• Communications for Statistical Applications and Methods
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• v.26 no.4
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• pp.359-370
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• 2019

Grouping structures in covariates are often ignored in regression models. Recent statistical developments considering grouping structure shows clear advantages; however, reflecting the grouping structure on the quantile regression model has been relatively rare in the literature. Treating the grouping structure is usually conducted by employing a group penalty. In this work, we explore the idea of group penalty to the quantile regression models. The grouping structure is assumed to be known, which is commonly true for some cases. For example, group of dummy variables transformed from one categorical variable can be regarded as one group of covariates. We examine the group quantile regression models via two real data analyses and simulation studies that reveal the beneficial performance of group quantile regression models to the non-group version methods if there exists grouping structures among variables.

• KIISE Transactions on Computing Practices
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• v.23 no.12
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• pp.677-683
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• 2017

Apache Storm is a representative real-time distributed processing system, which is able to process data streams quickly over distributed servers. Storm currently provides several stream grouping methods to distribute data traffic to multiple servers. Among them, the shuffle grouping may cause a processing delay problem and the local-or-shuffle grouping used to solve the problem may cause the problem of concentrating the traffic on a specific node. In this paper, we propose the locality-aware grouping to solve the problems that may arise in the existing Storm grouping methods. Experimental results show that the proposed locality-aware grouping is considerably superior to the existing shuffle grouping and the local-or-shuffle grouping. These results show that the new grouping is an excellent approach considering both the locality and load balancing which are limitations of the existing Storm.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.6A
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• pp.115-127
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• 2008

The Native API is a system call which can only be accessed with the authentication of the administrator. It can be used to detect a variety of malicious codes which can only be executed with the administrator's authority. Therefore, much research is being done on detection methods using the characteristics of the Native API. Most of these researches are being done by using supervised learning methods of machine learning. However, the classification standards of Anti-Virus companies do not reflect the characteristics of the Native API. As a result the population data used in the supervised learning methods are not accurate. Therefore, more research is needed on the topic of classification standards using the Native API for detection. This paper proposes a method for re-grouping malicious codes using fuzzy clustering methods with the Native API standard. The accuracy of the proposed re-grouping method uses machine learning to compare detection rates with previous classifying methods for evaluation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.1
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• pp.109-126
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• 2014

The level of service (LOS) of the Highway Capacity Manual (KHCM) has been used as a basic criterion at decision making processes for signalized intersections in Korea. The KHCM provides five steps for the signalized intersection analysis. Among them, lane grouping, which is the third step, significantly influence the final LOS. The current method presented in the KHCM, however, classifies a shared lane as a de facto turning lane group, even though the turning traffic of the shared lane is few. Thus, this research was initiated to provide an alternative. To this end, three alternatives were suggested, including the method based on the lane grouping presented in the U.S. Highway Capacity Manual, the method using turning ratio of shared turning lane, and the method using a threshold traffic volume in lane grouping. The feasibilities of the three methods were evaluated using a calibrated CORSIM model. Conclusively, the method using a threshold traffic volume in lane grouping outperformed.

• Journal of Digital Contents Society
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• v.14 no.2
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• pp.171-178
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• 2013

Desktop grid computing is the computing paradigm that can execute large-scale computing jobs using the desktop resources with heterogeneity and volatility. However, such the computing environment can not guarantee the stability and reliability of task execution because the desktop resources with different performance can freely participate and leave in task execution. Therefore, in this paper, we design resource grouping scheme using k-means clustering algorithm with an aim to provide desktop grid computing with the stability and reliability of task execution. Moreover, we conduct resource grouping using the execution log data of actual desktop grid systems and present application methods of desktop resource groups to fault-tolerance.

• Journal of the Korea Computer Industry Society
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• v.2 no.4
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• pp.435-442
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• 2001

According to the current increase of the usefulness of information in Internet, several methods are proposed in which multimedia information may be efficiently managed and retrieved. The purpose of this paper is to propose the new grouping method by SRR(Semantic Reference Relation) and DRR(Direct Reference Relation). The important point of this method proposed in this paper is to group MDI(Multimedia Document Informations) as a cluster of this multimedia objects. According to the result of experimental simulation, which has been tested by by the 1,000 multimedia items in internet, this method has made more efficiently the service and grouping of MDI possible than any other methods do in internet.

• Journal of The Korean Association For Science Education
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• v.32 no.4
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• pp.686-702
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• 2012

This study investigated the impact of grouping methods on free inquiry implementation through the use of mixed research methods. Some 113 7th graders and 2 science teachers in two middle schools participated in this study. The 113 students who participated in this study were grouped by homogeneity and heterogeneity according to scientific inquiry skills and personality types respectively, and performed free inquiry activities on the same subject for three weeks. Data were collected by means of a test on science inquiry skills and from focus group interviews with 36 students and in-depth interviews with 2 teachers. The quantitative results of this study showed that homogeneous grouping was more effective than heterogeneous grouping in improvement of scientific inquiry skills. Meanwhile, the qualitative results revealed both the students and teachers perceived that it was effective to compose a small group according to their affective quality than their cognitive quality. Particularly, most of the students preferred the method of small group from the personality types. Some students and both teachers proposed that it is necessary to collect enough information on students and to use them in mixture with the method of small group according to the affective quality.

• Journal of the Korea Computer Industry Society
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• v.8 no.2
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• pp.113-122
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• 2007

Recently, there are many research interests in providing efficient, temporal and spatial distribution multimedia streaming service. This paper proposed a temporal and spatial object grouping method for distribution multimedia streaming service. The proposed method performs the grouping structure by filtering and mapping with the collected frame object in application domains and it's peformed by JM relationship with the mapped frame objects. The results show that the performance provides the better than the other methods.

• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3336-3346
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• 2022

In general, a number of severe accident scenarios derived from Level 2 probabilistic safety assessment (PSA) are typically grouped into several categories to efficiently evaluate their potential impacts on the public with the assumption that scenarios within the same group have similar source term characteristics. To date, however, grouping by similar source terms has been completely reliant on qualitative methods such as logical trees or expert judgements. Recently, an exhaustive simulation approach has been developed to provide quantitative information on the source terms of a large number of severe accident scenarios. With this motivation, this paper proposes a machine learning-based categorization method based on exhaustive simulation for grouping scenarios with similar accident consequences. The proposed method employs clustering with an autoencoder for grouping unlabeled scenarios after dimensionality reductions and feature extractions from the source term data. To validate the suggested method, source term data for 658 severe accident scenarios were used. Results confirmed that the proposed method successfully characterized the severe accident scenarios with similar behavior more precisely than the conventional grouping method.

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2418-2426
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• 2022

The grouping analysis is a method guided by the Korea Radioactive Waste Agency for efficient analysis of radioactive waste for disposal. In this study, experiments to verify the adequacy of grouping analysis were conducted with radioactive soil, concrete, and dry active waste in similar environments. First, analysis results of the major radionuclide concentrations in individual waste samples were reviewed to evaluate whether wastes from similar environments correspond to a single waste stream. As a result, the soil and concrete waste were identified as a single waste stream because the distribution range of radionuclide concentrations was "within a factor of 10", the range that meet the criterion of the U.S. Nuclear Regulatory Commission for a single waste stream. On the other hand, the dry active waste was judged to correspond to distinct waste streams. Second, after analyzing the composite samples prepared by grouping the individual samples, the population means of the values of "composite sample analysis results/individual sample analysis results" were estimated at a 95% confidence level. The results showed that all evaluation values for soil and concrete waste were within the set reference values (0.1-10) when five-package and ten-package grouping analyses were conducted, verifying the adequacy of the grouping analysis.