• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.1
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• pp.28-35
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• 2002

In cavity flow, the rectangular step generates so strong sound that many researchers have investigated method to suppress the nois during interaction between vortical flow and rectangular forward step. In this study the flow noise from the vortex motion in two-dimentional low Mach number flow past a forward step with various chamfering angle is calculated numerically. Inviscid incompressible discrete vortex model and matched asymptotic expansion(MAE) theory are applied to obtain the inner flow field and the outer noise field. Both source acoustic pressure and sound intensity are obtained with various chamfering height, chamfering angle and initial vortex position. The pressure amplitude is most suppressed when the chamfering angle is between $15^{\circ}C$ and $30^{\circC}$ at the chamfering length of 30% of the step height.

• Preventive Nutrition and Food Science
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• v.21 no.3
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• pp.236-244
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• 2016

In this study, we compared the anti-inflammatory activity of Pinus koraiensis cone bark extracts prepared by conventional extraction and microwave-assisted extraction (MAE). Water extracts and 50% ethanol extracts prepared using MAE were applied to RAW 264.7 cell at 5, 10, 25, and $50{\mu}g/mL$ of concentrations, and tested for cytoxicity. The group treated with $50{\mu}g/mL$ of 50% ethanol extracts showed toxicity. In order to investigate the inhibition of nitric oxide (NO) production in RAW 264.7 cells, extracts of water and ethanol were treated with 5, 10, and $25{\mu}g/mL$ concentrations. The inhibitory activity of water and 50% ethanol extracts groups were determined as 40% and 60% at $25{\mu}g/mL$ concentration, respectively. We found concentration dependent decreases on inducible NO synthase. The inhibitory effect against forming inflammatory cytokines, prostaglandin $E_2$, tumor necrosis factor-${\alpha}$, interleukin (IL)-6, and IL-$1{\beta}$, was also superior in the $25{\mu}g/mL$ treated group than the control group. According to these results, the water extracts and 50% ethanol extracts both inhibited inflammatory mediators by reducing the inflammatory response. Therefore, The MAE extracts of P. koraiensis cone bark can be developed as a functional ingredient with anti-inflammatory activity.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.513-522
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• 2014

Using the assumption that the price of a stock follows a geometric Brownian motion with constant volatility, Black and Scholes (BS) derived a formula that gives the price of a European call option on the stock as a function of the stock price, the strike price, the time to maturity, the risk-free interest rate, the dividend rate paid by the stock, and the volatility of the stock's return. However, implied volatilities of BS method tend to depend on the stock prices and the time to maturity in practice. To address this shortcoming, we estimate the implied volatility function as a function of the strike priceand the time to maturity for data consisting of the daily prices for KOSPI200 call options from January 2007 to May 2009 using support vector regression (SVR), the multiple additive regression trees (MART) algorithm, and ordinary least squaress (OLS) regression. In conclusion, use of MART or SVR in the BS pricing model reduced both RMSE and MAE, compared to the OLS-based BS pricing model.

• Imaging Science in Dentistry
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• v.49 no.1
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• pp.19-26
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• 2019

Purpose: It has been proposed that using new prediction methods, such as neural networks based on dental data, could improve age estimation. This study aimed to assess the possibility of exploiting neural networks for estimating age by means of the pulp-to-tooth ratio in canines as a non-destructive, non-expensive, and accurate method. In addition, the predictive performance of neural networks was compared with that of a linear regression model. Materials and Methods: Three hundred subjects whose age ranged from 14 to 60 years and were well distributed among various age groups were included in the study. Two statistical software programs, SPSS 21 (IBM Corp., Armonk, NY, USA) and R, were used for statistical analyses. Results: The results indicated that the neural network model generally performed better than the regression model for estimation of age with pulp-to-tooth ratio data. The prediction errors of the developed neural network model were acceptable, with a root mean square error (RMSE) of 4.40 years and a mean absolute error (MAE) of 4.12 years for the unseen dataset. The prediction errors of the regression model were higher than those of the neural network, with an RMSE of 10.26 years and a MAE of 8.17 years for the test dataset. Conclusion: The neural network method showed relatively acceptable performance, with an MAE of 4.12 years. The application of neural networks creates new opportunities to obtain more accurate estimations of age in forensic research.

