• Journal of Information Technology Applications and Management
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• v.19 no.4
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• pp.227-242
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• 2012

It is a daunting task to theorize the process effectiveness of IT convergence based service model. Despite the criticalness of investigating process enhancement impact of IT-convergence based service model, the theoretical research in this field is relatively scarce, possibly due to the too wide and comprehensiveness of research scope. In this vein, we conducted exploratory study to understand the contributional impact of IT convergence based service model on resolving service process limitations. We first identified five IT convergence based service models in the area of typical service industry, which include entertainment, learning, location based services, tourism, and healthcare. Our research model classified value creation factors of the IT convergence model in twofold. The one is defined as basic value creation factor of the IT convergence, which is treated as the second-order factor that consists of two first-order factors of mobile functionality and Internet with digital contents merging functionality. The other is defined as service process limitations resolving factor which are comprised with the two first-order factors of simultaneousity and perishability. Both the second-order factors are modeled, each respectively, with the two first-order factors in formative manner. Using PLS, empirical validation is executed to analyze each value creating factor's contribution impact on the relative advantage, as well as the mediating effect of basic value creation factor on resolving service process limitations. On the basis of the insights revealed from this paper, further theory building research could be elaborated in the area of IT convergence applications for service industry.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.931-936
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• 1993

Optimal model following control scheme is to design the controller which makes the response of real system follow that of desirable model. This kind of design scheme is developed for first order system. We extends the scheme for second order system regarding the characteristics of mechanical second order system for vibration suppression of flexible structures. The model of mechanical second order system is obtained using suitable damping ratios and natural frequencies. Using this scheme, we can design the good controller which uses the characteristic of second order system. Numerical examples are presented which were used optimal model following control scheme.

• Magazine of the Korean Society of Agricultural Engineers
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• v.38 no.4
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• pp.110-124
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• 1996

This study was conducted to simulate long seres of synthetic monthly flows by multi-season first order Markov model with selection of best fitting frequency distribution, harmonic synthetic and harmonic regression models and to make a comparison of statistical parameters between observes and synthetic flows of five watersheds in Geum river system. The results obtained through this study can be summarized as follow. 1. Both gamma and two parameter lognormal distributions were found to be suitable ones for monthly flows in all watersheds by Kolmogorov-Smirnov test. 2. It was found that arithmetic mean values of synthetic monthly flows simulated by multi-season first order Markov model with gamma distribution are much closer to the results of the observed data in comparison with those of the other models in the applied watersheds. 3. The coefficients of variation, index of fluctuation for monthly flows simulated by multi-season first order Markov model with gamma distribution are appeared closer to those of the observed data in comparison with those of the other models in Geum river system. 4. Synthetic monthly flows were simulated over 100 years by multi-season first order Markov model with gamma distribution which is acknowledged as a suitable simulation modal in this study.

• Journal of Power System Engineering
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• v.19 no.3
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• pp.55-62
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• 2015

This paper discusses a new model for investigating the micro-mechanical behavior of beam-like structures composed of various elastic moduli and complex geometries varying through the cross-sectional directions and also periodically-repeated along the axial directions. The original three-dimensional problem is first formulated in an unified and compact intrinsic form using the concept of decomposition of the rotation tensor. Taking advantage of two smallness of the cross-sectional dimension-to-length parameter and the micro-to-macro heterogeneity and performing homogenization along dimensional reduction simultaneously, the variational asymptotic method is used to rigorously construct an effective zeroth-order beam model, which is similar a generalized Timoshenko one (the first-order shear deformation model) capable of capturing the transverse shear deformations, but still carries out the zeroth-order approximation which can maximize simplicity and promote efficiency. Two examples available in literature are used to demonstrate the consistence and efficiency of this new model, especially for the structures, in which the effects of transverse shear deformations are significant.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.1
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• pp.141-157
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• 1997

