본 연구에서는 호흡동조화기법의 대안으로 딥러닝 자유호흡기법에서 b-value 별 겉보기확산계수 값을 평가하고 확 산강조영상과 겉보기확산계수 지도의 해부학적 일치성을 분석하여 적절한 여기횟수 값을 알아보고자 하였다. 연구 방법은 2023년 7월부터 2024년 1월까지 간 자기공명영상 검사가 의뢰된 성인 남녀 35명을 대상으로 하였고 사용 장비는 Magnetom Skyra 3.0T(Siemens, Germany)를 이용하였다. 자유호흡기법의 비교를 위해 b-value 50, 400, 800(s/mm2)의 여기횟수를 각각 딥러닝 호흡동조화기법에서 2,3,4으로 딥러닝을 이용하지 않은 일반 자유호 흡기법에서 4,6,8으로 검사하였다. 딥러닝을 추가한 일반 자유호흡기법에서는 1,2,3 여기횟수, 2,3,4 여기횟수, 3,5,6 여기횟수, 4,6,8 여기횟수로 변화하였다. 연구 결과 딥러닝 자유호흡기법에서 간의 좌엽과 우엽, 담낭의 평균 겉보기확산계수 값은 딥러닝 호흡동조화기법과 비교하여 모두 통계적 유의성을 확인하였다. 한편 정성적 평가의 해 부학적 일치성을 분석한 결과 딥러닝 자유호흡기법의 3,5,6 여기횟수와 4,6,8 여기횟수에서 가장 높은 점수를 얻었 으며 검사 시간에서는 딥러닝 호흡동조화기법과 비교하여 약 51%, 40% 감소하였다. 따라서 간 진단에 있어 딥러닝 자유호흡기법에서 b-value 별 적절한 여기횟수 값을 이용한다면 겉보기확산계수 지도의 정확도 유지와 함께 검사 시간을 감소시킬 수 있어 임상적으로 유용한 검사가 될 것으로 사료된다.

PURPOSES : The skid resistance between tires and the pavement surface is an important factor that directly affects driving safety and must be considered when evaluating the road performance. In especially wet conditions, the skid resistance of the pavement surface decreases considerably, increasing the risk of accidents. Moreover, poor drainage can lead to hydroplaning. This study aimed to develop a prediction equation for the roughness coefficient—that is, an index of frictional resistance at the interface of the water flow and surface material—to estimate the thickness of the water film in advance to prevent human and material damage. METHODS : The roughness coefficient can be changed depending on the surface material and can be calculated using Manning's theory. Here, the water level (h), which is included in the cross-sectional area and wetted perimeter calculations, can be used to calculate the roughness coefficient by using the water film thickness measurements generated after simulating specific rainfall conditions. In this study, the pavement slope, drainage path length, and mean texture depth for each concrete surface type (non-tined, and tined surfaces with 25-mm and 16-mm spacings) were used as variables. A water film thickness scale was manufactured and used to measure the water film thickness by placing it vertically on top of the pavement surface along the length of the scale protrusion. Based on the measured water film thickness, the roughness coefficient could be back-calculated by applying Manning's formula. A regression analysis was then performed to develop a prediction equation for the roughness coefficient based on the water film thickness data using the water film thickness, mean texture depth, pavement slope, and drainage path length as independent variables. RESULTS : To calculate the roughness coefficient, the results of the water film thickness measurements using rainfall simulations demonstrated that the water film thickness increased as the rainfall intensity increased under N/T, T25, and T16 conditions. Moreover, the water film thickness decreased owing to the linear increase in drainage capacity as the mean texture depth and pavement slope increased, and the shorter the drainage path length, the faster the drainage, resulting in a low water film thickness. Based on the measured water film thickness data, the roughness coefficient was calculated, and it was evident that the roughness coefficient decreased as the rainfall intensity increased. Moreover, the higher the pavement slope and the shorter the drainage path length, the faster the drainage reduced the water film thickness and increased the roughness coefficient (which is an indicator of the friction resistance). It was also evident that as the mean texture depth increased, the drainage capacity increased, which also reduced the roughness coefficient. CONCLUSIONS : As the roughness coefficient of the concrete road surface changes based on the environmental factors, road geometry, and pavement surface characteristics, we developed a prediction equation for the concrete pavement roughness coefficient that considered these factors. To validate the proposed prediction equation, a sensitivity analysis was conducted using the water film thickness prediction equation from previous studies. Existing models have limitations on the impact of the pavement type and rainfall intensity and can be biased toward underestimation; in contrast, the proposed model demonstrated a high correlation between the calculated and measured values. The water film thickness was calculated based on the road design standards in Korea—in the order of normal, caution, and danger scenarios—by using the proposed concrete pavement roughness coefficient prediction model under rainy weather conditions. Specifically, because the normal and caution stages occur before the manifestation of hydroplaning, it should be possible to prevent damage before it leads to the danger stage if it is predicted and managed in advance.

