• Journal of Distribution Research
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• v.15 no.5
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• pp.103-126
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• 2010

The development of an industry including distribution sector is influenced by not only government policy but the related firms' behaviors. Recently the large-scale retailers have had more enormous channel power than any other distributors including monopolistic makers. Now is the time for government to prepare some policies against the unfair transaction behaviors by large-scale retailers. In this paper I tried to inquire into the distribution competition policy from a political correspondent point of view related with the transition of distribution system. For the purpose of this article I compared the case of Korea with Japan. According to the results so far inquired, there are some commons and differences in the cases of the two. Some suggestions are as follows. Considering the predominant position the concept of large-scale retailers is to be extended from a single store to numerous chain stores in the political level. Government needs to examine the standard propriety for large-scale retailer; the size of selling area and amount of sales a year. When a large-scale retailer store is to be established, it need to be taken a permit or a pre-inspection. The Fair Trade Commission have to secure the neutrality from Government's strategies. And government should find out the examples of unfair transaction behavior types and prepare some proper guidelines continually. For the last time statistical data by distributors are to be fitted out and the actual investigations for estimating the effects of government policies need to be enforced.

• Research in Community and Public Health Nursing
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• v.17 no.2
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• pp.195-209
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• 2006

Purpose: This study attempted to access the health promotion activities and employee's health promotion needs in workplaces. Methods: Subjects were 280 health care managers employed at small to large scale enterprises in national-wide areas of Korea. The instrument was a structured questionnaire included characteristics of workplace and respondents, health promotion activities, health promotion needs, and bottlenecks to operate programs. Data was analyzed using SAS 8.1 by applying $x^2-test$, t-test and ANOVA. Results: 1, 25.4% of the total workplaces employed health care managers. 2. Musculoskeletal management programs(49.6%) were the highest operating program. 3. The highest needs of health promotion programs were lifestyle management and disease prevention. 4. Health promotion activities were significantly different according to the type and size of workplaces. The programs were more frequently applied in manufacturing industries than non-manufacturing and in large-scale enterprises than small and middle-scale enterprises. 5. The needs of health promotion programs were high in non-manufacturing industries than manufacturing industries in all programs. 6. The major bottlenecks to operate programs were the difficulty in securing time, lack of budgets and lack of legal regulations. Conclusions: Health promotion activities were linked to their work environments including budgets, time, and law. Therefore, to operate effective health promotion programs in workplaces, various health promotion programs are required to be developed and systems for governmental support and management should be established.

• Journal of Korean Tunnelling and Underground Space Association
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• v.12 no.4
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• pp.321-328
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• 2010

The study performed an actual fire experiment in order to propose the heat release rate of automobile that is the most basic architectural element for the fire safety design in a tunnel, whose importance has been recognized as the underground traffic tunnels are planned in Korean metropolitan cities. The heat release rate of a van is measured by the large scale calorimeter, in which the law of oxygen consumption is applied, and the fire expansion characteristics in a tunnel by placing two passenger cars nearby one another in the tunnel. As the results, the heat release rate of the van was revealed to be 5.9 MW, and carbon monoxide was emitted 482 ppm at a maximum. In case of two passenger car experiment for the fire expansion characteristics, the adjacent car was ignited about 3 minutes 30 seconds after the fire occurrence, and the complete fire was developed after 15 minutes. The maximum heat release was 9 MW. The results from the actual fire experiment can be an important input data for future quantitative analysis as well as an element applicable to a tunnel disaster preventive equipment design.

• Journal of Multimedia Information System
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• v.5 no.3
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• pp.195-200
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• 2018

Recently, as the sensor and big data analysis technology have been developed, there have been a lot of researches that analyze the purchase-related data such as the trajectory information and the stay time. Such purchase-related data is usefully used for the purchase pattern prediction and the purchase time prediction. Because it is difficult to find periodic patterns in large-scale human data, it is necessary to look at actual data sets, find various feature patterns, and then apply a machine learning algorithm appropriate to the pattern and purpose. Although existing papers have been used to analyze data using various machine learning methods, there is a lack of statistical analysis such as finding feature patterns before applying the machine learning algorithm. Therefore, we analyze the purchasing data of Songjeong Maeil Market, which is a data gathering place, and finds some characteristic patterns through statistical data analysis. Based on the results of 1, we derive meaningful conclusions by applying the machine learning algorithm and present future research directions. Through the data analysis, it was confirmed that the number of visits was different according to the regional characteristics around Songjeong Maeil Market, and the distribution of time spent by consumers could be grasped.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5271-5289
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• 2016

