• Journal of Korea Multimedia Society
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• v.14 no.3
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• pp.348-355
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• 2011

In this paper, we propose an automatic prostate segmentation method from dynamic magnetic resonance (MR) images. Our method detects contrast-enhanced images among the dynamic MR images using an average intensity analysis. Then, the candidate regions of prostate are detected by the B-spline non-rigid registration and subtraction between the pre-contrast and contrast-enhanced MR images. Finally, the prostate is segmented by performing a dilation operation outward, and sequential shape propagation inward. Our method was validated by ten data sets and the results were compared with the manually segmented results. The average volumetric overlap error was 6.8%, and average absolute volumetric measurement error was 2.5%. Our method could be used for the computer-aided prostate diagnosis, which requires an accurate prostate segmentation.

• Fire Science and Engineering
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• v.20 no.2 s.62
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• pp.14-20
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• 2006

The aim of this study is to predict the ignition times and the HRR(heat release rate) for building interior materials. By using the literature data and RSM(response surface methodology), the new equations for predicting the ignition time and the HRR of building interior materials are proposed. The A.A.P.E.(average absolute percent error) and the A.A.D.(average absolute deviation) of the reported and the calculated ignition times by means of the thickness and the density were 4.35 sec and 1.57 sec, and the correlation coefficient was 0.987. The correlation coefficient of the reported and the calculated the net HRR by means of burner width and power was 0.983. Also the correlation coefficient of the reported and the calculated the total HHR by means of burner width and power was 0.999. The correlation coefficient of the reported and the calculated the maximum flame height by means of burner width and power was 0.999. The values calculated by the proposed equations were in good agreement with the literature data.

• Fire Science and Engineering
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• v.19 no.3 s.59
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• pp.70-75
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• 2005

The heat of combustion of polymer materials is an important fire characteristics, which can be used with other fire parameter to predict the potential fire hazard in the polymer handling process. The aim of this study is to predict the heat of combustion for polymers which used in the building interior materials. By using the literature data and multiple regression, the new equation for predicting the heat of combustion of polymers is proposed. The A.A.p.E.(average absolute percent error) and the A.A.D.(average absolute deviation) of the reported and the calculated heat of combustion by means of the oxygen consumption calorimeter and the stoichiometric coefficient were 4.46 and 1.09, and the correlation coefficient was 0.972. The values calculated by the proposed equations were in good agreement with the literature data. Therefore, it is expected that this proposed equations will support the use of the research for other polymer materials.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.903-911
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• 2022

Groundwater, one of the resources for supplying water, fluctuates in water level due to various natural factors. Recently, research has been conducted to predict fluctuations in groundwater levels using Artificial Neural Network (ANN). Previously, among operators in ANN, Gradient Descent (GD)-based Optimizers were used as Optimizer that affect learning. GD-based Optimizers have disadvantages of initial correlation dependence and absence of solution comparison and storage structure. This study developed Gradient Descent combined with Harmony Search (GDHS), a new Optimizer that combined GD and Harmony Search (HS) to improve the shortcomings of GD-based Optimizers. To evaluate the performance of GDHS, groundwater level at Icheon Yullhyeon observation station were learned and predicted using Multi Layer Perceptron (MLP). Mean Squared Error (MSE) and Mean Absolute Error (MAE) were used to compare the performance of MLP using GD and GDHS. Comparing the learning results, GDHS had lower maximum, minimum, average and Standard Deviation (SD) of MSE than GD. Comparing the prediction results, GDHS was evaluated to have a lower error in all of the evaluation index than GD.

• Computers and Concrete
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• v.26 no.3
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• pp.293-307
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• 2020

The aim of this paper is to develop design-oriented models for the prediction of the ultimate strength and ultimate axial strain for concrete confined with glass fiber-reinforced polymer (GFRP) wraps. Twenty of most used and recent design-oriented models developed to predict the strength and strain of GFRP-confined concrete in circular sections are selected and evaluated basing on a database of 163 test results of concrete cylinders confined with GFRP wraps subjected to uniaxial compression. The evaluation of these models is performed using three statistical indices namely the coefficient of the determination (R²), the root mean square error (RMSE), and the average absolute error (AAE). Based on this study, new strength and strain models for GFRP-wrapped concrete are developed using regression analysis. The obtained results show that the proposed models exhibit better performance and provide accurate predictions over the existing models.

• Environmental Engineering Research
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• v.21 no.2
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• pp.171-179
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• 2016

This study analyzes guidelines to select optimum number of grids to represent behavior of a given water system appropriately. The EFDC model was chosen as a 3-D hydrodynamic and water quality model and salt was chosen as a surrogate variable of pollutant. The model is applied to an artificial canal that receives salt water from coastal area and fresh water from a river from respective gate according to previously developed gate operation rule. Grids are subdivided in vertical and horizontal (longitudinal) directions, respectively until no significant changes are found in salinity concentrations. The optimum grid size was determined by comparing errors in average salt concentrations between a test grid systems against the most complicated grid system. MSE (mean squared error) and MAE (mean absolute error) are used to compare errors. The CFL (Courant-Friedrichs-Lewy) number was used to determine the optimum number of grid systems for the study site though it can be used when explicit numerical method is applied only. This study suggests errors seem acceptable when both MSE and MAE are less than unity approximately.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.32 no.3
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• pp.235-240
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• 1996

Forecasts of monthly landings of walleye pollock, Theragra chalcogramma, in Korea were carried out by the seasonal Autoregressive Integrated Moving Average(ARlMA) model. The Box - Cox transformation on the walleye pollock catch data handles nonstationary variance. The equation of Box - Cox transformation was Y'=($Y^0.31$_ 1)/0.31. The model identification was determined by minimum AIC(Akaike Information Criteria). And the seasonal ARlMA model is presented (1- O.583B)(1- $B^1$)(l- $B^12$)$Z_t$ =(l- O.912B)(1- O.732$B^12$)et where: $Z_t$=value at month t ; $B^p$ is a backward shift operator, that is, $B^p$$Z_t$=$Z_t$-P; and et= error term at month t, which is to forecast 24 months ahead the walleye pollock landings in Korea. Monthly forecasts of the walleye pollock landings for 1993~ 1994, which were compared with the actual landings, had an absolute percentage error(APE) range of 20.2-226.1 %. Thtal observed annual landings in 1993 and 1994 were 16, 61OM/T and 1O, 748M/T respectively, while the model predicted 10, 7 48M/T and 8, 203M/T(APE 37.0% and 23.7%, respectively).

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.623-632
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• 2021

Through this study, we studied how to consider environment variables (such as temperatures, weekend, holiday) closely related to electricity demand, and how to consider the characteristics of Korea electricity demand. In order to conduct this study, Smoothing method, Seasonal ARIMA model and regression model with AR-GARCH errors are compared with mean absolute error criteria. The performance comparison results of the model showed that the predictive method using AR-GARCH error regression model with environment variables had the best predictive power.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.152-158
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• 2005

Commercial lower limb prostheses or orthotics help patients achieve a normal life. However, patients who use such aids need prolonged training to achieve a normal gait, and their fatigability increases. To improve patient comfort, this study proposed a method of predicting gait angle using neural networks and EMG signals. Experimental results using our method show that the absolute average error of the estimated gait angles is $0.25^{\circ}$. This performance data used reference input from a controller for the lower limb orthotic or prosthesis controllers while the patients were walking.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.256-267
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• 2017

An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.