• Journal of the Korean Data and Information Science Society
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• 제24권4호
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• pp.867-876
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• 2013

Metabolic syndrome has been known as a major factor of cardiovascular disease. Several metabolic disorders, particularly chronic disease is complex, and from individuals that appear in our country, the prevalence of the metabolic syndrome is increasing gradually. Therefore, this study, using a multi-factor dimensionality reduction method, checks the major single risk factor of metabolic syndrome and suggests a new diagnosis results of metabolic syndrome. Data of 3990 adults who responded to all the questionnaires of health interview are used from the database of the 5th Korea national health and nutrition examination survey conducted in 2010. As the result, the most dangerous single risk factor for metabolic syndrome was waist circumference and the most dangerous combination factors were waist circumference, triglyceride, and hypertension. This is the result of a new diagnosis of the metabolic syndrome. Especially, waist circumference, low HDL-cholesterol and hypertension were the most dangerous combination for male. In particular, the combination of waist circumference, triglyceride and diabetes was dangerous for obese people.

• The Transactions of The Korean Institute of Electrical Engineers
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• 제67권3호
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• pp.419-426
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• 2018

An useful and effective design method for the gas identification system is presented in this paper. The proposed gas identification system adopts hierarchical structure with two level rule base combining fuzzy sets with rough sets. At first, a hybrid genetic algorithm is used in grouping the array sensors of which the measured patterns are similar in order to reduce the dimensionality of patterns to be analyzed and to make rule construction easy and simple. Next, for low level identification, fuzzy inference systems for each divided group are designed by using TSK fuzzy rule, which allow handling the drift and the uncertainty of sensor data effectively. Finally, rough set theory is applied to derive the identification rules at high level which reflect the identification characteristics of each divided group. Thus, the proposed method is able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, we demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• Smart Structures and Systems
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• 제14권3호
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• pp.377-395
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• 2014

Vibration-based fault detection and condition monitoring of rotating machinery, using statistical process control (SPC) combined with statistical pattern recognition methodology, has been widely investigated by many researchers. In particular, the discrete wavelet transform (DWT) is considered as a powerful tool for feature extraction in detecting fault on rotating machinery. Although DWT significantly reduces the dimensionality of the data, the number of retained wavelet features can still be significantly large. Then, the use of standard multivariate SPC techniques is not advised, because the sample covariance matrix is likely to be singular, so that the common multivariate statistics cannot be calculated. Even though many feature-based SPC methods have been introduced to tackle this deficiency, most methods require a parametric distributional assumption that restricts their feasibility to specific problems of process control, and thus limit their application. This study proposes a nonparametric multivariate control chart method, based on multiscale wavelet scalogram (MWS) features, that overcomes the limitation posed by the parametric assumption in existing SPC methods. The presented approach takes advantage of multi-resolution analysis using DWT, and obtains MWS features with significantly low dimensionality. We calculate Hotelling's $T^2$-type monitoring statistic using MWS, which has enough damage-discrimination ability. A bootstrap approach is used to determine the upper control limit of the monitoring statistic, without any distributional assumption. Numerical simulations demonstrate the performance of the proposed control charting method, under various damage-level scenarios for a bearing system.

• Journal of the Korean Society of Clothing and Textiles
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• 제38권3호
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• pp.355-371
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• 2014

The present study investigated the dimensionality of satisfaction and dissatisfaction as well as evaluated the effects of social commerce characteristics on satisfaction and dissatisfaction. The conceptual framework of the study was built on Herzberg's motivation-hygiene Theory and the Kano Model. We gathered 519 data by social commerce users through an online survey and used SPSS 20.0 for the analysis. The findings showed that satisfaction and dissatisfaction are two distinct constructs; in addition, nine characteristics of social commerce were derived from factor analysis. Among the nine factors of social commerce characteristics, diversity had a positive influence only on satisfaction and uncertainty had only a positive impact on dissatisfaction; however, price discount, product quality and transaction safety, influenced both satisfaction and dissatisfaction. There were several factors that had no significant influence on both satisfaction and dissatisfaction. The findings of the study support Herzberg's motivation-hygiene Theory and the Kano Model. The present study helps social commerce managers establish a plan to maximize factors that influence consumer satisfaction and minimize the factor influencing dissatisfaction.

• Journal of Fashion Business
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• 제23권6호
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• pp.27-36
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• 2019

This study aimed to analyze the color differences by warp direction of textile mosaics by focusing on two representative textile wallcovering types, woven and string. Mosaics made of string can be expressed as having three-dimensionality based on color differences resulting from the warp direction of the string. String wallcoverings, unlike woven or non-woven wallcoverings, only have vertically oriented warp lamination on the backing paper without weft, and therefore, the reflection and backing color can be expressed differently depending on the angle of the mosaic. In this study, two identical wallcoverings were manufactured using the same materials but using different textile types, woven and string. The wallcoverings underwent die-cutting by each angle and were deployed in cube form. The analysis was based on ISO 5631-1:2015. The color difference between the two wallcoverings, woven and string, was shown as ΔE* 9.29. Based on the standard deviation of the color difference for each mosaic angle, woven ranged from ΔE* 0.09 to 0.94 and string ranged from ΔE* 1.92 to 3.74, showing a larger color difference. Thus, using the color differences of string to create a mosaic wallcovering improved dimensionality.

