• Journal of Families and Better Life
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• v.30 no.5
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• pp.121-134
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• 2012

The purpose of this study was to investigate the influences of adolescents' body image and communication with their parents on the alienation of male and female middle school students. The subjects were 253 middle school students. The data were analyzed with SPSS win 18.0 using Cronbach's ${\alpha}$, t-test, one-way ANOVA, Scheff$\acute{e}$ test and stepwise regression. The findings showed that problematic communication with the father was the strongest factor in explaining the alienation of male middle school students. The second strongest factor was the middles school student's open communication with the mother, and the third strongest factor was the middle school student's perception of physical health. Unlike male students, open communication with the father was the strongest factor in explaining the alienation of female middle school students. Problematic communication with the mother was the second strongest factor. Perception of physical appearance was the third strongest, and perception of physical health was the fourth strongest factor. Based on the results of the study, implications were discussed in terms of the alienation of male and female middle school students.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.2
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• pp.210-219
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• 2005

This research was designed to investigate the effect of hand and sendibility image on the preference to textiles for shirts. 85 subjects majored in fashion design were surveyed and 10 kinds of fabrics used as specimen at each season. Factor analysis, t-test, Pearson correlation, regression were used for statistical analysis by SPSS WIN 11.0. The result of this study were af follows : 1. In Spring ${\cdot}$Fall season, 5 factors were extracted as hand factor and 3 factors as sensibility factor of textiles for shirts. 2. 6 factors were extracted as hand factor and 3 factors as sensibility image in Summer. 3. 5 factors were extracted as hand factor and 3 factors as sensibility image in Winter season. 4. There were significant differences according to sex between hand factor and sensibility image at each season. 5. There were significant correlations between hand and sensibility image in Spring${\cdot}$Fall and Summer. 6. Hand and sensibility image were related to the preferene to texitiles for shirts in Spring and Winter.

• Fashion & Textile Research Journal
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• v.21 no.5
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• pp.597-605
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• 2019

This paper examined the garment formability and appearance of perspiration absorption, fast dry, and breathable fabrics. The mechanical properties and seam pucker properties of these fabrics were measured and regression analysis was conducted between fabric structural parameters and their mechanical and seam pucker properties. The superior total appearance value (TAV) of fast dry knitted fabrics for sports-wear was achieved in fabrics with high extensibility and bending rigidity; consequently, it increased with increasing stitch density and tightness factor. The formability of the fast dry knitted fabric also improved with an increasing stitch density and tightness factor. The seam pucker was influenced by bending rigidity and a good seam pucker was exhibited in the fast dry knitted fabrics with low stitch density and tightness factor. However, the formability (F) of the breathable fabric improved by increasing extensibility and bending rigidity that decreased with an increasing cover factor and the thickness of the breathable fabric. In addition, seam pucker deteriorated with an increasing cover factor and the thickness of the breathable fabric, which was similar to the results of the formability predicted in fabric mechanical properties. A superior seam pucker was achieved in fabrics with high extensibility and low bending rigidity.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.168-173
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• 2022

As the function of a product is advanced and the process is refined, the yield in the fine manufacturing process becomes an important variable that determines the cost and quality of the product. Since a fine manufacturing process generally produces a product through many steps, it is difficult to find which process or equipment has a defect, and thus it is practically difficult to ensure a high yield. This paper presents the system architecture of how to build a smart manufacturing system to analyze the big data of the manufacturing plant, and the equipment factor analysis methodology to increase the yield of products in the smart manufacturing system. In order to improve the yield of the product, it is necessary to analyze the defect factor that causes the low yield among the numerous factors of the equipment, and find and manage the equipment factor that affects the defect factor. This study analyzed the key factors of abnormal equipment that affect the yield of products in the manufacturing process using the data mining technique. Eventually, a methodology for finding key factors of abnormal equipment that directly affect the yield of products in smart manufacturing systems is presented. The methodology presented in this study was applied to the actual manufacturing plant to confirm the effect of key factors of important facilities on yield.

• Bulletin of the Society of Naval Architects of Korea
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• v.26 no.4
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• pp.14-26
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• 1989

This paper describes the method of statistical analysis and its programs for predicting the ship's powering performance. The equation for the wavemaking resistance coefficient is derived as the sectional area coefficients by using the wavemaking resistance theory and its regression coefficients are determined from the regression analysis of the model test results. The equations for the form factor, wake franction and thrust deduction fraction are derived by purely regression analysis of the principal dimensions, sectional area coefficients and model test results. The statistical analyses are performed using the various descriptive statistic and stepwise regression analysis techniques. The powering performance prognosis program is developed to cover the prediction of resistance coefficients, propulsive coefficients, propeller open-water efficiency and various scale effect corrections.

