• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.225-236
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• 2008

The techniques to calculate the probability precipitation for the design of hydrological projects can be determined by the point frequency analysis and the regional frequency analysis. Probability precipitation usually calculated by point frequency analysis using rainfall data that is observed in rainfall observatory which is situated in the basin. Therefore, Probability precipitation through point frequency analysis need observed rainfall data for enough periods. But, lacking precipitation data can be calculated to wrong parameters. Consequently, the regional frequency analysis can supplement the lacking precipitation data. Therefore, the regional frequency analysis has weaknesses compared to point frequency analysis because of suppositions about probability distributions. In this paper, rainfall observatory in Korea did grouping by cluster analysis using position of timely precipitation observatory and characteristic time rainfall. Discordancy and heterogeneity measures verified the grouping precipitation observatory by the cluster analysis. So, there divided rainfall observatory in Korea to 6 areas, and the regional frequency analysis applies index-flood techniques and L-moment techniques. Also, the probability precipitation was calculated by the regional frequency analysis using variable kernel density function. At the results, the regional frequency analysis of the variable kernel function can utilize for decision difficulty of suitable probability distribution in other methods.

• Korean Journal of School Psychology
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• v.16 no.1
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• pp.39-63
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• 2019

The purpose of this study was to explore how teachers perceive barriers to consultation with school counselors. For this purpose, the opinions of 16 teachers working in Seoul, Gyeonggi, Incheon, Chungcheong, and Daegu were collected through one-on-one interviews and qualitatively analyzed using the concept-mapping method. A second set of data was gathered to classify the similarity and importance of the teachers' statements through one-on-one interviews or the mail. The data was analyzed using multidimensional scaling and hierarchical cluster analyses. The results were as follows. The barriers to the consultation with school counselors as perceived by teachers were represented in 51 statements. Dimensional statement analysis revealed two dimensions: (a) 'School counseling's traits - School counselors' traits' and (b) 'Psychological difficulties - Environmental difficulties' Hierarchical cluster analysis identified 5 clusters: 'The responsibilities as homeroom teachers interfere with communication with counselors', 'Teachers lack of awareness of their ability to seek consultation with counselors', 'Teachers lack of trust in school counselors', 'Perceptions of the school counselors' role and lack of relevant experience with school counselors prevent teachers from seeking consultation', and 'School counselors are overworked due to the school counseling environment' The most important cluster was 'Teachers lack of trust in school counselors'.

• Restorative Dentistry and Endodontics
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• v.32 no.3
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• pp.180-190
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• 2007

The purpose of this study was to evaluate the abrasion resistance of surface penetrating sealant which was applied on a composite resin restoration and to provide proper time to reapply sealant on composite resin surface. Two hundred rectangular specimens, sized $8\times3\times2mm$, were made of Micronew (Bisco, Inc., Schaumburg, IL, U.S.A) and divided into two groups; F group (n = 10) was finished with coarse and medium grit of Sof-Lex discs and BisCoverwas applied B group (n = 190) after finishing with discs. B group was again subdivided into nineteen subgroups From B-1 group to B-18 group were subjected to toothbrush abrasion test using a distilled water-dentifrice slurry and toothbrush heads B-IM group was not subjected to toothbrush abrasion test. Average surface roughness (Ra) of each group was calculated using a surface roughness tester (Surfcorder MSE-1700: Kosaka Laboratory Ltd., Tokyo, Japan) . A representative specimen of each group was examined by FE-SEM (S-4700: Hitachi High Technologies Co., Tokyo, Japan). The data were analysed using cluster analysis, paired t-test, and repeated measure ANOVA. The results of this study were as follows; 1. Ra off group was $0.898{\pm}0.145{\mu}m$ and B-IM group was $0.289{\pm}0.142{\mu}m$. Ra became higher from B-1 group $(0.299{\pm}0.48{\mu}m$ to B-18 group $(0.642{\pm}0.313{\mu}m$. 2. Final cluster center of Ra was $0.361{\mu}m$ in cluster 1 $(B-IM\simB-7)$, $0.511{\mu}m$ in cluster 2 $(B-8\simB-14)$ and $0.624{\mu}m$ in cluster 3 ($(B-15\simB-18)$. There were significant difference among Ra of three clusters. 3 Ra of B-IM group was decreased 210.72% than Ra of F group. Ra of B-8 group and B-15 group was increased 35.49% and 51.35% respectively than Ra of B-IM group. 4. On FE-SEM, B-IM group showed the smoothest resin surface. B-8 group and B-15 group showed vertically shallow scratches , and wide and irregular vertical scratches on composite resin surface respectively. Within a limitation of this study, finished resin surface will be again smooth and glazy if BisCover would be reapplied within 8 to 14 months after applying to resin surface.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.10
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• pp.221-230
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• 2020

