• 한국안전학회지
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• 제29권3호
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• pp.98-106
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• 2014

This study proposes severity analysis for pedestrian accidents by improving variables which were used for general severity analysis. The existing variables were collected based on the interviews with policeman or witnesses and evidence of accidents. Therefore, existing variables were subjective and had several measurement errors. In order to improve such problems, this study collected variables from vehicle recorder of taxi which recorded the moment of accidents. As a result, explanatory power of independent variables was enhanced and the complete objective variables could be collected. After collecting variables, ordered probit model was developed by utilizing vehicle recorder database. Fitness of ordered probit model was 0.23. Vehicle speed and pedestrian's eye direction variables were the most critical factors for severity of pedestrian accident. In addition, severity analysis for vulnerable pedestrian was carried out. As a result, it was revealed that vehicle speed, pedestrian's eye direction and safety zone variables affected the severity of pedestrian accidents most. Particularly, vehicle speed variable is the most important factor. Consequently, driver's defensive driving and compliance to the regulations are the priority to reduce severity of pedestrian accidents and prevent pedestrian accident.

• 대한산업공학회지
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• 제35권2호
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• pp.178-184
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• 2009

The Nuclear Power Plant(NPP) industry in Korea has been making efforts to reduce the human errors which have largely contributed to about 150 nuclear reactor trips since 2001. Recently, the Crew Resource Management(CRM) training has risen as an alternative countermeasure against the nuclear reactor trips caused by human errors. The effectiveness of CRM training in NPP industry, however, has not been proven to be significant yet. In this study a return on investment(ROI) model is developed to measure the effectiveness of CRM training for the operators in Korean NPP. The model consists of mathematical expressions including multiple variables affecting the CRM training impacts and nuclear reactor trips. Implication of the model is discussed further in detail.

• 한국농공학회논문집
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• 제57권3호
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• pp.9-19
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• 2015

Internal air temperature of greenhouse is an important variable that can be influenced by the complex interaction between outside weather and greenhouse inside climate. This paper focuses on a data-based model approach to predict internal air temperature of the greenhouse. External air temperature, solar radiation, wind speed and wind direction were measured next to an experimental greenhouse supported by the Electronics and Telecommunications Research Institute and used as input variables for the model. Internal air temperature was measured at the center of three sections of the greenhouse and used as an output variable. The proposed model consisted of a transfer function including the four input variables and tested the prediction accuracy according to the sampling interval of the input variables, the orders of model polynomials and the time delay variable. As a result, a second-order model was suitable to predict the internal air temperature having the predictable time of 20-30 minutes and average errors of less than ${\pm}1K$. Afterwards mechanistic interpretation was conducted based on the energy balance equation, and it was found that the resulting model was considered physically acceptable and satisfied the physical reality of the heat transfer phenomena in a greenhouse. The proposed data-based model approach is applicable to any input variables and is expected to be useful for predicting complex greenhouse microclimate involving environmental control systems.

• 한국농공학회지
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• 제22권3호
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• Communications for Statistical Applications and Methods
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• 제28권6호
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• pp.595-610
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• 2021

Recently a few articles have derived robust first-order rotatable and D-optimal designs for the lifetime response having distributions gamma, lognormal, Weibull, exponential assuming errors that are correlated with different correlation structures such as autocorrelated, intra-class, inter-class, tri-diagonal, compound symmetry. Practically, a first-order model is an adequate approximation to the true surface in a small region of the explanatory variables. A second-order model is always appropriate for an unknown region, or if there is any curvature in the system. The current article aims to extend the ideas of these articles for second-order models. Invariant (free of the above four distributions) robust (free of correlation parameter values) second-order rotatable designs have been derived for the intra-class and inter-class correlated error structures. Second-order rotatability conditions have been derived herein assuming the response follows non-normal distribution (any one of the above four distributions) and errors have a general correlated error structure. These conditions are further simplified under intra-class and inter-class correlated error structures, and second-order rotatable designs are developed under these two structures for the response having anyone of the above four distributions. It is derived herein that robust second-order rotatable designs depend on the respective error variance covariance structure but they are independent of the correlation parameter values, as well as the considered four response lifetime distributions.

• 한국산림과학회지
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• 제95권2호
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• pp.203-208
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• 2006

This study compared the roundwood demand prediction accuracy of econometric and time-series models using Korean data. The roundwood was divided into softwood and hardwood by species. The econometric model of roundwood demand was specified with four explanatory variables; own price, substitute price, gross domestic product, dummy. The time-series model was specified with lagged endogenous variable. The dummy variable reflected the abrupt decrease in roundwood demand in the late 1990's in the case of softwood roundwood, and the boom of plywood export in the late 1970's in the case of hardwood roundwood. On the other hand, the prediction accuracy was estimated on the basis of Residual Mean Square Errors(RMSE). The results showed that the softwood roundwood demand prediction can be performed more accurately by econometric model than by time-series model. However, the hardwood roundwood demand prediction accuracy was similar in the case of using econometric and time-series model.

