• The Journal of Information Systems
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• v.31 no.1
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• pp.25-58
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• 2022

Purpose The purpose of this study is to analyzes the RPA fitting processes by the casese of manufacturing SMEs(Small and Medium-sized Enterprises) in an exploraty approach. Based on the findings on the RPA fitting processes, we intend to provide a cornerstone for developing a general-purpose RPA introduction model in the future. Design/methodology/approach In this study, empirical cases of RPA fitting processes were analyzed based on interviews with project managers of specialized IT suppliers in charge of RPA development and managers of IT departments of manufacturing SMEs that actually introduced RPA. In order to explore various RPA fitting process in the manufacturing value chain, a total of 7 manufacturing SMEs were interviewed, ranging from companies using a legacy system to companies without a legacy system. Over the primary and secondary activity processes, the details of RPA processes were analyzed in the steps of 'Frequency Identification, Input Processing, Source Identification, Inquiry and Processing, Information Registration, Result Reporting'. Findings From the analysis, we derived some exploratory results that the processes over 0.25 FTE and related with many suppliers and clients are fitting for RPA introduction in manufacturing SMEs Our results will provide basic data for the development of the future general-purpose RPA introduction model for manufacturing SMEs, providing practical reference for RPA introduction.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.409-414
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• 2007

Active Appearance Model is a well-known model that can represent a non-rigid object effectively. However, since it uses the fixed appearance model, the fitting results are often unsatisfactory when the imaging condition of the target image is different from that of training images. To alleviate this problem, incremental AAM was proposed which updates its appearance bases in an on-line manner. However, it cannot deal with the sudden changes of illumination. To overcome this, we propose a novel scheme to update the appearance bases. When a new person appears in the input image, we synthesize illuminated images of that person and update the appearance bases of AAM using it. Since we update the appearance bases using synthesized illuminated images in advance, the AAM can fit their model to a target image well when the illumination changes drastically. The experimental results show that our proposed algorithm improves the fitting performance over both the incremental AAM and the original AAM.

• Communications for Statistical Applications and Methods
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• v.24 no.4
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• pp.383-396
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• 2017

The model in our approach assumes that computer responses are a realization of a Gaussian processes superimposed on a regression model called a Gaussian process regression model (GPRM). Selecting a subset of variables or building a good reduced model in classical regression is an important process to identify variables influential to responses and for further analysis such as prediction or classification. One reason to select some variables in the prediction aspect is to prevent the over-fitting or under-fitting to data. The same reasoning and approach can be applicable to GPRM. However, only a few works on the variable selection in GPRM were done. In this paper, we propose a new algorithm to build a good prediction model among some GPRMs. It is a post-work of the algorithm that includes the Welch method suggested by previous researchers. The proposed algorithms select some non-zero regression coefficients (${\beta}^{\prime}s$) using forward and backward methods along with the Lasso guided approach. During this process, the fixed were covariance parameters (${\theta}^{\prime}s$) that were pre-selected by the Welch algorithm. We illustrated the superiority of our proposed models over the Welch method and non-selection models using four test functions and one real data example. Future extensions are also discussed.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.3 s.162
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• pp.463-474
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• 2007

With rapid expansion in e-retailing of apparel business, personalized fitting model service shows the possibility as the differentiated marketing strategy in cyber shopping. According as necessity of personalized fitting model construction rises, it is tried personalized fitting model creation in several fields such as computer engineering, mechanical engineering, information engineering. But, because existent study was concentrated only on human body modeling, it does not reflect average morphological characteristics of human body properly. In this study, we wish to examine if morphing is fit for expressing characteristic of average human body shape and suggest desirable morphing. We used 3-D scan data of 254 Korean middle aged men collected by Size Korea 2004. The result of this study are as follows: Lower body types were categorized by height hip girth and lower drop(hip girth-navel girth) which were main factors of lower body shape. Then each factor was divided into 3 groups respectively, 30% in the middle, over 30%, under 30%. In 27 groups, the group which belonged to 30% in the middle of height, 30% in the middle of hip girth, 30% in the middle of lower drop was selected as a representative group. We tested geometrical figure by differ volume, tilt, position of point. And we created a representative type of men's lower bodies by morphing the representative group and analyzed it's horizontal, vertical sections. A representative type which was created by morphing reflected a real body and changed realistically at the part of hip, crotch, calf muscle and so on. A cross sections of a representative type were similar to average cross sections of the representative group in size and shape. So it was proved that morphing was successful.