The structure and dimensionality of the User Information Satisfaction (UIS) construct is an important theoretical issue that received considerable attentions. The acceptance of UIS as a standardized instrument requires confirmation that it explains and measures the user information satisfaction construct and its component. Based on a simple of 670 respondents who participated in dealing with the Public Management Information System (PMIS), this research used a confirmatory factor analysis to test the alternavtive models of underlying factor structure and assessed the reliability and validity of these factors and items in the PMIS. The result provided a support for a revised UIS model with four first-order factors and one PMIS The result provided a support for a revised UIS model with four first-order factors and one second-order (higher-order) factor in PMIS. To cross-validata these results, the author reexamined two prior data sets. The results showed that the revised model provides better model-data fit in all three data sets.

• The Korean Journal of Pesticide Science
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• v.9 no.3
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• pp.231-236
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• 2005

This study was conducted to determine the amounts of pesticide residues after treatment of criterion dose with 4 pesticides(tolclofos-m, folpet, procymidone, and triflumizole) under cultivated period and to compare the biological half-life of pesticides with 6 kinetic models(first, zero and second order kinetics, power function, elovich and parabolic model) and to establish proposed field tolerance using biological half-lives. Recovery of 4 pesticides form strawberry was ranged from 85.1 to 105.5%. For all of 4 pesticides, dissipation rate was over 73% at 5 days after application. Among 6 kinetic models, first order kinetic model (FO) was best fit to describe the relationship between residual pattern of pesticides and time. Therefore, half-lives were calculated by FO for establishing the field tolerance. These results showed that half-life should be calculated by comparative best fit kinetic model and field tolerance can help to prevent unacceptable agricultural products from marketing. It is good for both consumers and farmers having safe agricultural products and financial benefits, respectively.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.333-344
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• 2012

We consider some statistical properties of the first order difference-based error variance estimator in nonparametric regression models with a single outlier. So far under an outlier(s) such difference-based estimators has been rarely discussed. We propose the first order difference-based estimator using the leave-one-out method to detect a single outlier and simulate the outlier detection in a nonparametric regression model with the single outlier. Moreover, the outlier detection works well. The results are promising even in nonparametric regression models with many outliers using some difference based estimators.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.74-82
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• 2015

Two key challenges in statistical relational learning are uncertainty and complexity. Standard frameworks for handling uncertainty are probability and first-order logic respectively. A Markov logic network (MLN) is a first-order knowledge base with weights attached to each formula and is suitable for classification of dataset which have variables correlated with each other. But we need domain knowledge to construct first-order logics and a computational complexity problem arises when calculating weights of first-order logics. To overcome these problems we suggest a method to generate first-order logics and learn weights using association analysis in this study.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1062-1070
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• 2018

This paper presents a simple identification method for discrete-time low-order model of unknown delay process from pulse response. The key idea is to find the parameters of the model such that the first N moments of the unknown process and the model are equal. We first show that the k-th moment of a process can be determined by the moments of the input and output. The parameters and delay are estimated separately. It is shown that for a given delay, the parameters of the low-order model can be determined by solving linear equations in a matrix form. Delay of the model is estimated such that the integral of the absolute errors (IAE) of the candidate models with possible delays minimizes. The illustrative example shows that the proposed method can directly identify low-order models without order reduction process from a single pulse response.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.119-121
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• 2003

In small signal stability analysis of power systems, eigenvalue analysis is the most useful method and the detailed modeling of generator gives an important effect to the eigenvalues. Generator full model is used for precise dynamic analysis of generators and controllers while two-axis model is used for multimachine systems because of the reduced order of the state matrix. Also, the eigenvalue sensitivity coefficients are used for optimization of controller parameters to improve system stability. This paper compare the first order eigenvalue sensitivity coefficients of controllers in case of generator full model with those of two-axis model. As a result of an example the estimated eigenvalues using sensitivity coefficients in case of generator full model is very close to those of state matrix within 1% error ratios.