본 논문에서는 충격파 형태의 폭발 하중을 받는 부재의 소성 범위를 고려한 SDOF 해석의 수정계수를 개발하였다. SDOF 해석의 수 정계수는 MDOF 해석 결과 값을 비교하여 도출하였다. SDOF 해석에 영향을 미치는 매개변수로 부재의 경계조건, 폭발 하중 지속시 간과 고유주기 비를 선정하였다. 수정계수는 탄성 하중-질량 변환 계수를 기준으로 산정하였다. 수정계수 곡선은 상한, 하한 매개변수 경계 사이에 있도록 타원 방정식을 이용하여 도출하였다. 서로 다른 단면과 경계조건을 가지는 예제에 수정계수를 적용한 결과 SDOF 해석의 오차율이 15%에서 3%로 감소하였다. 본 연구의 결과는 수정계수를 적용하여 SDOF 해석의 정확도를 높임에 따라 폭발 해석 에 널리 활용될 수 있다.

여름철은 타 계절에 비해 장마와 불안정한 대기 등으로 인하여 빗길 교통사고의 위험성이 크게 증대될 수 있으며, 최근 5년 (2018~2022)간 여름철 빗길 교통사고는 전체 빗길 교통사고의 39%를 차지할 정도로 높은 수준이다. 이러한 빗길 운전은 노면의 배수 불량 및 미끄럼 저항 감소 등으로 인하여 수막현상을 발생시키게 된다. 이에 본 연구에서는 우천 시 도로의 안전성 강화 및 사고 위 험을 최소화하기 위한 요소인 수막두께를 산정하기 위하여 Manning의 평균 유속식을 기반으로 콘크리트 노면의 조도계수 예측 모델을 개발하는 것을 목표로 하였다. 조도계수의 영향인자를 고려하기 위하여 실외 강우 모의 장비를 이용하여 콘크리트를 타설한 뒤 실험 인자로 포장 경사, 배수거리, 강우강도, 노면 조직 특성을 고려하였으며, 이 중 노면 조직 특성은 타이닝 처리를 하지 않은 구간만 고 려한 타 연구의 기존 예측 모델 단점을 보완하기 위하여 16, 25mm 간격의 타이닝 표면 처리한 구간을 추가로 고려하였다. 수막두께 측정은 측정 범위 0.3~5mm의 수막두께 측정 게이지를 제작하여 강우가 모사된 조건에서 배수 거리 1~5m 이내 지점의 노면 조직 상 단과 수면이 접하는 수직 높이를 총 3회 측정하여 평균값을 사용하였다. 실측된 수막두께 데이터베이스를 기반으로 Manning 공식을 이용하여 조도계수를 역산한 결과, 강우강도가 증가함에 따라 조도계수는 감소하였으며, 이는 강우의 증가로 인해 물의 흐름과 콘크리 트 노면 사이의 마찰 저항 감소에 기인한 것으로 판단되었다. 또한 포장 경사가 높고 배수 거리가 짧을수록 배수성이 증가하여 마찰 저항의 지표인 조도계수가 증가하는 것으로 확인되었다. 평균 조직 깊이에 따른 조도계수 영향의 경우, 평균 조직 깊이가 증가할수록 콘크리트 표면에 노출되는 표면적이 증가하여 수막두께가 얕게 생성되고, 얕은 수심으로 인해 물의 흐름 저항이 감소하여 조도계수는 감소하는 것으로 산정되었다. 이후 135개의 데이터를 종합하여 조도계수를 종속변수로 하고 강우강도, 포장경사, 배수거리, 평균 조직 깊이, 수막두께를 독립변수로 하는 회귀분석을 수행하여 조도계수 산정식을 개발하였다.