• Journal of Biosystems Engineering
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• v.35 no.3
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• pp.175-181
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• 2010

This study compared moisture content measured by moisture meter of microwave resistance type(MMMRT) and standard moisture content of paddy, and developed the correction equation using linear and curvilinear regression analysis, and to explore its significance test. The correction factor according to the range of moisture content was developed to improve the measurement precision of MMMRT. The results were as followings. The coefficients of determination of correction equation by linear and curvilinear regression analysis with comparing the MMMRT and standard moisture content were 0.946 and 0.968, respectively. The moisture content error of MMMRT and standard moisture content measured after the MMMRT were corrected by moisture content rate of every 5% using the correction equation by curvilinear regression analysis appeared with 0~0.5% and 0.9~1.8% respectively in the moisture content range of 15~20% and 20~25%.

• Tribology and Lubricants
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• v.23 no.4
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• pp.156-161
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• 2007

The friction test using disk-on-pad type was carried out and regression analysis with friction parameters was applied fur wear loss presumption of motorcycle break disk. The wear loss has an effect on the frictional factor such as applied load, sliding speed, and number of ventilated disk hole. However, it is difficult to know the mutual relation of these factors on wear loss of motorcycle break disk. From this study, the result was shown that the regression analysis equation containing 4 elements were constructed and this equation had a trust of 95% in wear loss presumption of motorcycle break disk. It is possible to apply for another automobile parts.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.1
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• pp.47-53
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• 2001

In this paper, we compare the classification performances of both ensemble and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. In view of the unknown relationship between input and output function, we use a Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: When the level of the variance is medium, Bagging & Parameter Combining performs worse than Logistic Regression, Variable Selection Bagging and Clustering. However, classification performances of Logistic Regression, Variable Selection Bagging, Bagging and Clustering are not significantly different when the variance of input data is either small or large. When there is strong correlation in input variables, Variable Selection Bagging outperforms both Logistic Regression and Parameter combining. In general, Parameter Combining algorithm appears to be the worst at our disappointment.

• Proceedings of the KSRS Conference
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• v.2
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• pp.574-577
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• 2006

In this paper, we developed a forest fire detection algorithm which uses a regression function between NDVI and land surface temperature. Previous detection algorithms use the land surface temperature as a main factor to discriminate fire pixels from non-fire pixels. These algorithms assume that the surface temperatures of non-fire pixels are intrinsically analogous and obey Gaussian normal distribution, regardless of land surface types and conditions. And the temperature thresholds for detecting fire pixels are derived from the statistical distribution of non-fire pixels’ temperature using heuristic methods. This assumption makes the temperature distribution of non-fire pixels very diverse and sometimes slightly overlapped with that of fire pixel. So, sometimes there occur omission errors in the cases of small fires. To ease such problem somewhat, we separated non-fire pixels into each land cover type by clustering algorithm and calculated the residuals between the temperature of a pixel under examination whether fire pixel or not and estimated temperature of the pixel using the linear regression between surface temperature and NDVI. As a result, this algorithm could modify the temperature threshold considering land types and conditions and showed improved detection accuracy.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.847-859
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• 2011

cDNA microarray-based comparative genomic hybridization(CGH) data includes low-intensity spots and thus a statistical strategy is needed to detect subtle differences between different cancer classes. In this study, genes displaying a high frequency of alteration in one of the different classes were selected among the pre-selected genes that show relatively large variations between genes compared to total variations. Utilizing copy-number changes of the selected genes, this study suggests a statistical approach to predict patients' classes with increased performance by pre-classifying patients with similar genetic alteration scores. Two-stage logistic regression model(TLRM) was suggested to pre-classify homogeneous patients and predict patients' classes for cancer prediction; a decision tree(DT) was combined with logistic regression on the set of informative genes. TLRM was constructed in cDNA microarray-based CGH data from the Cancer Metastasis Research Center(CMRC) at Yonsei University; it predicted the patients' clinical diagnoses with perfect matches (except for one patient among the high-risk and low-risk classified patients where the performance of predictions is critical due to the high sensitivity and specificity requirements for clinical treatments. Accuracy validated by leave-one-out cross-validation(LOOCV) was 83.3% while other classification methods of CART and DT performed as comparisons showed worse performances than TLRM.