• Journal of Environmental Science International
• /
• v.32 no.8
• /
• pp.595-611
• /
• 2023

The purpose of this study is to improve the daily prediction results of PM2.5 from the air quality diagnosis and evaluation system operated by the Busan Institute of Health and Environment in real time. The air quality diagnosis and evaluation system is based on the photochemical numerical model, CMAQ (Community multiscale air quality modeling system), and includes a 3-day forecast at the end of the model's calculation. The photochemical numerical model basically has limitations because of the uncertainty of input data and simplification of physical and chemical processes. To overcome these limitations, this study applied DNN (Deep Neural Network), a deep learning technique, to the results of the numerical model. As a result of applying DNN, the r of the model was significantly improved. The r value for GFS (Global forecast system) and UM (Unified model) increased from 0.77 to 0.87 and 0.70 to 0.83, respectively. The RMSE (Root mean square error), which indicates the model's error rate, was also significantly improved (GFS: 5.01 to 6.52 ug/m³ , UM: 5.76 to 7.44 ug/m³ ). The prediction results for each concentration grade performed in the field also improved significantly (GFS: 74.4 to 80.1%, UM: 70.0 to 77.9%). In particular, it was confirmed that the improvement effect at the high concentration grade was excellent.

• Imaging Science in Dentistry
• /
• v.53 no.3
• /
• pp.221-227
• /
• 2023

Purpose: This study was performed to develop a linear regression model using the pulp-to-tooth volume ratio (PTVR) ratio of the maxillary canine, assessed through cone-beam computed tomography (CBCT) images, to predict chronological age (CA) in Indonesian adults. Materials and Methods: A sample of 99 maxillary canines was collected from patients between 20 and 49.99 years old. These samples were obtained from CBCT scans taken at the Universitas Padjadjaran Dental Hospital in Indonesia between 2018 and 2022. Pulp volume (PV) and tooth volume (TV) were measured using ITK-SNAP, while PTVR was calculated from the PV/TV ratio. Using RStudio, a linear regression was performed to predict CA using PTVR. Additionally, correlation and observer agreement were assessed. Results: The PTVR method demonstrated excellent reproducibility, and a significant correlation was found between the PTVR of the maxillary canine and CA(r= -0.74, P<0.01). The linear regression analysis showed an R² of 0.58, a root mean square error of 5.85, and a mean absolute error of 4.31. Conclusion: Linear regression using the PTVR can be effectively applied to predict CA in Indonesian adults between 20 and 49.99 years of age. As models of this type can be population-specific, recalibration for each population is encouraged. Additionally, future research should explore the use of other teeth, such as molars.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.119-119
• /
• 2023

본 연구에서는 Terra MODIS(MODerate resolution Imaging Spectroradiometer) 위성영상과 토양수분 부족지수(Soil Water Deficit Index, SWDI)를 이용하여 2012년부터 2022년까지 한반도 전국의 1km 공간 증발산량을 산정하였다. 공간 증발산량을 산정하기 위한 과정은 크게 두 가지로 구분된다. 첫 번째로 MODIS의 LST(Land Surface Temperature), NDVI(Normalized Difference Vegetation Index), 선행강우 및 무강우 누적일수를 이용해 1 km 공간 토양수분을 산정하였다. 농촌진흥청 토양수분관측망 자료 중 토지피복, 토양 속성을 고려하여 선정된 70개소 토양수분 실측데이터와 비교한 결과 지점별 평균 R² 0.63~0.90으로 유의미한 상관성을 나타내었다. 산정된 공간 토양수분은 생장저해수분점과 초기위조점의 관계를 이용한 SWDI로 변환하였다. 두 번째로 순 복사량, 기온 및 NDVI의 적은 수문인자를 통해 증발산량 산정이 가능한 MS-PT(Modified Satellite-based Priestley-Taylor) 모형을 기반으로 계절별 식생과 토양수분 상태를 고려하여 1 km 공간 증발산량을 산정하였다. MS-PT 모형에서 가정한 대기 증발 변수 Diurnal temperature (DT)와 지표 수분의 상관성 문제를 해결하기 위해 DT를 SWDI로 적용하였다. 모형 결과의 검증을 위해 국내 플럭스 타워 (설마천, 청미천, 덕유산) 증발산량 관측자료와의 결정계수(Coefficient of determination, R²), RMSE(Root Mean Square Error) 및 IOA(Index of Agreement)를 산정하였다. 본 연구의 결과로 생산되는 국내 증발산량의 시, 공간적 변동성은 증발산량을 통한 수문학적 가뭄지수 및 급성 가뭄을 파악하는데 활용될 수 있을 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.91-91
• /
• 2023