• 산업경영시스템학회지
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• 제35권4호
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• pp.24-32
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• 2012

Of many approaches to reduce motion analysis errors, the compensation method of anatomical landmarks estimates the position of anatomical landmarks during motion. The method models the position of anatomical landmarks with joint angle or skin marker displacement using the data of the so-called dynamic calibration in which anatomical landmark positions are calibrated in ad hoc motions. Then the anatomical landmark positions are calibrated in target motions using the model. This study applies the compensation methods with joint angle and skin marker displacement to three lower extremity motions (walking, sit-to-stand/stand-to-sit, and step up/down) in ten healthy males and compares their performance. To compare the performance of the methods, two sets of kinematic variables were calculated using different two marker clusters, and the difference was obtained. Results showed that the compensation method with skin marker displacement had less differences by 30~60% compared to without compensation. And, it had significantly less difference in some kinematic variables (7 of 18) by 25~40% compared to the compensation method with joint angle. This study supports that compensation with skin marker displacement reduced the motion analysis STA errors more reliably than with joint angle in lower extremity motion analysis.

• 전기학회논문지
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• 제66권4호
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• pp.621-628
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• 2017

This paper presents an artificial neural network (ANN) based model with a back-propagation algorithm for short-term load forecasting in microgrid power systems. Owing to the significant weather factors for such purpose, relevant input variables were selected in order to improve the forecasting accuracy. As remarked above, forecasting is more complex in a microgrid because of the increased variability of disaggregated load curves. Accurate forecasting in a microgrid will depend on the variables employed and the way they are presented to the ANN. This study also shows numerically that there is a close relationship between forecast errors and the number of training patterns used, and so it is necessary to carefully select the training data to be employed with the system. Finally, this work demonstrates that the concept of load forecasting and the ANN tools employed are also applicable to the microgrid domain with very good results, showing that small errors of Mean Absolute Percentage Error (MAPE) around 3% are achievable.

• 간호행정학회지
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• 제6권1호
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• pp.109-122
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• 2000

The present study examined the causal relationships among nurses' perceived autonomy, job satisfaction, work environment (work overload, role conflict, situational support, head nurses' leadership), personal aspects(experiences, need for achievement, professional knowledge and skill) by constructing and testing a theoretical framework. Based on literature review nurses' perceived autonomy and job satisfaction were conceived of as outcomes of the interplay among work environment and personal characteristics. Work environment factors involved work overload, role conflict, situational support, and head nurses' leadership (task oriented leadership, relation oriented leadership). Personal charateristics included experiences, need for achievement, and professional knowledge and skill. Three large general hospital in Chonbuk were selected to participate. The total sample of 516 registered nurses represents a response rate of 92 percent. Data for this study was collected from July to September in 1998 by Questionnaire. Path analyses with LISREL 7.16 program were used to test the fit of the proposed conceptual model to the data and to examine the causal relationship among variables. The result showed that both the proposed model and the modified model fit the data excellently. It needs to be notified, however, that path analisis can not count measurement errors; measurement error can attenuate estimates of coefficient and explanatory power. Nevertheless the model revealed relatively high explanatory power. 42 percent of nurses' perceived autonomy was explained by predicted variables; 32 percent of nurses' job satisfaction was explained by by predicted variables. Tn predicting nurses' perceived autonomy the findings of this study clearly demonstrated the work overload might be the most important variable of all the antecedent variables. Head nurses' relation oriented leadership, situational supports, need for achievement, and role conflict were also found to be important determinants for nurses' perceived autonomy. As for the job satisfaction, role conflict, situational supports, need for the achievement, and head nurses' relation oriented leadership were in turn important predictors. Unexpectedly the result showed perceived autonomy have few influence on job satisfaction. The results were discussed, including directions for the future research and practical implication drawn from the research were suggested.

• 소성∙가공
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• 제21권8호
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• pp.479-484
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• 2012

The objective of this study is to compensate the elastic recovery in the flexible forming process using the predictive models. The target shape was limited to two-dimensional shape having only one curvature radius in the longitudinal-direction. In order to predict the shape error the regression and neural network models were established based on the finite element (FE) simulations. A series of simulations were conducted considering input variables such as the elastic pad thickness, the thickness of plate, and the objective curvature radius. Then, at sampling points in the longitudinal-direction, the shape errors between formed and objective shapes could be calculated from the FE simulations as an output variable. These shape errors were expressed to a representative error value by the root mean square error (RMSE). To obtain the correct objective shape the die shape was adjusted by the closed-loop using the neural network model since the neural network model shows a higher capability of estimating the shape error than the regression model. Finally the experimental result shows that the formed shape almost agreed with the objective shape.