• Ocean and Polar Research
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• v.42 no.1
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• pp.89-95
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• 2020

Our study was conducted to examine the nest attendance of the Black-faced Spoonbill (Platalea minor) from 2015 to 2018 at two Islets located in Incheon, South Korea. We visited study sites in March-April and set up the remote sensor cameras at two breeding sites (Mae-do, Guji-do) to observe chick-rearing behavior. Mean nest bout length at Mae-do (female: 4.2 ± 0.1 hr, male: 4.0 ± 0.1 hr) was shorter than at Guji-do (female: 5.3 ± 0.2, male: 6.0 ± 0.3 hr), and trip duration at Mae-do (female: 4.4 ± 0.1 hr, male: 4.0 ± 0.1) was also shorter than at Guji-do (female: 7.0 ± 0.2 hr, male: 7.0 ± 0.3). Mean nest bout length and trip duration at both study sites decreased as chick rearing progressed. Males attended their nests during the daytime, and females attended their nests during the nighttime. Our results showed that females and males' duties were divided by the diel cycle, but the pattern of nest attendance could change depending on the environment of their breeding sites during the chick-rearing period.

• Geomechanics and Engineering
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• v.30 no.6
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• pp.551-564
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• 2022

Fragmenting the rock mass is considered as the most important work in open-pit mines. Ground vibration is the most hazardous issue of blasting which can cause critical damage to the surrounding structures. This paper focuses on developing an explicit model to predict the ground vibration through an multi objective evolutionary polynomial regression (MOGA-EPR). To this end, a database including 79 sets of data related to a quarry site in Malaysia were used. In addition, a gene expression programming (GEP) model and several empirical equations were employed to predict ground vibration, and their performances were then compared with the MOGA-EPR model using the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²) and a20-index. Comparing the results, it was found that the MOGA-EPR model predicted the ground vibration more precisely than the GEP model and the empirical equations, where the MOGA-EPR scored lower MAE and RMSE, 𝜇 and 𝜎 closer to the optimum value, and higher R² and a20-index. Accordingly, the proposed MOGA-EPR model can be introduced as a useful method to predict ground vibration and has the capacity to be generalized to predict other blasting effects.

• 한국방재학회:학술대회논문집
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• 2009.02b
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• pp.132.2-132.2
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• 2009

• Journal of the Korean housing association
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• v.27 no.2
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• pp.43-51
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• 2016

The purpose of this study was to search the architectural forms including the residence plan and wooden structure and the measuring unit in building sites named Bang in Gyeong_Ju, the capiatal of Silla dynasty. It includes the following three historic sites: JaMaeJung-ji, the east district of HwangLyong-sa (S1E1) and InWang-dong historic site. Especially, the data from excavating and studying the east district of HwangLyong-sa were very precious and meaningful. The results were as follows: 1) There are individual characteristics at the sites of JaMaeJung-ji, the east district of HwangLyong-sa and InWang-dong. 2) It is assumed that the builldings of the east district of HwangLyong-sa had most various types of plan and various types of wooden structures. 3) There were various types of wooden structures like as symmetrical or asymmetrical style, regular columnar or irregular columnar style and a third purlin location or a forth purlin location. 4) This study also showed there's a high probability that buildings at Bang sites had been built by the Tang dynaty scale. In Silla dynasty, there was similar tendency to BackJe Dynasty in the way that measuring unit changed according to the times.

• Journal of radiological science and technology
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• v.38 no.3
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• pp.187-197
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• 2015

This study analysed the effectiveness of aluminium(Al) filter in the added compound filtration for the removal of characteristic radiation by energy spectrum and image evaluation. 0.1, 0.2, 0.3 mm copper with and without 1 mm Al were evaluated. The energy spectrum was measured using the GATE and evaluated separately by each energy. Image quality was evaluated by PSNR, MAE, MSE, CNR, SNR and qualitative analysis was performed by seven items for resolution and contrast from chest x-ray criteria of National Cancer Screening and Cardiovascular evaluation table. In the analysis of the quality of the energy per photon spectrum with the exception of a low energy region, without Al were superior in all area. PSNR MAE, MSE, CNR, SNR and qualitative analysis were the same or slightly better. PSNR was over 30 dB and all significant and the p>0.05 in the T-test of qualitative analysis. The energy spectrum and image quality have little difference between before and after use of Al filter. Therefore, it is effective to use the Al filter for the radiation dose management with the compensation capability of DR system.

• Korea Journal of Hospital Management
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• v.11 no.3
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• pp.19-41
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• 2006

