• Korean Journal of Materials Research
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• v.1 no.1
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• pp.29-36
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• 1991

Pressurized Water Reactor (PWR) pressure vessels are made of forged low alloy steel plates internally clad with an austenitic stainless steel by welding to improve anti-corrosion properties. They display a characteristic behavior of dissimilar metal weld interface during post weld heat treatment (PWHT) and service at high temperature and pressure. In this Study, Metallugical structure of weld interface of SA 508 Class 3 forged steel clad with 309L, Austenitic stainless steel after PWHT was investigated. To estimate the width of the carburized/decarburized bands quantitatively, a model for carbon diffusion was proposed and a theoretical equation was derived.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.144-151
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• 2023

In this study, a database was established by collecting experimental results on various concrete mixtures subjected to freeze-thaw cycles, based on which an artificial neural network-based prediction model was developed to estimate durability resistance of concrete. A regression analysis was also conducted to derive an equation for estimating relative dynamic modulus of elasticity subjected to freeze-thaw loads. The error rate and coefficient of determination of the proposed artificial neural network model were approximately 11% and 0.72, respectively, and the regression equation also provided very similar accuracy. Thus, it is considered that the proposed artificial neural network model and regression equation can be used for estimating relative dynamic modulus of elasticity for various concrete mixtures subjected to freeze-thaw loads.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.2
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• pp.93-101
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• 2019

The objective of this study is to develop a model which can predict the seismic performance of the slope relatively accurately and efficiently by using artificial neural network(ANN) technique. The quantification of such the seismic performance of the slope is not easy task due to the randomness and the uncertainty of the earthquake input and slope model. Under these circumstances, probabilistic seismic fragility analyses of slope have been carried out by several researchers, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, a traditional statistical linear regression analysis has shown a limit that cannot accurately represent the nonlinearistic relationship between the slope of various conditions and seismic performance. In order to overcome these problems, in this study, we attempted to apply the ANN to generate prediction models of the seismic performance of the slope. The validity of the derived model was verified by comparing this with the conventional multi-linear and multi-nonlinear regression models. As a result, the models obtained through the ANN basically showed excellent performance in predicting the seismic performance of the slope, compared to the models obtained by the statistical regression analyses of the previous study.

• Korean Chemical Engineering Research
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• v.45 no.2
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• pp.133-142
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• 2007

As the environmental pollution caused by excessive uses of chemical fertilizers and pesticides is aggravated, organic farming using pasture and livestock manure is gaining an increased necessity. The application rate of the organic farming materials to the field is determined as a function of crops and soil types, weather and cultivation surroundings. When livestock manure is used for organic farming materials, the volatilization of ammonia from field-spread animal manure is a major source of atmospheric pollution and leads to a significant reduction in the fertilizer value of the manure. Therefore, an ammonia emission model should be presented to reduce the ammonia emission and to know appropriate application rate of manure. In this study, the ammonia emission rate from field-applied pig manure is predicted using an artificial neural network (ANN) method, where the Michaelis-Menten equation is employed for the ammonia emission rate model. Two model parameters (total loss of ammonia emission rate and time to reach the half of the total emission rate) of the model are predicted using a feedforward-backpropagation ANN on the basis of the ALFAM (Ammonia Loss from Field-applied Animal Manure) database in Europe. The relative importance among 15 input variables influencing ammonia loss is identified using the weight partitioning method. As a result, the ammonia emission is influenced mush by the weather and the manure state.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.26-67
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• 2020

Synthetic Aperture Radar(SAR) is able to photograph the earth's surface regardless of weather conditions, day and night. Because of its possibility to search for hydrological factors such as soil moisture and groundwater, and its importance is gradually increasing in the field of water resources. SAR began to be mounted on satellites in the 1970s, and about 15 or more satellites were launched as of 2020, which around 10 satellites will be launched within the next 5 years. Recently, various types of SAR technologies such as enhancement of observation width and resolution, multiple polarization and multiple frequencies, and diversification of observation angles were being developed and utilized. In this paper, a brief history of the SAR system, as well as studies for estimating soil moisture and hydrological components were investigated. Up to now hydrological components that can be estimated using SAR satellites include soil moisture, subsurface groundwater discharge, precipitation, snow cover area, leaf area index(LAI), and normalized difference vegetation index(NDVI) and among them, soil moisture is being studied in 17 countries in South Korea, North America, Europe, and India by using the physical model, the IEM(Integral Equation Model) and the artificial intelligence-based ANN(Artificial Neural Network). RADARSAT-1, ENVISAT, ASAR, and ERS-1/2 were the most widely used satellite, but the operation has ended, and utilization of RADARSAT-2, Sentinel-1, and SMAP, which are currently in operation, is gradually increasing. Since Korea is developing a medium-sized satellite for water resources and water disasters equipped with C-band SAR with the goal of launching in 2025, various hydrological components estimation researches using SAR are expected to be active.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.43-54
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• 2018

The accurate estimation of reference crop evapotranspiration ($ET_o$) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of $ET_o$ has resulted in many direct and indirect methods to approximate its value and include pan evaporation, meteorological-based estimations, lysimetry, soil moisture depletion, and soil water balance equations. Artificial neural networks (ANNs) have been intensively implemented for process-based hydrologic modeling due to their superior performance using nonlinear modeling, pattern recognition, and classification. This study adapted two well-known ANN algorithms, Backpropagation neural network (BPNN) and Generalized regression neural network (GRNN), to evaluate their capability to accurately predict $ET_o$ using daily meteorological data. All data were obtained from two automated weather stations (Chupungryeong and Jangsu) located in the Yeongdong-gun (2002-2017) and Jangsu-gun (1988-2017), respectively. Daily $ET_o$ was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of $ET_o$ and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against $ET_o$ calculated from the benchmark method and multiple linear regression (MLR) model. The overall results showed that the BPNN algorithm performed best followed by the MLR and GRNN in a statistical sense and this could contribute to provide valuable information to farmers, water managers and policy makers for effective agricultural water governance.

