• Journal of the Korean Home Economics Association
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• v.34 no.3
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• pp.137-155
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• 1996

The purpose of this study was to identify the relative influence of OCM and BSM's family functioning dimensions and develop a new family system model related to adolescent adjustment. The 443 subjects were selected randomly from the second grade of middle and high schools in the city of Taegu. The survey instruments were FACESⅢ, SFI-Ⅱ, State-Trait Anxiety Inventory, Depression Scale, Self-Esteem Scale, and Delinquency Scale, Factor Analysis, Cronbach's α, Multiple Regression, MANOVA, Scheffe test were conducted for the data analysis. The major findings of this study were as follows: First, OCM's and BSM's family functioning dimensions respectively had different relative influence that affected adolescent adjustment level. In anxiety and depression. BSM's family health/competence dimension had superior influence to any other family functioning dimensions and in self-esteem and delinquency, OCM's cohesion dimension was superior to any other family functions. Second, family system classification method by a new family system model using family cohesion(OCM's relationship dimension) and family health/competence(BSM's change dimension) was more useful than OCM in evaluating adolescent adjustment.

• Computers and Concrete
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• v.26 no.6
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• pp.505-513
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• 2020

Grading of aggregate influences significantly almost all of the concrete performances. The purpose of this paper is to propose practicable equations that express the optimized total aggregate gradation, by weight or by number of particles in a concrete mix. The principle is based on the fractal feature of the grading of combined aggregate in a solid skeleton of concrete. Therefore, equations are derived based on the so-called fractal dimension of the grain size distribution of aggregates. Obtained model was then applied in such a way a correlation between some properties of the dry concrete mix and the fractal dimension of the aggregate gradation has been built. This demonstrates that the parameter fractal dimension is an efficacious tool to establish a unified model to study the solid phase of concrete in order to design aggregate gradation to meet certain requirements or even to predict some characteristics of the dry concrete mixture.

• Journal of the Korean Society of Hazard Mitigation
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• v.7 no.4
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• pp.75-83
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• 2007

Urbanized area has complex terrain with many flow paths. Almost stormwater is drained through pipe network because most area is impervious. And overland flow from the pipe network reform the surface flow. Therefore, it should be considered the drainage system and surface runoff both in urban inundation analysis. It is analyzed by using MIKE FLOOD integrated 1 dimension - 2 dimension model about Incheon Gyo urbanized watershed and compared with the results of 1 dimension model and 2 dimension model. At the result this approach linking of 2 dimension and 1 dimension pipe hydraulic model in MIKE FLOOD give accuracy that offers substantial improvement over earlier approach and more information about inundation such as water dapth, velocity or risk of flood, because it is possible to present storage of overland flow and topographical characteristic of area.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.107-115
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• 2012

Yoo and Cook (2007) developed an optimal sufficient dimension reduction methodology for the conditional mean in multivariate regression and it is known that their method is asymptotically optimal and its test statistic has a chi-squared distribution asymptotically under the null hypothesis. To check the effect of dimension used in estimation on regression coefficients and the explanatory power of the conditional mean model in multivariate regression, we applied their method to several simulated data sets with various dimensions. A small simulation study showed that it is quite helpful to search for an appropriate dimension for a given data set if we use the asymptotic test for the dimension as well as results from the estimation with several dimensions simultaneously.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.825-835
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• 2008

Multivariate GARCH(MGARCH) has been useful in financial studies and econometrics for modeling volatilities and correlations between components of multivariate time series. An obvious drawback lies in that the number of parameters increases rapidly with the number of variables involved. This thesis tries to resolve the problem by using dimension reduction technique. We briefly review both factor models for dimension reduction and the MGARCH models including EWMA (Exponentially weighted moving-average model), DVEC(Diagonal VEC model), BEKK and CCC(Constant conditional correlation model). We create meaningful portfolios obtained after reducing dimension through statistical factor models and fundamental factor models and in turn these portfolios are applied to MGARCH. In addition, we compare portfolios by assessing MSE, MAD(Mean absolute deviation) and VaR(Value at Risk). Various financial time series are analyzed for illustration.