General circulation models(GCMs)은 여러 국가 기관들의 물리적 기후 모의 프로세스를 기반으로 과거 및 미래 기후변화의 영향을 정량화하기 위해 개발되었으며 현재 미래 기후변화를 예측하는데 가장 효과적인 도구이다. 그러나 GCMs에 내포된 여러 불확실성 요소 및 넓은 격자형식의 기후 데이터는 GCMs 기후 데이터를 사용한 지역적 기후 모의 시 주요 걸림돌로 인식되어지고 있다. 편의보정 방법은 GCMs을 사용한 지역적 기후 모의 시 기후 모의 성능을 향상시키기 위해 여러 연구에서 사용되어져 왔으나 다른 연구에서는 이러한 편의보정 방법의 문제점을 언급했다. 따라서 본 연구는 편의보정 방법이 GCMs 기후 모의 결과에 미치는 영향을 정량화하고 더 나아가 GCMs 기후 변수에 따른 유량 모의 결과에 미치는 영향을 분석했다. 연구대상지 과거 기간 기후 모의를 위해 coupled model intercomparison project(CMIP)6의 GCMs을 사용했으며, 미래 기후 모의를 위해 shared socioeconomic pathway(SSP) 시나리오를 사용했다. 편의보정 방법으로는 분위사상법을 사용했으며, 편의보정 전후 GCMs 기후 모의 성능평가를 위해 5개 평가 지표를 사용했다. 연구대상지 장기 유출 모의를 위해 storm water management model(SWMM)이 사용되었으며, 기후 입력 자료로는 일 단위 강수량, 최고 및 최저온도를 고려했다. 미래 기후 및 유량 모의 결과의 불확실성은 square root of error variance(SREV) 방법을 통해 정량화됐다. 결과적으로 과거 기간 GCMs 기후 및 유량 모의성능은 편의보정 전보다 편의보정 후에서 향상되었으며 특히, 강수 및 유량 모의 성능이 크게 향상되었다. 미래 기간의 경우 편의보정 후에서 기후 및 유량의 극값을 더 잘 반영함을 확인했다. 본 연구의 결과는 GCMs 기후 변수를 사용한 지역적 기후 및 유량 모의 시 편의보정 방법이 미치는 영향에 대한 구체적인 정보를 제공할 수 있다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.244-244
• /
• 2023

Both the frequency and the magnitude of hydrometeorological extreme events such as severe floods and droughts are increasing. In order to prevent a damage from the climatic disaster, hydrological models are often simulated under various meteorological conditions. While performing the simulations, a synthetic data generated through time series models which maintains the key statistical characteristics of the sample data are widely applied. However, the synthetic data can easily maintains both the average and the variance of the sample data, but the quantile is not maintained well. In this study, we proposes a data generation method which maintains the quantile of the sample data well. The equations of the former maintenance of variance extension (MOVE) are expanded to maintain quantile rather than the average or the variance of the sample data. The equations are derived and the coefficients are determined based on the characteristics of the sample data that we aim to preserve. Monte Carlo simulation is utilized to assess the performance of the proposed data generation method. A time series data (data length of 500) is regarded as the sample data and selected randomly from the sample data to create the data set (data length of 30) for simulation. Data length of the selected data set is expanded from 30 to 500 by using the proposed method. Then, the average, the variance, and the quantile difference between the sample data, and the expanded data are evaluated with relative root mean square error for each simulation. As a result of the simulation, each equation which is designed to maintain the characteristic of data performs well. Moreover, expanded data can preserve the quantile of sample data more precisely than that those expanded through the conventional time series model.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.223-223
• /
• 2023

토양수분은 지표면과 지하 영역 사이에 존재하는 수분 및 열에너지의 분배를 제어하거나, 토양 영양분, 식물 성장 및 미생물 활동과 같은 다양한 환경 과정에 영향을 미치는 핵심 구성요소이다. 토양수분은 생태수문학 및 생지화학적 역학, 저수지 관리, 가뭄 및 홍수의 경고, 토양 수분 변화에 따른 작물 수확량 등을 이해하는 데 매우 중요한 역할을 한다. 따라서, 토양 수분의 정확한 측정은 필수적이며, 이러한 필요성에 따라 중력 측정법, 장력 측정법, 전기 저항법 및 시간-주파수 영역반사측정법 등의 다양한 측정 방법들이 다년간 개발되어 사용되었다. 다만, 앞선 방법들은 철저한 실험을 통해 높은 정확성을 확보하였지만, 토양 교란이 발생하는 단점이 존재하며 실험 현장 토양의 물리적, 생물학적, 그리고 화학적 특성의 보존은 매우 어려운 한계점을 가지고 있다. 따라서, 이러한 단점을 극복하기 위해, 본 연구에서는 레일리파를 이용한 비접촉식 비교란 토양수분 센서 개발을 목표로 한다. 모래, 실트, 점토와 같은 세 가지 특징적인 토양 유형에 따른 파동을 측정하고, 측정된 파동으로부터 토양 수분을 추정하기 위해 기존에 개발된 시간-주파수 방법을 활용하여 토양수분을 함께 측정하였다. 비접촉 파동신호를 토양수분으로 변환하기 위하여, fully convolutional network을 개발하였다. 개발한 모델의 결과 검증은 RMSE(Root Mean Square Error)를 활용하여 검증하였으며, 모래, 실트, 점토에서 각각 0.0131, 0.0021, 0.0034 m³ m^-3으로 상대적으로 높은 정확성을 보였다. 즉, 본 연구에서 제시한 누출 레일리파를 사용한 비교란-비접촉 토양수분 측정 방법으로, 토양을 교란하지 않고 토양수분을 측정 할 수 있는 높은 가능성을 제시하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.8
• /
• pp.2068-2082
• /
• 2023

