• Journal of Environmental Health Sciences
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• v.9 no.2
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• pp.55-65
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• 1983

This survey was carried out to investigate the pattern of taking psychotropic drugs for 618 cases who visited 48 drugstores located as such four types of areas as business sections, gay quarters, residential sections and quasi-industrial areas from May, 1982 to March, 1983. The results are summarized as follows: I. The age distribution: The age group of 20-29 showed the highest distribution covering 35.6% as 220 out of 618 cases. The age groups of thirties and forties covered 23.0% and 19.0% respectively. The sex ratio was estimated as 1:1.86. 2. The occupational distribution: The unemployees composed the largest portion covering 53.7% as 332 out of 618. Above all the class of the housewives was 32.7%. 3. The marital status: The degree of distribution was higher on the sides of the group of married people than that of single and its percentage was 30.1. 4. The educational level: Most of the people who purchased the drugs had no knowledge of the effect of the drugs, and they covered 80.9%. 5. As for the motives, the twenties took psychotropic drugs in order to relief insomnia and that was the biggest major motive at the portion of 59.1%, 130 out of 618. 6. The age group of twenties who took the drugs for about 6 months showed the highest percentage of 52.7%. 7. The highest distribution appeared in the case that takes one or two tablets a day for less than 6 months. 8. The dosage distribution by the number of times taking the drugs The group of people that took the drugs more than 3 to 4 tablets a day as the number of 1 to 3 times covered 41.7\ulcorner0 of 187. 9. The most favorite psychotropic drugs: Lorazepam was showed to be the most favorite drugs by either male or female covered 50.9o70, 54.2\ulcornero respectively. 10. The motives of selecting drugs: The optional motives of selecting psychotropic drugs were showed 269 (43.5%) out of 618 cases that chose the drugs for themselves.

• Journal of Distribution Science
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• v.6 no.2
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• pp.41-60
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• 2008

This study examined state of local pharmaceuticals industry and investigated distribution system. The study found out associated problems as well as improvements of distribution system of local pharmaceuticals. Finally, to improve distribution system of local pharmaceuticals, the study investigated distribution system of 10 kinds of pharmaceuticals of 'J' Company being a leading local manufacturer of local pharmaceuticals, and found out improvements of the distribution system. The study collected and compiled 'J' Company's internal slips and reporting material from April 1, 2008 to July 31, 2008, and interviewed persons in charge continuously to find out state and problems of the distribution system.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.1
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• pp.1-8
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• 2021

Detecting Out-of-Distribution (OOD) data is fundamentally required when Deep Neural Network (DNN) is applied to real-world AI such as autonomous driving. However, modern DNNs are quite vulnerable to the over-confidence problem even if the test data are far away from the trained data distribution. To solve the problem, this paper proposes a novel Adversarial-Mixup training method to let the DNN model be more robust by detecting OOD data effectively. Experimental results show that the proposed Adversarial-Mixup method improves the overall performance of OOD detection by 78% comparing with the State-of-the-Art methods. Furthermore, we show that the proposed method can alleviate the over-confidence problem by reducing the confidence score of OOD data than the previous methods, resulting in more reliable and robust DNNs.

• Journal of Information Technology Services
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• v.4 no.1
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• pp.129-139
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• 2005

This paper proposes a framework of Operational Risk-based Business Continuity System(ORBCS), and develops protection system for operational risk through operational risk assessment and loss distribution approach based on risk management guideline announced in the basel II. In order to find out financial operational risk, business processes of domestic bank are assorted by seven event factors and eight business activities so that we can construct the system. After we find out KRI(Key Risk Indicator) index, tasks and risks, we calculated risk possibility and expected cost by analyzing quantitative data, questionnaire and qualitative approach for AHP model from the past events. Furthermore, we can assume unexpected cost loss by using loss distribution approach presented in the basel II. Each bank can also assume expected loss distributions of operational risk by seven event factors and eight business activities. In this research, we choose loss distribution approach so that we can calculate operational risk. In order to explain number of case happened, we choose poisson distribution, log-normal distribution for loss cost, and estimate model for Monte-Carlo simulation. Through this process which is measured by operational risk. of ABC bank, we find out that loss distribution approach explains closer unexpected cost directly compared than internal measurement approach, and makes less unexpected cost loss.

• Journal of the Korean Society of Clothing and Textiles
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• v.18 no.4
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• pp.574-590
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• 1994

The purpose of this research is to provide a plan for improvement distribution structure of Korean fashion industry. This research points out the importance of Korean fashion industry and the change of consumer consciousness which is followed by domestic fashion market and also) this study deals the present condition of fashion distribution industry in Korea, the effects of opening of the distribution structure, the characteristics of domestic's and advanced contries, the problems of them and providing the improvements. 1. Korean fashion industry is needed to change to 'the industry of living culture' 2. Promodern distribution structure in Korean fashion industry has to be turning out to the distribution industry. 3. The returning goods system and buying system of department stores in Korea have to be executed gradually and also the corporation with department stores and fashion organizations are needed. 4. The information system which can make connection from up stream to down stream is needed . 5. It is needed that renovation of technology and development of manpower.

