• Journal of Information Technology Applications and Management
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• v.10 no.3
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• pp.75-92
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• 2003

In order to investigate IS(Information Systems) outsourcing cost estimation model preference of firms, this study reviews previous literatures on outsourcing and firm characteristics. The relationships between firm characteristics and IS outsourcing cost estimation model preference are analysed. Four major factors of firms characteristics are found and classified. IS outsourcing cost estimation model with SLA are found to have a strong relationship with organizational culture. Future research will be needed to verify the result of this exploratory study.

• Journal of Information Technology Applications and Management
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• v.14 no.4
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• pp.1-16
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• 2007

In the microcell- or picocell-based system the frequent movements of the mobile bring about excessive traffics into the networks. A mobile location estimation mechanism can facilitate both efficient resource allocation and better QoS provisioning through handoff optimization. Existing location estimation schemes consider only LOS model and have poor performance in presence of multi-path and shadowing. In this paper we study a novel scheme which can increase estimation accuracy by considering NLOS environment and other multiple decision parameters than the received signal strength.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.1
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• pp.20-27
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• 2001

In this paper, we propose a method for estimating the peak load of pole-transformer in summer season considering the degree of cooling load possession in customer. The cooling load of customer is selected as the most reliable parameter of peak load in summer season. The proposed estimation method is restricted to the aspect of load management for pole-transformer. The main concept of proposed method is that the error of peak load estimation using load regression equation reduces with considering the degree of cooling load possession in customer. We propose an index for estimation of cooling load possession in each customer. The proposed index is defined as cooling load possession in customer (CLPC) and obtained from the increment of monthly electric energy. The membership function for deciding the uncertainty of cooling load possession in customer is used. The database of pole-transformer in Korea Electric Power Corporation (KEPCO) is used for case studies. Through the case studies, we verify that the proposed method reduces the error of peak load estimation than the conventional method in domestic.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.702-708
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• 2019

This paper deals with break size estimation of loss of coolant accidents (LOCA) using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Previous studies used static approaches, requiring time-integrated parameters and independent firing algorithms. NARX neural network is able to directly deal with time-dependent signals for dynamic estimation of break sizes in real-time. The case studied is a LOCA in the primary system of Bushehr nuclear power plant (NPP). In this study, number of hidden layers, neurons, feedbacks, inputs, and training duration of transients are selected by performing parametric studies to determine the network architecture with minimum error. The developed NARX neural network is trained by error back propagation algorithm with different break sizes, covering 5% -100% of main coolant pipeline area. This database of LOCA scenarios is developed using RELAP5 thermal-hydraulic code. The results are satisfactory and indicate feasibility of implementing NARX neural network for break size estimation in NPPs. It is able to find a general solution for break size estimation problem in real-time, using a limited number of training data sets. This study has been performed in the framework of a research project, aiming to develop an appropriate accident management support tool for Bushehr NPP.

• Industrial Engineering and Management Systems
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• v.1 no.1
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• pp.73-78
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• 2002

In this paper, we propose a constrained optimization model for conjoint analysis (a preference decomposition technique) to improve parameter estimation by restricting the relative importance of the attributes to an extent as decided by the respondents. Quite simply, respondents are asked to provide some pairwise attribute comparisons that are then incorporated as additional constraints in a linear programming model that estimates the partial preference values. This data collection method is typical in the analytic hierarchy process. Results of a simulation study show the new model can improve the predictive accuracy in partial value estimation by ordinal east squares (OLS) regression.

• Journal of applied mathematics & informatics
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• v.41 no.2
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• pp.449-468
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• 2023

This paper deals in the study of the estimation of the parameters of Erlang distribution based on rank set sampling and some of its modifications. Here we considered Maximum Likelihood (ML) and the Bayesian technique to estimate the shape and scale parameter of Erlang distribution based on RSS and its some modifications such as ERSS, MRSS, and MRSSu. The derivation for unknown parameters of Erlang distribution is well presented using normal approximation to the asymptotic distribution of ML estimators. But due to the complexity involves in the integral, the Bayes estimator of unknown parameters is obtained using MCMC method. Further, we compared the MSE of estimation in different sampling schemes with different set sizes and cycle size. A real-life data application is also given to illustrate the efficiency of the proposed scheme.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.2
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• pp.109-118
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• 2013

We propose a method for estimating coefficients of AR (autoregressive) model which named MLPAR (Maximum Likelihood of Pearson system for Auto-Regressive model). In the present method for estimating coefficients of AR model, there is an assumption that residual or error term of the model follows the normal distribution. In common cases, we can observe that the error of AR model does not follow the normal distribution. So the normal assumption will cause decreasing prediction accuracy of AR model. In the paper, we propose the MLPAR which does not assume the normal distribution of error term. The MLPAR estimates coefficients of auto-regressive model and distribution moments of residual by using pearson distribution system and maximum likelihood estimation. Comparing proposed method to auto-regressive model, results are shown to verify improved performance of the MLPAR in terms of prediction accuracy.

• Management Science and Financial Engineering
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• v.2 no.1
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• pp.103-122
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• 1996

This paper presents an easily used graphical procedure for simultaneous estimation of the index, skewness, scale, and location parameters of the stable laws. First, the index $\alpha$ and skewness $\beta$ are estimated through the joint use of a tail length statistic $\widetilde{K_t}$ and a skewness statistic $\widetilde{K_s}$, both of which are functions of order statistics. Next, the function of order statistics needed for estimation of scale $\sigma$ and location $\mu$ are determined from a nomogram indexed on the estimates of $\alpha$ and $\beta$. Some applications and examples are provided.

• Journal of Soil and Groundwater Environment
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• v.17 no.6
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• pp.69-81
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• 2012

Diverse geostatistical tools such as kriging have been used to estimate the volume and spatial coverage of contaminated soil needed for remediation. However, many approaches frequently yield estimation errors, due to inherent geostatistical uncertainties. Such errors may yield over- or under-estimation of the amounts of polluted soils, which cause an over-estimation of remediation cost as well as an incomplete clean-up of a contaminated land. Therefore, it is very important to use a better estimation tool considering uncertainties arising from incomplete field investigation (i.e., contamination survey) and mathematical spatial estimation. In the current work, as better estimation tools we propose stochastic simulation approaches which allow the remediation volume to be assessed more accurately along with uncertainty estimation. To test the efficiency of proposed methods, heavy metals (esp., Pb) contaminated soil of a shooting range area was selected. In addition, we suggest a quantitative method to delineate the confident interval of estimated volume (and spatial extent) of polluted soil based on the spatial aspect of uncertainty. The methods proposed in this work can improve a better decision making on soil remediation.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.208-219
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• 2004

This paper considers a nonparametric estimation of lifetime distribution for ramp stress tests with stress bound under intermittent inspection. The test items are inspected only at specified time points an⊂1 so the collected observations are grouped data. Under the cumulative exposure model, two nonparametric estimation methods of estimating the lifetime distribution at use condition stress are proposed for the situation which the time transformation function relating stress to lifetime is a type of the inverse power law. Each of items is initially put on test under ramp stress and then survivors are put on test under constant stress, where all failures in the Inspection interval are assumed to occur at the midi)oint or the endpoint of that interval. Two proposed estimators of quantile from grouped data consisting of the number of items failed in each inspection interval are numerically compared with the maximum likelihood estimator(MLE) based on Weibull distribution.