• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.697-710
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• 2021

Relative error prediction is preferred over ordinary prediction methods when relative/percentile errors are regarded as important, especially in econometrics, software engineering and government official statistics. The relative error prediction techniques have been developed in linear/nonlinear regression, nonparametric regression using kernel regression smoother, and stationary time series models. However, random effect models have not been used in relative error prediction. The purpose of this article is to extend relative error prediction to some of generalized linear mixed model (GLMM) with panel data, which is the random effect models based on gamma, lognormal, or inverse gaussian distribution. For better understanding, the real auto insurance data is used to predict the claim size, and the best predictor and the best relative error predictor are comparatively illustrated.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.1
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• pp.1-9
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• 2009

This paper presents stress distribution around a central crack tip in a tensile plate using phase-shifting photoelasticity and a power series stress function. Isochromatic data along the straight lines far from the crack tip were obtained by phase shifting photoelasticity and were used as input data of the hybrid experimental analysis. By using the complex-type power series stress equations, the photoelastic stress distribution fields in the vicinity of the crack and the mode I stress intensity factor were obtained. With the help of image processing software, accuracy and reliability was enhanced by twice multiplying and sharpening the measured isochromatics. Actual and reconstructed fringes were compared qualitatively. For quantitative comparison, percentage errors and standard deviations of the percentage errors were calculated for all measured input data by varying the number of terms in the stress function. The experimental results agreed with those predicted by finite element analysis and empirical equation within 2 percent error.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.78-96
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• 2023

Urban traffic information is collected by various systems such as VDS, DSRC, and radar. Recently, with the development of deep learning technology, smart intersection systems are expanding, are more widely distributed, and it is possible to collect a variety of information such as traffic volume, and vehicle type and speed. However, as a result of reviewing related literature, the performance evaluation criteria so far are rbs-based evaluation systems that do not consider the deep learning area, and only consider the percent error of 'reference value-measured value'. Therefore, a new performance evaluation method is needed. Therefore, in this study, individual error, interval error, and overall error are calculated by using a formula that considers deep learning performance indicators such as precision and recall based on data ratio and weight. As a result, error rates for measurement value 1 were 3.99 and 3.54, and rates for measurement value 2 were 5.34 and 5.07.

• Journal of Broadcast Engineering
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• v.5 no.2
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• pp.260-272
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• 2000

In this paper, we propose a simple auto calibration method for a CCD camera with wide an91e lens that causes high degree of distortion. We formulate a cubic warping equation for the relationship between the cross points on the distorted calibration target and the corresponding points from the standard grid image, and calibrate distorted images using the computed parameters. The experiment has been performed with the distorted images resulted from wide angle CCD camera. The experimental results show that the proposed method, in terms of the average and maximum distorted error, has higher accuracy than the existing methods because of maintaining the calibration ratio more than 95 percent. The proposed method is applicable to wide variety of images regardless a type of lens or distortion.

• The Korean Journal of Applied Statistics
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• v.2 no.1
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• pp.48-53
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• 1989

In contrast to the customary approximations based on standard normal percentage points, direct approximations involve simple functions of parameters (such as degrees of freedom and tail area of the t distributions). This article used techniques of exploratory data analysis following Hoaglin to develop direct approximations for percentage points in the commonly used portions of upper tail of the t distribution with small to moderate numbers of degrees of freedom. These approximations are convenient to use and they compare favorably in accuracy with the popular approximations based on standard normal percentage points such as Peiser's. They can be used as an initial value generator in algorithms for getting more accurate percentage points.

