• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.155-155
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• 2019

The Agricultural Policy/Environmental eXtender (APEX) models have been developed for assessing agricultural management efforts and their effects on soil and water at the field scale as well as more complex multi-subarea landscapes, whole farms, and watersheds. National Academy of Agricultural Sciences, Wanju, Korea, has modified a key component of APEX application, named APEX-Paddy for simulating water quality with considering appropriate paddy management practices, such as puddling and flood irrigation management. Calibration and validation are an anticipated step before any model application. Simple techniques are essential to assess whether or not a parameter should be adjusted for calibration. However, very few study has been done to evaluate the ability of APEX-Paddy to simulate the impact of multiple management scenarios on nutrients loss. In this study, the observation data from experimental fields at Iksan in South Kora was used in calibration and evaluation process during 2013-2015. The APEX auto- calibration tool (APEX-CUTE) was used for model calibration and sensitivity analysis. Four quantitative statistics, the coefficient of determination ($R^2$),Nash-Sutcliffe(NSE),percentbias(PBIAS)androotmeansquareerror(RMSE)were used in model evaluation. In this study, the hydrological process of the modified model, APEX-Paddy, is being calibrated and tested in predicting runoff discharge rate and nutrient yield. Field-scale calibration and validation processes are described with an emphasis on essential calibration parameters and direction regarding logical sequences of calibration steps. This study helps to understand the calibration and validation way is further provided for applications of APEX-Paddy at the field scales.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.959-964
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• 2022

Lead is an important material, both for fusion or fission reactors. The cross sections of natural lead should be validated because lead is a main component of lithium-lead modules suggested for fusion power plants and it directly affects the crucial variable, tritium breeding ratio. The presented study discusses a validation of the lead transport libraries by dint of the activation of carefully selected activation samples. The high emission standard ²⁵²Cf neutron source was used as a neutron source for the presented validation experiment. In the irradiation setup, the samples were placed behind 5 and 10 cm of the lead material. Samples were measured using a gamma spectrometry to infer the reaction rate and compared with MCNP6 calculations using ENDF/B-VIII.0 lead cross sections. The experiment used validated IRDFF-II dosimetric reactions to validate lead cross sections, namely ¹⁹⁷Au(n, 2n)¹⁹⁶Au, ⁵⁸Ni(n,p)⁵⁸Co, ⁹³Nb(n, 2n)^92mNb, ¹¹⁵In(n,n')^115mIn, ¹¹⁵In(n,γ)^116mIn, ¹⁹⁷Au(n,γ)¹⁹⁸Au and ⁶³Cu(n,γ)⁶⁴Cu reactions. The threshold reactions agree reasonably with calculations; however, the experimental data suggests a higher thermal neutron flux behind lead bricks. The paper also suggests ²⁵²Cf isotropic source as a valuable tool for validation of some cross-sections important for fusion applications, i.e. reactions on structural materials, e.g. Cu, Pb, etc.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.1-16
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• 2023

Accurate traffic information prediction is considered to be one of the most important aspects of intelligent transport systems(ITS), as it can be used to guide users of transportation facilities to avoid congested routes. Various deep learning models have been developed for accurate traffic prediction. Recently, ensemble techniques have been utilized to combine the strengths and weaknesses of various models in various ways to improve prediction accuracy and stability. Therefore, in this study, we developed and evaluated a traffic information prediction model using various deep learning models, and evaluated the performance of the developed deep learning models as a stacking ensemble. The individual models showed error rates within 10% for traffic volume prediction and 3% for speed prediction. The ensemble model showed higher accuracy compared to other models when no cross-validation was performed, and when cross-validation was performed, it showed a uniform error rate in long-term forecasting.

• Computers and Concrete
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• v.32 no.5
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• pp.499-511
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• 2023

Anchor channels are commonly used for façade, tunnel, and structural connections. These connections encounter various types of loadings during their service life, including high rate or impact loading. For anchor channels that are placed close and parallel to an edge and loaded in shear perpendicular to and towards the edge, the failure is often governed by concrete edge breakout. This study investigates the transverse shear behavior of the anchor channels under quasi-static and high rate loadings using a numerical approach (3D finite element analysis) utilizing a rate-sensitive microplane model for concrete as constitutive law. Following the validation of the numerical model against a test performed under quasi-static loading, the rate-sensitive static, and rate-sensitive dynamic analyses are performed for various displacement loading rates varying from moderately high to impact. The increment in resistance due to the high loading rate is evaluated using the dynamic increase factor (DIF). Furthermore, it is shown that the failure mode of the anchor channel changes from global concrete edge failure to local concrete crushing due to the activation of structural inertia at high displacement loading rates. The research outcomes could be valuable for application in various types of connection systems where a high rate of loading is expected.

• Nuclear Engineering and Technology
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• v.50 no.3
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• pp.379-388
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• 2018

Models for the rate of atomization and deposition of droplets for stratified and annular flow in horizontal pipes are presented. The entrained fraction is the result of a balance between the rate of atomization of the liquid layer that is in contact with air and the rate of deposition of droplets. The rate of deposition is strongly affected by gravity in horizontal pipes. The gravitational settling of droplets is influenced by droplet size: heavier droplets deposit more rapidly. Model calculation and simulation results are compared with experimental data from various diameter pipes. Validation for the suggested models was performed by comparing the Safety and Performance Analysis Code for Nuclear Power Plants calculation results with the droplet experimental data obtained in various diameter horizontal pipes.