• Communications for Statistical Applications and Methods
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• v.21 no.3
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• pp.225-234
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• 2014

We propose a novel hierarchical clustering for distribution valued dissimilarities. Analysis of large and complex data has attracted significant interest. Symbolic Data Analysis (SDA) was proposed by Diday in 1980's, which provides a new framework for statistical analysis. In SDA, we analyze an object with internal variation, including an interval, a histogram and a distribution, called a symbolic object. In the study, we focus on a cluster analysis for distribution valued dissimilarities, one of the symbolic objects. A hierarchical clustering has two steps in general: find out step and update step. In the find out step, we find the nearest pair of clusters. We extend it for distribution valued dissimilarities, introducing a measure on their order relations. In the update step, dissimilarities between clusters are redefined by mixture of distributions with a mixing ratio. We show an actual example of the proposed method and a simulation study.

• The Journal of Industrial Distribution & Business
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• v.10 no.8
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• pp.17-24
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• 2019

Purpose - The purpose of this study is to find out research directions for distribution and fusion and complex field to many domestic and foreign researchers carrying out related academic research by confirming research trends in the Journal of Distribution Science (JDS). Research Design, Data, and Methodology - To do this, I used keywords from a total of 904 papers published in the JDS excluding 19 papers that were not presented with keywords among 923. The analysis utilized word clouding, topic modeling, and weighted frequency analysis using the R program. Results - As a result of word clouding analysis, customer satisfaction was the most utilized keyword. Topic modeling results were divided into ten topics such as distribution channels, communication, supply chain, brand, business, customer, comparative study, performance, KODISA journal, and trade. It is confirmed that only the service quality part is increased in the weighted frequency analysis result of applying to the year group. Conclusion - The results of this study confirm that the JDS has developed into various convergence and integration researches from the past studies limited to the field of distribution. However, JDS's identity is based on distribution. Therefore, it is also necessary to establish identity continuously through special editions of fields related to distribution.

• Journal of Korean Society for Quality Management
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• v.27 no.4
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• pp.143-152
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• 1999

In Shewhart control chart, the average run length(ARL) is calculated using the mean of a conventional geometric distribution(CGD) assuming a sequence of identical and independent Bernoulli trials. In this, the success probability of CGB is the probability that any point exceeds the control limits. When the process is in-control state, there is no problem in the above assumption since the probability that any point exceeds the control limits does not change if the in-control state continues. However, if the out-of-control state begins and continues during the process, the probability of exceeding the control limits may take two forms. First, once the out-of-control state begins with exceeding probability p, it continues with the same exceeding probability p. Second, after the out-of-control state begins, the exceeding probabilities may very according to some pattern. In the first case, ARL is the mean of CGD with success probability p as usual. But in the second case, the assumption of a sequence of identical and independent Bernoulli trials is invalid and we can not use the mean of CGD as ARL. This paper concentrate on that point. By adopting one generalized binomial distribution(GBD) model that allows correlated Bernoulli trials, generalized geometric distribution(GGD) is defined and its mean is derived to find an alternative ARL when the process is in out-of-control state and the exceeding probabilities take the second form mentioned in the above. Small-scale simulation is performed to show how an alternative ARL works.

• Journal of Distribution Science
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• v.18 no.3
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• pp.25-36
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• 2020

Purpose: Based on previous researches on social factors of digital item purchase in digital contents distribution platforms such as SNS, we aim to develop the integrated model that accounts for the dynamic and interactive relationship between social structure indicators and digital item purchase. Research design, data and methodology: A PVAR model was used to capture endogenous and dynamic relationships between digital item purchase and network indicators. Results: We find that there exist considerable endogenous and dynamic relationships between digital item purchase and network structure variables. Not only lagged in-degree and out-degree but also in-closeness and out-closeness centrality have significant and positive impacts on digital item purchase. Lagged clustering has a significant and negative effect on digital item purchase. Lagged purchase has a significant and positive impact just on the present in-closeness and out-closeness centrality; but there is no significant effect of lagged purchase on the other two degree variables and clustering coefficient. We also find that both closeness centralities have much higher carryover effect on digital item purchase and that the elasticity of both closeness centralities on the purchase of digital items is even higher than that of other network structure variables. Conclusions: In-closeness and out-closeness are the most influential factors among social structure variables of this study on digital item purchase.

• Smart Media Journal
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• v.9 no.3
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• pp.80-89
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• 2020

Out - of - distribution (OOD) samples are frequently encountered when deploying a classification model in plenty of real-world machine learning-based applications. Those samples are normally sampling far away from the training distribution, but many classifiers still assign them high reliability to belong to one of the training categories. In this study, we address the problem of removing OOD examples by estimating marginal density estimation using variational autoencoder (VAE). We also investigate other proper methods, such as temperature scaling, Gaussian discrimination analysis, and label smoothing. We use Chonnam National University (CNU) weeds dataset as the in - distribution dataset and CIFAR-10, CalTeach as the OOD datasets. Quantitative results show that the proposed framework can reject the OOD test samples with a suitable threshold.