• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.277-281
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• 2006

Cyber counseling, one of the most compatible type of consultation for the information society, enables people to reveal their mental agonies and private problems anonymously, since it does not require face-to-face interview between a counsellor and a client. However, there are few cyber counseling centers which provide high quality and trustworthy service, although the number of cyber counseling center has highly increased. Therefore, this paper is intended to enable an appropriate consultation for each client by analyzing client propensity using Bayesian variable selection. Bayesian variable selection is superior to stepwise regression analysis method in finding out a regression model. Stepwise regression analysis method, which has been generally used to analyze individual propensity in linear regression model, is not efficient since it is hard to select a proper model for its own defects. In this paper, based on the case database of current cyber counseling centers in the web, we will analyze clients' propensities using Bayesian variable selection to enable individually target counseling and to activate cyber counseling programs.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.10
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• pp.747-754
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• 2003

An enhancement of the probabilistic data association filter is presented for tracking a single maneuvering target in clutter environment. The use of the variable dimensional structure leads the probabilistic data association filter to adjust to real motion of a target. The detection of the maneuver for the model switching is performed by the acceleration estimates taken from a bias estimator of the two stage Kalman filter. The proposed algorithm needs low computational power since it is implemented with a single filtering procedure. A simple Monte Carlo simulation was performed to compare the performance of the proposed algorithm and the IMMPDA filter.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.11
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• pp.881-888
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• 2020

In this paper, research on estimation of states of a target of interest using Line Of Sight(LOS) angle measurement is performed. Target's position, velocity, and acceleration are chosen to be the states of interests. The LOS measurement is known to be highly non-linear, making target dynamic modeling hard to be implemented into a filter. To solve this issue, the Pseudomeasurement equation was applied to the LOS measurement equation. With the help of this equation, 3D variable turn target dynamic model is applied to the filter model. For better performance, Kinematic Constraint is also implemented into the filter model. As for the filter, Bias Compensation Pseudomeasurement Filter (BCPMF) is used which is known for its robustness to initial conditions. Moreover, Two-Stage Kalman Filter (TSKF) form was also implemented to benefit from the parallel computation. As a result, TBCPMF 3DVT-KC is proposed and simulated to assess performance.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.61-78
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• 2007

A method for poll analysis using information theory and data visualization is proposed in this paper. Questions of opinion poll consist of a target variable and many explanation variables. The type of explanation variables is either numerical or categorical. In this study, explanation variables of mixed types have been ranked according to the magnitude of their effect on target variable by using mutual information. Likewise, the order of explanation variables has been evaluated using data visualization. This is the first study to quantify the impact of specific explanation variable on the related target variable.

• Journal of Korea Trade
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• v.24 no.6
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• pp.1-18
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• 2020

Purpose - This study investigates whether a listing effect exists in cross-border M&As and whether the effect can be attributed to the uncertainty of the GDP growth rate in the target firm's home country. We apply a joint variable analysis using M&A announcement data from the Korea Exchange (KRX), Shanghai Stock Exchange (SSE), and the Taiwan Stock Exchange (TWSE) from 2004 to 2013. We also conduct an event study using the measure of the uncertainty of the GDP growth rate (based on IMF statistics) in 55 target countries. Design/methodology - We measure the abnormal return (AR) using the market-adjusted model. We test the significance of the AR and the cumulative abnormal return (CAR) using a one-sample t-test. We examine the characteristics of the CARs depending on whether the target company is listed by applying a difference analysis using CAR as a test variable. In addition, we set CAR (-5, +5) as a dependent variable to identify the cause of the listing effect, and test both the financial characteristic variables of the acquirer and the collective characteristic variables of the merger as independent variables in the multiple regression analysis. Findings - First, we find the listing effect of cross-border M&As in the KRX, SSE, and TWSE, which represent the capital markets in Korea, China, and Taiwan, respectively. This listing effect persists during the global financial crisis and has a negative effect on the wealth of acquiring shareholders, especially when the target countries are emerging markets. Second, greater uncertainty regarding the target countries' economic growth in cross-border M&As has a negative effect on the wealth of acquiring firms' shareholders. Third, our empirical analysis demonstrates that the listing effect is attributable to the fact that firms listed in a target country with greater uncertainty of economic growth are more directly and greatly exposed to uncertain capital markets through stock markets, than are unlisted firms. Originality/value - This study is significant in that it presents a new strategic perspective in the study of cross-border M&As by demonstrating empirically that the listing effect is attributable to the uncertainty regarding the economic development of the target firms' home countries.

