• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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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.

• The Research Journal of the Costume Culture
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• v.8 no.6
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• pp.930-939
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• 2000

This study was done to provide the fundamental data for scientific and rational children's clothing sizing system by investigating their somatometric characteristics and classifying somatotypes. The subjects were 269 elementary schoolgirls aged from 7 to 8 years old living in Pusan and Kyungsangnam-do. Data from each girl comprises 28 anthropometic measurments and 4 photographic measurments, related to the lower half of body. To analyze somatotypes of the lower half of body, factor analysis, cluster analysis, discriminant analysis were performed for statistical analysis of the data. As to the analysis to draw somatometric factors by this age group, five factors which explain 76.85% of the whole variances were extracted. The first and second factors which explain more than 60% of the whole variances represent 'horizontal size'and 'vertical size', which characterize most aspects of the body shape of the subjects. On the basis of the cluster analysis, three different lower half of body types were categorized. Type Ⅰ has biggest horizontal size, average vertical size and most protruded belly. Type Ⅱ has average degree of horizontal size, quite big vertical size and most protruded hips. Type Ⅲ has smallest horizontal and vertical size. According to the analysis to discriminate somatotypes of the lower half of body of this age group, weight and waist circumference of discriminant function 1 and abdominal circumference of discriminant function 2 have coefficient values.

• Women's Health Nursing
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• v.4 no.1
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• pp.123-138
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• 1998

The purpose of this study was to describe perimenstrual symptom severity levels and perimenstrual distress patterns of women. The study performed the discriminant analysis in which included seven factors : age, pariety, social support, menstrual socialization(mother's symptom, sister's symptom, and menstrual effect), attitude of sex role and depression. The subjects were 283 women that they were not pregnant or lactating, had at least one period in past three months, would understand the purpose of study and willingly accepted the participation. The data analysis was done by pc-SAS program after data collection from Nov. 20, 1997 to Dec. 18, 1997. The descriptive analysis was done to explore general characteristics of the subjects and the stepwise discriminant analysis was done to verify factors in relation to perimenstrual symptom severity levels(severe vs mild menstrual symptom group) and perimenstrual distress patterns(spasmodic vs congestive menstrual symptom group). The instruments were selected for this study from Interpersonal Support Evaluation List(ISEL) by Cohen and Hoberman(1983), Center for Epidemic Studies Depression(CES-D) by Radloff(1977), and Sex Role Attitude Scale by Yunok Suh(1995), Mother's symptom and sister's symptom measurements by Woods, Mitchell & Lentz(1995), and menstrual effect by Brooks-Gun & Ruble(1980). The major findings of this study are as follows : 1. Of the 283 women, 93 women(32.9%) were assessed to severe perimenstrual symptom group and 190 women(67.1%) were assessed to mild perimenstrual symptom group. Results from the stepwise discriminant analysis showed three factors, such as depression, menstrual effect, and age, significantly related to perimenstrual symptom severity and they explained 20% of the total variance. The linear discriminant equation included three factors related to perimenstrual symptom groups was showed(Z=1.445 depression+0.174 menstrual effect-0.054 age). The cutting score(Z) was 2.809. We classified the severe perimenstrual symptom group by more than the cutting score 2.809 and the mild perimenstrual symptom by less or equal than the cutting score 2.809. The correctedness of posterior probability from discriminant equation was 72% as two perimenstrual symptom group classifications. 2. Of the 264 women, 139 women(52.7%) were assessed to spasmodic perimenstrual distress group and women(47.3%) were assessed to congestive perimenstrual distress group. Results from the stepwise discriminant analysis showed two factors, such as depression, age, significantly related to perimenstrual distress groups and they explained 8% of the total variance. The linear discriminant equation included two factors related to perimenstrual distress group was showed(Z=-0.084 age-0.776 depression). The cutting score(Z) was -3.759. We classified the spasmodic perimenstrual distress group by more than cutting score -3.759 and the congestive perimenstrual distress group by less or equal than cutting score -3.759. The correctedness of posterior probability from discriminant equation was 65% as two perimenstrual distress group classifications.