When the parent radionuclide decays, the progeny radionuclide is produced. Accordingly, the dose contribution of the progeny radionuclide should be considered when assessing dose. For this purpose, European Commission (EC) and International Atomic Energy Agency (IAEA) provide weighting factors for dose coefficient. However, these weighting factors have a limitation that does not reflect the latest nuclide data. Therefore, in this study, we analyzed the EC and IAEA methodology for derivation of weighting factor and used the latest nuclide data from ICRP 107 to derive weighting factors for dose coefficient. Weighting factor calculation is carried out through 1) selection of nuclide, 2) setting of evaluation period, and 3) derivation based on ICRP 107 radionuclide data. Firstly, in order to derive the weighting factor, we need to select the radionuclides whose dose contribution should be considered. If the half-life of progeny radionuclides sufficiently short compared to the parent radionuclide to achieve radioactive equilibrium, or if the dose coefficient is greater of similar to that of the parent radionuclide and cannot be ignored, the dose contribution of the progeny radionuclide should be considered. In order not to underestimate the dose contribution of progeny radionuclides, the weighting factors for the progeny nuclides are taken as the maximum activity ratio that the respective progeny radionuclides will reach during a time span of 100 years. Finally, the weighting factor can be derived by considering the radioactivity ratio and branch fraction. In order to calculate the weighting factor, decay data such as the half-life of the radionuclide, decay chain, and branch fraction are required. In this study, radionuclide data from ICRP 107 was used. As a result of the evaluation, for most radionuclides, the weighting factors were derived similarly to the existing EC and IAEA weighting factors. However, for some nuclides, the weighting factors were significantly different from EC and IAEA. This is judged to be a difference in the half-life and branch fraction of the radionuclide. For example, in the case of 95Zr, the weighting factor for 95mNb showed a 35.8% difference between this study and previous study. For ICRP 38, when 95Zr decays, the branch fraction for 95mNb is 6.98×10-3. In contrast, for ICRP 107, the branch fraction is 1.08×10-2, a difference of 54.7%. Therefore, the weighting factor for the dose coefficient based on ICRP 107 data may differ from existing studies depending on the half-life and decay information of the nuclide. This suggests the need for a weighting factor based on the latest nuclide data. The results of this study can be used as a basis for the consideration of dose contributions for progeny radionuclides in various dose assessments.

