• Journal of the Korean Society of Safety
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• v.30 no.4
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• pp.113-119
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• 2015

This study is to examine the workers' awareness of the safety in logistic centers. For that purpose, the exploratory factor analysis of workers' safety awareness in logistic centers was performed at first, and the 6 variables extracted from the factor analysis were then used to investigate the difference in intergroup awareness of the safety environment in the logistic centers. We administered a survey to 147 workers attending the logistic centers and collected data from them. The results of the study showed that the intergroup awareness of the safety environment turned out to be statistically different from each other in terms of working environment, safe behavior, work risk, safety knowledge and effort, risk justification and compromising attitudes. Experiences in industrial accidents influenced awareness of working environment, work risk and risk justification. The group who experienced accidents is more likely to feel risky and unsatisfied with working place, and their awareness toward risk justification was high as well. It was also observed that there exists awareness difference between manager group and worker group. The group who manages the working place showed more positive awareness of working environment, safe behavior, work risk, safety knowledge and effort, risk justification and compromising attitudes than the worker group. On the contrary, the worker group showed high recognition in risk of working place, and felt that they are willing to compromise on safety for increasing production. The scale of the logistic center produced negative influence on awareness of safety. The group in small logistic center showed the highest awareness in safety, whereas the group in large logistic center with more than 100 workers showed the highest awareness in risk. They are more likely to deviate from correct and safe work procedures due to over-familiarity with the job, as well. The findings suggest that there is a need for the safety management and education to change the workers' understanding and attitudes towards safety.

• Journal of Distribution Research
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• v.3 no.1
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• pp.145-158
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• 1998

The selection of proposed sites for the general logistic center of agriculture products would be made the most suitable place by considering the spread of population as real consumers, the prospect of the demand, the expansion of traffic system, the regional, hourly and carring traffic volume and the use of land based urban planning, etc. As the preconsideration, the possible occupant companies have to be selected on the category of business and the district. After posing questions and having interview, several selected regions would be compared and analysed for deciding the most suitable place. The model for the general logistic center of agricultural products must be selected taking key factors approach for choosing key factors at first and referring to many documentary records. And the more, cooperating with the specialists for location selection and making objective questions to concerned companies, the most suitable place is selected by marking high score for the moderate land cost, the low traffic jam, the connection with the back cities and the possible expansion as the general logistic center of agriculture products.

• Proceedings of KOSOMES biannual meeting
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• 2017.04a
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• pp.262-262
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• 2017

• Design Convergence Study
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• v.16 no.3
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• pp.17-37
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• 2017

The purpose of this study is to evaluate the value of the center of age-friendly goods to confirm the public value of the center. 152 elderly participants, either who have utilized the center themselves or who have experienced the center indirectly through the image have participated in the study. The experience of age-friendly product center was evaluated by the contingent valuation method, which is widely used in the public services. A double-bounded dichotomous choice (DB-DC) log-logistic model and a log-normal model were conducted. As the result of the estimation, the truncated mean of the log-logistic model was 3,401 Korean won, with the mean of 4,937 won. In addition, the truncated mean of the log-normal model was 3,433 won, with the mean of 4,144 won. Elderly who have not experienced the center placed a higher value on the center, compared to the experienced elderly. The results imply the necessity to enlarge the opportunity for future suppliers to experience the center for age-friendly products, in order to improve their objective understandings. In addition, the necessity of expanding exhibition products and applying age-friendly designs is proposed, to offer various attractive experiences to elderly.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5883-5888
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• 2014

