• 한국항공우주학회지
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• 제30권1호
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• pp.28-35
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• 2002

공동흐름에서 직각 계단은 커다란 소음의 원인이 되어 많은 사람들의 와류와 직각계단의 상호 작용 시에 발생되는 소음의 저감 방안을 연구하여 왔다. 본 연구에서는 2차원 저아음속 와류 흐름이 전향계단을 지날 때 발생되는 유동 소음을 전향계단의 형상 변화를 통한, 즉, 계단의 모따기 양과 각도를 바꾸어 가면서 수치적으로 계산하였다. 내부 유동장을 구하기 위해서 비압축성 비점성 이산와류 모델을 가정하였으며, 유동정보로부터 원거리로의 음향장 계산은 MAE 이론을 적용하여 구하였다. 음원에서의 음압과 음압강도를 모따기 높이 및 모따기 각도와 최기 와류의 높이를 변화시켜가면서 다양하게 수치적인 결과를 얻어내었다. 본 연구를 통해서 계단에 접근하는 와류에 의한 원거리 음압은 모따기 양이 계단 높이의 30%에서 모따기 각도가 $15^{\circ}C$에서 $30^{\circ}C$°사이일 때 가장 소음이 적게 발생되는 결과를 얻어내었다.

• Preventive Nutrition and Food Science
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• 제21권3호
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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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• 제42권1호
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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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• 제25권3호
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• pp.513-522
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• 2014

옵션가격의 결정에 있어서 실제 변동성은 사후에 알 수 있는 정보이므로 대용값으로 내재변동성을 가장 많이 사용하는데 본 연구에서는 동일한 기초자산을 가진 옵션의 잔존만기와 행사가격을 이용하여 내재변동성을 추정하고자 한다. KOSPI200 옵션 데이터와 서포트벡터회귀, 나무모형 및 회귀모형을 통해 모형의 설명력을 평균제곱근오차 (RMSE)와 평균절대오차 (MAE)를 사용하여 살펴보았다. 그 결과 서포트벡터회귀와 MART의 성능이 최소제곱회귀보다 우수한 것으로 나타났으며, 서포트벡터회귀와 MART의 성능은 거의 비슷하였다.

• Imaging Science in Dentistry
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• 제49권1호
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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.

• 한국전문물리치료학회지
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• 제26권1호
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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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• 제41권3호
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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.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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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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• 제84권2호
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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.

• 한국재난정보학회 논문집
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• 제20권1호
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• pp.82-92
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• 2024

연구목적: 본 연구는 화재시뮬레이션 데이터를 증강현실에 연동할시 발생하는 방대한 데이터 구축과 그로 인한 데이터 과부하 문제 해결 방안을 강구하기 위함이다. 연구방법: 데이터 추정 기술인 선형 보간법의 신뢰도와 계산 복잡도를 개선하기 위한 적정 Input 데이터의 간격을 설정하기 위한 실험을 진행하였다. 또한, 선형 보간법이 화재의 동적 변화를 잘 반영하는지 확인하기 위한 타당성 검증을 진행하였다. 연구결과: 연구 대상 건축물인 지하 공동구에 적용 결과 10m 간격으로 데이터 입력시 보간법의 신뢰성과 시뮬레이션의 연산처리 속도 개선에서 높은 만족성을 보였다. 또한, 보간법을 활용한 화재시뮬레이션 데이터의 추정 방식이 높은 설명력과 신뢰성을 가진다는 것을 MAE와 R-Squared를 이용한 평가를 통해 검증하였다. 결론: 본 연구는 화재시뮬레이션에 디지털트윈 기술을 적용하면서 발생하는 데이터 과부하 문제를 보간법을 통해 해결하였으며, 이를 통한 화재 정보 예측과 시각화가 실시간 화재 예방에 크게 기여함을 확인하였다.