The operation of nuclear facilities involves the potential for on-site contamination of soil, primarily resulting from pipe leaks and other operational incidents. Globally, decommissioning process for commercial nuclear power plants have revealed huge-amounts of soil waste contaminated with Cs-137, Sr-90, Co-60, and H-3. For example, Connecticut Yankee in the United States produced approximately 52,800 ton of contaminated soil waste, constituting 10% of the total waste generated during its decommissioning. Environmental remediation costs associated with nuclear decommissioning in the US averaged $60 million per unit, representing a significant 10% of the whole decommissioning expenses. Consequently, this study undertook a preliminary investigation to identify important factors for establishing a site remediation strategy based on radionuclide- and site-specific media- characteristics, focusing the efficiency enhancement for the environmental remediation. The factors considered for this investigation were categorized into physical/environmental, socioeconomic, technical, and management aspects. Physical/environmental factors contained the site characteristics, contamination levels, and environmental sensitivity, while socio-economic factors included the social concerns and economic costs. Technical and management factors included subcategories such as technical considerations, policy aspects, and management factors. Especially, technical factors were further subdivided to consider the site reuse potential, secondary waste generation by site remediation, remediation efficiency, and remediation time. Additionally, our study focused the key factors that facilitate the systematic planning for the site remediation, considering the distribution coefficient (Kd) and hydrogeological characteristics associated with each radionuclide in specific site conditions. Therefore, key factors in this study focus the geochemical characteristics of site media including the particle size distribution, chemical composition, organic and inorganic constituents, and soil moisture content. Moreover, the adsorption properties of site media were examined concerning the distribution coefficient (Kd) of radionuclides and their migration characteristics. Furthermore, this study supported the development of a conceptual framework, containing the remediation strategies that incorporate the mobility of radionuclides, according to the site-specific media. This conceptual framework would necessitate the spatial analysis techniques involving the whole contamination surveys and radionuclide mobility modeling data. By integrating these key factors, the study provides the selection and simulation of optimal remediation methods, ultimately offering the estimated amounts of radioactive waste and its disposal costs. Therefore, these key factors offer foundational insights for designing the site remediation strategies according the sitespecific information such as the distribution coefficient (Kd) and hydrogeological characteristics.

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure’s safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

The mobility of radionuclides is largely determined by their radiological properties, geochemical conditions, and adsorption reactions, such as surface adsorption, chemical precipitation, and ion exchange. To evaluate the safety assessments of radionuclides in nuclear sites, it is essential to understand the behavior and mechanism of radionuclides onto soils. Since nuclear power plants are located in coastal areas, the chemical composition of groundwater can vary depending on the intrusion of seawater, altering the adsorption distribution coefficient (Kd) values of radionuclides. This study examines the impact of seawater on the Kd values of clay minerals for cesium (Cs) and strontium (Sr). The results of Cs+ adsorption experiments showed a broad range of Kd values from 36 to 1,820 mL/g at an initial concentration of 1 mg/L and a high sorption coefficient of 15-613 mL/g at an initial concentration of 10 mg/L. Montmorillonite, hydrobiotite, illite, and kaolinite were ranked in terms of their CEC values for Cs+ adsorption, with hydrobiotite having the highest adsorption at 1 mg/L. The results of Sr adsorption experiments showed a wide range of Kd values from 82 to 1,209 mL/g at an initial concentration of 1 mg/L and a lower adsorption coefficient of 6.68-344 mL/g at an initial concentration of 10 mg/L. Both Cs+ and Sr2+ demonstrated lower Kd values at higher initial concentrations. CEC of clays found to have a significant impact on Sr2+ Kd values. Ca2+ ions showed a significant impact on Sr2+ adsorption distribution coefficients, demonstrating the greater impact of seawater on Sr2+ compared to Cs+. These findings can inform future safety assessments of radionuclides in nuclear sites.

Radioactive contaminants, such as 137Cs, are a significant concern for long-term storage of nuclear waste. Migration and retention of these contaminants in various environmental media can pose a risk to the surrounding environment. The distribution coefficient (Kd) is a critical parameter for assessing the behavior of these contaminants and can introduce significant errors in predicting migration and remediation options. Accurate prediction of Kd values is essential to assess the behavior of radioactive contaminants and to ensure environmental safety. In this study, we present machine learning models based on the Japan Atomic Energy Agency Sorption Database (JAEA-SDB) to predict Kd values for Cs in soils. We used three different machine learning models, namely the random forest (RF), artificial neural network (ANN), and convolutional neural network (CNN), to predict Kd values. The models were trained on 14 input variables from the JAEA-SDB, including factors such as Cs concentration, solid phase properties, and solution conditions which are preprocessed by normalization and log transformation. We evaluated the performance of our models using the coefficient of determination (R2) value. The RF, ANN, and CNN models achieved R2 values of over 0.97, 0.86, and 0.88, respectively. Additionally, we analyzed the variable importance of RF using out-of-bag (OOB) and CNN with an attention module. Our results showed that the initial radionuclide concentration and properties of solid phase were important variables for Kd prediction. Our machine learning models provide accurate predictions of Kd values for different soil conditions. The Kd values predicted by our models can be used to assess the behavior of radioactive contaminants in various environmental media. This can help in predicting the potential migration and retention of contaminants in soils and the selection of appropriate site remediation options. Our study provides a reliable and efficient method for predicting Kd values that can be used in environmental risk assessment and waste management.