• International Journal of Reliability and Applications
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• v.3 no.1
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• pp.1-16
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• 2002

In reliability engineering, the bathtub-shaped hazard rates play an important role in survival analysis and many other applications as well. For the bathtub-shaped, initially the hazard rate decreases from a relatively high value due to manufacturing defects or infant mortality to a relatively stable middle useful life value and then slowly increases with the onset of old age or wear out. In this paper, we present a new two-parameter lifetime distribution function, called the Loglog distribution, with Vtub-shaped hazard rate function. We illustrate the usefulness of the new Vtub-shaped hazard rate function by evaluating the reliability of several helicopter parts based on the data obtained in the maintenance malfunction information reporting system database collected from October 1995 to September 1999. We develop the S-Plus add-in software tool, called Reliability and Safety Assessment (RSA), to calculate reliability measures include mean time to failure, mean residual function, and confidence Intervals of the two helicopter critical parts. We use the mean squared error to compare relative goodness of fit test of the distribution models include normal, lognormal, and Weibull within the two data sets. This research indicates that the result of the new Vtub-shaped hazard rate function is worth the extra function-complexity for a better relative fit. More application in broader validation of this conclusion is needed using other data sets for reliability modeling in a general industrial setting.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.3
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• pp.261-269
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• 2015

In this study, numerical analysis of curved channel steam-methanol reformer was conducted using the computational fluid dynamics (CFD) commercial code STAR-CCM. A pre-numerical analysis of reference model with a cylindrical channel reactor was performed to validate the combustion model of the CFD commercial code. The result of advance validation was in agreement with reference model over 95%. After completing the validation, a curved channel reactor was designed to determine the effects of shape and length of flow path on methanol conversion efficiency and generation of hydrogen. Numerical analysis of the curved-channel reformer was conducted under various flow rate ($10/15/20{\mu}l/min$). As a result, the characteristics of flow and mass transfer were confirmed in the cylindrical channel and curved channel reactor, and useful information about methanol conversion efficiency and hydrogen generation was obtained for various flow rate.

• Korean Journal of Remote Sensing
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• v.17 no.3
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• pp.199-210
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• 2001

The purpose of this study is to compare the landslide prediction models quantitatively using prediction rate curve. A case study from the Jangheung area was used to illustrate the methodologies. The landslide locations were detected from remote sensing data and field survey, and geospatial information related to landslide occurrences were built as a spatial database in GIS. As prediction models, joint conditional probability model and certainty factor model were applied. For cross-validation approach, landslide locations were partitioned into two groups randomly. One group was used to construct prediction models, and the other group was used to validate prediction results. From the cross-validation analysis, it is possible to compare two models to each other in this study area. It is expected that these approaches will be used effectively to compare other prediction models and to analyze the causal factors in prediction models.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.36 no.3
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• pp.113-120
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• 2020

The purpose of this study is to compare the models of Deep Learning-based Convolution Neural Network(CNN) for concrete crack detection. The comparison models are AlexNet, GoogLeNet, VGG16, VGG19, ResNet-18, ResNet-50, ResNet-101, and SqueezeNet which won ImageNet Large Scale Visual Recognition Challenge(ILSVRC). To train, validate and test these models, we constructed 3000 training data and 12000 validation data with 256×256 pixel resolution consisting of cracked and non-cracked images, and constructed 5 test data with 4160×3120 pixel resolution consisting of concrete images with crack. In order to increase the efficiency of the training, transfer learning was performed by taking the weight from the pre-trained network supported by MATLAB. From the trained network, the validation data is classified into crack image and non-crack image, yielding True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), and 6 performance indicators, False Negative Rate (FNR), False Positive Rate (FPR), Error Rate, Recall, Precision, Accuracy were calculated. The test image was scanned twice with a sliding window of 256×256 pixel resolution to classify the cracks, resulting in a crack map. From the comparison of the performance indicators and the crack map, it was concluded that VGG16 and VGG19 were the most suitable for detecting concrete cracks.

• Journal of Environmental Health Sciences
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• v.28 no.2
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• pp.183-192
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• 2002

Indoor air quality is affected by source strength of pollutants, ventilation rate, decay rate, outdoor level and so on. Although technologies exist to measure these factors directly, direct measurements of all factors are impractical in most field studies. The purpose of this study was to develop an alternative methods to estimate these factors by multiple measurements. Daily indoor and outdoor NO$_2$concentrations for 21 days in 20 houses in summer and winter, Seoul. Using a mass balance model and linear regression analysis, penetration factor (ventilation divided by sum of air exchange rate and deposition constant) and source strength factor(emission rate divided by sum of air exchange rate and deposition constant) were calculated. Subsequently, the ventilation and source strength were estimated. During sampling period, geometric mean of natural ventilation was estimated to be 1.10$\pm$1.53 ACH, assuming a residential NO$_2$decay rate of 0.8 hr$^{-1}$ in summer. In winter, natural ventilation was 0.75$\pm$1.31 ACH. And mean source strengths in summer and winter were 14.8ppb/hr and 22.4ppb/hr, respectively. Although the method showed similar finding previous studies, the study did not measure ACH or the source strength of the house directly. As validation of natural ventilations, infiltrations were measured with $CO_2$tracer gas in 18 houses. Relationship between ventilation and infiltration was statistically correlated (Pearson r=0.63, p=0.02).