• Journal of the Korean association of regional geographers
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• v.12 no.5
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• pp.614-630
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• 2006

Improved estimates of populations at risk for quick and effective response to natural and man-made disasters require spatial disaggregation of zonal population data because of the spatial mismatch problem in areal units between census and impact zones. This paper implements a dasymetric surface model to facilitate spatial disaggregation of the population of a census block group into populations associated with each constituent pixel and evaluates the performance of the surface-based spatial disaggregation model visually and statistically. The surface-based spatial disaggregation model employed geographic information systems (GIS) to enable dasymetric interpolation to be guided by satellite-derived land use and land cover data as additional information about the geographic distributor of population. In the spatial disaggregation, percent cover based empirical sampling and areal weighting techniques were used to objectively determine dasymetric weights for each grid cell. The dasymetric population surface for the Atlanta metropolitan area was generated by the surface-based spatial disaggregation model. The accuracy of the dasymetric population surface was tested on census counts using the root mean square error (RMSE) and an adjusted RMSE. The errors related to each census track and block group were also visualized by percent error maps. Results indicate that the dasymetric population surface provides high-precision estimates of populations as well as the detailed spatial distribution of population within census block groups. The results also demonstrate that the population surface largely tends to overestimate or underestimate population for both the rural and forested and the urban core areas.

• Journal of Korea Water Resources Association
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• v.49 no.4
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• pp.327-333
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• 2016

Due to climate changes accelerated by global warming, South Korea has experienced regional climate variations as well as increasing severities and frequencies of extreme weather. The precipitation in South Korea during the summer season in 2013 was concentrated mainly in the central region; the maximum number of rainy days were recorded in the central region while the southern region had the minimum number of rainy days. As a result, much attention has been paid to the importance of flood control due to damage caused by spatiotemporal intensive rainfalls. In this study, forecast rainfall data was used for rapid responses to prevent disasters during flood seasons. For this purpose, the applicability of numerical weather forecast data was analyzed using the ground observation rainfall and inflow rate. Correlation coefficient, maximum rainfall intensity percent error and total rainfall percent error were used for the quantitative comparison of ground observation rainfall data. In addition, correlation coefficient, Nash-Sutcliffe efficiency coefficient, and standardized RMSE were used for the quantitative comparison of inflow rate. As a result of the simulation, the correlation coefficient up to six hours was 0.7 or higher, indicating a high correlation. Furthermore, the Nash-Sutcliffe efficiency coefficient was positive until six hours, confirming the applicability of forecast rainfall.

• Journal of IKEEE
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• v.26 no.4
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• pp.531-536
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• 2022

In order to efficeintly manage a battery, it is important to accurately estimate and manage the SOH(State of Health) and RUL(Remaining Useful Life) of the batteries. Even if the batteries are of the same type, the characteristics such as facility capacity and voltage are different, and when the battery for the training model and the battery for prediction through the model are different, there is a limit to measuring the accuracy. In this paper, We proposed the entropy index using voltage distribution and discharge time is generalized, and four batteries are defined as a training set and a test set alternately one by one to predict the health status of batteries through linear regression analysis of machine learning. The proposed method showed a high accuracy of more than 95% using the MAPE(Mean Absolute Percentage Error).

• Journal of IKEEE
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• v.26 no.4
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• pp.545-552
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• 2022

The number of patent application filing for a specific technology has a good relation with the technology's life cycle and future industry development on that area. So industry and governments are highly interested in forecasting the number of patent application filing in order to take appropriate preparations in advance. In this paper, a new method based on the bidirectional long short-term memory(LSTM), a kind of recurrent neural network(RNN), is proposed to improve the forecasting accuracy compared to related methods. Compared with the Bass model which is one of conventional diffusion modeling methods, the proposed method shows the 16% higher performance with the Korean patent filing data on the five selected technology areas.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.23 no.4
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• pp.173-179
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• 2013

Even though a rough diamond shape is irregular and rugged, it is easy to be processed to make gem, shaped facets (table, crown, pavilion and girdle) were precisely treated because they influence on the evaluation of diamond grading. Those specifications suitable for the standard round brilliant cut diamond polishing were investigated and in 95 % statistical confidence interval, standard deviation, mean and acceptable tolerance were examined. According to these variables (size, angle, depth, and thickness) distribution, the frequency analysis of ratings and proportions were compared with each other. The correlation between each variables and the evidence of influence represented in proportion were determined by the regression analysis applying LSM (Least Square Method). In this research, it was recognized that table sizes of the diamond jewels and pavilion depth (in %) influence the rating decision and in particular, the depth of pavilion acting as the main factor of proportions, also plays an important role in optical phenomena.