previous background modeling methods in the video of crowded situations.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.417-426
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• 2024

In general, the implementation of machine learning requires prior knowledge and experience with deep learning models, and substantial computational resources and time are necessary for data processing. As a result, machine learning encounters several limitations when deployed on embedded processors. To address these challenges, this paper introduces a novel approach where a genetic algorithm is applied to the convolution operation within the machine learning process, specifically for performing a selective convolution operation.In the selective convolution operation, the convolution is executed exclusively on pixels identified by a genetic algorithm. This method selects and computes pixels based on a ratio determined by the genetic algorithm, effectively reducing the computational workload by the specified ratio. The paper thoroughly explores the integration of genetic algorithms into machine learning computations, monitoring the fitness of each generation to ascertain if it reaches the target value. This approach is then compared with the computational requirements of existing methods.The learning process involves iteratively training generations to ensure that the fitness adequately converges.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.21 no.2
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• pp.129-139
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• 1995

To confirm the usefulness of partial least-squares(PLS) and multiple scattering correction(MSC) method for quantitation of surfactants in [quantitative methods using FT-lR, reconsitituted mixtures of LAS, MES and ELA-9 were tested. Each mixture was dissolved in 50% EtOH, dried, and applied to the KBr cell. From the IR spectra of these mixture, the variance spectrum was obtained. After repeated calibrations for the various regios of this spectrum, we found that 1245-1130cm-1 and 1070-1010cm-1 showed the strong correlation with each component of the sample mixture: all the correlation coefficients were 1.000 and quantitative errors did not exceed 0.32%. From this result, we concluded that PLS method and MSC method are very useful and can be successfully applied to Quality control.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.763-782
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• 2019

This paper proposes a probabilistic document ranking model incorporating term dependencies. Document ranking is a fundamental information retrieval task. The task is to sort documents in a collection according to the relevance to the user query (Qin et al., Information Retrieval Journal, 13, 346-374, 2010). A probabilistic model is a model for computing the conditional probability of the relevance of each document given query. Most of the widely used models assume the term independence because it is challenging to compute the joint probabilities of multiple terms. Words in natural language texts are obviously highly correlated. In this paper, we assume a multinomial distribution model to calculate the relevance probability of a document by considering the dependency structure of words, and propose an information retrieval model to rank a document by estimating the probability with the maximum entropy method. The results of the ranking simulation experiment in various multinomial situations show better retrieval results than a model that assumes the independence of words. The results of document ranking experiments using real-world datasets LETOR OHSUMED also show better retrieval results.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2007.04a
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• pp.257-263
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• 2007

본 논문은 내구성(durability) 및 내오염성(soiling resistance)이 매우 중요한 성질로 인식되는 유통지의 평가방법을 제안하는데 목적이 있다. 즉, 종이의 내구성 및 내오염성의 측정 및 평가에 있어서, 재현성 및 반복성이 우수하고 합리적인 측정방법 및 평가방법을 구축하는데 목적이 있다. 연구목적에 적합한 시료의 준비, 오염물의 조성, 실험장치 및 실험후의 타당한 평가방법 등에 대하여 선행 연구결과를 고찰하였다. 기존 방법의 수정과 새로운 장치의 제작 및 평가방법의 설정을 통해 재현성이 높은 평가방법을 정립하고자 하였다. 유통지의 내구성을 평가하는 방법으로는 습식, 내오염성, 건식 내오염성, 구김기공도, 내세탁성, 잉크 내마모도 등의 항목을 측정하여 개별항목으로 내구성을 가늠하거나, 몇가지항목에 각기 다른 가중치를 부여하여 구한 내구성 지수(durability index)의 개념으로 내구성을 평가하기도 한다. 본 논문에서는 내구성에 영향하는 요소로 강도적 성질요소(strength property value)와 내오염성 요소(artificial soiling value)로 구분하였다. 강도적 성질요소에서는 전통적으로 내구성과 관련이 깊은 것으로 인식되어 온 내절도(folding endurance), 인열강도(tearing resistance) 및 구김기공도(crumpled porosity)를 인쇄하기 전 용지상태에서 측정하여 4:4:2의 가중치를 주어 구하였다. 내오염성 요소는 국가별 기후특성에 따라 연평균 상대습도가 60% 이상인 경우 인쇄 시료를 대상으로하여 습식 내오염성, 건식 내오염성 및 내석검성을 평가한 후 6:3:1의 가중치를 주어 구하였다. 또한 연평균 60% 미만인 국가의 경우 3:6:1의 가중치를 부여하였다. 이렇게 구해진 강도적 성질요소와 내오염성 요소에 있어 강도적 성질요소는 궁극적으로 내오염성 요소에 영향하기 때문에 3:7의 비율로 가중치를 부여하여 최종적으로 내구성 지수를 구하였다. 이때의 점수가 60점 이상이면 내구성 용지로 정의하였다. 본 논문에서는 현장실험을 통하여 시제품을 제조하였다. 상기 설명된 방법으로 내구성 지수를 구한 결과, 일반 유통지 제조방법으로 제조한 경우 내구성 지수가 약 45점이었다. 반면, 새로 개발된 방법에 의한 고내구성용지(durability paper)의 경우 70점 이상을 나타내어 내구성이 향상되었음을 확인할 수 있었다.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1171-1180
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• 2010

It is well known that the asymptotic convergence rates of nonparametric regression estimator gets worse as the dimension of covariates gets larger. One possible way to overcome this problem is reducing the dimension of covariates by using single index models. Two coefficient estimation methods in single index models are introduced. One is semiparametric least square estimation method, which tries to find approximate solution by using iterative computation. The other one is weighted average derivative estimation method, which is non-iterative method. Both of these methods offer the parametric convergence rate to normal distribution. However, practical comparison of these two methods has not been done yet. In this article, we compare these methods by examining the variances of estimators in various models.