• Analyses & Alternatives
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• v.5 no.1
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• pp.3-24
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• 2021

This paper introduces a multilevel item-response-theory (IRT) model as a unifying model for hypothesis testing using legislative voting data. This paper shows that a probit or logit model is a special type of multilevel IRT model. In particular, it is demonstrated that, when a probit or logit model is applied to multiple votes, it makes unrealistic assumptions and produces incorrect coefficient estimates. The advantages of a multilevel IRT model over a probit or logit model are illustrated with a Monte Carlo experiment and an example from the U.S. House. Finally, this paper provides a practical guide to fitting this model to legislative voting data.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.47-47
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• 2023

Drought occurs due to lack of water resources over an extended period and its intensity has been magnified globally by climate change. In recent years, drought over South Korea has also been intensed, and the prediction was inevitable for the water resource management and water industry. Therefore, drought forecasting over South Korea was performed in the current study with the following procedure. First, accumulated spring precipitation(ASP) driven by the 93 weather stations in South Korea was taken with their median. Then, correlation analysis was followed between ASP and Df4m, the differences of two pair of the global winter MSLP. The 37 Df4m variables with high correlations over 0.55 was chosen and sorted into three regions. The selected Df4m variables in the same region showed high similarity, leading the multicollinearity problem. To avoid this problem, a model that performs variable selection and model fitting at once, least absolute shrinkage and selection operator(LASSO) was applied. The LASSO model selected 5 variables which showed a good agreement of the predicted with the observed value, R²=0.72. Other models such as multiple linear regression model and ElasticNet were also performed, but did not present a performance as good as LASSO. Therefore, LASSO model can be an appropriate model to forecast spring drought over South Korea and can be used to mange water resources efficiently.

• Journal of Korean Society for Quality Management
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• v.24 no.1
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• pp.137-145
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• 1996

We propose a model which is useful for the analysis of the spatial time series data. The proposed model utilized the linear dependences across the spatial units as well as over time. Three stage model fitting procedures are suggested and the real data is analyzed.

• Journal of the Korean Statistical Society
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• v.5 no.1
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• pp.19-28
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• 1976

In empirical study for fitting a multiple linear regression model for successive cross-sections data observed on the same set of independent variables over several time periods, one often faces the problem of poor $R^2$, the multiple coefficient of determination, which provides a standard measure of how good a specified regression line fits the sample data.

• Journal of the Korea Computer Graphics Society
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• v.28 no.4
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• pp.1-11
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• 2022

The Skinned Multi-Person Linear Model (SMPL) is the most widely used parametric 3D Human Model optimized and learned from CAESAR, a 3D human scanned database created with measurements from 3,800 people living in United States in the 1990s. We point out the lack of racial diversity of body types in SMPL and propose K-SMPL that better represents Korean 3D body shapes. To this end, we develop a fitting algorithm to estimate 2,773 Korean 3D body shapes from Korean body measurement data. By conducting principle component analysis to the estimated Korean body shapes, we construct K-SMPL model that can generate various Korean body shape in 3D. K-SMPL model allows to improve the fitting accuracy over SMPL with respect to the Korean body measurement data. K-SMPL model can be widely used for avatar generation and human shape fitting for Korean.

• Journal of the Korean Society of Clothing and Textiles
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• v.41 no.6
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• pp.1109-1123
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• 2017

This study analyzed a somatotype of teenager's that was suitable to improve the reality of a virtual model size. We analyzed 843 teenagers 12-18 years old from the 6th Size Korea data. First, factor analysis was done for abstracting new criteria and dividing the somatotype; subsequently, we selected the waist height proportion to stature (body proportion) and drop (torso shape). Next, the cluster analysis was done with these criteria; subsequently, 5 body proportion types and 7 torso shapes were distinguished. A virtual model size for 4 somatotype with more than 50 persons was also designed by a regression analysis that constituted sizes for each factor. The designed model size was compared with body size as well as with Clo's virtual model size. The research model showed a high similarity in sizes with body as well as improved reality over the Clo model that presented size problems such as low waist height, bigger bust, and smaller thigh circumference than the real body.