• Journal of the Korea Society of Computer and Information
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• v.22 no.1
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• pp.23-32
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• 2017

We can obtain useful knowledge from data by using a subgroup discovery algorithm. Subgroup discovery is a rule model learning method that finds data subgroups containing specific information from data and expresses them in a rule form. Subgroups are meaningful as they account for a high percentage of total data and tend to differ significantly from the overall data. Subgroup is expressed with conjunction of only literals previously. So, the scope of the rules that can be derived from the learning process is limited. In this paper, we propose a method to increase expressiveness of rules through internal disjunctive representation of attribute values. Also, we analyze the characteristics of existing subgroup discovery algorithms and propose an improved algorithm that complements their defects and takes advantage of them. Experiments are conducted with the traffic accident data given from Busan metropolitan city. The results shows that performance of the proposed method is better than that of existing methods. Rule set learned by proposed method has interesting and general rules more.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.513-516
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• 2007

Because many scientific applications require large data processing, the importance of parallel I/O has been increasingly recognized. Collective I/O is one of the considerable features of parallel I/O and enables application programmers to easily handle their large data volume. In this paper we measure and analyze the performance of original collective I/Os and the subgroup method, the way of using collective I/O of MPI effectively. From the experimental results, we found that the two kinds of subgroup method showed different performance. In terms of collective write operation, the subgroup method caused the performance degradation. However, the subgroup method for collective read showed good performance with small data size.

• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1269-1286
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• 2017

This paper presents a newly developed resonance self-shielding method based on Tone's method in $APOLLO3^{(R)}$ for fast and thermal reactor calculations. The new method is based on simplified models, the narrow resonance approximation for the slowing down source and Tone's approximation for group collision probability matrix. It utilizes mathematical probability tables as quadrature formulas in calculating effective cross-sections. Numerical results for the ZPPR drawer calculations in 1,968 groups show that, in the case of the double-column fuel drawer, Tone's method gives equivalent precision to the subgroup method while markedly reducing the total number of collision probability matrix calculations and hence the central processing unit time. In the case of a single-column fuel drawer with the presence of a uranium metal material, Tone's method obtains less precise results than those of the subgroup method due to less precise heterogeneous-homogeneous equivalence. The same options are also applied to PWR UOX, MOX, and Gd cells using the SHEM 361-group library, with the objective of analyzing whether this energy mesh might be suitable for the application of this methodology to thermal systems. The numerical results show that comparable precision is reached with both Tone's and the subgroup methods, with the satisfactory representation of intrapellet spatial effects.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.50-53
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• 2003

We describe a method for estimating new hand views from a single 2D hand image using decomposed approach with subgroup-based scheme. With this method, we can get the simplicity in the sense of computation by comparing the image with models in the promising subgroup instead of comparing with all models. It shows more effectiveness in recognition by process depend on each subgroup and easy of extension.

• Restorative Dentistry and Endodontics
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• v.17 no.2
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• pp.355-364
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• 1992

This is a study on the effect of the dentinal pretreatment method to the bond strength between dentin and glass ionomer cement. In this study, 196 human molar teeth with sound crown were used. The dentin surfaces of these teeth were exposed with wet trimmer and polished with # 800 Emory paper and teeth were divided into 7 groups according to the pretreatment agent and method. Each group has 4 subroups of the kinds of glass ionomers. The shear bond strength were measured by Instron Universal Testing machine model 1122. The data of the evaluations were then subjected to statistical analysis using one way ANOVA and the result were as follows : 1. In Durelon liquid 20 sec scrubbing & Vitrebond filling subgroup, shear bond strength was highest with measurements of 72.41(kg/$cm^2$) and in no pretreatment & Shofu lining cement filling subgroup, lowest with measurements of 4.77(kg/$cm^2$). 2. In no pretreatment group, statistical significant differences were found between the subgroups of G-C lining cement and Shofu lining cement. 3. In Ketac conditioner 20 sec scrubbing group, Vitrebond were bonded stronger than others, and in Ketac conditioner 10 sec passive contact group, it has the significant difference with other glass ionomers except G-C lining cement. 4. The subgroup og G-C dentin conditioner 20 sec scrubbing & G-C lining cement filling was bonded to dentin stronger than the subgroup of no pretreatment & Shofu lining cement. In G-C dentin conditioner groups, both of 10 sec passive contact and 20 sec scrubbing, Vitrebond has highest bond strength among the subgroups. 5. The subgroup of Durelon liquid 10 sec passive contact & G-C lining cement filling was bonded to dentin stronger than the subgroup of no pretreatment & Shofu lining cement. Also in both Durelon liquid groups, Vitrebond were bonded to dentin with the highest strength among the subgroups.