Understanding the light environment in greenhouse cultivation and the light utilization characteristics of crops is important in the study of photosynthesis and transpiration. Also, as the plant grows, the form of light utilization changes. Therefore, this study aims to develop a light extinction coefficient model reflecting the plant growth. To measure the extinction coefficient, five pyranometers were installed vertically according to the height of the plant, and the light intensity by height was collected every second during the entire growing season. According to each growth stage in the early, middle, and late stages, the difference between the top and bottom light intensity tended to increase to 69%, 72%, and 81%. When leaf area index and plant height increased, the extinction coefficient decreased, and it showed an exponential decay relationship. Three-dimensional model reflecting the two growth indexes, the paraboloid had the lowest RMSE of 1.340 and the highest regression constant of 0.968. Through this study, it was possible to predict the more precise light extinction coefficient during the growing period of plants. Furthermore, it is judged that this can be utilized for predicting and analyzing photosynthesis and transpiration according to the plant height.

Research is being actively conducted on the continuous thin plate casting method, which is used to manufacture magnesium alloy plate for plastic processing. This study applied a heat transfer solidification analysis method to the melt drag process. The heat transfer coefficient between the molten magnesium alloy metal and the roll in the thin plate manufacturing process using the melt drag method has not been clearly established until now, and the results were used to determine the temperature change. The estimated heat transfer coefficient for a roll speed of 30 m/min was 1.33 × 105 W/m2·K, which was very large compared to the heat transfer coefficient used in the solidification analysis of general aluminum castings. The heat transfer coefficient between the molten metal and the roll estimated in the range of the roll speed of 5 to 90 m/min was 1.42 × 105 to 8.95 × 104 W/m2·K. The cooling rate was calculated using a method based on the results of deriving the temperature change of the molten metal and the roll, using the estimated heat transfer coefficient. The DAS was estimated from the relationship between the cooling rate and DAS, and compared with the experimental value. When the magnesium alloy is manufactured by the melt drag method, the cooling rate of the thin plate is in the range of about 1.4 × 103 to 1.0 × 104 K/s.

Phosphate coating is applied to the surface of the round bar material used in the multi-stage cold forging process for the purpose of lubrication. The film characteristics are determined according to the conditions of the phosphate film treatment process. In this study, the film properties according to the phosphate treatment conditions were defined as the coefficient of repeated friction and quantitative analysis was performed. Different friction behaviors were exhibited depending on the film properties, suggesting that optimization of the phosphate film treatment conditions is possible based on this. Finally, as a practical example, friction behavior according to the film characteristics was applied to the automotive engine bolt forming process. As a final conclusion, the need for linkage analysis with phosphating conditions for optimizing the forging process was raised. In addition, it can be seen that damage to the phosphate film should be considered in the process of predicting the limiting life of the die.

In this study, a mixed resin containing Bis-GMA was developed to produce a light-emitting sign using quantum dots. As a result of measuring the viscosity, color coordinates change, and luminance of the mixed resin, the following conclusions were obtained. The viscosity of the mixed resin decreased as the content of the diluent increased, and viscosity values ​​ranged from 3,627 to 1,349cps showed as a result. The viscosity of the mixed resin decreased as the temperature increased, and the viscosity showed a value of 5,156 to 1,132cps. For the optical properties of InP/GaP/ZnSe/ZnS quantum dots, the absolute quantum efficiency was 91% at 522nm and 90% at 618nm when the gallium was 0.01%. The luminance of the light-emitting sign using the resin mixed with quantum dots was showed 142.6cd/m2 in white and 104.2cd/m2 in the red region.

