• 기술혁신학회지
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• 제10권2호
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• pp.183-205
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• 2007

본 연구는 기술력평가에 근거해서 중소기업 부실예측 가능성을 사전에 예측할 수 있는 최적 판별 모형을 개발 제안하였다. 판별모형에 포함될 설명변수는 요인분석과 판별모형의 단계별 선택방법에 의하여 선정되었다. 분석결과 선형판별모형이 로지스틱판별모형보다 임계확률 관점에서 적절한 것으로 나타났다. 최적 선형판별모형의 분류 정분류율은 70.4%, 분류 예측력은 67.5%로 나타났다. 최적 선형판별모형의 활용도를 높이기 위해서 확실 범주와 유보범주를 구분할 수 있는 경계값을 설정하였다. 분석결과를 활용하면 기술금융 취급기관은 부실위험 평가와 더불어 기술금융 신청기업의 순위를 부여할 때 유용하게 사용할 수 있을 것으로 기대된다.

• 음성과학
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• 제14권1호
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• pp.151-162
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• 2007

This study focuses on the classification of pathological voice using GMM (Gaussian Mixture Model) and compares the results to the previous work which was done by ANN (Artificial Neural Network). Speech data from normal people and patients were collected, then diagnosed and classified into two different categories. Six characteristic parameters (Jitter, Shimmer, NHR, SPI, APQ and RAP) were chosen. Then the classification method based on the artificial neural network and Gaussian mixture method was employed to discriminate the data into normal and pathological speech. The GMM method attained 98.4% average correct classification rate with training data and 95.2% average correct classification rate with test data. The different mixture number (3 to 15) of GMM was used in order to obtain an optimal condition for classification. We also compared the average classification rate based on GMM, ANN and HMM. The proper number of mixtures on Gaussian model needs to be investigated in our future work.

• 한국안전학회지
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• 제25권4호
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• pp.90-95
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• 2010

This study deals with the accident model of arterial link section in Cheongju. The objective is to develop the accident model of arterial link section using the logistic regression. In pursuing the above, the study uses the 258 accident data occurred at the 322 arterial link section. The main results are as follows. First, Nagellerke $R^2$ of developed accident model is analyzed to be 0.309 and t-values of variable that explains goodness of fit are evaluated to be significant. Second, the variables adopted in the model are AADT, the number of exit and entry. These variables are all analyzed to be statistically significant. Finally, the analysis of correct classification rate shows that the total accident of correct classification rate is analyzed to be 72.7% at the arterial link section.

• 한국컴퓨터정보학회논문지
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• 제20권8호
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• pp.15-21
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• 2015

This study proposed a Sasang constitution classification system that can increase the objectivity and reliability of Sasang constitution diagnosis using the image of frontal face, in order to solve problems in the subjective classification of Sasang constitution based on Sasang constitution specialists' experiences. For classification, characteristics indicating the shapes of the eyes, nose, mouth and chin were defined, and such characteristics were extracted using the morphological statistic analysis of face images. Then, Sasang constitution was classified through a SVM (Support Vector Machine) classifier using the extracted characteristics as its input, and according to the results of experiment, the proposed system showed a correct recognition rate of 93.33%. Different from existing systems that designate characteristic points directly, this system showed a high correct recognition rate and therefore it is expected to be useful as a more objective Sasang constitution classification system.

• 한국멀티미디어학회논문지
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• 제10권9호
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• pp.1125-1134
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• 2007

본 논문에서는 가보 웨이블릿(Gabor Wavelet)을 이용한 회전 변화에 무관한 질감 분류 기법을 제안한다. 기존의 방법들은 대용량 질감 데이터베이스에서 낮은 정정분류비(Correct Classification Rate)를 나타내었다. 제안한 방법은 가보 웨이블릿 필터링 된 영상에서 전역 특징 벡터(Global Feature Vector)와 지역 특징행렬(Local Feature Matrix)을 정의하였다. 회전 변화에 무관한 두 가지 특징 그룹을 이용하여 개선된 유사도 측정 판별식(Discriminant)을 정의하였으며, 실험을 통하여 대용량 질감 데이터베이스에 적용한 결과 향상된 정정분류비를 얻을 수 있었다. 또한 질감 영상 스펙트럼의 대칭성을 이용하여 기존의 방법보다 실험회수를 50% 가까이 감소시켰다 결론적으로 112개의 브로다츠(Brodatz) 질감 클래스에서 비교 방법에 따라 차이는 있으나 $2.3%{\sim}15.6%$의 향상된 정정분류비를 얻었다.

