• Korean Journal of Adult Nursing
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• v.16 no.3
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• pp.355-365
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• 2004

Purpose: The aims of this study were to develope a structural model of health insensitivity and to verify the model of health insensitivity. Method: There were three theoretical variables in the hypothetical model. The endogenous variable was health insensitivity which is a concept including bluntness of health risk perception and unhealthy behavior. The exogenous variables were composed of personal factors and socio-cultural factors. In personal factors, neuroticism, external health locus of control, blunting style of information-seeking, deficit of self-efficacy, knowledge deficit related to health, health-related experience, age and education were included. Whereas socio-cultural factors include perceived group size of unhealthy behavior and stereotypes of unhealthy behavior. Result: Personal factors and sociocultural factors were significant in explanation of the health insensitivity. Relationship between personal factors and sociocultural factors was significant, too. However, the optimistic bias as part of health insensitivity was not supported by these data. GFI, AGFI and PGFI were .95, .92, .65, respectively. Therefore, this model was verified to be a good fit to the data and parsimonious. Conclusion: Nursing to change unhealthy behavior has focused on personal factors rather than sociocultural factors. Based on this result, however, the sociocultural factors should be considered as well.

• Journal of agriculture & life science
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• v.45 no.6
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• pp.279-289
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• 2011

The purpose of this study is to estimate efficiency of item-based agricultural cooperative by using Data Envelopment Analysis. A proposed method employs a bootstrapping approach to generating efficiency estimates through Monte Carlo simulation resampling process. The technical efficiency, pure technical efficiency, and scale efficiency measure of item-based agricultural cooperative is 0.80, 0.87, 0.93 respectively. However the bias-corrected estimates are less than those of DEA. We know that the DEA estimator is an upward biased estimator. In technical efficiency, average lower and upper confidence bounds of 0.726 and 0.8747. According to these results, the DEA bootstrapping model used here provides bias-corrected and confidence intervals for the point estimates, it is more preferable.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.49-60
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• 2021

BCSA (Bias-Correction and Stochastic Analog) is a statistical downscaling technique designed to effectively correct the systematic errors of GCM (General Circulation Model) output and reproduce basic statistics and spatial variability of the observed precipitation filed. In this study, the applicability of BCSA was evaluated using the ASOS observation data over South Korea, which belongs to the monsoon climatic zone with large spatial variability of rainfall and different rainfall characteristics. The results presented the reproducibility of temporal and spatial variability of daily precipitation in various manners. As a result of comparing the spatial correlation with the observation data, it was found that the reproducibility of various climate indices including the average spatial correlation (variability) of rainfall events in South Korea was superior to the raw GCM output. In addition, the needs of future related studies to improve BCSA, such as supplementing algorithms to reduce calculation time, enhancing reproducibility of temporal rainfall patterns, and evaluating applicability to other meteorological factors, were pointed out. The results of this study can be used as the logical background for applying BCSA for reproducing spatial details of the rainfall characteristic over the Korean Peninsula.

• Journal of muscle and joint health
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• v.28 no.1
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• pp.10-17
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• 2021

Purpose: This was integrative literature review for analyzing interventions of musculoskeletal symptom and pain on nurses. Methods: Data were searched by using databases, namely SAGE journals, EBSCOhost, PubMed, RISS, NDSL and KCI Search terminology included nurse and musculoskeletal and the language was Korean or Engli.sh. Data analysis was classified by risk of bias, inconsistency, indirectness and imprecision. Total six studies, one Korean and five English articles analyzed. Results: Risk of bias was serious in three of six studies. Regarding inconsistency, one study was moderate effect size and two studies were small effect size. There was no serious indirectness except one study. Regarding imprecision, three studies were not serious. Conclusion: Evidence from six studies was insufficient to support intervention's effect in means of no repeated studies and no high effect size of intervention. Evidence-based interventions must be applied on nurses for reducing musculoskeletal symptoms.