• Journal of computational fluids engineering
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• v.19 no.2
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• pp.86-92
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• 2014

In this paper, prediction of separation trajectory for Two-stage-To-Orbit space launch vehicle has been numerically simulated by using an aerodynamic database based on steady state analysis. Aerodynamic database were obtained for matrix of longitudinal and vertical positions. The steady flow simulations around the launch vehicle have been made by using a 3-D RANS flow solver based on unstructured meshes. For this purpose, a vertex-centered finite-volume method was adopted to discretize inviscid and viscous fluxes. Roe's finite difference splitting was utilized to discretize the inviscid fluxes, and the viscous fluxes were computed based on central differencing. To validate this flow solver, calculations were made for the wind-tunnel experiment model of the LGBB TSTO vehicle configuration on steady state conditions. Aerodynamic database was constructed by using flow simulations based on test matrix from the wind-tunnel experiment. ANN(Artificial Neural Network) was applied to construct interpolation function among aerodynamic variables. Separation trajectory for TSTO launch vehicle was predicted from 6-DOF equation of motion based on the interpolated function. The result of present separation trajectory calculation was compared with the trajectory using experimental database. The predicted results for the separation trajectory shows fair agreement with reference[4] solution.

• Journal of the Korean Dietetic Association
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• v.15 no.1
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• pp.52-68
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• 2009

This study compared activity factor. predicted resting metabolic rate (RMR), and nutrient intakes between athletic and non-athletic high school students in Gangwon-do. Fifty soccer players (30 males and 20 females; mean ages 16.7${\pm}$1.0 years and 16.4${\pm}$1.1 years. respectively) and 50 non-athletic (30 males and 20 females: mean ages 17.5${\pm}$0.4 years and 16.4${\pm}$1.1 years respectively) high school students were included. Anthropometric measurements included: weight and height. triceps skinfold, mid-ann circumference, and body fat. Prediction equations consisted of those from the Harris-Benedict. FAO/WHO/VNU, IMNA, Cunningham, Mifflin et al., and Owen et al. A one-day activity diary was collected by interview, and the 24-hour recall method was used to analyze nutrient intakes of subjects. The activity factors of the male and female athletic groups (2.23 and 2.16, respectively) were significantly higher than those (1.52 and 1.46, respectively) of the non-athletic group. There was only a significant difference in RMR by use of the Cunningham's equation between two groups. For the males. almost all nutrient intakes of the athletic group (except carbohydrate, iron, vitamin $B_1$, $B_6$, and niacin) of athletic group were significantly higher than those of the non-athletic group. The female athletic group showed significantly higher nutrient intakes with the exception of most vitamins. These results suggest that assessments of energy balance between energy intake and energy expenditure by employing RMR and activity factors would be useful to prevent and treat obesity in high school athletes. In addition, the Cunningham's equation would be appropriate for predicting their energy needs.

• Journal of Nutrition and Health
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• v.28 no.1
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• pp.71-83
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• 1995

The purpose of the study was to develop generalized equations for estimating stature and weight for the nonambulatory elderly persons. Height weight recumbent knee height total ann length, midarm, waist and calf circumferences, triceps and subscapular skinfolds were measured from over 60 years old 315 ambulatory elderly. The equations to predict stature and weight were derived from participants in the validation sample and were applied to the participants in the cross-validation to test the accuracy and validity of equations. Stature and weight were significantly and negatively associated with age of women and similar patterns observed in men but associated to a slight degree. Knee height and total arm length were highly correlated with stature but the majority of the variances in stature was accounted for by knee height for both the men and women. In men, waist circumference was the most significantly correlated with weight and am, calf circumferences and so forth. But in women arm circumference was the highest then waist and calf circumference in order. The possible predictor variables to estimate of stature were knee height total arm length and age for both elderly men and women. Predictor variables to estimate of weight were recumbent measures of waist am, calf circumferences and knee height for both sexes. Inclusion of skinfold thickness measurements did not improve the prediction power of estimation for weight. When both equations developed from the present study and Chumlea's study were applied to cross-valida-tions samples, the equations derived from present study showed better accuracy and validity. The presentation of prediction equations using two, three, or four recommended measurements allows the selection of an equation based upon the measurements that are possible to collect on an individual basis.

• Journal of the Korean Society of Safety
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• v.24 no.2
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• pp.69-75
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• 2009

It is well-known that lifting capacity of a worker is influenced by body posture during the task. When a task analyst make use of RULA and REBA Trunk and upper arm angles are recorded in a separate item. It means that the interaction between the angles of two body segments may be ignored in a final score. The NLE(NIOSH Lifting Equation) has been used to supplement this problem. However, there is no study to validate the result of RWL (Recommended Workload Limit) under the existence of interactions between trunk and upper arm angles. The goal of this study was to assess the effect of the interaction between trunk and upper arm angles. Three responses, including NMVC(normalized maximum voluntary contraction), RWL(Recommended Weight Limit) and subjective judgment in psychophysical method (Borg's scale), were recorded according to the combinations of three trunk angles and nine upper arm angles. The results showed that lifting capacity is highly influenced by interaction of two body segments(trunk and upper arm). It means that the task workload has to be analyzed along with the interaction of trunk angles and upper arm angles when the task analyst assesses potential risk factors on the postures. This study may be able to be a fundamental study to develop an assessment method for lifting task analyses according to body postures.