• Journal of Korean Critical Care Nursing
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• v.8 no.2
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• pp.13-22
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• 2015

Purpose: This study identified the current status and perception of intensive care unit nurses' handover. Methods: A cross sectional descriptive survey was employed. The population included nurse managers and staff nurses who worked in intensive care units in hospitals with more than 500 beds and excluded nursing homes, psychiatric hospitals, and military hospitals. Results: Of the nurses, 61.7% were satisfied with the current handover method, 68.36% had no handover-related guidelines, and 83.2% of them perceived that the handover was important for patients' safety. The most frequent cause for errors related to handover was that the "nursing workload is heavy." The nurses perceived that their handover was informative ($5.62{\pm}0.79$) and efficient ($5.04{\pm}0.98$). The variables associated with their perception of the handover were the level of satisfaction with the current handover method, existence of handover guidelines, and importance of handover for patient safety. Conclusion: The development of standardized handover guidelines, especially for intensive care units, is necessary to reduce handover time and errors and to improve handover quality for patients'safety and high standards of nursing care.

• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.248-256
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• 2020

In this study, predictive models based on machine learning for evaluating the extent of hazardous zone of explosive gases are developed. They are able to provide important guidelines for installing the explosion proof apparatus. 1,200 research data sets including 12 combustible gases and their extents of hazardous zone are generated to train predictive models. The extent of hazardous zone is set to an output variable and 12 variables affecting an output are set as input variables. Multiple linear regression, principal component regression, and artificial neural network are employed to train predictive models. Mean absolute percentage errors of multiple linear regression, principal component regression, and artificial neural network are 44.2%, 49.3%, and 5.7% and root mean square errors are 1.389m, 1.602m, and 0.203 m respectively. Therefore, it can be concluded that the artificial neural network shows the best performance. This model can be easily used to evaluate the extent of hazardous zone for explosive gases.

• Journal of the Korean Society of Visualization
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• v.3 no.2
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• pp.71-78
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• 2005

Uncertainty introduced by a cross-correlation algorithm based on FFT has been investigated using PIV standard images. The standard images were generated by the Monte Carlo simulation method. Both bias and random errors from the velocity vector have been analyzed with regard to the particle diameter, displacement, and the number of particles. The uncertainty of velocity is evaluated based upon the IS0/IEC standard. As a result, a total error of $0.26\%$ is included in the PIV cross-correlation algorithm. In addition, the uncertainty budget is presented, where the effect of the above three variables is examined. According to the budget, the variation of the number of particles within the interrogation window mainly contributes to the combined standard uncertainty of the real measured velocity field when excluding the effect of errors by the experiments itself. Finally, the expanded uncertainty is found to be about $12\%$ at the $95\%$ confidence level.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.591-598
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• 2018

Short term load forecasts complexly affected by socioeconomic factors and weather variables have non-linear characteristics. Thus far, researchers have improved load forecast technologies through diverse techniques such as artificial neural networks, fuzzy theories, and statistical methods in order to enhance the accuracy of load forecasts. Short term load forecast errors for special days are relatively much higher than that of weekdays. The errors are mainly caused by the irregularity of social activities and insufficient similar past data required for constructing load forecast models. In this study, the load characteristics of Lunar New Year's Day holidays well known for the highest error occurrence holiday period are analyzed to propose a load forecast technique for Lunar New Year's Day holidays. To solve the insufficient input data problem, the similarity of the load patterns of past Lunar New Year's Day holidays having similar patterns was judged by Euclid distance. Lunar New Year's Day holidays periods for 2011-2012 were forecasted by the proposed method which shows that the proposed algorithm yields better results than the comprehensive analysis method or the knowledge-based method.

• Journal of Korean Academy of Nursing Administration
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• v.4 no.1
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• pp.193-214
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• 1998

The purpose of the present study was to examine the causal model of nurses' organizational commitment. Based on literature review and Fishbein's behavioral intentions model ((Fishbein. 1967: Fishbein & Ajzen. 1975). the organizational commitment was conceptualized within a motivational framework that mediate between antecedents variables and outcome variables. Antecedent variables were pay, promotional chances. continuing education opportunity. rigidity of the administration. paticipative decision making, latitude, group support, role conflict, work load, need for achievement. experience and pride for professional nursing. Outcome variable was turnover intention. The subjects were 373 nurses who were working at 2 large general hospitals located in Seoul. It represents a response rate of 94%. Data for this study was collected from August 29 to September 22 in 1997 by Questionnaire. Path analysis with LISREL 7.16 prigram was used to test the fit of the proposed conceptual model to data and to examine the causal relationships 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 analysis can not count measurment errors: measurement error can attenuate estimates of coefficient and explanatory power. Nontheless the model revealed considerable explanatory power for organizational commitment (58%), pride for professional nursing (50%) and turnover intention(40%). In predicting nurses' organizational commitment, the findings of this study clearly demonstrated 'the pride for professional nursing' might be the most important variables of all the antecedent variables. Group support, role conflict, need for achievement were also found to be important determinants for the organizational commitment and turnover intention, The result showed experience might be a predictor for 'pride for professional nursing' and 'turnover intention' but not 'organizational commitment', 'Rigidity of the administration' and latitude were also found to have important roles in predictingr the organizational commitment, while participative decision making might have an impact on turnover intention. On the other hand promotional chance had an influence on all the outcome variables, while pay only on turnover intention. In predicting turnover intention, the result clearly revealed 'the pride for professional nursing' and 'organizational commitment' might be the most powerful predictors among all the variables. Theses results were discussed, including directions for the future research and practical implications drawn from the research were suggested.