• Journal of Families and Better Life
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• v.16 no.3
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• pp.53-66
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• 1998

This study presents the Family FIRO model for stepfamily development. The Family FIRO model conceptualizes interactional patterns in stepfamily for dealing with developmental tasks and suggests a method for organizing assessment and for prioritizing treatment strategies. Boundary ambiguity role confusion intergenerational coalition and loyalty conflict lack of relationship commitment and resource management issues of stepfamily constitute the inclusion interaction dimension in The Family FIRO model. While power role negotiation conflict decision making and discipline issues represent the control interaction dimension lack of emotional exchange and open self-disclosure issues are intimacy interaction dimension in the stepfamily. The family FIRO model suggests that stepfamily should attend to the developmental tasks related to inclusion before placing a major emphasis on control issues which in turn should come before emphasis on stepfamily intimacy.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.2947-2959
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• 1997

Temporal data model is able to handle the time varying information, which is to add temporal attributes to conventional data model. The temporal data model is classified into three models depending upon supporting time dimension, that are the valid time model to support valid time, the transaction time model to support transaction model, and the bitemporal data model to support valid time and transaction time. Most temporal data models are designed to process the temporal data by extending the relational model. There are two types or temporal data model, which are the tuple timestamping and the attribute timestamping depending on time dimension. In this research, a concepts of temporal data model, the time dimension, types of thc data model, and a consideration for the data model design are discussed Also, temporal data models in terms of the time dimension are compared. And the aggregate function model of valid time model is proposed, and then logical analysis for its computing consts has been done.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.79-86
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• 2019

Software dimension reduction identifies the commonality of elements and extracts important feature elements. So it reduces complexity by simplify and solves multi-collinearity problems. And it reduces redundancy by performing redundancy and noise detection. In this study, we proposed defect severity-based dimension reduction model. Proposed model is applied defect severity-based NASA dataset. And it is verified the number of dimensions in the column that affect the severity of the defect. Then it is compares and analyzes the dimensions of the data before and after reduction. In this study experiment result, the number of dimensions of PC4's dataset is 2 to 3. It was possible to reduce the dimension.

• Journal of the Korean Wood Science and Technology
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• v.17 no.3
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• pp.67-81
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• 1989

This experiment was carried out to know the mothod of changing the step of moisture content schedule with time in conventional kiln drying. For the purpose of this object. we made drying model by applying the moisture diffusion model by J.FSiau(1984) to average moisture content equation by J.Crank(1956) derived it from Fick's second law. And to verify this method of drying model. 2.5cm-thick boards and 5.0cm-thick dimension lumbers of Pinus densiflora were kiln-dried with the schedule of T11-C3 and T10-C4, respectively. And then the drying rates were investigated and compared with those calculated from drying model. The results obtained were as follows 1. Average drying rate and total drying time of board to dry to 6.5% moisture content were 0.64%/hr and 109hr., and those of dimension lumber to dry to 8.3% moisture content were 0.4%/hr. and 162hr., respectively. 2. The moisture content of shell and core decreased by equalizing treatment and increased by conditioning treatment both on board and dimension lumber. But the moisture gradient was lower after conditioning than after equalizing. 3. As the drying was proceeded, the transverse bound water diffusion coefficient all but linearly decreased, the water vapor diffusion coefficient abruptly curvilinearly increased, while the transverse diffusion coefficient curvilinearly decreased both on board and dimension lumber. But each of diffusion coefficients on board was larger than that on dimension lumber. 4. Compared to experimential drying rate of board. theoretical drying rate was larger at 30.0%-21.8% moisture content range and was similiar at 21.8%-5.4% moisture content. And in case of dimension lumber, the drying rate was similiar at 30.0%-16.1% moisture content range but theoretical drying rate was much lower at 16.1%-8.3% moisture content range. 5. The possibility of adapting this drying model to changing the moisture content schedule step with time was in the range of 21.8%-5.4% moisture content on board. And in the case of dimension lumber that was in the range of 30.0%-16.1% moisture content.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.323-335
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• 2023

In causal analysis of high dimensional data, it is important to reduce the dimension of covariates and transform them appropriately to control confounders that affect treatment and potential outcomes. The augmented inverse probability weighting (AIPW) method is mainly used for estimation of average treatment effect (ATE). AIPW estimator can be obtained by using estimated propensity score and outcome model. ATE estimator can be inconsistent or have large asymptotic variance when using estimated propensity score and outcome model obtained by parametric methods that includes all covariates, especially for high dimensional data. For this reason, an ATE estimation using an appropriate dimension reduction method and semiparametric model for high dimensional data is attracting attention. Semiparametric method or sparse sufficient dimensionality reduction method can be uesd for dimension reduction for the estimation of propensity score and outcome model. Recently, another method has been proposed that does not use propensity score and outcome regression. After reducing dimension of covariates, ATE estimation can be performed using matching. Among the studies on ATE estimation methods for high dimensional data, four recently proposed studies will be introduced, and how to interpret the estimated ATE will be discussed.