On large-scale data analysis platforms deployed on cloud infrastructures over the Internet, the instability of the data transfer time and the dynamics of the processing rate require a more sophisticated data distribution scheme which maximizes parallel efficiency by achieving the balanced load among participated computing elements and by eliminating the idle time of each computing element. In particular, under the constraints that have the real-time and limited data buffer (in-memory storage) are given, it needs more controllable mechanism to prevent both the overflow and the underflow of the finite buffer. In this paper, we propose an auto regulated data provisioning model based on receiver-driven data pull model. On this model, we provide a synchronized data replenishment mechanism that implicitly avoids the data buffer overflow as well as explicitly regulates the data buffer underflow by adequately adjusting the buffer resilience. To estimate the optimal size of buffer resilience, we exploits an adaptive buffer resilience control scheme that minimizes both data buffer space and idle time of the processing elements based on directly measured sample path analysis. The simulation results show that the proposed scheme provides allowable approximation compared to the numerical results. Also, it is suitably efficient to apply for such a dynamic environment that cannot postulate the stochastic characteristic for the data transfer time, the data processing rate, or even an environment where the fluctuation of the both is presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3714-3732
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• 2019

With the rapid development of network, Intrusion Detection System(IDS) plays a more and more important role in network applications. Many data mining algorithms are used to build IDS. However, due to the advent of big data era, massive data are generated. When dealing with large-scale data sets, most data mining algorithms suffer from a high computational burden which makes IDS much less efficient. To build an efficient IDS over big data, we propose a classification algorithm based on data clustering and data reduction. In the training stage, the training data are divided into clusters with similar size by Mini Batch K-Means algorithm, meanwhile, the center of each cluster is used as its index. Then, we select representative instances for each cluster to perform the task of data reduction and use the clusters that consist of representative instances to build a K-Nearest Neighbor(KNN) detection model. In the detection stage, we sort clusters according to the distances between the test sample and cluster indexes, and obtain k nearest clusters where we find k nearest neighbors. Experimental results show that searching neighbors by cluster indexes reduces the computational complexity significantly, and classification with reduced data of representative instances not only improves the efficiency, but also maintains high accuracy.

• Advances in Traditional Medicine
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• v.5 no.4
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• pp.336-346
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• 2005

Experts of Sasang Constitution Medicine of Traditional Korean Medicine have classified stroke patients with four types of Sasang constitutions in their clinical practice and some types of Sasang constitutions have been regarded as risk factors of stroke, but this is uncertain because there were no evidences by large scale of prospective studies. The purpose of this was to study the association between strokes and Sasang constitutions. Case-control study has been conducted to the patients admitted to the research hospitals. The patients were confirmed stroke by brain MRI or CT scans and recruited from May 2003 to August 2005. The subjects who met the requirement of inclusion and exclusion criteria were 108 patients as the cases and 107 as healthy controls. Data collection has been performed by the trained specialists majoring neurologists through interviews, physical examinations, and laboratory testes. No statistical significance was obtained between the strokes and Sasang constitutions, yet Taeumin, and Soyangin types showed a trend of increase in the incidence of strokes as compared with Soeumin. To acquire more concrete data on this theme, we need further and large scale of prospective researches.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.5
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• pp.433-441
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• 2009

Generally, the events in nature have multi-disciplinary characteristics. To solve this problems, these days loosely coupled methods are widely applied because of advantage of solvers which are already developed and well proved. Those solvers use different mesh system, so transformation and mapping of data are vital in the field of fluid-structure interaction(FSI). In this paper, the interpolation of deformation which is used globally and compactly supported radial basis functions(RBF), and mapping of force which use principle of virtual work are examined for computing time and accuracy to compare ability with simple 3-D problem. As the results, interpolation scheme of compactly supported radial basis functions are useful to interpolation and mapping for large-scale airplane in FSI with a k-dimensional tree(kd-tree) which is a space-partitioning data structure for organizing points in a k-dimensional space.

• Journal of Satellite, Information and Communications
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• v.9 no.3
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• pp.9-14
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• 2014

Recently, precision guided munition systems and missile defense systems based on GPS have been taking a key role in modern warfare. In warfare however, unexpected interferences cause by large/small scale fading, radio frequency interferences, etc. These interferences result in a severe GPS positioning error, which could occur late supports and friendly fires. To solve the problems, this paper proposes an interference mitigation positioning method by adopting a wavelet denoising filter algorithm. The algorithm is applied to a GPS/QZSS/Wi-Fi combined positioning system which was performed by this laboratory. Experimental results of this paper are based on a real field test data of a GPS/QZSS/Wi-Fi combined positioning system and a simulation data of a wavelet denoising filter algorithm. At the end, the simulation result demonstrates its superiority by showing a 21.6% improved result in comparison to a conventional GPS system.

• Bioinformatics and Biosystems
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• v.1 no.1
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• pp.38-45
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• 2006

We present a client-server system architecture for inferring genetic interaction networks based on Bayesian networks. It is typical to take tens of hours when genome-wide large-scale genetic interaction networks are inferred in the form of Bayesian networks. To deal with this situation, batch-style distributed system architectures are preferable to interactive standalone architectures. Thus, we have implemented a loosely coupled client-server system for network inference and user interface. The network inference consists of two stages. Firstly, the proposed method divides a whole gene set into overlapped modules, based on biological annotations and expression data together. Secondly, it infers Bayesian networks for each module, and integrates the learned subnetworks to a global network through common genes across the modules.