• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.14-22
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• 1994

This paper proposes two Bayes estimators and their evaluation algorithms of the software reliability at the end testing stage in the Smith's Bayesian software reliability growth model under the data prior distribution BE(a, b), which is more general than uniform distribution, as a class of prior information. We consider both a squared-error loss function and the Harris loss function in the Bayesian estimation procedures. We also compare the MSE performances of the Bayes estimators and their algorithms of software reliability using computer simulations. And we conclude that the Bayes estimator of software reliability under the Harris loss function is more efficient than other estimators in terms of the MSE performances as a is larger and b is smaller, and that the Bayes estimators using the beta prior distribution as a conjugate prior is better than the Bayes estimators under the uniform prior distribution as a noninformative prior when a＞b.

• Korean Journal of Health Education and Promotion
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• v.30 no.1
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• pp.65-81
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• 2013

Objectives: The present study was carried out to develop and evaluate comprehensive health care program to prevent infectious disease and promote health in child-care centers by Doctor of Korean medicine. Methods: A nonequivalent control group pretest-posttest design study was conducted on 568 children and 85 child care teacher at 12 child care facilities for 12 weeks from July to October 2012. The program was consist of management, education, screening under concepts of traditional preventive medicine, Yangsaeng and Chimibyeong. Children's medical utilization due to infectious disease and attendance means functional status were measured by reports from parents. The Difference in difference(DID) estimator was applied data analysis, and added Zero-inflated negative binomial regression model. Also, attitudes on the infection of teacher was measured and analyzed through t-test. Results: After the intervention, the total medical utilization due to infectious disease decreased, but not significantly. Total absence, early leave and lateness decreased significantly. But, Attitude on the infection of child care teacher was not changed. The parent's satisfaction showed positive overall. Conclusions: The intervention program may be effective in preventing infectious disease and managing health in child-care center partially. To measure long-term effect, long-term study improved is requested.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.1
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• pp.81-90
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• 2002

This paper presents an Implementation of digital high-performance position sensorless control system of Reluctance Synchronous Motor(RSM) drives with Direct Torque Control(DTC). The system consists of stator flux observer, speed and torque estimator, two digital hysteresis controllers, an optimal switching look-up table, Insulated Gate Bipolar Transistor(IGBT) voltage source inverter, and TMS320C31 DSP board. The stator flux observer Is based on the combined voltage and current model with stator flux feedback adaptive control of which inputs are current and voltage sensed on motor terminal for wide speed range. In order to prove the suggested sensorless control algorithm for industrial field application, we have some simulation and actual experiment at low and high speed range. The developed high-performance speed control by fully digital system are shown a good response characteristic of control results and high performance features using 1.0[kW] RSM having 2.57 reluctance ratio of $L_d/L_q$.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.129-138
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• 2019

Ammunition is an integral element of a weapon systems and in calculating fighting strength. The Korea Army utilizes the basic load (B/L) concept to supply ammunition smoothly. The required supply rate (RSR) is the basis of a B/L that is estimated from real combat data that includes a troop's mission and operation terrain. The current RSR is based on Korean War data and the sample mean has some problems in applications to modern combat. Therefore, this study used Korea Combat Training Center (KCTC) data that is similar to real combat to estimate rifle ammunition RSR. We used a quantile of truncated Weibull distribution to estimate rifle ammunition RSR considering that rifle ammunition consumption data in KCTC is truncated. As a result, we obtained a rifle ammunition RSR which covers most ammunition consumption by reflecting the individual consumption of rifle ammunition.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.761-772
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• 2021

Simulation results for the comparison of estimators of interest are usually reported in tables or plots. However, if the simulations are conducted under various conditions for many estimators, the comparison can be difficult to be made with tables or plots. Furthermore, for algorithms that take a long time to run, the number of iterations of the simulation is costly to to be increased. The analysis of simulation results using regression models allows us to compare the estimators more systematically and effectively. Since variances in performance measures may vary depending on the simulation conditions and estimators, the heteroscedasticity of the error term should be allowed in the regression model. And multiple comparisons should be made because multiple estimators should be compared simultaneously. We introduce background theories of heteroscedasticity and multiple comparisons in the context of analyzing simulation results. We also present a concrete example.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.494-500
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• 2020

