• 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.

• 대한전자공학회논문지TE
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• 제39권3호
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• pp.97-104
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• 2002

이 논문에서는 퍼지뉴럴 시스템을 위하여 measure of fuzziness에 의한 입력공간의 분할을 최적화하는 방법을 제안한다. 이에 따라 최적화된 퍼지 부공간에 대하여 퍼지 제어규칙을 자동으로 생성하는 방법을 제안한다. 또한 시계열 예측 문제에서 입력패턴의 간격을 조정하여 그 성능을 검증한다. 이 방법은 샤논 함수와 index of fuzziness를 이용하여 입력공간을 분할하고, 분할된 부 공간에 대해 입력 데이터와 부합할 수 있는 각각의 규칙에 등급을 정하여 불필요한 제어규칙을 제거하여 최적의 규칙베이스를 구성하도록 한다. 적용되는 퍼지 신경망의 기본적인 구조는 퍼지 제어기의 규칙베이스와 추론의 과정을 신경회로망을 이용하여 구현하며 퍼지 제어규칙의 매개변수들은 최대 급경사 강하법에 의해 적응되어진다. 제안된 알고리즘을 토대로 여덟 가지의 입력패턴에 대하여 추론한 결과 입력공간의 최적분할에 의하여 수렴과정에서 초기에 오차(RMSE)가 빠르게 수렴함을 알 수 있었다.

• 한국토양비료학회지
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• 제50권4호
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• pp.306-317
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• 2017

Unmanned aerial vehicles (UAVs) became popular platforms for the collection of remotely sensed data in the last years. This study deals with the monitoring of multi-temporal onion growth with very high resolution by means of low-cost equipment. The concept of the monitoring was estimation of multi-temporal onion growth using normalized difference vegetation index (NDVI) and meteorological factors. For this study, UAV imagery was taken on the Changnyeong, Hapcheon and Muan regions eight times from early February to late June during the onion growing season. In precision agriculture frequent remote sensing on such scales during the vegetation period provided important spatial information on the crop status. Meanwhile, four plant growth parameters, plant height (P.H.), leaf number (L.N.), plant diameter (P.D.) and fresh weight (F.W.) were measured for about three hundred plants (twenty plants per plot) for each field campaign. Three meteorological factors included average temperature, rainfall and irradiation over an entire onion growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, $NDVI_{UAV}$ and rainfall in the model explain 88% and 68% of the P.H. and F.W. with a root mean square error (RMSE) of 7.29 cm and 59.47 g, respectively. And $NDVI_{UAV}$ in the model explain 43% of the L.N. with a RMSE of 0.96. These lead to the result that the characteristics of variations in onion growth according to $NDVI_{UAV}$ and other meteorological factors were well reflected in the model.

• Journal of Forest and Environmental Science
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• 제33권3호
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• pp.187-196
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• 2017

The use of unmanned aerial vehicles (UAV) for the estimation of tree height is gaining recognition. This study aims to assess the effectiveness of tree height estimation of Pinus densiflora Sieb. et Zucc. and Pinus koraiensis Sieb. et Zucc. using digital surface model (DSM) generated from UAV-acquired imageries. Images were taken with the $Trimble^{(R)}$ UX5 equipped with Sony ${\alpha}5100$. The generated DSM, together with the digital elevation model (DEM) generated from a digital map of the study areas, were used in the estimation of tree height. Field measurements were conducted in order to generate a regression model and carry out accuracy assessment. The obtained coefficients of determination (R2) and root mean square error (RMSE) for P. densiflora (R2=0.71; RMSE=1.00 m) and P. koraiensis (R2=0.64; RMSE=0.85 m) are comparable to the results of similar studies. The results of the paired two-tailed t-test show that the two tree height estimation methods are not significantly different (p-value=0.04 and 0.10, alpha level=0.01), which means that tree height estimation using UAV imagery could be used as an alternative to field measurement.

• 한국농공학회논문집
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• 제55권4호
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• pp.55-63
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• 2013

This study investigates the trends and uncertainty of the impacts of climate change on paddy rice production in the Geumho river basin. The Long Ashton Research Station stochastic Weather Generator (LARS-WG) was used to derive future climate data for the Geumho river basin from 15 General Circulation models (GCMs) for 3 Special Report on Emissions Scenarios (SRES) (A2, A1B and B1) included in the Intergovernmental Panel on Climate Change (IPCC) 4th assessment report. The Food and Agricultural Organization (FAO) AquaCrop, a water-driven crop model, was statistically calibrated for the 1982 to 2010 climate. The index of agreement (IoA), prediction efficiency ($R^2$), percent bias (PBIAS), root mean square error (RMSE) and a visual technique were used to evaluate the adjusted AquaCrop simulated yield values. The adjusted simulated yields showed RMSE, NSE, IoA and PBIAS of 0.40, 0.26, 0.76 and 0.59 respectively. The 5, 9 and 15 year central moving averages showed $R^2$ of 0.78, 0.90 and 0.96 respectively after adjustment. AquaCrop was run for the 2020s (2011-2030), 2050s (2046-2065) and 2090s (2080-2099). Climate change projections for Geumho river basin generally indicate a hotter and wetter future climate with maximum increase in the annual temperature of $4.5^{\circ}C$ in the 2090s A1B, as well as maximum increase in the rainfall of 45 % in the 2090s A2. The means (and ranges) of paddy rice yields are projected to increase by 21 % (17-25 %), 34 % (27-42 %) and 43 % (31-54 %) for the 2020s, 2050s and 2090s, respectively. The A1B shows the largest rice yield uncertainty in all time slices with standard deviation of 0.148, 0.189 and $0.173t{\cdot}ha^{-1}$ for the 2020s, 2050s and 2090s, respectively.