• Journal of the Korean Data and Information Science Society
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• 제23권3호
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• pp.467-474
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• 2012

Human diseases and livestock economic traits are not typically the result of variation of a single genetic locus, but are rather the result of interplay between interactions among multiple genes and a variety of environmental exposures. We have used linear regression model for adjusted environmental effects and multifactor dimensionality reduction (MDR) method to identify gene-gene interaction effect of statistical model in general. Of course, we use 5 SNPs (single uncleotide polymorphism) which were studied recently by Oh et al. (2011). We apply the MDR (multifactor demensionality reduction) method on the identify major interaction effects of single nucleotide polymorphisms responsible for economic traits in a Korean cattle population.

• Genomics & Informatics
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• 제18권4호
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• pp.38.1-38.9
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• 2020

Chronotype is an important moderator of psychiatric illnesses, which seems to be controlled in some part by genetic factors. Clock genes are the most relevant genes for chronotype. In addition to the roles of individual genes, gene-gene interactions of clock genes substantially contribute to chronotype. We investigated genetic associations and gene-gene interactions of the clock genes BHLHB2, CLOCK, CSNK1E, NR1D1, PER1, PER2, PER3, and TIMELESS for chronotype in 1,293 healthy Korean individuals. Regression analysis was conducted to find associations between single nucleotide polymorphism (SNP) and chronotype. For gene-gene interaction analyses, the quantitative multifactor dimensionality reduction (QMDR) method, a nonparametric model-free method for quantitative phenotypes, were performed. No individual SNP or haplotype showed a significant association with chronotype by both regression analysis and single-locus model of QMDR. QMDR analysis identified NR1D1 rs2314339 and TIMELESS rs4630333 as the best SNP pairs among two-locus interaction models associated with chronotype (cross-validation consistency [CVC] = 8/10, p = 0.041). For the three-locus interaction model, the SNP combination of NR1D1 rs2314339, TIMELESS rs4630333, and PER3 rs228669 showed the best results (CVC = 4/10, p < 0.001). However, because the mean differences between genotype combinations were minor, the clinical roles of clock gene interactions are unlikely to be critical.

• Korean Journal of Social Welfare
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• 제58권3호
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• pp.27-49
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• 2006

The purpose of the present study was to investigate dimensionality and psychometric properties of identified theoretical constructs of the 'Life Satisfaction Scale for Elderly(LSSE)', which was developed by Choi, Sung-Jae in 1986. Data was obtained from 'The survey of health and welfare status of the elderly aged 65 or older in Chung-Choo city'. The subjects were 275 elderly. Results showed that LSSE had a multi-dimensional structure with three theoretical constructs. Each theoretical construct was named as 'positive affect and subjective satisfaction', 'negative self image and affect', and 'self-value'. Three theoretical constructs had high levels of reliability and validity based on internal construct. 'Positive affect and subjective satisfaction' and 'negative self image and affect' showed high levels of convergent and discriminant validity. 'Self-value' had a high level of convergent validity but acceptable level of discriminant validity. Results of this study revealed that there was a difference in theoretical dimensionality of LSSE between this study and Choi's study, which explained the dimensionality of LSSE as a single dimension. However, the result of this study regarding theoretical dimensionality supported findings from existing studies which insisted that life satisfaction had a multi-dimensional structure.

• The Journal of the Korea Contents Association
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• 제4권3호
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• pp.81-90
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• 2004

Recently, It has been increased to use a multi-dimensional data in various applications with a rapid growth of the computing environment. In this paper, we propose the vector approximate tree for content-based retrieval of multi-dimensional data. The proposed index structure reduces the depth of tree by storing the many region information in a node because of representing region information using space partition based method and vector approximation method. Also it efficiently handles 'dimensionality curse' that causes a problem of multi-dimensional index structure by assigning the multi-dimensional data space to dynamic bit. And it provides the more correct regions by representing the child region information as the parent region information relatively. We show that our index structure outperforms the existing index structure by various experimental evaluations.

• Journal of Information Processing Systems
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• 제15권3호
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• pp.682-693
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• 2019

The traditional classification methods mostly assume that the data for class distribution is balanced, while imbalanced data is widely found in the real world. So it is important to solve the problem of classification with imbalanced data. In Mahalanobis-Taguchi system (MTS) algorithm, data classification model is constructed with the reference space and measurement reference scale which is come from a single normal group, and thus it is suitable to handle the imbalanced data problem. In this paper, an improved method of MTS-CBPSO is constructed by introducing the chaotic mapping and binary particle swarm optimization algorithm instead of orthogonal array and signal-to-noise ratio (SNR) to select the valid variables, in which G-means, F-measure, dimensionality reduction are regarded as the classification optimization target. This proposed method is also applied to the financial distress prediction of Chinese listed companies. Compared with the traditional MTS and the common classification methods such as SVM, C4.5, k-NN, it is showed that the MTS-CBPSO method has better result of prediction accuracy and dimensionality reduction.