• Physical Therapy Korea
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• v.26 no.1
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• pp.1-7
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• 2019

Background: The Microsoft Kinect which is a low-cost gaming device has been studied as a promise clinical gait analysis tool having satisfactory reliability and validity. However, its accuracy is only guaranteed when it is properly positioned in front of a subject. Objects: The purpose of this study was to identify the error when the Kinect was positioned at a $45^{\circ}$ angle to the longitudinal walking plane compare with those when the Kinect was positioned in front of a subject. Methods: Sixteen healthy adults performed two testing sessions consisting of walking toward and $45^{\circ}$ obliquely the Kinect. Spatiotemporal outcome measures related to stride length, stride time, step length, step time and walking speed were examined. To assess the error between Kinect and 3D motion analysis systems, mean absolute errors (MAE) were determined and compared. Results: MAE of stride length, stride time, step time and walking speed when the Kinect set in front of subjects were investigated as .36, .04, .20 and .32 respectively. MAE of those when the Kinect placed obliquely were investigated as .67, .09, .37, and .58 respectively. There were significant differences in spatiotemporal outcomes between the two conditions. Conclusion: Based on our study experience, positioning the Kinect directly in front of the person walking towards it provides the optimal spatiotemporal data. Therefore, we concluded that the Kinect should be placed carefully and adequately in clinical settings.

• Ocean and Polar Research
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• v.41 no.3
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• pp.147-157
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• 2019

Our study was conducted to examine differences in incubation behavior among breeding sites and the relationship between factor affecting environmental change and incubation behavior of the Black-faced Spoonbill (Platalea minor). We set up the remote sensor cameras at three breeding sites (Mae-do, Namdongji, Guji-do) to observe incubation behavior in Gyeonggi Bay, South Korea from 2015 to 2018. We analyzed effects of breeding year, day of incubation started, day of incubation, the time of incubation exchanges and sex on incubation bout length. Mean incubation bout length of females (Mae-do: $7.19{\pm}0.23$ hours, Namdongji: $6.08{\pm}0.23$ hours, Guji-do: $7.96{\pm}0.30$ hours) was longer than males (Mae-do: $6.14{\pm}0.21$ hours, Namdongji: $5.45{\pm}0.28$ hours, Guji-do: $7.38{\pm}0.29$ hours). Mean incubation bout length was longer in Guji-do than other study sites. Incubation bout length tended to increase with the clutch initiation date. Males incubated their eggs at day time while female did at night time, these tendencies were observed more clearly in Guji-do. The proportion of time spent incubating of females was higher than males. Males' proportion increased as incubation progressed and increased rate in Guji-do was higher than other study sites. Our results showed that incubation rhythm of the Black-faced Spoonbill differed among breeding sites and varied with the environmental cycle.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.317-317
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• 2021

