• Korea Journal of Hospital Management
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• v.6 no.2
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• pp.22-36
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• 2001

The hospital bankruptcy rate is increasing, therefore it is very important to predict the bankruptcy using the existing hospital management information. The hospital bankruptcy is often measured in year intervals, called grouped duration data, not by the continuous time elapsed to the bankruptcy. This study introduces a hierarchical generalized linear model(HGLM) for analysis of hospital bankruptcy data. The hazard function for each hospital may be influenced by unobservable latent variables, and these unknown variables are usually termed as random effects or frailties which explain correlations among repeated measures of the same hospital and describe individual heterogeneities of hospitals. Practically, the data of twenty bankrupt and sixty profitable hospitals were collected for five years, and were fitted to HGLM. The results were compared with those of the logit model. While the logit model resulted only in the effects of explanatory variables on the bankruptcy status at specific period, the HGLM showed variables with significant effects over all observed years. It is concluded that the HGLM with a fixed ratio and a period of total asset turnrounds was justified, and could find significant within and between hospital variations.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.26 no.11
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• pp.529-534
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• 2014

This research developed a hybrid heat pump system with the functions of dehumidification and heating, which uses simulated air that is like underground air, from an environmental chamber as a heat source. The system consisted of three evaporators and three condensers that were installed in series in the air passage, between the underground and load space. As results, the total amount of dehumidification was 2.726 kg/h, and the heating $COP_h$ was 1.84 at air intake temperature $17^{\circ}C$ and relative humidity 70%, which is a similar condition to underground air. We found that the total amount of dehumidification also increased with the air temperature and humidity. The system $COP_s$ was reached at 2.5, if we include the latent heat of dehumidification in the conventional heat pump system's COP.

• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.143-154
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• 2017

A variable selection method based on probabilistic principal component analysis (PCA) using penalized likelihood method is proposed. The proposed method is a two-step variable reduction method. The first step is based on the probabilistic principal component idea to identify principle components. The penalty function is used to identify important variables in each component. We then build a model on the original data space instead of building on the rotated data space through latent variables (principal components) because the proposed method achieves the goal of dimension reduction through identifying important observed variables. Consequently, the proposed method is of more practical use. The proposed estimators perform as the oracle procedure and are root-n consistent with a proper choice of regularization parameters. The proposed method can be successfully applied to high-dimensional PCA problems with a relatively large portion of irrelevant variables included in the data set. It is straightforward to extend our likelihood method in handling problems with missing observations using EM algorithms. Further, it could be effectively applied in cases where some data vectors exhibit one or more missing values at random.

• Korean Institute of Interior Design Journal
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• v.17 no.4
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• pp.3-10
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• 2008

In the Western and Oriental architecture, corridor and stairs have not been important from the whole architectural composition. The purpose of this study is to analyze their elements from theoretical viewpoint of modern architecture. The subject of analysis are Yangjindang and Daesanru of the Mid-Chosun Dynasty in Sangju. The conclusions are the followings : First, the moving line in the Western and Oriental architecture has the linear axis, and in general moving axis is straighten in Hanok architecture. But unlike common traditional architecture, the two buildings are right-angled in the moving axies. Second, Toenmaru in Yangjindang is the element of visual experience in the whole architectural space as promenade architecture called by Le Corbusier. On the other hand, Toenmaru in Daesanru plays a role the space of thinking in extending a visual field to the nature than its pure function. Third, the stairs of Yangjindang is diagonal shape with a role of entrance, but that of Daesanru was concealed in the wall as interior step. Yangjindang has two different stairs. One is broad and shallow stairs with ceremonial or public expression, and the other is narrow and steep stairs with unstable or private expression. This paper intends to show the latent architectural possibility of our traditional architecture.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.377-387
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• 1998

A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For each observed failure epoch, we introduced latent variables that indicates with component of the Superposition model. This data augmentation approach facilitates specification of the transitional measure in the Markov Chain. Metropolis algorithms along with Gibbs steps are proposed to preform the Bayesian inference of such models. for model determination, we explored the Pre-quential conditional predictive Ordinate(PCPO) criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions, we consider in this paper Superposition of Musa-Okumoto and Erlang(2) models. A numerical example with simulated dataset is given.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2299-2318
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• 2019

Location-aware networks are of great importance for both civil lives and military applications. Methods based on line-of-sight (LOS) measurements suffer sever performance loss in harsh environments such as indoor scenarios, where sensors can receive both LOS and non-line-of-sight (NLOS) measurements. In this paper, we propose a data association (DA) process based on the expectation maximization (EM) algorithm, which enables us to exploit multipath components (MPCs). By setting the mapping relationship between the measurements and scatters as a latent variable, coefficients of the Gaussian mixture model are estimated. Moreover, considering the misalignment of sensor position, we propose a space-alternating generalized expectation maximization (SAGE)-based algorithms to jointly update the target localization and sensor position information. A two dimensional (2-D) circularly symmetric Gaussian distribution is employed to approximate the probability density function of the sensor's position uncertainty via the minimization of the Kullback-Leibler divergence (KLD), which enables us to calculate the expectation step with low computational complexity. Moreover, a distributed implementation is derived based on the average consensus method to improve the scalability of the proposed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms can perform close to the Monte Carlo-based method with much lower communication overhead and computational complexity.

