• Journal of the Korean Home Economics Association
• /
• v.36 no.10
• /
• pp.79-88
• /
• 1998

The purpose of this study is to identify environmental perception of clothing consumption behavior taking socioo-economic variables into account. An instrument, based on previous research was administered to 213 housewives in Taegu area. In analyzing the data, factor analysis, t-test, ANOVA, regression analysis were used. The finding of this indicated that the environmental perception of clothing consumption were significant in purchasing, managing, and disposal of clothing. Among socio-economics variables, income and educational level were found to be significant in explaining the variance of enviromental perception behavior of clothing consumption. The important results of this study are discussed and implications are provided.

• Korean Journal of Child Studies
• /
• v.12 no.2
• /
• pp.130-145
• /
• 1991

The purpose of this study was to investigate parental authority by sex and age of child. The subjects of this study were 546 elementary school and middle school children in Seoul. The instrument was the Parent Authority Scale (김경희, 1991). Statistical analysis of the data was by two-way multivaliate analysis of variance, simultaneous confidence interval and structure coefficients. There were sex and age differences in children's perception of parental authority. There was a significant interaction effect between children's sex and age on parental authority.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1244-1244
• /
• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from mastitic and healthy cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from mastitic and healthy cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA and FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference, thereby providing a useful means for spectroscopy-based clinic applications.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1042-1042
• /
• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from daily monitoring of two Japanese cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from two cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA md FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 1996.06a
• /
• pp.51-54
• /
• 1996

In this paper, an interchannel interference (ICI) and symbol error probability for orthogonal frequency division multiplexing (OFDM) on the two-ray fading environment are obtained analytically. From the analysis results, it is found that the ICI is a Gaussian random variable and its variance depends on the subchannel location, normalized time delay, and the number of subchannels. In addition, the OFDM signal without guard interveal is found to yield an irreducible error even at high signal to noise ratio due to the ICI.

• Proceedings of the Materials Research Society of Korea Conference
• /
• 1999.05a
• /
• pp.57-57
• /
• 1999

핵연료 펠렛제조공정의 단순화를 위하여 분말을 플라즈마로 용융시킨 후 이를 펠렛몰더에 직접 침적시키는 방법으로 핵연료를 제조하고자 하였다. 침적물의 밀도에 미치는 영향을 관찰하기 위하여 쉬스가스 조성, 플라즈마 동력, 챔버내부압력 및 분말 공급량, 입자크기, 분사관 위치, 분사거리 및 쉬스가스조성 등을 변수로 하였다$^{1)}$ . 실험으로 얻어진 결과는 ANOVA(Analysis of Variance)의 통계적 방법으로 각각의 인자가 밀도에 미치는 영향의 크기뿐만 아니라, 두 가지 이상의 인자가 조합되어 나타나는 영향에 대해서도 분석하였다.

• Journal of Positioning, Navigation, and Timing
• /
• v.8 no.1
• /
• pp.19-29
• /
• 2019

This paper analyzes the design factors for GNSS anti-jamming receiver system in which the adaptive beamforming algorithm is applied in GNSS receiver system. The design analysis factors used in this paper are divided into three: antenna, beamforming algorithm, and operation environment. This paper analyzes the above three factors and presents numerical simulation results on antenna and beamforming algorithm.

• Culinary science and hospitality research
• /
• v.21 no.2
• /
• pp.243-253
• /
• 2015

This study examined the factors that affect the relationship between customer consumption propensity, customer bakery selction and customer behavioral intention. A total of 300 questionnaires were distributed to the consumers, of which 27 were deemed suitable for analysis after the removal of 28 unusable responses. In order to perform statistical analyses required in the study, SPSS 18.0 Statistical Program was employed for frequency analysis, factor analysis, and reliability analysis. The results of exploratory factor analysis showed that three factors regarding customer consumption propensity were extracted from all measurements with a KMO of 0.778 and a total cumulative variance of 62.121%. With regard to bakery selection attributes, three factors were extracted with a total cumulative variance of 65.69% and a KMO score of 0.776. One factor for behavioral intention was extracted that accounted for a total cumulative variance of 69.82% and a KMO score of 0.803. All factors were significant to 0.000 and the correlation between variables was significant. Thus, based on the results, the main research hypothesis that identifies the relationships between bakery selection attributes and behavioral intention was partially adopted.

• Culinary science and hospitality research
• /
• v.22 no.5
• /
• pp.25-36
• /
• 2016

This study examined the factors that affect the relationship between single-person households, food related life style, restaurant selection attributes, and behavioral intention. In order to perform statistical analyses required in the study, SPSS 18.0 Statistical Program was employed for frequency analysis, factor analysis, reliability analysis, correlations, and regression analysis. The results of exploratory factor analysis showed that four factors regarding food related life style were extracted from all measurements with a KMO of 0.716 and a total cumulative variance of 64.437%, With regard to restaurant selection attributes, one factor was extracted with a total cumulative variance of 75.372% and a KMO score of 0.739. One factor for behavioral intention was extracted that accounted for a total cumulative variance of 61.312% and a KMO score of 0.666. All factors were significant to .000 and the correlation between variables was significant. Thus, based on the results, the main research hypotheses regarding the relationships among food related life style restaurant selection attributes, and behavioral intention were adopted.

• Culinary science and hospitality research
• /
• v.21 no.3
• /
• pp.29-39
• /
• 2015

This study examined the factors that affect the relationship among local specialties food quality, value perception and customer behavioral intention. A total of 280 questionnaires were distributed to consumers, of which 268 were deemed suitable for analysis after the removal of 12 unusable responses. In order to perform statistical analyses required for the study, SPSS 18.0 Statistical Program was employed for frequency analysis, factor analysis, and reliability analysis. The results of the exploratory factor analysis showed that three factors regarding local food specialties quality were extracted from all measurements with a KMO of 0.827 and a total cumulative variance of 65.638%. With regard to value perception, six factors were extracted with a total cumulative variance of 59.855% and a KMO score of 0.782. One factor for behavioral intention was extracted that accounted for a total cumulative variance of 64.427% and a KMO score of 0.757. All factors were significant to 0.000 and the correlation between variables was significant. Thus, based on the results, the main research hypothesis that identifies the relationships between value perception and behavioral intention was partially adopted.