• 대한음성학회지:말소리
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• 제44호
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• pp.105-113
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• 2002

In Korean, each digit is monosyllable and some pairs are known to have high confusability, causing performance degradation of connected digit recognition systems. To improve the performance, in this paper, we employ various discriminant analyses (DA) including Linear DA (LDA), Weighted Pairwise Scatter LDA WPS-LDA), Heteroscedastic Discriminant Analysis (HDA), and Maximum Likelihood Linear Transformation (MLLT). We also examine several combinations of various DA for additional performance improvement. Experimental results show that applying any DA mentioned above improves the string accuracy, but the amount of improvement of each DA method varies according to the model complexity or number of mixtures per state. Especially, more than 20% of string error reduction is achieved by applying MLLT after WPS-LDA, compared with the baseline system, when class level of DA is defined as a tied state and 1 mixture per state is used.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.II
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• pp.270-278
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• 2000

A machine vision system using charge coupled device(CCD) camera for the weed detection in a radish farm was developed. Shape features were analyzed with the binary images obtained from color images of radish and weeds. Aspect, Elongation and PTB were selected as significant variables for discriminant models using the STEPDISC option. The selected variables were used in the DISCRIM procedure to compute a discriminant function for classifying images into one of the two classes. Using discriminant analysis, the successful recognition rate was 92% for radish and 98% for weeds. To recognize radish and weeds more effectively than the discriminant analysis, an artificial neural network(ANN) was used. The developed ANN model distinguished the radish from the weeds with 100%. The performance of ANNs was improved to prevent overfitting and to generalize well using a regularization method. The successful recognition rate in the farms was 93.3% for radish and 93.8% for weeds. As a whole, the machine vision system using CCD camera with the artificial neural network was useful to detect weeds in the radish farms.

• 교육시설
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• 제4권4호
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• pp.29-41
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• 1997

The objective of this paper is to construct a model to be used in deciding whether to repair or rebuild school buildings is depending on their ages and other factors. The theme of this paper is the age is the main variable but other factors such as floor, innerwall, ceiling, door, inner window of the class room, outer window of the class room, inner window of the corridor, outer window of the corridor, middle window between the classroom and the corridor, light, heater, speaker, fire protection sensor, TV monitor, and telephone status would influence the final decisions. This paper employs an experimental case study method. Using the stepwise, statistical, classification method commonly used in discriminant analysis, it evaluates 12,766 rooms of 87 different high schools in Seoul. The result of this study indicates that some critical variables influencing the final decisions are the status of TV monitor, middle window between the classroom and the corridor, light, inner window of the corridor, fire protection sensor, innerwall, speaker utensil, outer window of the class room, and door of the class room. This paper also suggests a linear discriminant function will be used for this kind of studies. Finally the paper recommends policies with respect to the variables and discriminant functions evaluated.

• 통상정보연구
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• 제9권2호
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• pp.19-40
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• 2007

In recent, T-commerce is widely dispersed as alternative type of commerce. It is forecasted that t-commerce system is used more than e-commerce system. Therefore more and more t-commerce-related industries are also recognizing that t-commerce is a critical business model. It is needed to understand the concept of t-commerce and develop the t-commerce marketing strategy. CEO analyses consumer's behaviors according to the data about buyers and applies the advantage of t-commerce to the communication with customers. This t-commerce system plays an important role in maximizing customer satisfaction and affecting their intention to reuse it. Therefore this paper attempts to identify T-commerce critical success factors and divide between use-intention group and unuse-intention group by taking out a discriminant function by the discriminant analysis. This lays a foundation in developing T-commerce strategy. According to the discriminant function extracted, convenience factor, amusement factor, system quality factor, product perception factor are significant in the sequence of influential degree. However, usefulness factor and speedy connection factor are not significant. In result, the target hitting rate is 77.9% in the first unuse-intention group and it is 95.2% in the second use-intention group. The total discriminant target hitting rate is computed to higher value, 86.55%. The statistic package, SPSS 12.0, is used to survey and analyse data and test the hypothesis. The validity and reliability of variables are verified by both reliability analysis and factor analysis. The discriminant analysis is used to tell the difference between use-intention group and unuse-intention group.

• 응용통계연구
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• 제21권3호
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• pp.455-468
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• 2008

야구경기의 승패에 영향을 미치는 중요한 요인으로 간주되는 도루의 성공모형을 개발하기 위하여 2007년 한국프로야구 기록자료를 바탕으로 로지스틱 회귀모형들을 제안한다. 또한 한국프로야구의 도루성공과 실패에 대해 판별분석을 실시하고 분류 기준값을 결정하였으며, 판별분석 분류표를 이용해 로지스틱 회귀분석과 판별분석의 효율성을 비교한다. 전체적인 모형의 정확도는 로지스틱 회귀모형이 판별분석보다 더 좋은 것으로 나타났고, 연속형 자료를 범주형으로 변환한 자료에 대한 로지스틱 회귀모형도 유사한 효율성을 갖고있다.