• Journal of the Ergonomics Society of Korea
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• v.34 no.6
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• pp.563-581
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• 2015

Objective: This study was conducted to investigate the potential of sematic network analysis for design research. Background: As HCD (Human-Centered Design) was emphasized, lots of design research methodologies were developed and used in order to find user needs. However, it is still difficult to discover users' latent needs. This study suggests the semantic network analysis as a complementary means for design research, and proved its potential through the practical application, which compares multi-screen purchase and usage behaviors between America and China. Method: We conducted an in-depth interview with 32 consumers from USA and China, and analyzed interview texts through semantic network analysis. Cross cultural differences in purchase and usage behaviors were investigated, based on measuring centrality and community modularity of devices, functions, key buying factors and brands. Results: Americans use more services and functions in the multi-screen environment, compared to Chinese. As a device substitutes other devices, traditional boundaries of the devices are disappearing in the USA. Americans consider function to recall Apple, but Chinese consider function, design and brand to recall Apple, Sony and Samsung as an important brand at the time of their purchase. Conclusion: This study shows the potential of semantic network analysis for design research through the practical application. Semantic network analysis presents how the concepts regarding a theme are structured in the cognitive map of users with visual images and quantitative data. Therefore, it can complement the qualitative analysis of the existing design research. Application: As the design environment becomes more and more complicated like multi-screen environment, semantic network analysis, which is able to provide design insights in the intuitive and holistic perspective, will be acknowledged as an effective tool for further design research.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.77-84
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• 2014

The purpose of this study is to explore factors affecting the effect of e-learning, and to find out the casual relationship among these factors. Subjects are 2,091 students who have participated in e-learning based class during the period of second semester in 2013. Those of them, 1,732 students response to the survey questions. After gathering data, they are analyzed by using Confirmative Factor Analysis and Structural Equation Modeling. From the result of Confirmative Factor analysis, data have reduced four factors, and are named as four latent variables likes e-learning effect, contents satisfaction, managing assistant factor, and system functional factor. From the result of Structural Equation Modeling, it is known as the relation and impact among factors: (a) "managing assistant factor" affects to "contents satisfaction" directly. (b) "contents satisfaction" affects to "e-learning effect" directly. (c) "system function factor" affects directly to "contents satisfaction", but does not affect directly to "e-learning effect". (d) both "managing assistant factor" and "system function factor" have an indirect effect on "e-learning effect" via "contents satisfaction".

• Journal of the Korean Society of Child Welfare
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• no.59
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• pp.25-58
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• 2017

This study aimed to identify self-worth and self-deprecation trajectories and their associated factors among Korean adolescents. For these purposes, we used latent growth curve modeling involving 2,350 students who participated in the Korea Children and Youth Panel Survey in 2010, 2012, 2014, and 2015. Major findings are as follows: 1) Adolescents' self-worth and self-deprecation increased with time, but the speed gradually changed to a quadratic function model; and 2) the types of predictors affecting self-worth and self-deprecation were different. Specifically, the factors that affected only self-worth were adolescents' relationship with teachers and household income, and the factors that affected only self-deprecation were presence of disease and parental over interference. Factors affecting both self-worth and self-deprecation were child's sex, parental affection, peer trust, and peer alienation. These results suggest that independent intervention is needed for self-worth and self-deprecation. Furthermore, the results can be an important basis for establishing a more focused intervention strategy when intervening in self-worth and self-deprecation in adolescents.

• Smart Media Journal
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• v.12 no.8
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• pp.9-17
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• 2023

The development of information and communication technology and the improvement of the development and dissemination of smart devices have caused changes in the form of tourism, and the concept of smart tourism has since emerged. In this regard, researches related to smart tourism has been conducted in various fields such as policy implementation and surveys, but there is a lack of research on application reviews. This study collects Trip Advisor application review data in the Google Play Store to identify usage of the application and user satisfaction through Latent Dirichlet Allocation (LDA) topic modeling. The analysis results in four topics, two of which are positive and the other two are negative. We found that users were satisfied with the application's recommendation system, but were dissatisfied when the filters they set during search were not applied or that reviews were not published after updates of the application. We suggest more categories can be added to the application to provide users with different experiences. In addition, it is expected that user satisfaction can be improved by identifying problems within the application, including the filter function, and checking the application environment and resolving the error occurring during the application usage.