• The Journal of Information Systems
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• v.31 no.4
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• pp.145-160
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• 2022

Purpose The purpose of this study is to develop a virtual try-on deep learning model that can efficiently learn front and back clothes images. It is expected that the application of virtual try-on clothing service in the fashion and textile industry field will be vitalization. Design/methodology/approach The data used in this study used 232,355 clothes and product images. The image data input to the model is divided into 5 categories: original clothing image and wearer image, clothing segmentation, wearer's body Densepose heatmap, wearer's clothing-agnosting. We advanced the HR-VITON model in the way of Mixed-Precison, Gradient Accumulation, and sharing model weights. Findings As a result of this study, we demonstrated that the weight-shared MP-GA HR-VITON model can efficiently learn front and back fashion images. As a result, this proposed model quantitatively improves the quality of the generated image compared to the existing technique, and natural fitting is possible in both front and back images. SSIM was 0.8385 and 0.9204 in CP-VTON and the proposed model, LPIPS 0.2133 and 0.0642, FID 74.5421 and 11.8463, and KID 0.064 and 0.006. Using the deep learning model of this study, it is possible to naturally fit one color clothes, but when there are complex pictures and logos as shown in <Figure 6>, an unnatural pattern occurred in the generated image. If it is advanced based on the transformer, this problem may also be improved.

• Applied Chemistry for Engineering
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• v.31 no.1
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• pp.19-24
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• 2020

In this study, the O/W emulsions prepared from coconut oil and the non-ionic mixed surfactant as Tween-Span system were evaluated and optimized in order to upgrade the stability of manufactured emulsions. For the optimization, a central composite design model-response surface methodology, so called as CCD-RSM was implemented. Quantitative factors were the hydrophile-lipophilie balance (HLB), amount of non-ionic mixed surfactant and emulsification speed while experimental results included the mean droplet size (MDS), emulsion stability index (ESI), and thermal instability index (TII). Optimized values of the HLB, amount of non-ionic mixed surfactant and emulsification speed obtained from CCD-RSM were 9.1, 8.7 wt.%, and 6,200.8 rpm, respectively. Expected experimental results for MDS, ESI, and TII under the optimized experimental condition were 151.0 nm, 99.86, and 3.17%, respectively. The average error from actual experiments which established for validation of the conclusions was lower than 3.5%. Therefore, a highly favorable level could be obtained when the optimized CCD-RSM was applied to manufacturing the O/W emulsion in this study.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.881-888
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• 2008

We present a novel fault diagnosis methodology using acoustic sensor systems and neural independent component analysis for large-scaled power machines. Acoustic sensors are carried out to measure sounds generated from power machines whose signal is used to determine whether fault is occurred or not. Acoustic measurements are independently mixed and deteriorated from original source signals. We propose a demixing algorithm against such mixed signals by means of independent component analysis which is achieved based on information theory and higher-order statistics to derive learning mechanism.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.2
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• pp.74-81
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• 2006

In this paper, we deal with the separation of data by concurrently determined, piecewise nonlinear discriminant functions. Toward the end, we develop a new $l_1$-distance norm error metric and cast the problem as a mixed 0-1 integer and linear programming (MILP) model. Given a finite number of discriminant functions as an input, the proposed model considers the synergy as well as the individual role of the functions involved and implements a simplest nonlinear decision surface that best separates the data on hand. Hence, exploiting powerful MILP solvers, the model efficiently analyzes any given data set for its piecewise nonlinear separability. The classification of four sets of artificial data demonstrates the aforementioned strength of the proposed model. Classification results on five machine learning benchmark databases prove that the data separation via the proposed MILP model is an effective supervised learning methodology that compares quite favorably to well-established learning methodologies.

