• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.23-32
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• 2007

We consider penalized likelihood regression with data from the negative binomial distribution with unknown shape parameter. Smoothing parameter selection and asymptotically efficient low dimensional approximations are employed for negative binomial data along with shape parameter estimation through several different algorithms.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.743-758
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• 2005

This paper consider penalized likelihood regression with data from exponential family. The fast computation method applied to Gaussian data(Kim and Gu, 2004) is extended to non Gaussian data through asymptotically efficient low dimensional approximations and corresponding algorithm is proposed. Also smoothing parameter selection is explored for various exponential families, which extends the existing cross validation method of Xiang and Wahba evaluated only with Bernoulli data.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.861-870
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• 2005

The Bayesian model selection procedures for the shape parameter of gamma distribution are proposed in order to test that the failure rate of gamma distribution is constant, increasing or decreasing. The encompassing intrinsic Bayes factor by Beger and Pericchi (1996) based on Jeffreys prior for shape parameter is used to investigate the usefulness of the proposed Bayesian model selection procedures via both real data and pseudo data.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.1
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• pp.47-53
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• 2001

In this paper, we compare the classification performances of both ensemble and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. In view of the unknown relationship between input and output function, we use a Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: When the level of the variance is medium, Bagging & Parameter Combining performs worse than Logistic Regression, Variable Selection Bagging and Clustering. However, classification performances of Logistic Regression, Variable Selection Bagging, Bagging and Clustering are not significantly different when the variance of input data is either small or large. When there is strong correlation in input variables, Variable Selection Bagging outperforms both Logistic Regression and Parameter combining. In general, Parameter Combining algorithm appears to be the worst at our disappointment.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.876-881
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• 2004

Finite element model updating is an inverse problem to identify and correct uncertain modeling parameters that leads to better predictions of the dynamic behavior of a target structure. Unlike other inverse problems, the restrictions on selecting parameters all: very high since the updated model should maintains its physical meaning. That is, only the regions with modeling errors should be parameterized. And the variations of the parameters should be kept small while the updated results give acceptable correlations with experimental data. To avoid an ill-conditioned numerical problem, the number of parameters should be kept as small as possible. Thus it is very difficult to select an adequate set of updating parameters which meet all these requirements. In this paper, the importance of updating parameter selection is illustrated through a case study, and an automated procedure to guide the parameter selection is suggested based on simple observations. The effectiveness of the suggested procedure is tested with two example problems, ones is a simulated case study and the other is a real engineering structure.

• Journal of the Korean Society for Library and Information Science
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• v.27
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• pp.291-339
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• 1994

The purpose of this study is to find personal characteristics that affect users' cognitive characteristics of system, and to verify correlations between this users' cognitive characteristics and selection of system usage in using end -user searching systems (EUSS), For corroborative analysis of this study, preliminary model was constructed referring to Davis' Technology Acceptance Model. The model consists of exogenous variables (personal characteristics) , parameter variables (perceived usefulness, perceived ease of use), and effect variables (selection of system usage), When exogenous variables affect parameter variables, exogenous variables are independent variables and parameter variables are dependent variables. In addition, in correlation of parameter variables, which have been affected by exogenous variables, with effect variables, parameter variables are independent variables and effect variables are dependent variables, As for the research methodology, this study regards the Academic Information System connected with the Internet as EUSS, So questionnaires have been sent to researchers in universities who were conducting direct searching for the system. 229 valid responses to questionnaires have been analyzed according to Pearson Correlation Analysis and Stepwise Selection of Multiple Regression in the statistical software packages, 'SPSS PC+'. The findings and conclusions made in this study are summarized as follows; 1. Among the personal characteristics (age, disciplinary, computer literacy level, perceived usefulness of use education and training, perceived satisfaction of end-user searching, perceived satisfaction of system characteristics), all characteristics but age affect perceived usefulness and perceived ease of use. Specifically, perceived satisfaction of end user searching and perceived satisfaction of system characteristics most affect perceived usefulness and perceived ease of use respectively. 2. Perceived usefulness and perceived ease of use have a direct effect on selection of system usage in using EUSS. 3, Perceived usefulness more affect selection of system usage than perceived ease of use in using EUSS.

