• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.8
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• pp.1386-1397
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• 2001

The purpose of this study was to classify and analyze the somatotype of early middle-aged women and to provide its total data for clothing construction, and to improve clothing culture. The subjects were 277 early middle-aged women between 35 and 44 years old. Data were collected through anthropometry and photometry and analyzed by factor analysis, cluster analysis and discriminant analysis. The results were as follows; 1. The result of factor analysis indicated that 10 factors were extracted through factor analysis and those factors comprised 86.13 percent of total variance. 2. Using factor scores, cluster analysis was carried out and the subject were classified into 4 cluster. Type 1 is tall, slim, and X type in front. Type 2 is standard height and weight, short upper body, and hip-protruded on the side. Type 3 is standard height, thin, H type in front, back and hip are clearly protruded, and lean-back type on the side. Type 4 is standard height, fat, and long upper body. 3. According to the stepwise discriminant analysis, the 8 important iems is classifying the somatotype of early middle-aged women are as follows : bust girth, back length hip breadth-waist breadth, back protruded point depth(back)-back waist depth(back), hip tangent tilt, hip depth(back) waist dapth(back), bust depth-waist depth, and cervical hight, The correct classification rate for these items is as exact as 83.20%.

• ETRI Journal
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• v.40 no.6
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• pp.802-812
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• 2018

Disease discrimination using an electronic nose is achieved by measuring the presence of a specific gas contained in the exhaled breath of patients. Many studies have reported the presence of acetone in the breath of diabetic patients. These studies suggest that acetone can be used as a biomarker of diabetes, enabling diagnoses to be made by measuring acetone levels in exhaled breath. In this study, we perform a chemical sensor array optimization to improve the performance of an electronic nose system using Wilks' lambda, sensor selection based on a principal component (B4), and a stepwise elimination (SE) technique to detect the presence of acetone gas in human breath. By applying five different temperatures to four sensors fabricated from different synthetic materials, a total of 20 sensing combinations are created, and three sensing combinations are selected for the sensor array using optimization techniques. The measurements and analyses of the exhaled breath using the electronic nose system together with the optimized sensor array show that diabetic patients and control groups can be easily differentiated. The results are confirmed using principal component analysis (PCA).

• Korean journal of food and cookery science
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• v.18 no.1
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• pp.109-118
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• 2002

The purpose of this study was to identify the behavioral, attitudinal, and demographic correlates of light, medium, and heavy users of eating out at family restaurants. Among 358 reponses from the subjects, 224 responses were utilized for the analysis, and 134 responses were reserved for validating the discriminant function. Descriptive statistics, reliability analysis, stepwise discriminant analysis, canonical discriminant analysis, and anova analysis were used for this study. The findings from this study were as follows: First, He behavioral characteristics were found to discriminate among the three usage groups. Second, it was found that heavy users expressed greater difference between perception and expectation on the quantity of food that are appropriately served and the consistent quality of food at every visit. Third, the usage rate of eating out was not dependent on the sex, but dependent on the companion, average expenditure, and the time of eating out in chi-square test. Finally, the results of the study provide some insight into the pattern of marketing strategies that can be successfully used by the managers of family restaurants.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.12
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• pp.6181-6185
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• 2012

Background: The aim of the study was to evaluate the available breast nomograms (MSKCC, Stanford, Tenon) to predict non-sentinel lymph node metastasis (NSLNM) and to determine variables for NSLNM in SLN positive breast cancer patients in our population. Materials and Methods: We retrospectively reviewed 170 patients who underwent completion axillary lymph node dissection between Jul 2008 and Aug 2010 in our hospital. We validated three nomograms (MSKCC, Stanford, Tenon). The likelihood of having positive NSLNM based on various factors was evaluated by use of univariate analysis. Stepwise multivariate analysis was applied to estimate a predictive model for NSLNM. Four factors were found to contribute significantly to the logistic regression model, allowing design of a new formula to predict non-sentinel lymph node metastasis. The AUCs of the ROCs were used to describe the performance of the diagnostic value of MSKCC, Stanford, Tenon nomograms and our new nomogram. Results: After stepwise multiple logistic regression analysis, multifocality, proportion of positive SLN to total SLN, LVI, SLN extracapsular extention were found to be statistically significant. AUC results were MSKCC: 0.713/Tenon: 0.671/Stanford: 0.534/DEU: 0.814. Conclusions: The MSKCC nomogram proved to be a good discriminator of NSLN metastasis in SLN positive BC patients for our population. Stanford and Tenon nomograms were not as predictive of NSLN metastasis. Our newly created formula was the best prediction tool for discriminate of NSLN metastasis in SLN positive BC patients for our population. We recommend that nomograms be validated before use in specific populations, and more than one validated nomogram may be used together while consulting patients.

• Journal of Sensor Science and Technology
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• v.24 no.4
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• pp.258-263
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• 2015

In order to prevent drink-driving by detecting concentration of alcohol from driver's exhale breath, twenty chemical sensors fabricated. The one of purposes for sensor array which consists of those sensors is to discriminate between target gas(alcohol) and interference gases($CH_3CH_2OH$, CO, NOx, Toluene, and Xylene). Wilks's lambda was presented to achieve above purpose and optimal sensors were selected using the method. In this paper, step-wise sensor elimination based on Euclidean distance was investigated for selecting optimal sensors and compared with a result of Wilks's lambda method. The selectivity and sensitivity of sensor array were used for comparing performance of sensor array as a result of two methods. The data acquired from selected sensor were analyzed by pattern analysis methods, principal component analysis and Sammon's mapping to analyze cluster tendency in the low space (2D). The sensor array by stepwise sensor elimination method had a better sensitivity and selectivity compared to a result of Wilks's lambda method.

• Journal of Biosystems Engineering
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• v.22 no.3
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• pp.295-302
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• 1997

An ultimate purpose of this study was to develop an automatic system for brown rice quality inspection using image processing technique. In this study emphasis was put on developing an algorithm for discriminating the brown rice kernels depending on their external quality with a color image processing system equipped with an adaptor magnifying the input image and optical fiber for oblique lightening. Primarily, geometical and optical features of images were analyzed with paddy and the various brown rice kernel samples such as a sound, cracked, peen-transparent, green-opaque, colored, white-opaque and brokens. Secondary, geometrical and optical parameters significant for identifying each rice kernels were screened by a statistical analysis(STEPWISE and DISCRIM procedure, SAS wer. 6) and an algorithm fur on- line discrimination of the rice kernels in static state were developed, and finally its performance was evaluated. The results are summarized as follows. 1) It was ascertained that the cracked kernels can be detected when e incident angle of the oblique light is less than 2$0^{\circ}C$ but detectivity was significantly affected by the angle between the direction of the oblique light and the longitudinal axis of the rice kernel and also by the location of the embryo with respect to the oblique light. 2) The most significant Parameters which can discriminate brown rice kernels are area, length and R, B and r values among the several geometrical and optical parameters. 3) Discrimination accuracies of the algorithm were ranged from 90% to 96% for a sound, cracked, colored, broken and unhulled, about 81 % for green-transparent and white-opaque and 75 % for green-opaque, respectively.