• Earthquakes and Structures
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• 제18권1호
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• pp.73-82
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• 2020

With the coal mining depth increasing, both stress and gas pressure rapidly enhance, causing coal and gas outburst risk to become more complex and severe. The conventional method for prediction of coal and gas outburst adopts one prediction index and corresponding critical value to forecast and cannot reflect all the factors impacting coal and gas outburst, thus it is characteristic of false and missing forecasts and poor accuracy. For the reason, based on analyses of both the prediction indicators and the factors impacting coal and gas outburst at the test site, this work carefully selected 6 prediction indicators such as the index of gas desorption from drill cuttings Δh₂, the amount of drill cuttings S, gas content W, the gas initial diffusion velocity index ΔP, the intensity of electromagnetic radiation E and its number of pulse N, constructed the Bayes discriminant analysis (BDA) index system, studied the BDA-based multi-index comprehensive model for forecast of coal and gas outburst risk, and used the established discriminant model to conduct coal and gas outburst prediction. Results showed that the BDA - based multi-index comprehensive model for prediction of coal and gas outburst has an 100% of prediction accuracy, without wrong and omitted predictions, can also accurately forecast the outburst risk even for the low indicators outburst. The prediction method set up by this study has a broad application prospect in the prediction of coal and gas outburst risk.

• 한국조경학회지
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• 제29권1호
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• pp.11-21
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• 2001

This study investigates how motivations, preferences, and past experiences vary by each hikers trail choice at the Mt.Keyryong National Park. The purpose of this study is to find out the factors influencing behavioral choice in the recreation areas, and establish the fundamental theory for the efficient management of the resource and visitors. For this study, we have collected 472 respondents by on-site self-administrated questionnaire from the hikers in the park. The collected data were analyzed by the descriptive statistics and the discriminant analysis. The motivations variable of hiking participation on mountain trail were categorized three types; close-nature, escapism, and physical improvement. The preferences for trail environment were classified as four categories by factor analysis; preference for nature, safety, use density, and facilities. In descriptive statistics, the study showed that the experienced hikers prefer natural trials and hikers who have preference for close-nature select longer and deeper forest trails. The results of discriminant analysis indicate that the level of past experience is the most affectable in classification of trail choice. Such variables as motivation for close-nature and preference for nature were also appeared as affecting factors on classification of trail choice. Two discriminant functions were available, and 90.5 percent of analysis sample were correctly classified. In the validity analysis, 89 percent of holdout sample were correctly classified. These hit ratios ensures an accuracy by Press Q test. The result of this study is to be useful knowledge of the choice of detailed use environments in the same recreation areas.

• 품질경영학회지
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• 제29권3호
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• pp.124-137
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• 2001

In general, it is very efficient and effective to use screening variables that are correlated with the performance variable in case that measuring the performance variable is impossible (destructive) or expensive. The general methodology for searching surrogate variables is regression analysis. This paper considers the inspection problem in CRT (Cathode Ray Tube) production line, in which the performance variable (dependent variable) is binary type and screening variables are continuous. The general regression with dummy variable, discriminant analysis and binary logistic regression are considered. The cost model is also formulated to determine economically inspection procedure with screening variables.

• 한국산학기술학회논문지
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• 제17권10호
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• pp.520-526
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• 2016

본 연구는 서울지역 특1급 호텔을 대상으로 2015년도 재무비율을 변수로 활용하여 표준재무비율을 산출하며, 다변량 판별분석에 의한 부실예측모형 개발 및 부실예측력 평가에 목적이 있다. 서울소재 19개 특1급 호텔의 14개 재무비율을 분석대상으로 선정하여 실증분석을 실시하였으며 분석결과는 다음과 같다. 첫째, 분석결과 우수기업과 부실기업을 판별하는 7개 재무비율은 유동비율, 차입금의존도, 영업이익대비 이자보상비율, 매출액영업이익율, 자기자본순이익율, 영업현금흐름비율, 총자산회전율로 나타났다. 둘째, 7개 재무비율을 활용하여 우수기업과 부실기업을 판별하는 판별함수를 다변량판별분석에 의해 추정하였으며, 추정된 판별함수를 실제 소속집단과 예측집단으로 분류가 가능한가의 예측력 검정 결과, 예측 판별력의 정확도는 87.9%로 분석되었다. 셋째, 추정된 판별함수의 예측 판별력의 정확도 검증결과 판별분석에 의한 부실예측모형의 예측력은 78.95%로 분석되었다. 이러한 분석결과, 호텔 경영진은 호텔기업의 부실기업집단을 판별하는 7개 재무비율을 중점적으로 관리해야 함을 시사하고 있다. 또한 호텔기업이 타 산업과는 뚜렷한 재무구조의 차이와 부실예측 지표가 상이하며, 이에 호텔기업 대상의 신용평가시스템 구축 시 호텔기업의 재무적 특성을 반영한 시스템 구축이 필요함을 시사하고 있다.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2003년도 추계학술대회
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• pp.57-72
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• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.