Background: Breast cancer is the most common cancers in female populations. The exact cause is not known, but is most likely to be a combination of genetic and environmental factors. Log-logistic model (LLM) is applied as a statistical method for predicting survival and it influencing factors. In recent decades, artificial neural network (ANN) models have been increasingly applied to predict survival data. The present research was conducted to compare log-logistic regression and artificial neural network models in prediction of breast cancer (BC) survival. Materials and Methods: A historical cohort study was established with 104 patients suffering from BC from 1997 to 2005. To compare the ANN and LLM in our setting, we used the estimated areas under the receiver-operating characteristic (ROC) curve (AUC) and integrated AUC (iAUC). The data were analyzed using R statistical software. Results: The AUC for the first, second and third years after diagnosis are 0.918, 0.780 and 0.800 in ANN, and 0.834, 0.733 and 0.616 in LLM, respectively. The mean AUC for ANN was statistically higher than that of the LLM (0.845 vs. 0.744). Hence, this study showed a significant difference between the performance in terms of prediction by ANN and LLM. Conclusions: This study demonstrated that the ability of prediction with ANN was higher than with the LLM model. Thus, the use of ANN method for prediction of survival in field of breast cancer is suggested.

• Journal of Korean Institute of Industrial Engineers
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• v.7 no.2
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• pp.19-26
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• 1981

A special case of the probabilistic multi-location problem is studied in a two-echelon logistic system for deteriorating items. The objective is to determine the location of the minimum number of supply centers among a discrete set of location sites of supply centers, such that the probability each retailer being covered by some supply center is not less than a specified value. A logistic cost is introduced as performance measure of the system, and leads us to analyze the impact of deterioration rate on the location problem. The results obtained from numerical examples are discussed, which provides effective guidelines that can be used for the logistic managerial decisions.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.310-313
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• 2004

The aim of this study is to evaluate the hazard of landslides at Penang, Malaysia, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from TM satellite images; and the vegetation index value from SPOT satellite images. Landslide hazardous area were analysed and mapped using the landslide-occurrence factors by logistic regression model. The results of the analysis were verified using the landslide location data and compared with probabilistic model. The validation results showed that the logistic regression model is better prediction accuracy than probabilistic model.

• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.445-453
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• 2011

Almost all small area estimations are obtained by minimizing the mean squared error. Recently relative error prediction methods have been developed and adapted to small area estimation. Usually the estimators obtained by using relative error prediction is called a shrinkage estimator. Especially when data set consists of large range values, the shrinkage estimator is known as having good statistical properties and an easy interpretation. In this paper we study the shrinkage estimators based on logistic regression type estimators for small area estimation. Some simulation studies are performed and the Economically Active Population Survey data of 2005 is used for comparison.

• Journal of the Korean Mathematical Society
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• v.50 no.6
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• pp.1165-1181
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• 2013

In this paper, we consider a class of periodic It$\hat{o}$ stochastic delay differential equations by using the properties of periodic Markov processes, and some sufficient conditions for the existence of periodic solution of the delay equations are given. These existence theorems improve the results obtained by It$\hat{o}$ et al. [6], Bainov et al. [1] and Xu et al. [15]. As applications, we study the existence of periodic solution of periodic stochastic logistic equation and periodic stochastic neural networks with infinite delays, respectively. The theorem for the existence of periodic solution of periodic stochastic logistic equation improve the result obtained by Jiang et al. [7].

• Journal of Korean Academy of Nursing
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• v.43 no.2
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• pp.154-164
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• 2013

Purpose: The purpose of this article is twofold: 1) introducing logistic regression (LR), a multivariable method for modeling the relationship between multiple independent variables and a categorical dependent variable, and 2) examining use and reporting of LR in the nursing literature. Methods: Text books on LR and research articles employing LR as main statistical analysis were reviewed. Twenty-three articles published between 2010 and 2011 in the Journal of Korean Academy of Nursing were analyzed for proper use and reporting of LR models. Results: Logistic regression from basic concepts such as odds, odds ratio, logit transformation and logistic curve, assumption, fitting, reporting and interpreting to cautions were presented. Substantial shortcomings were found in both use of LR and reporting of results. For many studies, sample size was not sufficiently large to call into question the accuracy of the regression model. Additionally, only one study reported validation analysis. Conclusion: Nursing researchers need to pay greater attention to guidelines concerning the use and reporting of LR models.