• 품질경영학회지
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• 제23권2호
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• pp.53-67
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• 1995

The problem considered here is to find the optimal discriminant analysis method in growth curve model. It has been studied how to find correct prior probability for the effective classification in discriminant analysis. We use the balanced condition to calculate prior probability. From the informative simulation study, new classification rule for the growth curve model is suggested. The suggested classification rule has better classification result than the other previously suggested method in terms of error rate criterion.

• 한국경영과학회지
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• 제26권4호
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• pp.55-70
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• 2001

Artificial neural network(ANN) models have been widely used for the classification problems in business such as bankruptcy prediction, credit evaluation, etc. Although the application of ANN to classification of consumers＇ choice behavior is a promising research area, there have been only a few researches. In general, most of the researches have reported that the classification performance of the ANN models were better than conventional statistical model Because the survey data on consumer behavior may include much noise and missing data, ANN model will be more robust than conventional statistical models welch need various assumptions. The purpose of this paper is to study the potential of the ANN model for forecasting consumers＇ choice behavior based on survey data. The data was collected by questionnaires to the shoppers of department stores and discount stores. Then the correct classification rates of the ANN models for the training and test sample with that of multiple discriminant analysis(MDA) and logistic regression(Logit) model. The performance of the ANN models were betted than the performance of the MDA and Logit model with respect to correct classification rate. By using input variables identified as significant in the stepwise MDA, the performance of the ANN models were improved.

• 전기학회논문지
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• 제65권9호
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• pp.1557-1563
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• 2016

According to recent studies, poor sitting posture of the spine has been shown to lead to a variety of spinal disorders. For this reason, it is important to measure the sitting posture. We proposed a strategy for classification of sitting posture using machine learning. We retrieved acceleration data from single tri-axial accelerometer attached on the back of the subject's neck in 5-types of sitting posture. 6 subjects without any spinal disorder were participated in this experiment. Acceleration data were transformed to the feature vectors of principle component analysis. Support vector machine (SVM) and K-means clustering were used to classify sitting posture with the transformed feature vectors. To evaluate performance, we calculated the correct rate for each classification strategy. Although the correct rate of SVM in sitting back arch was lower than that of K-means clustering by 2.0%, SVM's correct rate was higher by 1.3%, 5.2%, 16.6%, 7.1% in a normal posture, sitting front arch, sitting cross-legged, sitting leaning right, respectively. In conclusion, the overall correction rates were 94.5% and 88.84% in SVM and K-means clustering respectively, which means that SVM have more advantage than K-means method for classification of sitting posture.

• 전자공학회논문지B
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• 제32B권3호
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• pp.522-532
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• 1995

Multilayer neural networks with backpropagation learning algorithm are widely used for pattern classification problems. For many real applications, it is more important to reduce the misclassification rate than to increase the rate of successful classification. But multilayer perceptrons(MLP's) have drawbacks of slow learning speed and false convergence to local minima. In this paper, we propose a new method for character recognition problems with a single-layer network and double rejection mechanisms, which guarantees a very low misclassification rate. Comparing to the MLP's, it yields fast learning and requires a simple hardware architecture. We also introduce a new coding scheme to reduce the misclassification rate. We have prepared two databases: one with 135,000 digit patterns and the other with 117,000 letter patterns, and have applied the proposed method for printed character recognition, which shows that the method reduces the misclassification rate significantly without sacrificing the correct recognition rate.

• 반도체디스플레이기술학회지
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• 제10권2호
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• pp.1-5
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• 2011

In recent years, worldwide production of solar wafers increased rapidly. Therefore, the solar wafer technology in the developed countries already has become an industry, and related industries such as solar wafer manufacturing equipment have developed rapidly. In this paper we propose the color classification method of the polycrystalline solar wafer that needed in manufacturing equipment. The solar wafer produced in the manufacturing process does not have a uniform color. Therefore, the solar wafer panels made with insensitive color uniformity will fall off the aesthetics. Gaussian mixture models (GMM) are among the most statistically mature methods for clustering and we use the Gaussian mixture models for the classification of the polycrystalline solar wafers. In addition, we compare the performance of the color feature vector from various color space for color classification. Experimental results show that the feature vector from YCbCr color space has the most efficient performance and the correct classification rate is 97.4%.