• Journal of Korean Society for Quality Management
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• v.31 no.1
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• pp.21-41
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• 2003

The purpose of this research is to identify if psychological temptation, site quality and sense of community influence user's flow and addiction and if causalities among flow, addiction, customer satisfaction and customer loyalty are different between Korean and Japanese online games. To perform our research, we use MCSF(Multi-group Confirmatory Factor Analysis) and MSEM(Multi-group Structural Equation Model). The empirical results of SEM(Structural Equation Model) including high-order factor analysis indicate that all of paths in our model are the same for both countries. Therefore, site quality and sense of community have impacts on the flow, while on the other hand, psychological temptation has impacts on the addiction. Customer satisfaction and loyalty are positively related not with the addiction but with the flow. In addition, customer loyalty is significantly influenced by the flow and the customer satisfaction. In Conclusion, the empirical results of MSEM(Multi-group Structural Equation Model) indicate sense of community to flow, flow to loyalty and customer satisfaction to loyalty are different between Korea and Japan. This indicates that companies to penetrate into Japa online game industry should have a concern with Japanese Social and Cultural features and to develop strategies which correspond with Japanese culture.

• East Asian Economic Review
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• v.18 no.2
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• pp.111-142
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• 2014

We examine whether country fundamentals help explain the cross-section of currency excess returns. For this purpose, we consider fundamental variables such as default risk, foreign exchange rate regime, capital control as well as interest rate in the multi-factor model framework. Our empirical results show that fundamental factors explain a large part of the cross-section of currency excess returns. The zero-intercept restriction of the factor model is not rejected for most currencies. They also reveal that our factor model with country fundamentals performs better than a factor model with usual investment-style factors. Our main empirical results are based on 2001-2010 balanced panel data of 19 major currencies. This paper may fill the gap between country fundamentals and practitioners' strategies on currency investment.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.7
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• pp.663-671
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• 2008

This paper presents a real-time haptic rendering method for multi-contact interaction with virtual environments. Haptic systems often employ physics-based deformation models such as finite-element models and mass-spring models which demand heavy computational overhead. The haptic system can be designed to have two sampling times, T and JT, for the haptic loop and the graphic loop, respectively. A multi-rate output-estimation with an exponential forgetting factor is proposed to implement real-time haptic rendering for the haptic systems with two sampling rates. The computational burden of the output-estimation increases rapidly as the number of contact points increases. To reduce the computation of the estimation, the multi-rate output-estimation with reduced parameters is developed in this paper. Performance of the new output-estimation with reduced parameters is compared with the original output-estimation with full parameters and an exponential forgetting factor. Estimated outputs are computed from the estimated input-output model at a high rate, and trace the analytical outputs computed from the deformation model. The performance is demonstrated by simulation with a linear tensor-mass model.

• Journal of Distribution Science
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• v.12 no.9
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• pp.95-99
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• 2014

Purpose - This study aims to develop and empirically test a multi-dimensional service convenience scale with the dimensions and measurement items of service convenience perceived by Korean consumers in retail contexts. Research design, data, and methodology - The study adopts the framework suggested by Berry et al. (2002) and conceptualizes service convenience as a second-order reflective construct comprising 31 items. Three department store chains (Hyundai, Lotte, and Shinsegae) and three discount store chains (E-mart, Homeplus, and Lotte Mart) were involved 510 valid responses were used for the empirical testing. Results - The measurement model is acceptable for internal consistency, convergent validity, and discriminant validity. Further, the structural model results show that service convenience is positively related to satisfaction. Results of the rival model comparison indicate that the proposed second-order factor model provides a better fit to the data than both the five-factor and the one-factor model. Conclusions - The multi-dimensional, second-order conceptualization of service convenience is robustly supported. This study provides psychometrically valid scales to measure service convenience in retail contexts as conceptualized by Berry et al. (2002).

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.11
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• pp.1079-1088
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• 2001

A dynamic simulation model was developed to analyse the transient characteristics of a multi-inverter heat pump. The programs included a basic air conditioning system such as a evaporator, condenser, compressor, linear electronic expansion valve (LEV) and by-pass circuit. The theoretical model was derived from mass conservation and energy conservation equations to predict the performance of the multi-inverter heat pump at various operating conditions. Calculated results were compared with the values obtained from the experiments at different operation frequencies of compressor, area of the LEV and configuration of indoor units operation. The results of the simulation model showed a good agreement with the experimental ones, so that the model could be used as an efficient tool for thermodynamic design and control factor design of air-conditioners.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.7
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• pp.1-14
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• 1999

In the present study, the flame factor which primarily influence the simulation accuracy of the combustion process in a gasoline engine was modeled as a nonlinear function of turbulent intensity to laminar flame speed ratio. Multi-length-scale production rate model for turbulent kinetic energy equation was introduced to consider the different length scales of the swirling and tumbling motions in cylinder on the production rte of turbulent kinetic energy. By7 introducing the multi-length-scale production rate model for the turbulent kinetic energy equation, the predictions of turbulent burning velocity , cylinder pressure, mass burning rate and engine performance of a gasoline engine can much be improved.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.47-50
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• 2000

In this paper, an optimal identification method using Multi-FNN(Fuzzy-Neural Network) is proposed for model ins of nonlinear complex system. In order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM clustering algorithm which carry out the input-output data preprocessing function and Genetic Algorithm which carry out optimization of model. The proposed Multi-FNN is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. HCM clustering method which carry out the data preprocessing function for system modeling, is utilized to determine the structure of Multi-FNN by means of the divisions of input-output space. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model, To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.639-645
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• 2009

Fractional Brwonian motion(fBm) has properties of behaving tails and exhibiting long memory while remaining Gaussian. In particular, it is well known that interest rates show some long memories and non-Markovian. We present no aribitrage condition for HJM model under the multi-factor fBm reflecting the long range dependence in the interest rate model.

• Computers and Concrete
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• v.19 no.6
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• pp.725-736
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• 2017

This paper presents the two-, three-, and four-lane transverse reduction factor based on FEA method, probability theory, and the recently actual traffic flow data. A total of 72 composite girder bridges with various spans, number of lanes, loading mode, and bridge type are analyzed with time-varying static load FEA method by ANSYS, and the probability models of vehicle load effects at arbitrary-time point are developed. Based on these probability models, in accordance to the principle of the same exceeding probability, the multi-lane transverse reduction factor of these composite girder bridges and the relationship between the multi-lane transverse reduction factor and the span of bridge are determined. Finally, the multi-lane transverse reduction factor obtained is compared with those from AASHTO LRFD, BS5400, JTG D60 or Eurocode. The results show that the vehicle load effect at arbitrary-time point follows lognormal distribution. The two-, three-, and four-lane transverse reduction factors calculated by using FEA method and probability respectively range between 0.781 and 1.027, 0.616 and 0.795, 0.468 and 0.645. Furthermore, a correlation between the FEA and AASHTO LRFD, BS5400, JTG D60 or Eurocode transverse reduction factors is made for composite girder bridges. For the two-, three-, and four-lane bridge cases, the Eurocode code underestimated the FEA transverse reduction factors by 27%, 25% and 13%, respectively. This underestimation is more pronounced in short-span bridges. The AASHTO LRFD, BS5400 and JTG D60 codes overestimated the FEA transverse reduction factors. The FEA results highlight the importance of considering span length in determining the multi-lane transverse reduction factors when designing two-lane or more composite girder bridges. This paper will assist bridge engineers in quantifying the adjustment factors used in analyzing and designing multi-lane composite girder bridges.