• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.619-627
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• 2002

It is unpractical for the optimal design theory based on the given model and assumption to be applied to the real-world experimentation. Particularly, when the experimenter feels it necessary to consider multiple objectives in experimentation, its modified version of optimality criteria is indeed desired. The constrained optimal design is one of many methods developed in this context. But when the number of constraints exceeds two, there always exists a problem in specifying the lower limit for the efficiencies of the constraints because the “infeasible solution” issue arises very quickly. In this paper, we developed a sequential approach to tackle this problem assuming that all the constraints can be ranked in terms of importance. This approach has been applied to the polynomial regression model.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.4
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• pp.135-141
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• 2000

The major role of temperature sensors in thermal error compensation system of machine tools is improving machining accuracy by supplying reliable temperature data on the machine structure. This paper presents a new method for fault diagnosis of temperature sensors and recovery of faulted data to establish the reliability of thermal error compensation system. The detection of fault and its location is based on the correlation coefficients among temperature data from the sensors. The multiple linear regression model which is prepared using complete normal data is also used fur the recovery of faulted data. The effectiveness of this method was tested by comparing the computer simulation results and measured data in a CNC machining center.

• New & Renewable Energy
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• v.2 no.2 s.6
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• pp.37-43
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• 2006

In this paper, the first forecasting system of wind power generation, KIER Forecaster is presented. KIER Forecaster has been constructed based on statistical models and was trained with wind speed data observed at Gosan Weather Station nearby Walryong Site. Due to short period of measurements at Walryong Site for training the model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict(MCP) technique. The results of One to Three-hour advanced forecasting models are consistent with the measurement at Walryong site. In particular, the multiple regression model by classification of wind speed pattern, which has been developed in this work, shows the best performance comparing with neural network and auto-regressive models.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.384-388
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• 1997

Recently,direct rolling process has been drawing increasing interests because production cost be greatly reduced by eliminating subsequent hot rolling processes. Such a process has been characterized to prosuce thin steel strip (thickness 1~5mm) directly from molten metal and to skip over the conventional hot rolling processes. However, since there are several process parameters, which affect the quality of product,and their relationship between the parametersare very complex,it is therefore very difficult to realize the process design and the quality control. To overcome these difficulties quantitative relationship between the parameters are investigated through a numerical analysis. Form these results, it is found that solidification final point is the most important paramter which is critical to quality of the strip. Also,the multiple regression model is obtianed to determine their relationship from the solidification final point and roll separating force which can be easily estimated

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.365-370
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• 1999

This paper describes the Weekly Load Forecasting Expert System(Named WLoFy) which was developed and implemented for Korea Electric Power Corporation(KEPCO). WLoFy was designed to provide user oriented features with a graphical user interface to improve the user interaction. The various forecasting models such as exponential smoothing, multiple regression, artificial nerual networks, rult-based model, and relative coefficient model also have been included in WLofy to increase the forecasting accuracy. The simulation based on historical data shows that the weekly forecasting results form WLoFy is an improvement when compared to the results from the conventional methods. Especially the forecasting accuracy on special days has been improved remakably.

• IE interfaces
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• v.23 no.1
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• pp.78-88
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• 2010

In this paper we develop a model for the performance evaluation of defense R&D test development projects based on analytic hierarchy process. First, evaluation indicators are collected through the related literature survey and a delphi inquiry method. Second, stepwise multiple linear regression is used for developing a hierarchical structure for analytic hierarchy process in the evaluation model, which can make the selected evaluation indicators of the hierarchical structure independent. Also we verify the effectiveness of proposed indicators of the performance evaluation by comparing with the existing evaluation indicators. The developed indicators for the performance evaluation is more reasonable and practical than the previous indicators on defense R&D test development projects.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.187-189
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• 2004

This paper investigates the effect of weather factors(such as winds, rain, snows, temperature, clouds and humidity) on transmission line outages. The result shows that weather variables have significant effects on the transmission line historical outages and the relationship between them is nonlinear. Multiple regression analysis using Logit model is proved to be appropriate in forecasting line failure rate in KEPCO systems. It could also provide system operators with useful informations about system operation and planing.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.3
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• pp.416-424
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• 2019

This paper studies the optimal inventory levels of P-3 assemblies in order to assure the required operational availability. A simulation model is developed for identifying the impact of the inventory levels on operational availability. Based on the result of the simulation model, multiple regression analysis is performed. Finally, the optimal inventory levels of critical P-3 assemblies are determined with integer programming. Additionally, sensitivity analysis of depot maintenance period is also conducted for its impact on the operational availability.

• Journal of the Semiconductor & Display Technology
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• v.21 no.1
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• pp.17-21
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• 2022

In the case of the manufacturing industry that produces high-tech components such as semiconductors and large flat panel displays, the manufacturing space is made into a cleanroom to increase product yield and reliability, and various environmental factors have been managed to maintain the environment. Among them, airborne particle is a representative management item enough to be the standard for actual cleanroom grade, and a sequential particle monitoring system is usually used as one parts of the FMS (Fab or Facility monitoring system). However, this method has a problem in that the measurement efficiency decreases as the length of the sampling tube increases. In this study, in order to solve this problem, a multiple regression model was created. This model can correct the measurement error due to the decrease in efficiency by sampling tube length.

• Journal of the Korean Society of Safety
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• v.29 no.6
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• pp.151-157
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• 2014

This study deals with the rear-end collision at roundabouts. The purpose of this study is to develop the accident models of rear-end collision in Korea. In pursuing the above, this study gives particular attention to developing the appropriate models using Poisson, negative binomial model, ZAM, multiple linear and nonlinear regression models, and statistical analysis tools. The main results are as follows. First, the Vuong statistics and overdispersion parameters indicate that ZIP is the most appropriate model among count data models. Second, RMSE, MPB, MAD and correlation coefficient tests show that the multiple nonlinear model is the most suitable to the rear-end collision data. Finally, such the independent variables as traffic volume, ratio of heavy vehicle, number of circulatory roadway lane, number of crosswalk and stop line are adopted in the optimal model.