This paper proposes a new distance estimation technique for indoor localization in ultra wideband (UWB) systems. The proposed technique is based on recurrent neural network (RNN), one of the deep learning methods. The RNN is known to be useful to deal with time series data, and since UWB signals can be seen as a time series data, RNN is employed in this paper. Specifically, the transmitted UWB signal passes through IEEE802.15.4a indoor channel model, and from the received signal, the RNN regressor is trained to estimate the distance from the transmitter to the receiver. To verify the performance of the trained RNN regressor, new received UWB signals are used and the conventional threshold based technique is also compared. For the performance measure, root mean square error (RMSE) is assessed. According to the computer simulation results, the proposed distance estimator is always much better than the conventional technique in all signal-to-noise ratios and distances between the transmitter and the receiver.

• Research in Community and Public Health Nursing
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• v.32 no.1
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• pp.107-115
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• 2021

Purpose: The purpose of this study is to identify factors associated with smoking relapse within six months after quit attempts among workers in small and medium-sized enterprises in South Korea. Methods: The analysis was conducted for a total of 194 people who attempted to quit smoking by applying for a smoking cessation support service at the Regional Tobacco Control Center. The data used in the study were extracted from the Smoking Cessation Service Integrated Information System. Kaplan-Meier estimator and Cox proportional hazards regression model were used to identify variables associated with smoking relapse within six months' time period. Results: Smoking relapse rate within six months was 66.0%, and variables associated with relapse included the cases such as carbon monoxide (CO) at the time of registration (HR: 2.15, 95% CI: 1.10~4.22 for CO ≥20 ppm or more vs.CO <10 ppm), the average number of cigarettes smoked per day (HR: 1.04, 95% CI: 1.00~1.07), and the number of counseling(HR: 0.60, 95% CI: 0.54~0.67). Conclusion: Smoking characteristics and counseling showed one of the strongest correlations with relapse within six months. This implies that it is necessary to understand the smoking characteristics and patterns of workers and to provide continuous smoking cessation counseling tailored to individual characteristics for effective smoking relapse prevention.

• Journal of The Korean Astronomical Society
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• v.55 no.2
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• pp.37-57
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• 2022

Using I-band images of 35 nearby (z < 0.1) type 1 active galactic nuclei (AGNs) obtained with Hubble Space Telescope, selected from the 70-month Swift-BAT X-ray source catalog, we investigate the photometric properties of the host galaxies. With a careful treatment of the point-spread function (PSF) model and imaging decomposition, we robustly measure the I-band brightness and the effective radius of bulges in our sample. Along with black hole (BH) mass estimates from single-epoch spectroscopic data, we present the relation between BH mass and I-band bulge luminosity (M_BH-M_I,bul relation) of our sample AGNs. We find that our sample lies offset from the M_BH-M_I,bul relation of inactive galaxies by 0.4 dex, i.e., at a given bulge luminosity, the BH mass of our sample is systematically smaller than that of inactive galaxies. We also demonstrate that the zero point offset in the M_BH-M_I,bul relation with respect to inactive galaxies is correlated with the Eddington ratio. Based on the Kormendy relation, we find that the mean surface brightness of ellipticals and classical bulges in our sample is comparable to that of normal galaxies, revealing that bulge brightness is not enhanced in our sample. As a result, we conclude that the deviation in the M_BH-M_I,bul relation from inactive galaxies is possibly because the scaling factor in the virial BH mass estimator depends on the Eddington ratio.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1153-1165
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• 2017

Structural equation modeling (SEM) is a basic tool for testing theories in a variety of disciplines. A maximum likelihood (ML) method for parameter estimation is by far the most widely used in SEM. Alternatively, two-stage least squares (2SLS) estimator has been proposed as a more robust procedure to address model misspecification. A regularized extension of 2SLS, two-stage ridge least squares (2SRLS) has recently been introduced as an alternative to ML to effectively handle the small-sample-size issue. However, it is unclear whether and when misspecification and small sample sizes may pose problems in theory testing with 2SLS, 2SRLS, and ML. The purpose of this article is to evaluate the three estimation methods in terms of inferences errors as well as parameter recovery under two experimental conditions. We find that: 1) when the model is misspecified, 2SRLS tends to recover parameters better than the other two estimation methods; 2) Regardless of specification errors, 2SRLS produces small or relatively acceptable Type II error rates for the small sample sizes.