• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.1997-2003
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• 2010

Mean Residual Life (MRL) function plays a very important role in the area of engineering, medical science, survival studies, social sciences, and many other fields. Specially, in the reliability study of technical systems, the MRL estimation of a component is very important because the sudden stop of a system brings a serious problem. So, many simulation studies of MRL estimation have been done considering various situation variables. In this paper, four estimators of MRL are proposed under random censoring and their performances re compared through bias and Mean Square Error (MSE) by Monte Carlo simulation.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.15 no.7
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• pp.583-590
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• 2002

In this paper, a new Silicon on Insulator (SOI)-based photodetector was proposed, and its basic operation principle was explained. Fabrication steps of the detector are compatible with those of conventional SOI CMOS technology. With the proposed structure, RGB (Read, Green, Blue) which are three primary colors of light can be realized without using any organic color filters. It was shown that the characteristics of the SOI-based detector are better than those of bulk-based detector. To see the response characteristics to the green (G) among RGB, SOI and bulk NMOSFETS were fabricated using $1.5\mu m$ CMOS technology and characterized. We obtained optimum optical response characteristics at $V_{GS}=0.35 V$ in NMOSFET with threshold voltage of 0.72 V. Drain bias should be less than about 1.5 V to avoid any problem from floating body effect, since the body of the SOI NMOSFET was floated. The SOI and the bulk NMOSFETS shown maximum drain currents at the wavelengths of incident light around 550 nm and 750 nm, respectively. Therefore the SOI detector is more suitable for the G color detector.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1571-1580
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• 1999

When a hierarchical structure is derived from data set for data mining and machine learning, using a conceptual clustering algorithm, one of the unsupervised learning paradigms, it is not unusual to have a different set of outcomes with respect to the order of processing data objects. To overcome this problem, the first classification process is proceeded to construct an initial partition. The partition is expected to imply the possible range in the number of final classes. We apply center sorting to the data objects in the classes of the partition for new data ordering and build a new partition using ITERATE clustering procedure. We developed an algorithm, REIT that leads to the final partition with stable and best partition score. A number of experiments were performed to show the minimization of order bias effects using the algorithm.

• Nuclear Engineering and Technology
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• v.46 no.5
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• pp.619-632
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• 2014

The Best Estimate Plus Uncertainty (BEPU) method has been widely used to evaluate the uncertainty of a best-estimate thermal hydraulic system code against a figure of merit. This uncertainty is typically evaluated based on the physical model's uncertainties determined by expert judgment. This paper introduces the application of data assimilation methodology to determine the uncertainty bands of the physical models, e.g., the mean value and standard deviation of the parameters, based upon the statistical approach rather than expert judgment. Data assimilation suggests a mathematical methodology for the best estimate bias and the uncertainties of the physical models which optimize the system response following the calibration of model parameters and responses. The mathematical approaches include deterministic and probabilistic methods of data assimilation to solve both linear and nonlinear problems with the a posteriori distribution of parameters derived based on Bayes' theorem. The inverse problem was solved analytically to obtain the mean value and standard deviation of the parameters assuming Gaussian distributions for the parameters and responses, and a sampling method was utilized to illustrate the non-Gaussian a posteriori distributions of parameters. SPACE is used to demonstrate the data assimilation method by determining the bias and the uncertainty bands of the physical models employing Bennett's heated tube test data and Becker's post critical heat flux experimental data. Based on the results of the data assimilation process, the major sources of the modeling uncertainties were identified for further model development.

• ETRI Journal
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• v.37 no.5
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• pp.961-971
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• 2015

This paper proposes a 250 mV supply voltage digital low-dropout (LDO) regulator. The proposed LDO regulator reduces the supply voltage to 250 mV by implementing with all digital circuits in a$0.11{\mu}m$ CMOS process. The fast current tracking scheme achieves the fast settling time of the output voltage by eliminating the ringing problem. The over-voltage and under-voltage detection circuits decrease the overshoot and undershoot voltages by changing the switch array current rapidly. The switch bias circuit reduces the size of the current switch array to 1/3, which applies a forward body bias voltage at low supply voltage. The fabricated LDO regulator worked at 0.25 V to 1.2 V supply voltage. It achieved 250 mV supply voltage and 220 mV output voltage with 99.5% current efficiency and 8 mV ripple voltage at $20{\mu}A$ to $200{\mu}A$ load current.