• International Journal of Reliability and Applications
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• v.14 no.1
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• pp.41-54
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• 2013

In this paper, cost analysis is conducted using inter-failure interval under renewable warranty subject to imperfect repair for multi-component system. One way to model the imperfect repair is to use the quasi-renewal process (Wang and Pham 1996). Two alternative quasi-renewal processes were suggested by Park and Pham (2010) using quasi-renewal process; first is an altered quasi-renewal process with random variable parameter and second is a mixed quasi-renewal process considering replacement service and repair service, simultaneously. In this study, we use the altered and mixed quasi-renewal processes and develop the warranty cost model to obtain the expected value of warranty cost and to help company make important decisions regarding the warranty policy. Numerical examples are used to demonstrate the applicability of the methodology derived in the paper.

• Journal of the Korea Safety Management & Science
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• v.9 no.4
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• pp.121-127
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• 2007

CSP(Concurrent Spare Parts) is supplied with the procurement of new equipment or weapon system and is used to sustain the equipment without resupply during the initial coverage period. This study is concerned with a problem of determining the near optimal inventory level of the spare parts, especially Concurrent Spare Parts. For this, we utilize the mixed periodic and continuous review polices considering the CSP and (r,Q) Policies concurrently in a two-echelon distribution system. We propose the mathematical model to minimize the total cost which is composed with ordering cost, purchasing cost, holding cost, and stickout cost. If the mixed policy is compared to other policies(CSP, (r,Q)), the proposed methodology performs well and is best policy in the equipment maintenance expenses.

• Journal of Magnetics
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• v.15 no.2
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• pp.74-77
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• 2010

This paper deals with optimum design criteria for maximum torque density & minimum torque ripple of a Flux Switching Motor (FSM) using response surface methodology (RSM) & finite element method (FEM). The focus of this paper is to find a design solution through the comparison of torque density and torque ripple which vary with rotor shape. And then, a central composite design (CCD) mixed resolution was introduced and analysis of variance (ANOVA) was conducted to determine the significance of the fitted regression model. The proposed procedure allows one to define the rotor dimensions, starting from an existing motor or a preliminary design.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1055-1056
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• 2011

This paper deals with optimum design criteria for maximum torque density & minimum current density of a single phase line-start permanent-magnet motor (LSPMM) using RSM (Response Surface Methodology) & FEM (Finite Element Method). The focus of this paper is to find a design solution through the comparison of torque density and minimum current density resulting from rotor shape variations. And then, a central composite design (CCD) mixed resolution is introduced, and analysis of variance (ANOVA) is conducted to determine the significance of the fitted regression model.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.12
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• pp.570-575
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• 2006

This paper develops methodology in order to consider CHP(Combined Heat and Power) capacity in the Basic Plan of Long Term Electricity Supply & Demand. We develop generating cost of CHP considering electric and heat. Also we develop mixed load duration curve which includes the electric load and heat load and then apply CHP capacity to SCM(Screening Curve Method) considering CHP feature. Accordingly, it decide the optimal CHP capacity in the Basic Plan of Long Term Electricity Supply & Demand. Also, We perform the sensitivity analysis according to cost variation.

• Journal of Korean Academy of Nursing
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• v.45 no.4
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• pp.501-512
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• 2015

Purpose: The purpose of this study, which was guided by the Resiliency Model of Family Stress, Adjustment, and Adaptation, was twofold: (a) to explore family and parental adaptation and factors influencing family adaptation in Korean families of children with Down syndrome (DS) through a quantitative methodology and (b) to understand the life with a Korean child with DS through a qualitative method. Methods: A mixed-methods design was adopted. A total of 147 parents of children with DS completed a package of questionnaires, and 19 parents participated in the in-depth interviews. Quantitative and qualitative data were analyzed using stepwise multiple regression and content analysis respectively. Results: According to the quantitative data, the overall family adaptation scores indicated average family functioning. Financial status was an important variable in understanding both family and parental adaptation. Family adaptation was best explained by family problem solving and coping communication, condition management ability, and family hardiness. Family strains and family hardiness were the family factors with the most influence on parental adaption. Qualitative data analysis showed that family life with a child with DS encompassed both positive and negative aspects and was expressed with 5 themes, 10 categories, and 16 sub-categories. Conclusion: Results of this study expand our limited knowledge and understanding concerning families of children with DS in Korea and can be used to develop effective interventions to improve the adaptation of family as a unit as well as parental adaptation.