The Objective of this study was to design the model which predict the future cash flow of hospitals and on the basis of designed model to support sound hospital management by the prediction of future cash flow. The five cash flow measurement variables discussed in financial accrual part were used as variables and these variables were defined as NI, NIDPR, CFO, CFAI, CC. To measure the cash flow B/S related variables, P/L related variables and financial ratio related variables were utilized in this study. To measure cash flow models were designed and to estimate the prediction ability of five cash flow models, the martingale model and the market model were utilized. To estimate relative prediction outcome of cash flow prediction model and simple market model, MAE and MER were used to compare and analyze relative prediction ability of the cash flow model and the market model and to prove superiority of the model of the cash flow prediction model, 32 Regional Public Hospital's cross-section data and 4 year time series data were combined and pooled cross-sectional time series regression model was used for GLS-analysis. To analyze this data, Firstly, each cash flow prediction model, martingale model and market model were made and MAE and MER were estimated. Secondly difference-test was conducted to find the difference between MAE and MER of cash flow prediction model. Thirdly after ranking by size the prediction of cash flow model, martingale model and market model, Friedman-test was evaluated to find prediction ability. The results of this study were as follows: when t-test was conducted to find prediction ability among each model, the error of prediction of cash flow model was smaller than that of martingale and market model, and the difference of prediction error cash flow was significant, so cash flow model was analyzed as excellent compare with other models. This research results can be considered conductive in that present the suitable prediction model of future cash flow to the hospital. This research can provide valuable information in policy-making of hospital's policy decision. This research provide effects as follows; (1) the research is useful to estimate the benefit of hospital, solvency and capital supply ability for substitution of fixed equipment. (2) the research is useful to estimate hospital's liqudity, solvency and financial ability. (3) the research is useful to estimate evaluation ability in hospital management. Furthermore, the research should be continued by sampling all hospitals and constructed advanced cash flow model in dimension, established type and continued by studying unified model which is related each cash flow model.

• Food Science and Preservation
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• v.15 no.6
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• pp.840-846
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• 2008

Microwave-assisted extraction (MAE) of grape seeds was performed under the different conditions based on a central composite design for independent variables of microwave power ($0{\sim}120\;W$), ethanol concentration ($0{\sim}100%$) and extraction time ($1{\sim}5\;min$). Response surface methodology (RSM) was used to predict the optimum extraction conditions for three dependent variables in grape seed extracts: total yield, total catechin and electron donating ability. Determination coefficients ($R^2$) of regression equations for the three dependent variables were higher than 0.9 (p < 0.01). The optimal MAE conditions to yield the maximum value of total catechin (434.16 mg%) were 122.76 W microwave power, 42.88% ethanol and 4.67 min extraction time. The superimposed contour maps for maximizing the three dependent variables indicated that the MAE condition ranges were 75150 W, 4060% ethanol and 3.55.0 min. The predicted values at the optimized conditions (6.72% total yield, 408.65 mg% total catechin, and 83.33% electron donation ability) were similar to the experimental values. The optimized MAE (112.5 W, 50% EtOH, 4.2 min) was more efficient than the conventional solvent extraction using 80% EtOH, $60^{\circ}C$ for 3h and 150 rpm.

• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.362-373
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• 2015

This study analysed the effectiveness of aluminium(Al) filter in the added compound filtration for the removal of characteristic radiation from high atomic number material by DQE and image evaluation. 1mm Al was applied to each 0.1, 0.2, 0.3 mm copper and befere and after use were evaluated. Beam quality and DQE were tested by IEC regulations and image quality was evaluated by PSNR, MAE, MSE, CNR, SNR and qualitative analysis was performed by 7 items for resolution and contrast from chest x-ray criteria of national cancer checkup. MTF 10 and 50% were the same by 4.6, 2.54 cycle/mm and NPS, DQE, PSNR MAE, MSE, CNR, SNR and qualitative analysis were all the same or slightly better when Al was not used. PSNR is over 30dB and all significant and at the qualitative analysis, the p-value of t-test was over 0.05. The DQE and image quality evaluation have little difference between before and after use of Al filter and it is effective to use the Al filter for the reduction of skin dose by removal of characteristic radiation.

• Structural Engineering and Mechanics
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• v.60 no.3
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• pp.471-488
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• 2016

This study predicts the strength of rotary brace damper by analyzing a new set of probabilistic models using the usual method of multiple linear regressions (MLR) and advanced machine-learning methods of multivariate adaptive regression splines (MARS), Rotary brace damper can be easily assembled with high energy-dissipation capability. To investigate the behavior of this damper in structures, a steel frame is modeled with this device subjected to monotonic and cyclic loading. Several response parameters are considered, and the performance of damper in reducing each response is evaluated. MLR and MARS methods were used to predict the strength of this damper. Displacement was determined to be the most effective parameter of damper strength, whereas the thickness did not exhibit any effect. Adding thickness parameter as inputs to MARS and MLR models did not increase the accuracies of the models in predicting the strength of this damper. The MARS model with a root mean square error (RMSE) of 0.127 and mean absolute error (MAE) of 0.090 performed better than the MLR model with an RMSE of 0.221 and MAE of 0.181.

• Journal of Auto-vehicle Safety Association
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• v.15 no.1
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• pp.55-62
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• 2023

In this study, a chest deflection is predicted by introducing a deep learning technique with the results of the frontal impact of the USNCAP conducted for 110 car models from MY2018 to MY2020. The 120 data are divided into training data and test data, and the training data is divided into training data and validation data to determine the hyperparameters. In this process, the deceleration data of each vehicle is averaged in units of 10 ms from crash pulses measured up to 100 ms. The performance of the deep learning model is measured by the indices of the mean squared error and the mean absolute error on the test data. A DNN (Deep Neural Network) model can give different predictions for the same hyperparameter values at every run. Considering this, the mean and standard deviation of the MSE (Mean Squared Error) and the MAE (Mean Absolute Error) are calculated. In addition, the deep learning model performance according to the inclusion of CVW (Curb Vehicle Weight) is also reviewed.