• Journal of Information Technology Services
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• v.6 no.2
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• pp.99-112
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• 2007

In this study we designed the killer services for the scene of labor learning in ubiquitous computing. To achieve this study, we have explored the unmet needs of teachers in the scene of labor learning and examined whether the unmet needs could be served by the resources and capabilities of ubiquitous computing. Then, we have crafted a detail killer services that includes value propositions and resource maps by using statistical methodology. Finally, the killer services for the scene of labor learning proposed to serve educational users with the service architecture. The result of this study will be applied to develop new business model in ubiquitous computing as the basic research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4280-4300
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• 2017

The fact that cloud computing services have been proposed in recent years, organizations and individuals face with various challenges and problems such as how to migrate applications and software platforms into cloud or how to ensure security of migrated applications. This study reviews the current challenges and open issues in cloud computing, with the focus on autonomic resource management especially in federated clouds. In addition, this study provides recommendations and research roadmaps for scientific activities, as well as potential improvements in federated cloud computing. This survey study covers results achieved through 190 literatures including books, journal and conference papers, industrial reports, forums, and project reports. A solution is proposed for autonomic resource management in the federated clouds, using machine learning and statistical analysis in order to provide better and efficient resource management.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.739-745
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• 2010

This paper computes the repurchase index based on statistical modeling. Using the transaction record of a certain product, the repurchase index is obtained by fitting the Poisson regression model. The customers are classified into 5 groups based on the index giving the information about the propensity to repurchase.

• Journal of the Korean Statistical Society
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• v.25 no.4
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• pp.589-601
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• 1996

Geometric approach to extend the classical two-set theory of canonical correlation analysis to three or more sets is considered. It provides statistical graphs to represent the data in a low dimensional space. Procedures are developed for computing the canonical variables and the corresponding properties are investigated. The solution is equivalent to that of the usual problem in the case of two sets. Goodness-of-fit of the proposed plots is studied and a numerical example is included.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.845-853
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• 2004

This study is to adjust the focus on the statistical survey about the rates of application for the 2005 Susi second semester admission to Universities in Daegu, Kyungbook. The decrease of population for university admission and the change of paradigm of selecting a field of specialization in university will have an adverse effect on ratios of admission. This is very important to the future of university in Daegu, Kyungbook.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.171-177
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• 2011

Accuracy and computing time are considerable issues in machine learning. In general, the computing time for data analysis is increased in proportion to the size of given data. So, we need a sampling approach to reduce the size of training data. But, the accuracy of constructed model is decreased by going down the data size simultaneously. To solve this problem, we propose a new statistical sampling method having similar performance to the total data. We suggest a rule to select optimal sampling techniques according to given data structure. This paper shows a sampling method for reducing computing time with keeping the most of accuracy using cluster sampling, stratified sampling, and systematic sampling. We verify improved performance of proposed method by accuracy and computing time between sample data and total data using objective machine learning data sets.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1213-1228
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• 2017

The pitch tracking of music has been researched for several decades. Several possible improvements are available for creating a good t-distribution, using the instantaneous robust algorithm for pitch tracking framework to perfectly detect pitch. This article shows how to detect the pitch of music utilizing an improved detection method which applies a statistical method; this approach uses a pitch track, or a sequence of frequency bin numbers. This sequence is used to create an index that offers useful features for comparing similar songs. The pitch frequency spectrum is extracted using a modified instantaneous robust algorithm for pitch tracking (IRAPT) as a base combined with the statistical method. The pitch detection algorithm was implemented, and the percentage of performance matching in Thai classical music was assessed in order to test the accuracy of the algorithm. We used the longest common subsequence to compare the similarities in pitch sequence alignments in the music. The experimental results of this research show that the accuracy of retrieval of Thai classical music using the t-distribution of instantaneous robust algorithm for pitch tracking (t-IRAPT) is 99.01%, and is in the top five ranking, with the shortest query sample being five seconds long.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.13-17
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• 2003

본 논문에서는 낮은 대역폭, 높은 지연, 그리고 잦은 네트워크 단절로 인한 모바일 컴퓨팅 환경의 문제점들을 해결하기 위한 효과적인 캐시 적재 기법으로서 협업 추천 기반의 데이터 마이닝 전략을 제안하였다. 캐시 적재가 모바일 클라이언트의 이러한 문제점들을 해결하기 위한 효율적인 방법이 된다는 기존의 연구는 많이 진행되어 왔다. 하지만 모바일 컴퓨터의 요구에 대한 이력 정보만을 이용한 기존의 연구는 모바일 클라이언트가 필요로 하는 모든 정보 요구를 만족하지 못하였다. 특히 저장 공간의 제약을 갖는 모바일 컴퓨터의 한계 때문에 더욱 큰 어려움을 갖게 되었다. 본 연구에서는 모바일 클라이언트의 이력 정보에 대하여 데이터 마이닝 기법을 적용한 캐시 적재 기법을 제안하여 적은 캐시 용량만으로도 모바일 클라이언트의 요구를 만족할 수 있는 아이템들을 효과적으로 서비스할 수 있도록 하였다. CSIM Simulator를 이용하여 모의 데이터를 생성하여, 제안 모형의 성능 평가를 위한 실험을 수행하였다. Cache hit ratio를 이용한 객관적인 성능 평가를 통하여 제안된 모형이 모바일 클라이언트의 캐시 적재 기법으로서 우수한 성능을 보임이 확인되었다.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.377-383
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• 2020

The likelihood-based inference in a nonlinear generalized mixed model often requires computing moments of truncated multivariate normal random variables. Many methods have been proposed for the computation using a recurrence relation or the moment generating function; however, these methods rely on high dimensional numerical integrations. The numerical method is known to be inefficient for high dimensional integral in accuracy. Besides the accuracy, the methods demand too much computing time to use them in practical analyses. In this note, a moment calculation method is proposed under an assumption of a certain covariance structure that occurred mostly in generalized mixed models. The method needs only low dimensional numerical integrations.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.651-662
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• 2017

We propose an alternative procedure to select penalty parameter in $L_1$ penalized robust regression. This procedure is based on marginalization of prior distribution over the penalty parameter. Thus, resulting objective function does not include the penalty parameter due to marginalizing it out. In addition, its estimating algorithm automatically chooses a penalty parameter using the previous estimate of regression coefficients. The proposed approach bypasses cross validation as well as saves computing time. Variable-wise penalization also performs best in prediction and variable selection perspectives. Numerical studies using simulation data demonstrate the performance of our proposals. The proposed methods are applied to Boston housing data. Through simulation study and real data application we demonstrate that our proposals are competitive to or much better than cross-validation in prediction, variable selection, and computing time perspectives.