• Industrial Engineering and Management Systems
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• v.14 no.2
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• pp.210-219
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• 2015

A classification task requires an exponentially growing amount of computation time and number of observations as the variable dimensionality increases. Thus, reducing the dimensionality of the data is essential when the number of observations is limited. Often, dimensionality reduction or feature selection leads to better classification performance than using the whole number of features. In this paper, we study the possibility of utilizing the Markov blanket discovery algorithm as a new feature selection method. The Markov blanket of a target variable is the minimal variable set for explaining the target variable on the basis of conditional independence of all the variables to be connected in a Bayesian network. We apply several Markov blanket discovery algorithms to some high-dimensional categorical and continuous data sets, and compare their classification performance with other feature selection methods using well-known classifiers.

• International Journal of Automotive Technology
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• v.8 no.6
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• pp.713-722
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• 2007

Design variables for suspension systems cannot always be realized in the actual suspension systems due to tolerances in manufacturing and assembly processes. In order to deal with these tolerances, design variables associated with kinematic configuration and compliance characteristics of suspensions are treated as random variables. The reliability of a design target with respect to a design variable is defined as the probability that the design target is in the acceptable design range for all possible values of the design variable. To compute reliability, the limit state, which is the boundary between the acceptable and unacceptable design, is expressed mathematically by a limit state function with value greater than 0 for acceptable design, and less than 0 for unacceptable design. Through reliability analysis, the acceptable range of design variables that satisfy a reliability target is specified. Furthermore, through sensitivity analysis, a general procedure for optimization of the design target with respect to the design variables has been established.

• Journal of Korea Water Resources Association
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• v.37 no.3
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• pp.173-184
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• 2004

The objective of this study is to examine the usefulness of climate model simulations (GCM) in Korea water resource management. The methods are based on probabilistic measures of the effectiveness of GCM simulations of an indicator variable for discriminating high versus low regional observations of a target variable. The formulation uses the significance probability of the Kolmogorov-Smirnov test for detecting differences between two variables. AMIP-II(Atmospheric Model Intercomparison Project-II) type GCM simulation done by ECMWF(European Centre for Medium-Range Weather Forecasts) was used for indicator variable and observed mean average precipitation(MAP) values on 7 major river basins were used as target variable. Monte Carlo simulation is used to establish the significance of the estimator values. The results show that GCM simulations done by ECMWF are skillful in discriminating the high from the low of the observed MAP for wet season in all seven basins of Korea, but not enough for dry season.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.123-129
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• 2016

In the manufacturing industry fields, thousands of quality characteristics are measured in a day because the systems of process have been automated through the development of computer and improvement of techniques. Also, the process has been monitored in database in real time. Particularly, the data in the design step of the process have contributed to the product that customers have required through getting useful information from the data and reflecting them to the design of product. In this study, first, characteristics and variables affecting to them in the data of the design step of the process were analyzed by decision tree to find out the relation between explanatory and target variables. Second, the tolerance of continuous variables influencing on the target variable primarily was shown by the application of algorithm of decision tree, C4.5. Finally, the target variable, loss, was calculated by a loss function of Taguchi and analyzed. In this paper, the general method that the value of continuous explanatory variables has been used intactly not to be transformed to the discrete value and new method that the value of continuous explanatory variables was divided into 3 categories were compared. As a result, first, the tolerance obtained from the new method was more effective in decreasing the target variable, loss, than general method. In addition, the tolerance levels for the continuous explanatory variables to be chosen of the major variables were calculated. In further research, a systematic method using decision tree of data mining needs to be developed in order to categorize continuous variables under various scenarios of loss function.

• Progress in Medical Physics
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• v.28 no.4
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• pp.181-189
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• 2017

The conventional delivery quality assurance (DQA) process for RapidArc (Varian Medical Systems, Palo Alto, USA), has the limitation that it measures and analyzes the dose in a phantom material and cannot analyze the dosimetric changes under the motional organ condition. In this study, a DQA method was designed to overcome the limitations of the conventional DQA process for internal target volume (ITV) based RapidArc. The dynamic DQA measurement device was designed with a moving phantom that can simulate variable target motions. The dose distribution in the real volume of the target and organ-at-risk (OAR)s were reconstructed using 3DVH with the ArcCHECK (SunNuclear, Melbourne, USA) measurement data under the dynamic condition. A total of 10 ITV-based RapidArc plans for liver-cancer patients were analyzed with the designed dynamic DQA process. The average pass rate of gamma evaluation was $81.55{\pm}9.48%$ when the DQA dose was measured in the respiratory moving condition of the patient. Appropriate method was applied to correct the effect of moving phantom structures in the dose calculation, and DVH data of the real volume of target and OARs were created with the recalculated dose by the 3DVH program. We confirmed the valid dose coverage of a real target volume in the ITV-based RapidArc. The variable difference of the DVH of the OARs showed that dose variation can occur differently according to the location, shape, size and motion range of the target. The DQA process devised in this study can effectively evaluate the DVH of the real volume of the target and OARs in a respiratory moving condition in addition to the simple verification of the accuracy of the treatment machine. This can be helpful to predict the prognosis of treatment by the accurate dose analysis in the real target and OARs.