• IE interfaces
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• v.15 no.3
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• pp.308-315
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• 2002

This study presents the web based cutting parameter selection system using Group Technology (GT). The GT is basically applied to classify and code the work material and cutting process which are main factors to affect cutting parameter selection. The proposed system has been designed to electronically select proper cutting conditions based on the stored GT database. The existing approaches used in most small and medium sized companies are basically to use manufacturing engineer's experience or to find the recommended values from the manufacturing engineers handbook. These processes are often time consuming and inconsistent, especially when a new engineer is involved. Consequently, the proposed system could automatically and consistently generate the proper cutting conditions (feed, depth of cut, and cutting speed) as soon as relatively simple data input is given thanks to the classified GT database.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.2
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• pp.1-7
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• 2007

재료의 중량과 강도는 기계부품 특히 항공기의 부품에 중요한 요소가 되므로 가볍고 강인한 열처리 강화 알루미늄이나 티타늄 등이 많이 사용된다. 그러나 알루미늄은 용융점이 낮기 때문에 기계 가공 시 발생되는 열에 의해 부품이 얇고 길수록 쉽게 변형된다. 본 연구는 end milling 가공에서 최적의 절삭 parameter를 선정하여 열 변형을 최소화한다. 밀링 가공의 절삭속도, 이송속도, 절삭 깊이를 실험 인자로 정하여 다구찌 방법으로 실험을 계획하고 얇은 시편을 절삭하여 특성을 측정한다. 결과를 분산분석 (ANOVA) 과 signal to noise 비를 (SNR) 분석하여 최소 열 변형의 절삭 parameter를 찾는다. 실험의 data를 SQL database 프로그램화하여 다양한 절삭 환경에서 최소 열 변형과 최소 표면거칠기의 parameter를 찾을 수 있도록 하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10A
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• pp.760-768
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• 2009

In this paper, we propose the energy efficient transmission parameter selection method for Wireless Personal Area Network (WPAN) system which is applied to e-Navigation system considering various ship models environment. An appropriate selection of transmission parameters of HR-WPAN system is very essential to be considered for saving WPAN devices' energy consumption, when HR-WPAN system is applied to ship area network (SAN). Therefore, we propose an energy consumption model for a ship area network employing IEEE 802.15.3 based CSMA/CA HR-WPAN model and analyze the effect of transmission parameter selection on the performance of energy consumption. In particular, the path loss is the major performance decision parameter for the SAN employing HR-WPAN system, since it varies according to the material of shipbuilding such as steel(for large ship), FRP(for medium size ship) and compound wood(for small ship). Thus, we analyze and demonstrate that the proper transmission parameter selection of transmit power, PHY data rate and fragment size for each ship model could guarantee energy efficiency.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.9A
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• pp.712-718
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• 2009

This paper proposes the optimal transmission parameter selection method for an energy efficient Wireless Personal Area Network (WPAN) system which is applicable to the Maritime Telematics targeting for various ship models. Since the transmission parameter selection is an important factor for WPAN system to decide its energy efficiency, we propose an energy consumption model for ship area network (SAN) employing IEEE 802.15.3 based TDMA HR-WPAN model and analyzes the effect of transmission parameter selection on the performance of energy consumption. In particular, the main performance decision parameter of the SAN applying HR-WPAN is path loss, since it is very varied according to the material of shipbuilding such as steel (large ship), FRP (medium size ship) and compound wood (small ship). Thus, we analyzed and demonstrated that the proper transmission parameter selection among transmit power, PHY data rate and fragment size for each ship model guarantee the energy efficiency.