• Nuclear Engineering and Technology
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• v.48 no.2
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• pp.339-350
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• 2016

Based on the resonance integral (RI) tables produced by the NJOY program, the conventional subgroup method usually ignores both the resonance elastic scattering and the resonance interference effects. In this paper, on one hand, to correct the resonance elastic scattering effect, RI tables are regenerated by using the Monte Carlo code, OpenMC, which employs the Doppler broadening rejection correction method for the resonance elastic scattering. On the other hand, a fast resonance interference factor method is proposed to efficiently handle the resonance interference effect. Encouraging conclusions have been indicated by the numerical results. (1) For a hot full power pressurized water reactor fuel pin-cell, an error of about +200 percent mille could be introduced by neglecting the resonance elastic scattering effect. By contrast, the approach employed in this paper can eliminate the error. (2) The fast resonance interference factor method possesses higher precision and higher efficiency than the conventional Bondarenko iteration method. Correspondingly, if the fast resonance interference factor method proposed in this paper is employed, the $k_{inf}$ can be improved by ~100 percent mille with a speedup of about 4.56.

• Journal of Korean Society for Quality Management
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• v.35 no.2
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• pp.106-112
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• 2007

In statistical process control, it is assumed that the process data are independent. However, most of chemical processes such as semi-conduct processes do not satisfy the assumption because of presence of autocorrelation between process data. It causes abnormal out of control signal in the process control and misleading estimation in process capability. In this study, we adopted Shore's method to solve the problem and propose an optimal subgroup size to estimate the variance correctly for AR(1) processes. Especially, we focus on finding an actual subgroup size for small samples based on simulation study.

• Proceedings of the Korean Society for Quality Management Conference
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• 2007.04a
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• pp.302-309
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• 2007

To conduct statistical process control needs the assumption that the process data are independent. However, most of chemical processes, like a semi-conduct processes do not satisfy the assumption because of autocorrelation. It causes abnormal out of control signal in the process control and misleading process capability. In this study, we introduce that Shore's method to solve the problem and to find the optimal subgroup size to estimate variance for AR(l) model. Especially, we focus on finding an actual subgroup size for small samples using simulation. It may be very useful for statistical process control to analyze process capability and to make a Shewhart chart properly.

• Korean Journal of Plant Resources
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• v.20 no.6
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• pp.570-578
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• 2007

In order to investigate the basic vegetation information for the efficient management of the Sorbus commixta habitat in Korea, 31 plots in Mt. Chiri area were selected of which vegetation types were classified in phytosociological method and ecological characteristics were identified. As a result, the habitat was classified as S. commixta community group which was then subdivided to Prunus padus group and Picea jezoensis group. Prunus padus group was classified in Ainsliaea acerifolia subgroup and Magnolia sieboldii subgroup, while the Picea jezoensis group was classified in Rhododendron mucronulatum subgroup and Echinopanax horridum subgroup. Thus, the forest vegetation of S. commixta was classified in 1 community, 2 groups and 4 subgroups, and found to have 4 vegetation units in total.

• The Korean Journal of Pain
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• v.34 no.2
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• pp.139-155
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• 2021

The quality of subgroup analyses (SGAs) in chronic non-cancer pain trials is uncertain. The purpose of this study was to address this issue. We conducted a comprehensive search in MEDLINE and EMBASE from January 2012 to September 2018 to identify eligible trials. Two pairs of reviewers assessed the quality of the SGAs and the credibility of subgroup claims using the 10 criteria developed by Sun et al. in 2012. The associations between the quality of the SGAs and the studies' characteristics including risk of bias, funding sources, sample size, and the latest impact factor, were assessed using multivariable logistic regression. Our search retrieved 3,401 articles of which 66 were eligible. The total number of SGAs was 177 of which 52 (29.4%) made a subgroup claim. Of these, only 15 (8.5%) were evaluated as being of high quality. Among the 30 SGAs that claimed subgroup effects using an appropriate method of performing interaction tests, the credibility of only 5 were assessed as high. None of the subgroup claims met all the credibility criteria. No significant association was found between the quality of SGAs and the studies' characteristics. The quality of the SGAs performed in chronic pain trials was poor. To enhance the quality of SGAs, scholars should consider the developed criteria when designing and conducting trials, particularly those which need to be specified a priori.