• The korean journal of orthodontics
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• v.25 no.1 s.48
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• pp.1-12
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• 1995

The purpose of this study was to predict the respose to the chincap therapy from the initial cephalometric measurements and to obtain the indication of chincap therapy. 40 patients selected for this study were classified into two groups by the occlusal stability after completion of permanent dentition and the improvement of facial profile, after chincap therapy. One was good response group which consisted of 25 children and the other was poor response group with 15 patients. Various measurements of the craniofacial structure in the initial lateral cephalogram were calculated and analyzed by t-test and discriminant analysis. The results were as follows: 1. Good response group had more horizontal growth pattern in initial stage of treatment, and the contributing measurements were $Bj\ddot{o}rk$ sum anterior-posterior facial height ratio, genial angle, lower genial angle and occlusal plane to AB plane angle. 2. The critical points and predictive values of the influential skeletal measurements were calculated. 3. The discriminant function was obtained from three major influential measurements; $Bj\ddot{o}rk$ sum, genial angle and occlusal plane to hn plane angle, and this function could discreminate correctly in $85\%$ of this samples.

• Journal of Wetlands Research
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• v.20 no.1
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• pp.20-26
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• 2018

The purpose of this study is to construct a GIS / DB by grasping a small but ecologically valuable village wetland distribution, and to propose conservation management and wise use plan. The study area is Seocheon-gun, a typical farming village. Firstly, based on the digital topographical map (1:5,000), the Arc-GIS tool was used to identify the provisional(draft) village wetlands. In addition, for the management of village wetlands, wetlands with an area of more or less than $625m^2$ each were derived and according to ecological regions study area was classified into urban areas, inland areas and coastal areas. And finally, according to the wetland identifying indicators, the village wetlands were identified as the final village wetlands through indoor and field trips. The results of the study show that there are 570 village wetlands in Seocheon - gun province, which are 74 in urban areas, 220 in inland areas, and 276 in coastal areas. The case study for village wetland identification was conducted in one out of two urban areas (Seocheon - eup), two of four coastal areas (Biin - myeon and Seo - myeon), and three of seven inland areas (Masan - myeon, Hansan - myeon, and Sicho - myeon). The distribution of village wetlands was found mainly to be a village wetland with an area of less than $625m^2$. In addition, compared with inland areas, the discrimination rate of village wetlands in coastal areas and urban areas was relatively low, indicating that inland areas were still less disturbed, and land use in urban areas and coastal areas is changing rapidly. Especially, land with less awareness such as village wetlands is relatively easily damaged, and management strategy is urgent.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.73-86
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• 2024

A radial chart of Excel is very useful graphical method in delivering information for numerical data. However, it is not easy to discriminate or classify many individuals. In this case, after shaping each individual of a radial chart, we need to apply shape analysis. For a radial chart, since landmarks for shaping are formed as many as the number of variables representing the characteristics of the object, we consider a shape that connects them to a line. If the shape becomes complicated due to the large number of variables, it is difficult to easily grasp even if visualized using a radial chart. Principal component analysis (PCA) is performed on variables to create a visually effective shape. The classification table and classification rate are checked by applying the techniques of traditional discriminant analysis, support vector machine (SVM), and artificial neural network (ANN), before and after principal component analysis. In addition, the difference in discrimination between the two coordinates of generalized procrustes analysis (GPA) coordinates and Bookstein coordinates is compared. Bookstein coordinates are obtained by converting the position, rotation, and scale of the shape around the base landmarks, and show higher rate than GPA coordinates for the classification rate.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.745-760
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• 2021

Prediction of a stock price has been a subject of interest for a long time in financial markets, and thus, many studies have been conducted in various directions. As the efficient market hypothesis introduced in the 1970s acquired supports, it came to be the majority opinion that it was impossible to predict stock prices. However, recent advances in predictive models have led to new attempts to predict the future prices. Here, we summarize past studies on the price prediction by evaluation measures, and predict the direction of stock prices of Samsung Electronics, LG Chem, and NAVER by applying various machine learning models. In addition to widely used technical indicator variables, accounting indicators such as Price Earning Ratio and Price Book-value Ratio and outputs of the hidden Markov Model are used as predictors. From the results of our analysis, we conclude that no models show significantly better accuracy and it is not possible to predict the direction of stock prices with models used. Considering that the models with extra predictors show relatively high test accuracy, we may expect the possibility of a meaningful improvement in prediction accuracy if proper variables that reflect the opinions and sentiments of investors would be utilized.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.457-465
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• 2015

The long lasting world-wide recession and low economic progress have made it more important to predict future economic behavior. Accordingly, it is of interest to explore useful leading indicators, correlated with policy targets, to predict future economic growth. This study attempts to develop a model to evaluate the performance of consumer survey results from Statistics Korea to predict future economic activities. A statistical model is formulated and estimated to generate predictions by utilizing consumer expectations. The prediction is found improved in the distant future and consumer expectations appear to be a useful leading indicator to provide information of future real growth.