Supercomputer that shares limited resources to multiple users needs a way to optimize the execution of application. For this, it is useful for system administrators to get prior information and hint about the applications to be executed. In most high-performance computing system operations, system administrators strive to increase system productivity by receiving information about execution duration and resource requirements from users when executing tasks. They are also using profiling techniques that generates the necessary information using statistics such as system usage to increase system utilization. In a previous study, we have proposed a scheduling optimization technique by developing a hardware performance counter-based profiling technique that enables characterization of applications without further understanding of the source code. In this paper, we constructed a profiling testbed cluster to support optimal execution of the supercomputer and experimented with the scalability of the profiling method to analyze application characteristics in the built cluster environment. Also, we experimented that the profiling method can be utilized in actual scheduling optimization with scalability even if the application class is reduced or the number of nodes for profiling is minimized. Even though the number of nodes used for profiling was reduced to 1/4, the execution time of the application increased by 1.08% compared to profiling using all nodes, and the scheduling optimization performance improved by up to 37% compared to sequential execution. In addition, profiling by reducing the size of the problem resulted in a quarter of the cost of collecting profiling data and a performance improvement of up to 35%.

• Journal of Broadcast Engineering
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• v.13 no.6
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• pp.828-837
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• 2008

We propose a method to select representative views of single objects and classes of objects for 3D object retrieval and classification. Our method is based on projected 2D shapes, or views, of the 3D objects, where the representative views are selected by applying affinity propagation to cluster uniformly sampled views. Affinity propagation assigns prototypes to each cluster during the clustering process, thereby providing a natural criterion to select views. We recursively apply affinity propagation to the selected views of objects classified as single classes to obtain representative views of classes of objects. By enabling classification as well as retrieval, effective management of large scale databases for retrieval can be enhanced, since we can avoid exhaustive search over all objects by first classifying the object. We demonstrate the effectiveness of the proposed method for both retrieval and classification by experimental results based on the Princeton benchmark database [16].

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.29-36
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• 2017

In this paper, we implement a spatial big data analysis prototype based on Spark which is an in-memory system and compares the performance by the spatial split algorithm on this basis. In cluster computing environments, big data is divided into blocks of a certain size order to balance the computing load of big data. Existing research showed that in the case of the Hadoop based spatial big data system, the split method by spatial is more effective than the general sequential split method. Hadoop based spatial data system stores raw data as it is in spatial-divided blocks. However, in the proposed Spark-based spatial analysis system, there is a difference that spatial data is converted into a memory data structure and stored in a spatial block for search efficiency. Therefore, in this paper, we propose an in-memory spatial big data prototype and a spatial split block storage method. Also, we compare the performance of existing spatial split algorithms in the proposed prototype. We presented an appropriate spatial split strategy with the Spark based big data system. In the experiment, we compared the query execution time of the spatial split algorithm, and confirmed that the BSP algorithm shows the best performance.