• Nuclear Engineering and Technology
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• v.51 no.7
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• pp.1853-1859
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• 2019

The two-thermocouple method was investigated experimentally to evaluate its accuracy for the measurement of local wall temperature and heat flux on a heat transfer tube with an electric heater rod installed in an annulus channel. This work revealed that a thermocouple flush-mounted in a surface groove serves as a good reference method for the accurate measurement of the wall temperature, whereas two thermocouples installed at different depths in the tube wall yield large bias errors in the calculation of local heat flux and wall temperature. These errors result from conductive and convective changes due to the fin effect of the thermocouple sheath. To eliminate the bias errors, we proposed a calibration method based on both the local heat flux and Reynolds number of the cooling water. The calibration method was validated with the measurement of local heat flux and wall temperature against experimental data obtained for single-phase convection and two-phase condensation flows inside the tube. In the manuscript, Section 1 introduces the importance of local heat flux and wall temperature measurement, Section 2 explains the experimental setup, and Section 3 provides the measured data, causes of measurement errors, and the developed calibration method.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.177-190
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• 2021

Random forests is a popular method that improves the instability and accuracy of decision trees by ensembles. In contrast to increasing the accuracy, the ease of interpretation is sacrificed; hence, to compensate for this, variable importance is provided. The variable importance indicates which variable plays a role more importantly in constructing the random forests. However, when a predictor is correlated with other predictors, the variable importance of the existing importance algorithm may be distorted. The downward bias of correlated predictors may reduce the importance of truly important predictors. We propose a new algorithm remedying the downward bias of correlated predictors. The performance of the proposed algorithm is demonstrated by the simulated data and illustrated by the real data.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.62-66
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• 2023

With the advancement in technology and rapid increase in the demand for microelectromechanical systems (MEMS) based inertial measurement units (IMUs), high-volume production and test system remain a major challenge for the MEMS industry. To compete with the challenging market of Industry 4.0, here we developed an automatic test system to evaluate the performance of the ovenized IMU sensors as well as analyze the data. The automatic test system was developed by interfacing a commercial MEMS IMU (BMI 088) using LabVIEW. The BMI 088 was tested experimentally for long-term bias stability, ON/OFF bias repeatability, and root mean square (rms) noise. Furthermore, the data was analyzed through the developed test system. The results show that the automatic test system has improved the test time and reduced human effort. The developed automatic test system is a significant approach to MEMS research and development (R&D) to increase and improve the mass production of IMUs.

• Journal of the Korean Data and Information Science Society
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• v.5 no.2
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• pp.107-116
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• 1994

이 논문은 통계상담사례연구의 일종으로 설문지를 이용한 통계자료분석에 도움이 되도록 소개하고자 한다. 여러항목을 종합하여 한 척도를 구성하고 각 항목마다 중요도나 선호도 등에 따라 다품등 리커트형 척도를 묻는 자료는 개인의 성향이나 지리적 문화적 여건에 따라 그 응답경향이 달라 가질지도 모르는 반응편향성(Response Set Bias)을 감소시켜 보다 좋은 통계자료분석을 할 수 있는 통계적 방법을 소개하며, 이 방법을 적용시킨 통계상담을 예시하고자 한다.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.279-287
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• 2003

In this paper we propose a local smoothing of the Nelson type estimator for the survival function based on an approximation by the Weibull distribution function. It appears that Mean Square Error and Bias of the smoothed estimator of the Nelson type survival function estimators are significantly smaller than that of the smoothed estimator of the Kaplan-Meier survival function estimator.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.337-343
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• 2003

In this paper we present the prediction interval estimation method using bootstrap method for least squares support vector machine(LS-SVM) regression, which allows us to perform even nonlinear regression by constructing a linear regression function in a high dimensional feature space. The bootstrap method is applied to generate the bootstrap sample for estimation of the covariance of the regression parameters consisting of the optimal bias and Lagrange multipliers. Experimental results are then presented which indicate the performance of this algorithm.