• Proceedings of the KIPE Conference
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• 2020.08a
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• pp.218-220
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• 2020

배터리 팩을 구성하는 단위 셀들은 전기화학적 특성으로 인해 다양한 내부 파라미터들이 동일한 값을 가지지 않고 편차가 있으며, 편차가 심할 경우 과방전 및 과충전의 원인이 될 수 있다. 기존의 연구된 SOF (State-Of-Function) 알고리즘의 경우 SOC (State-Of-Charge), SOH (State-Of-Health)와 같은 파라미터를 하나의 수식으로 정의하여 배터리 팩의 가용 전력을 예측하는 지표로써 사용되어 왔으나, 본 논문에서 제안하는 새로운 SOF 알고리즘은 배터리 팩 내부의 단위 셀간 파라미터들의 편차를 하나의 수식으로 정의하여 배터리 팩의 안전 상태를 나타낼 수 있는 지표로써 활용한다. SOF 알고리즘을 통해 배터리 팩의 안전 상태를 확인하고 검증하기 위해 21700 NMC(LiNiMnCoO₂) 계열의 고용량 배터리를 14S40P로 구성한 배터리 팩을 사용했다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.60-60
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• 2022

본 연구에서는 적설 추정 알고리즘과 추계 일기 생성 모형을 활용하여 관측 적설의 특성을 재현하는 연속 적설심 자료 모의 방법을 소개한다. 적설 추정 알고리즘은 강수 유형 판단, Snow Ratio 추정, 그리고 적설 깊이 감소량 추정까지 총 3단계로 구성된다. 먼저 강수 발생시 지상기온과 상대습도를 지표로 활용하여 강수 유형을 판단하고, 강수가 적설로 판별되었을 때 강수량을 신적설심으로 환산하는 Snow Ratio를 추정한다. Snow Ratio는 지상 기온과의 sigmoid 함수 회귀분석을 통해 추정하였으며, precipitation rate 조건(5 mm/3hr 미만 및 이상)에 따라 두 가지 함수를 적용하였다. 마지막으로 적설 깊이 감소량은 온도 지표 snowmelt 식을 이용하여 추정하였으며, 매개변수는 적설 깊이 및 온도 관측 자료를 활용하여 보정하였다. 속초 관측소 자료를 활용하여 매개변수를 보정 및 검증하여 높은 NSE(보정기간 : 0.8671, 검증기간 : 0.7432)를 달성하였으며, 이 알고리즘을 추계 일기 생성 모형으로 모의한 합성 기상 자료(강수량, 지상기온, 습도)에 적용하여 합성 적설심 시계열을 모의하였다. 모의 자료는 관측 자료의 통계 및 극한값을 매우 정확하게 재현하였으며, 현행 건축구조기준과도 일치하는 것으로 나타났다. 이 모형을 통하여 적설 위험 분석 분야뿐 아니라 기후 전망 자료와의 결합, 미계측 지역에 대한 자료 모의 등에도 광범위하게 활용될 수 있을 것이다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.4
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• pp.13-22
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• 2018

In this study, we examined the accuracy of the assessment items for selecting start-ups used in the project to support start-ups and verified their validity in determining whether they are appropriate assessment items based on selection criteria. The results of 973 start-ups that applied for the project fostering startup leading universities were collected and logistic regression was performed using SPSS 18.0. The study results are summarized as follows. First, the differences in characteristics of start-ups were identified in terms of selection. Second, the impact of selection by assessment items was gender in 2015, capability of the founder, business establishment in 2016, performance and potential in the global market, and business startup in 2017. Third, the overall selection accuracy analysis for the last three years confirmed that the accuracy of the selection is lower each year and that the accuracy of the selection is lower than the accuracy of the non-selection. This means that the current assessment items for selecting start-ups are inaccurate for selection, and that changes in the items due to changes in the start-up environment each year have led to lower accuracy of selection. It is meaningful that this study raised the importance of assessment items and the need for improvement of assessment items for the screening functions of good start-ups to enhance efficiency of the policies for startup support.

• Journal of Preventive Medicine and Public Health
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• v.31 no.3 s.62
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• pp.490-502
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• 1998

This study purports to find the predictor of hospital bankruptcy in Korea and to examine the predictive power of the discriminant function model of hospital bankruptcy. Data on 17 financial and 4 non-financial indicators of 31 bankrupt and 31 profitable hospitals of 1, 2, and 3 years before bankruptcy were obtained from the hospital performance databank of Korea Institute of Health Services Management. Significant variables were identified through mean comparison of each indicator between bankrupt and profitable hospitals, and the discriminant function model of hospital bankruptcy was developed. The major findings are as follows 1. As for profitability indicators, net worth to total assets, operating profit to total capital, operating profit ratio to gross revenues, normal profit to total assets, normal profit to gross revenues, net profit to total assets were significantly different in mean comparison test in 1, 2, and 3 years before hospital bankruptcy. With regard to liquidity indicators, current ratio and quick ratio were significant in 1 year before bankruptcy. For activity indicators, patients receivable turnover was significant in 2 and 3 years before bankruptcy and added value per adjusted inpatient days was significant in 3 years before bankruptcy. 2. The discriminant function in 1, 2, and 3 years before bankruptcy were; $Z=-0.0166{\times}quick$ ratio-$0.1356{\times}normal$ profit to total assets-$1.545{\times}total$ assets turnrounds in 1 year before bankruptcy, $Z=-0.0119{\times}quick$ ratio-$0.1433{\times}operating$ profit to total assets-$0.0227{\times}value$ added to total assets in 2 years before bankruptcy, and $Z=-0.3533{\times}net$ profit to total assets-$0.1336{\times}patients$ receivables turn-rounds-$0.04301{\times}added$ value per adjusted $patient+0.00119{\times}average$ daily inpatient census in 3 years before bankruptcy. 3. The discriminant function's discriminant power in 1, 2, and 3 years before bankruptcy was 77.42, 79.03, 82.25% respectively.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.