• Science of Emotion and Sensibility
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• v.19 no.4
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• pp.55-70
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• 2016

It has been suggested that few studies were completed on psychological aspect of coffee drinking behavior though coffee researches have been mainly focused on marketing or business purposes. In this study, fifteen university students were participated to a group interview for concept mapping analysis asking about their meaning and purpose of coffee drinking behavior. Sixty nine statements were extracted from the interview, and categorized into seven clusters using multi dimensional scaling analysis and hierarchical cluster analysis. Seven clusters were Personal method of getting the desired physical effect, As a method of social activities, Method to get psychological consolation, Drink for spending time and using space, Habitual drink and use of caffeine's effect, Enjoying coffee's various characteristic and attraction, and Enjoying coffee's various characteristic and attraction. Two factors were identified based on these clusters such as internal-external motivation to drink coffee and emotional-physical effect of coffee. Participants ranked their priority of those clusters; the cluster of "Drink for spending time and using space" was first ranked. Limitations and suggestions for future research were also discussed.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.193-200
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• 2014

Due to the advent of ubiquitous computing environment, it is becoming a part of our common life style. And tremendous information is cumulated rapidly. In these trends, it is becoming a very important technology to find out exact information in a large data to present users. Collaborative filtering is the method based on other users' preferences, can not only reflect exact attributes of user but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, we propose clustering method by user's features based on SOM for predicting purchase pattern in u-Commerce. it is necessary for us to make the cluster with similarity by user's features to be able to reflect attributes of the customer information in order to find the items with same propensity in the cluster rapidly. The proposed makes the task of clustering to apply the variable of featured vector for the user's information and RFM factors based on purchase history data. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• Korean Journal of Microbiology
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• v.53 no.1
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• pp.9-19
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• 2017

We studied soil composition, $N_2O$ production, a number of denitrifying bacteria, community structure and T-RFLP patterns of denitrifying bacteria dependent on agricultural methods with the change of seasons. Analyses of the soil chemical composition revealed that total carbon and total organic carbon contents were 1.57% and 1.28% in the organic farming soil, 1.52% and 1.24% in the emptiness farming soil, and 1.40% and 0.95% in traditional farming soil, respectively. So, the amount of organic carbon was relatively high in the environment friendly farming soils than traditional farming soils. In case of $N_2O$ production, the amount of $N_2O$ production was high in May and November soils, but the rate of $N_2O$ production was fast in August soil. The average number of denitrifying bacteria were $1.32{\times}10^4MPN{\cdot}g^{-1}$ in the organic farming soil, $1.17{\times}10^4MPN{\cdot}g^{-1}$ in the emptiness farming soil, and $6.29{\times}10^3MPN{\cdot}g^{-1}$ in the traditional farming soil. It was confirmed that the environment friendly farming soil have a larger number of denitrifying bacteria than the traditional farming soil. As a result of the phylogenetic analyses, it was confirmed that six clusters were included in organic farming soil among total 10 clusters. And the result of PCA profile distribution of T-RFLP pattern on agricultural methods, the range of distribution showed wide in the organic farming method, relatively narrow in the conventional farming method, and middle in the emptiness farming method. Therefore, we could concluded that the distribution and the community structure of denitrifying bacteria were changed according to the agricultural methods and seasons.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.233-240
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• 2004

This paper presents a method to implicitly resolve ambiguities using dynamic incremental clustering in Korean-to-English and Japanese-to-English cross-language information retrieval (CLIR). The main objective of this paper shows that document clusters can effectively resolve the ambiguities tremendously increased in translated queries as well as take into account the context of all the terms in a document. In the framework we propose, a query in Korean/Japanese is first translated into English by looking up bilingual dictionaries, then documents are retrieved for the translated query terms based on the vector space retrieval model or the probabilistic retrieval model. For the top-ranked retrieved documents, query-oriented document clusters are incrementally created and the weight of each retrieved document is re-calculated by using the clusters. In the experiment based on TREC test collection, our method achieved 39.41% and 36.79% improvement for translated queries without ambiguity resolution in Korean-to-English CLIR, and 17.89% and 30.46% improvements in Japanese-to-English CLIR, on the vector space retrieval and on the probabilistic retrieval, respectively. Our method achieved 12.30% improvements for all translation queries, compared with blind feedback in Korean-to-English CLIR. These results indicate that cluster analysis help to resolve ambiguity.