• Physical Therapy Rehabilitation Science
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• v.9 no.1
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• pp.55-65
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• 2020

Objective: The most important goal of hippotherapy is to improve function, gait, and balance. The purpose of this study was to investigate the effects of hippotherapy on function, gait, and balance of children with cerebral palsy (CP). Design: A systematic review and meta-analysis. Methods: We've searched the PubMed, EMBASE, and Cochrane Library CENTRAL databases for English experimental studies published up until September 2018. To analyze the effects of hippotherapy on the function, gait, and balance of children with cerebral palsy, the systematic review and meta-analysis were performed on Patient/Participants/Population Problem, Intervention, Comparison, Outcome with Timing, Setting Study Design. Papers retrieved from the database were removed from the literature management database. Methodological quality evaluation was evaluated using Cochrane's risk of bias. Data was analyzed using the Revman 5.3 program of the Cochrane library. Results: In this study, we found that the effect size of hippotherapy, denoted as d, was -3.82, and that hippotherapy had the most effect on gait, but no statistical significant difference was observed. We also found no significant difference in function and balance after hippotherapy. It was found from the funnel plot that there was no publication bias as the plot was symmetrically distributed around the dotted line. Conclusions: There are many unclear studies and a lack of randomized controlled trials (RCTs) in this research area. More RCTs on the effects of hippotherapy on children with CP should be conducted in the future.

• Journal of Environmental Science International
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• v.27 no.7
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• pp.499-507
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• 2018

Agricultural meteorological information is an important resource that affects farmers' income, food security, and agricultural conditions. Thus, such data are used in various fields that are responsible for planning, enforcing, and evaluating agricultural policies. The meteorological information obtained from automatic weather observation systems operated by rural development agencies contains missing values owing to temporary mechanical or communication deficiencies. It is known that missing values lead to reduction in the reliability and validity of the model. In this study, the hierarchical Bayesian spatio-temporal model suggests replacements for missing values because the meteorological information includes spatio-temporal correlation. The prior distribution is very important in the Bayesian approach. However, we found a problem where the spatial decay parameter was not converged through the trace plot. A suitable spatial decay parameter, estimated on the bias of root-mean-square error (RMSE), which was determined to be the difference between the predicted and observed values. The latitude, longitude, and altitude were considered as covariates. The estimated spatial decay parameters were 0.041 and 0.039, for the spatio-temporal model with latitude and longitude and for latitude, longitude, and altitude, respectively. The posterior distributions were stable after the spatial decay parameter was fixed. root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and bias were calculated for model validation. Finally, the missing values were generated using the independent Gaussian process model.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.4
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• pp.531-536
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• 2014

There are trapping and stripping methods as the technique to remove the dither motion from RLG(Ring Laser Gyro) output. V/F converter output of angular sensor to measure the dither motion is used in stripping method. But bias and scale factor error is always included in V/F converter output and is a critical limiting factor for the wide application of stripping method to RLG. Therefore there have been many researches to solve this problem. The method to accurately estimate the bias and scale factor error of V/F converter using measurements of the angular sensor acquired at data sampling rate of INS is presented in this paper. To this end, stripping technique based on model of dither motion is newly applied.

• Journal of Korean Society of Rural Planning
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• v.27 no.4
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• pp.1-12
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• 2021

The purpose of this study is to analyze the effect of crop diversification on farm households' agricultural income. Abundant literature have explored the determinants and efficient strategies for crop diversification. Yet, there is a paucity of research studies that empirically test the effectiveness of crop diversification as a profitable farm management strategy. Utilizing the 2015 Agricultural Census, this study adopts a quasi-experimental research design to compare the outcomes between farm households that opted for crop diversification and farm households that did not engage in such a strategy. In doing so, this study applies the Heckman Selection Model and the decomposition technique to address the problem of selection bias and to identify the causal effect. Our empirical results show that farms that implement diversification are more likely to earn higher agricultural income than non-diversified farms, although the difference would not be much substantial. This study concludes with several policy proposals to stabilize agricultural income in conjunction with crop diversification.

• Journal of IKEEE
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• v.23 no.4
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• pp.1134-1139
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• 2019

One of the most important applications of computer vision algorithms is the detection of threat objects in x-ray security images. However, in the practical setting, this task is complicated by two properties inherent to the dataset, namely, the problem of class imbalance and visual complexity. In our previous work, we resolved the class imbalance problem by using a GAN-based anomaly detection to balance out the bias induced by training a classification model on a non-practical dataset. In this paper, we propose a new method to alleviate the visual complexity problem by using a KNN-based automatic cropping algorithm to remove distracting and irrelevant information from the x-ray images. We use the cropped images as inputs to our current model. Empirical results show substantial improvement to our model, e.g. about 3% in the practical dataset, thus further outperforming previous approaches, which is very critical for security-based applications.