• Journal of Physiology & Pathology in Korean Medicine
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• v.18 no.6
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• pp.1938-1944
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• 2004

In this study, when we make a diagnosis of constitution using QSCC Ⅱ(Questionnaire of Sasang Constitution Classification). data mining techniques are applied to seek the classification function for improving the accuracy. Data used in the analysis are the questionnaires of 1051 patients who had been treated in Dong Eui Oriental Medical Hospital and Kyung Hee Oriental Medical Hospital. The criteria for data cleansing are the response pattern in the opposite questionnaires and the positive proportion of specific questionnaires in each constitution. And the criteria for variable selection are the test of homogeneity in frequency analysis and the coefficients in the linear discriminant function. Discriminant analysis model and decision tree model are applied to seek the classification function into Sasang constitution. The accuracy in learning sample is similar in two models, the higher accuracy in test sample is obtained in discriminant analysis model.

• Journal of the Korean Institute of Landscape Architecture
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• v.29 no.1
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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.

• MALSORI
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• no.44
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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.

• KSTLE International Journal
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• v.2 no.1
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• pp.35-39
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• 2001

In this paper, statistical approach is undertaken to investigate the classification of wear debris which is the key function of objective assessment of wear debris morphology. Wear tests are run to produce various kinds of wear debris. The images of wear debris from wear tests are captured with image acquisition equipment. By thresholding, two-dimensional binary images of wear debris are made and, then, morphological parameters are used to quantify the images of debris. Parametric and nonparametric discriminant method are employed to classify wear debris into predefined wear conditions. It is demonstrated that classification accuracy of parametric and nonparametric discriminant method is similar. The selected use of morphological parameters by stepwise discriminant analysis can generally improve the classification accuracy of parametric and nonparametric discriminant method.

• Korean Journal of Construction Engineering and Management
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• v.11 no.5
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• pp.84-94
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• 2010

Partnering concept has been mentioned as an innovative arrangement that helps to reduce many of the disadvantages of the traditional arrangement. Partnering in construction has been widely applied in Vietnam from late 1990s. The application of the new has arrangement spread thanks to anecdotal proofs. This concept is quite new to Vietnamese practitioners. It is necessary to conduct study as a lesson-learn of the industry to encourage the partnering implementation. This paper attempts to develop a model, using discriminant analysis, which classifies the partnering in construction projects into success levels. Dedication, teamwork, sufficiency, and balance are the four significant components in discriminant model. The proposed model is helpful to practitioners in developing, adjusting and improving their strategy for partnering implementation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4502-4518
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• 2015

Fisher linear discriminant analysis (LDA) is one of the most popular projection techniques for feature extraction and has been widely applied in face recognition. However, it cannot be used when encountering the single sample per person problem (SSPP) because the intra-class variations cannot be evaluated. In this paper, we propose a novel method called local similarity based linear discriminant analysis (LS_LDA) to solve this problem. Motivated by the "divide-conquer" strategy, we first divide the face into local blocks, and classify each local block, and then integrate all the classification results to make final decision. To make LDA feasible for SSPP problem, we further divide each block into overlapped patches and assume that these patches are from the same class. To improve the robustness of LS_LDA to outliers, we further propose local similarity based median discriminant analysis (LS_MDA), which uses class median vector to estimate the class population mean in LDA modeling. Experimental results on three popular databases show that our methods not only generalize well SSPP problem but also have strong robustness to expression, illumination, occlusion and time variation.

• Korean Journal of Food Science and Technology
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• v.28 no.2
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• pp.298-304
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• 1996

Thirty-nine samples of roasted sesame seed oils were sensorially evaluated in terms of nutty odor, burnt odor and overall desirability, and their volatile compounds quantitatively analysed using direct sampling capillary GLC. Five volatile compounds were appeared to be significant for the sensory Properties of sesame oils through the multivariate analytical techniques such as stepwise discriminant analysis. canonical discriminant analysis, discriminant analysis and principal component analysis. The most important compounds were 2,5-dimethylpyrazine and 2-methylpyrazine which could be effectively used as chemical indicators related to nutty and burnt odor of sesame oils, respectively. The sesame oils which have represented a good grade of overall desirability have been always kept $35.82{\sim}4.43$ ppm of 2,5-dimethylpyrazine and also $28.90{\sim}6.35$ ppm of 2-methylpyrazine.