• 대한방사선기술학회지:방사선기술과학
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• 제44권6호
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• pp.655-661
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• 2021

Positron emission tomography (PET) images is affected by acquisition time, short acquisition times results in low gamma counts leading to degradation of image quality by statistical noise. Noise2Void(N2V) is self supervised denoising model that is convolutional neural network (CNN) based deep learning. The purpose of this study is to evaluate denoising performance of N2V for PET image with a short acquisition time. The phantom was scanned as a list mode for 10 min using Biograph mCT40 of PET/CT (Siemens Healthcare, Erlangen, Germany). We compared PET images using NEMA image-quality phantom for standard acquisition time (10 min), short acquisition time (2min) and simulated PET image (S2 min). To evaluate performance of N2V, the peak signal to noise ratio (PSNR), normalized root mean square error (NRMSE), structural similarity index (SSIM) and radio-activity recovery coefficient (RC) were used. The PSNR, NRMSE and SSIM for 2 min and S2 min PET images compared to 10min PET image were 30.983, 33.936, 9.954, 7.609 and 0.916, 0.934 respectively. The RC for spheres with S2 min PET image also met European Association of Nuclear Medicine Research Ltd. (EARL) FDG PET accreditation program. We confirmed generated S2 min PET image from N2V deep learning showed improvement results compared to 2 min PET image and The PET images on visual analysis were also comparable between 10 min and S2 min PET images. In conclusion, noisy PET image by means of short acquisition time using N2V denoising network model can be improved image quality without underestimation of radioactivity.

• 한국멀티미디어학회논문지
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• 제24권11호
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• pp.1481-1491
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• 2021

Since most biosignals rely on contact-based measurement, there is still a problem in that it is hard to provide convenience to users by applying them to daily life. In this paper, we present a mobile application for estimating heart rate based on a deep learning model. The proposed application measures heart rate by capturing real-time face images in a non-contact manner. We trained a three-dimensional convolutional neural network to predict photoplethysmography (PPG) from face images. The face images used for training were taken in various movements and situations. To evaluate the performance of the proposed system, we used a pulse oximeter to measure a ground truth PPG. As a result, the deviation of the calculated root means square error between the heart rate from remote PPG measured by the proposed system and the heart rate from the ground truth was about 1.14, showing no significant difference. Our findings suggest that heart rate measurement by mobile applications is accurate enough to help manage health during daily life.

• 인터넷정보학회논문지
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• 제24권2호
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• pp.47-56
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• 2023

수소 에너지는 높은 에너지 효율로 열과 전기를 생산하면서도 온실가스와 미세먼지 등 유해물질 배출이 없는 친환경 에너지로서, 전 세계적으로 탄소중립으로의 전환을 위한 핵심으로 주목받고 있다. 특히 스마트 수소에너지는 경제적이고 지속 가능하며, 안전한 미래 스마트 수소에너지 서비스로써 수소 에너지의 기반 시설이 디지털로 통합되어 '데이터' 기반으로 안정적으로 운영되는 서비스를 의미한다. 본 논문에서는 데이터 기반 수소 충전소 수요예측 모델 구현을 위해 강원도 내 설치되어 있는 수소 충전소 3곳(춘천, 속초, 평창)을 선정, 수소 충전소의 수요공급 데이터를 확보하였고, 머신러닝 및 딥러닝 알고리즘 7개를 선정하여 총 27종 입력 데이터(기상데이터+수소 충전소 수요량)로 모델을 학습하였고, 평균 제곱근 오차(RMSE)로 모델을 평가하였다. 이를 통해 본 논문에서는 최적의 수소 에너지 수요공급을 위한 머신러닝 기반 수소 충전소 에너지 수요 예측 모델을 제안한다.

• Geomechanics and Engineering
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• 제34권3호
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• pp.267-284
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• 2023

Landslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curve; the root means square error, and the correlation coefficient . Finally, the landslide potential model that was obtained using Gaussian's kernel was selected as the best one for susceptibility of landslides in the Oshvand watershed.

• 한국콘텐츠학회논문지
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• 제8권3호
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• pp.252-263
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• 2008

본 연구에서는 L-모멘트법에 의한 지역화 빈도분석에 따른 설계강우량 추정에 관한 연구를 수행하였다. 제주도와 울릉도의 강우관측소를 제외한 분석에 사용된 65개 강우관측소의 강우자료 수집과 선정된 강우관측지점의 강우자료의 지속시간, 즉 1, 3, 6, 12, 24, 36, 48 및 72시간 지속의 연최대치 계열을 구성하였다. 관측지점을 대상으로 Cluster분석을 실시한 결과 우리나라의 강우관측지점에 대한 합리적인 지역화로 5개의 지역으로 구분되었다. 지역화된 지역에 대한 지속기간별 극치강우자료의 적정분포모형 결정을 위한 6가지 분포모형의 적용하고 적용분포의 L-모멘트비를 산정하여 L-모멘트비도를 도시하고 K-S 검정에 의한 적정분포모형을 선정하였다. 선정된 적정분포는 GEV 분포이며 이 분포에 의해 강우관측치의 점빈도 및 지역빈도분석에 의한 설계강우량을 유도하였다. Monte Carlo 기법에 의해 모의발생된 강우량의 점빈도 및 지역빈도분석에 의한 설계강우량을 유도하였다. 실측치 및 모의발생치의 점빈도 및 지역빈도분석에 의한 설계강우량의 비교분석을 위해 상대제곱근오차와 상대편의오차에 의해 분석한 결과 점빈도 분석에 의한 설계강우량보다 지역빈도분석에 의한 설계강우량의 사용이 적정한 것으로 나타났다.