With the rapid development of deep learning, Depth Map Super-Resolution (DMSR) method has achieved more advanced performances. However, when the upsampling rate is very large, it is difficult to capture the structural consistency between color features and depth features by these DMSR methods. Therefore, we propose a color-image guided DMSR method based on iterative depth feature enhancement. Considering the feature difference between high-quality color features and low-quality depth features, we propose to decompose the depth features into High-Frequency (HF) and Low-Frequency (LF) components. Due to structural homogeneity of depth HF components and HF color features, only HF color features are used to enhance the depth HF features without using the LF color features. Before the HF and LF depth feature decomposition, the LF component of the previous depth decomposition and the updated HF component are combined together. After decomposing and reorganizing recursively-updated features, we combine all the depth LF features with the final updated depth HF features to obtain the enhanced-depth features. Next, the enhanced-depth features are input into the multistage depth map fusion reconstruction block, in which the cross enhancement module is introduced into the reconstruction block to fully mine the spatial correlation of depth map by interleaving various features between different convolution groups. Experimental results can show that the two objective assessments of root mean square error and mean absolute deviation of the proposed method are superior to those of many latest DMSR methods.

• Geomechanics and Engineering
• /
• v.34 no.6
• /
• pp.697-726
• /
• 2023

Brittleness as an important property of rock plays a crucial role both in the failure process of intact rock and rock mass response to excavation in engineering geological and geotechnical projects. Generally, rock brittleness indices are calculated from the mechanical properties of rocks such as uniaxial compressive strength, tensile strength and modulus of elasticity. These properties are generally determined from complicated, expensive and time-consuming tests in laboratory. For this reason, in the present research, an attempt has been made to predict the rock brittleness indices from simple, inexpensive, and quick laboratory test results namely dry unit weight, porosity, slake-durability index, P-wave velocity, Schmidt rebound hardness, and point load strength index using multiple linear regression, exponential regression, support vector machine (SVM) with various kernels, generating fuzzy inference system, and regression tree ensemble (RTE) with boosting framework. So, this could be considered as an innovation for the present research. For this purpose, the number of 39 rock samples including five igneous, twenty-six sedimentary, and eight metamorphic were collected from different regions of Iran. Mineralogical, physical and mechanical properties as well as five well known rock brittleness indices (i.e., B₁, B₂, B₃, B₄, and B₅) were measured for the selected rock samples before application of the above-mentioned machine learning techniques. The performance of the developed models was evaluated based on several statistical metrics such as mean square error, relative absolute error, root relative absolute error, determination coefficients, variance account for, mean absolute percentage error and standard deviation of the error. The comparison of the obtained results revealed that among the studied methods, SVM is the most suitable one for predicting B₁, B₂ and B₅, while RTE predicts B₃ and B₄ better than other methods.

• Journal of Radiation Protection and Research
• /
• v.48 no.1
• /
• pp.28-43
• /
• 2023

Background: High-fidelity meteorological data is a prerequisite for the realistic simulation of atmospheric dispersion of radioactive materials near nuclear power plants (NPPs). However, many meteorological models frequently overestimate near-surface wind speeds, failing to represent local meteorological conditions near NPPs. This study presents a new high-resolution (approximately 1 km) meteorological downscaling method for modeling short-range (< 100 km) atmospheric dispersion of accidental NPP plumes. Materials and Methods: Six considerations from literature reviews have been suggested for a new dynamic downscaling method. The dynamic downscaling method is developed based on the Weather Research and Forecasting (WRF) model version 3.6.1, applying high-resolution land-use and topography data. In addition, a new subgrid-scale topographic drag parameterization has been implemented for a realistic representation of the atmospheric surface-layer momentum transfer. Finally, a year-long simulation for the Kori and Wolsong NPPs, located in southeastern coastal areas, has been made for 2016 and evaluated against operational surface meteorological measurements and the NPPs' on-site weather stations. Results and Discussion: The new dynamic downscaling method can represent multiscale atmospheric motions from the synoptic to the boundary-layer scales and produce three-dimensional local meteorological fields near the NPPs with a 1.2 km grid resolution. Comparing the year-long simulation against the measurements showed a salient improvement in simulating near-surface wind fields by reducing the root mean square error of approximately 1 m/s. Furthermore, the improved wind field simulation led to a better agreement in the Eulerian estimate of the local atmospheric dispersion. The new subgrid-scale topographic drag parameterization was essential for improved performance, suggesting the importance of the subgrid-scale momentum interactions in the atmospheric surface layer. Conclusion: A new dynamic downscaling method has been developed to produce high-resolution local meteorological fields around the Kori and Wolsong NPPs, which can be used in short-range atmospheric dispersion modeling near the NPPs.