• Journal of Korean Academy of Nursing Administration
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• v.4 no.1
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• pp.183-214
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• 1998

The purpose of the present study was to examine the causal model of nurses' organizational commitment. Based on literature review and Fishbein's behavioral intentions model ((Fishbein, 1967;Fishbein & Ajzen. 1975), the organizational commitment was conceptualized within a motivational framework that mediate between antecedents variables and outcome variables. Antecedent variables were pay, promotional chances, continuing education opportunity, rigidity of the administration, paticipative decision making, latitude, group support, role conflict, work load, need for achievement, experience and pride for professional nursing. Outcome variable was turnover intention. The subjects were 373 nurses who were working at 2 large general hospitals located in Seoul. It represents a response rate of 94%. Data for this study was collected from August 29 to September 22 in 1997 by Questionnaire. Path analysis with LISREL 7.16 prigram was used to test the fit of the proposed conceptual model to data and to examine the causal relationships 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 analysis can not count measurement errors; measurement error can attenuate estimates of coefficient and explanatory power. Nontheless the model revealed considerable explanatory power for organizational commitment (58%). pride for professional nursing (50%) and turnover intention(40%). In predicting nurses' organizational commitment. the findings of this study clearly demonstrated 'the pride for professional nursing' might be the most important variables of all the antecedent variables. Group support. role conflict, need for achievement were also found to be important determinants for the organizational commitment and turnover intention. The result showed experience might be a predictor for 'pride for professional nursing' and 'turnover intention' but not 'organizational commitment'. 'Rigidity of the administration' and latitude were also found to have important roles in predictor for the organizational commitment, while participative decision making might have an impact on turnover intention. On the other hand promotional chance had an influence on all the outcome variables, while pay only on turnover intention. In predicting turnover intention, the result clearly revealed 'the pride for professional nursing' and 'organizational commitment' might be the most powerful predictors among all the variables. Theses results were discussed, including directions for the future research and practical implications drawn from the research were suggested.

• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.3
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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.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.792-795
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• 1996

In TIG welding of pipe, back bead size monitoring is important for weld quality assurance. Many researches have been performed on estimation of the back bead size by heat conduction analysis. However numerical conduction model based on many uncertain thermal parameters causes remarkable errors and thermomechanical phenomena in molten pool can not be considered. In this paper, filler wire feeding force in addition to weld current, wire feedrate, torch travel speed and orbital position angle is monitored to estimate back bead size in orbital TIG welding. Monitored welding process variables are fed into an artificial neural network estimator which has been trained with the monitored process variables (input patterns) and actual back bead size (output patterns). Experimental verification of the proposed estimation method was performed. The predicted results are in a good agreement with the actual back bead shape. The results are quite promising in that estimation of invisible back bead shape can be achieved by analyzing the welding parameters without any conventional NDT of welds.

• Journal of Ocean Engineering and Technology
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• v.19 no.2 s.63
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• pp.40-46
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• 2005

In order to consider the statistical properties of probability variables which are used in structural analysis, the conventional approach of using safety factors based on past experience, are usually used to estimate the safety of a structure. The real structures could only be analyzed with the error in estimation of loads, materials and dimensional characteristics. Errors should be considered systematically in the structural analysis. In this paper, we estimated the probability of failure of two pressure vessels, simultaneously, using computational analysis. One pressure vessel, theoretically, had no stiffener whereas the other had. This paper also discusses sensitivity values of random variables in the rounded parts of the pressure vessel which had ring-style stiffener in the center of the external area which had ring-style stiffener. Finally, we show that the reliability index, and the probability of failure, can be calculated to particular tolerance limits.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.2B no.3
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• pp.81-89
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• 2002

The sensorless AC motor drive is a popular topic of study due to the cost and reliability of speed and position sensors. Most sensorless algorithms are based on the mathematical modeling of motors including electrical variables such as phase current and voltage. Therefore, the accuracy of such variables largely affects the performance of the sensorless AC motor drive. However, the output voltage of the SVPWM-VSI, which is widely used in sensorless AC motor drives, has considerable errors. In particular, the SVPWM-VSI is error-prone in the low speed range because the constant DC link voltage causes poor resolution in a low output voltage command and the output voltage is distorted due to dead time and voltage drop. This paper investigates a novel high-performance strategy for overcoming these problems in a sensorless ac motor drive. In this paper, a variation of the DC link voltage and a direct compensation for dead time and voltage drop are proposed. The variable DC link voltage leads to an improved resolution of the inverter output voltage, especially in the motor's low speed range. The direct compensation for dead time and voltage drop directly calculates the duration of the switching voltage vector without the modification of the reference voltage and needs no additional circuits. In addition, the proposed strategy reduces a current ripple, which deteriorates the accuracy of a monitored current and causes torque ripple and additional loss. Simulation and experimentation have been performed to verify the proposed strategy.