효율적인 물관리를 위한 댐 유입량 대한 연구는 필수적이다. 본 연구에서는 다양한 머신러닝 알고리즘을 통해 40년동안의 기상 및 댐 유입량 데이터를 이용하여 소양강댐 유입량을 예측하였으며, 그 중 고유량과 저유량예측에 적합한 알고리즘을 각각 선정하여 머신러닝 알고리즘을 결합한 CombML을 개발하였다. 의사 결정 트리 (DT), 멀티 레이어 퍼셉트론 (MLP), 랜덤 포레스트(RF), 그래디언트 부스팅 (GB), RNN-LSTM 및 CNN-LSTM 알고리즘이 사용되었으며, 그 중 가장 정확도가 높은 모형과 고유량이 아닌 경우에서 특별히 예측 정확도가 높은 모형을 결합하여 결합 머신러닝 알고리즘 (CombML)을 개발 및 평가하였다. 사용된 알고리즘 중 MLP가 NSE 0.812, RMSE 77.218 m3/s, MAE 29.034 m3/s, R 0.924, R2 0.817로 댐 유입량 예측에서 최상의 결과를 보여주었으며, 댐 유입량이 100 m3/s 이하인 경우 앙상블 모델 (RF, GB) 이 댐 유입 예측에서 MLP보다 더 나은 성능을 보였다. 따라서, 유입량이 100 m3/s 이상 시의 평균 일일 강수량인 16 mm를 기준으로 강수가 16mm 이하인 경우 앙상블 방법 (RF 및 GB)을 사용하고 강수가 16 mm 이상인 경우 MLP를 사용하여 댐 유입을 예측하기 위해 두 가지 복합 머신러닝(CombML) 모델 (RF_MLP 및 GB_MLP)을 개발하였다. 그 결과 RF_MLP에서 NSE 0.857, RMSE 68.417 m3/s, MAE 18.063 m3/s, R 0.927, R2 0.859, GB_MLP의 경우 NSE 0.829, RMSE 73.918 m3/s, MAE 18.093 m3/s, R 0.912, R2 0.831로 CombML이 댐 유입을 가장 정확하게 예측하는 것으로 평가되었다. 본 연구를 통해 하천 유황을 고려한 여러 머신러닝 알고리즘의 결합을 통한 유입량 예측 결과, 알고리즘 결합 시 예측 모형의 정확도가 개선되는 것이 확인되었으며, 이는 추후 효율적인 물관리에 이용될 수 있을 것으로 판단된다.

• Structural Engineering and Mechanics
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• v.84 no.2
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• pp.143-154
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• 2022

Corrosion of steel reinforcement is considered as the main cause of concrete structures deterioration, especially those under humid environmental conditions. Hence, fiber reinforced polymer (FRP) bars are being increasingly used as a replacement for conventional steel owing to their non-corrodible characteristics. However, predicting the shear strength of beams reinforced with FRP bars still challenging due to the lack of robust shear theory. Thus, this paper aims to develop an explicit data driven based model to predict the shear strength of FRP reinforced beams using multi-objective evolutionary polynomial regression analysis (MOGA-EPR) as data driven models learn the behavior from the input data without the need to employee a theory that aid the derivation, and thus they have an enhanced accuracy. This study also evaluates the accuracy of predictive models of shear strength of FRP reinforced concrete beams employed by different design codes by calculating and comparing the values of the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²), and percentage of prediction within error range of ±20% (a20-index). Experimental database has been developed and employed in the model learning, validation, and accuracy examination. The statistical analysis illustrated the robustness of the developed model with MAE, RMSE, 𝜇, 𝜎, R², and a20-index of 14.6, 20.8, 1.05, 0.27, 0.85, and 0.61, respectively for training data and 10.4, 14.1, 0.98, 0.25, 0.94, and 0.60, respectively for validation data. Furthermore, the developed model achieved much better predictions than the standard predictive models as it scored lower MAE, RMSE, and 𝜎, and higher R² and a20-index. The new model can be used in future with confidence in optimized designs as its accuracy is higher than standard predictive models.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.82-92
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• 2024

Purpose: The purpose of this study is to find a solution to the massive data construction that occurs when fire simulation data is linked to augmented reality and the resulting data overload problem. Method: An experiment was conducted to set the interval between appropriate input data to improve the reliability and computational complexity of Linear Interpolation, a data estimation technology. In addition, a validity verification was conducted to confirm whether Linear Interpolation well reflected the dynamic changes of fire. Result: As a result of application to the underground common area, which is the study target building, it showed high satisfaction in improving the reliability of Interpolation and the operation processing speed of simulation when data was input at intervals of 10 m. In addition, it was verified through evaluation using MAE and R-Squared that the estimation method of fire simulation data using the Interpolation technique had high explanatory power and reliability. Conclusion: This study solved the data overload problem caused by applying digital twin technology to fire simulation through Interpolation techniques, and confirmed that fire information prediction and visualization were of great help in real-time fire prevention.