Based on the nonlinear static analysis and the approximate seismic evaluation method adopted in “Guidelines for seismic performance evaluation for existing buildings, two methods to calculate strength demand for retrofitting individual structural walls in unreinforced masonry buildings are proposed.” The displacement coefficient method to determine displacement demand from nonlinear static analysis results is used for the inverse calculation of overall strength demand required to reduce the displacement demand to a target value meeting the performance objective of the unreinforced masonry building to retrofit. A preliminary seismic evaluation method to screen out vulnerable buildings, of which detailed evaluation is necessary, is utilized to calculate overall strength demand without structural analysis based on the difference between the seismic demand and capacity. A system modification factor is introduced to the preliminary seismic evaluation method to reduce the strength demand considering inelastic deformation. The overall strength demand is distributed to the structural walls to retrofit based on the wall stiffness, including the remaining walls or otherwise. Four detached residential houses are modeled and analyzed using the nonlinear static and preliminary evaluation procedures to examine the proposed method.

A wire rod, a material for multistage cold forging, is subjected to spheroidization and low annealing heat treatment to secure formability, and a phosphate coating treatment on the material surface to secure lubricity. The film layer produced by the phosphate treatment process is involved in adhesion to the material surface, adhesion to the forging die surface, and lubricity. This results in the increase or decrease of the forming load and the increase or decrease of the die life in the cold forging process. In particular, as the cold forging process progresses, the phosphate film is damaged and the original performance is deteriorated, so there is a high possibility of process defects. In case of excessive damage, the film is completely lost and die soldering occurs. Therefore, in this study, quantitative criteria for phosphate film damage are presented and the effect on the cold forging process is analyzed based on this to improve process analysis prediction accuracy. Therefore, in this study, quantitative criteria for phosphate film damage are presented, and based on this, the friction coefficient in the multi-stage cold forging process is to be derived.

PURPOSES : The purpose of this study is to suggest a thermal expansion coefficient measurement method using an embedded strain transducer (EST) and vibrating wire gauge (VWG), as well as to evaluate the reliability of the proposed methods by comparing them with the AASHTO T 336-10 standard method. METHODS : To apply the AASHTO 336-10 test method, which is the criterion for reliability evaluation, a reference specimen using stainless steel (sus304) is manufactured, and a thermal expansion coefficient of 17.308με/°C is obtained based on ISO regulations. Using the reference specimen, the correction factor of the thermal expansion coefficient measurement equipment is measured to be 2.93με/°C, and using this value, the thermal expansion coefficient of the mortar specimen containing the embedded gauges is measured accurately. The reliability of the proposed experimental method is evaluated by measuring the thermal expansion coefficient of the embedded gauge with temperature compensation and then comparing it with that of the reference specimen. RESULTS : The coefficient of thermal expansion of the mortar specimen is measured to be 12.423με/°C based on AASHTO 336-10, 11.963με/°C using the EST method, and 12.522με/°C using the VWG method. Based on the results obtained using the AASHTO method, the embedded gauges show a difference of 1%~3% in terms of the average results, as well as a difference in the standard deviation of 0.059~0.186. Therefore, our level of confidence in the thermal expansion coefficient experiment using the embedded gauges is high. CONCLUSIONS : When using the AASHTO 336-10 test method, the thermal expansion coefficient should be obtained by measuring the length change of the specimen; however, some engineering judgment of the experimenter is required when the measurement values fluctuate during the temperature stabilization period. In the thermal expansion coefficient test using embedded gauges (EST and VWG), temperature compensation must be performed. Furthermore, it is assumed that the temperature difference between the water tank and test specimen does not significantly affect the thermal expansion coefficient measurement because the important point is not the actual temperature value but the temperature gradient. For reliability evaluation, a statistical significance review of the strain distribution by measurement method is performed via a T-test comparing with the AASHTO test result (12.423με/°C) and the reliability level for each measurement method remains confidential.