• Nuclear Engineering and Technology
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• v.35 no.1
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• pp.35-44
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• 2003

The neutron induced nuclear data for Eu-153, Gd-155 and Cd-157 are calculated and evaluated in the high energy region. The evaluation procedure for deformed nuclei is setup by using Ecis-Empire codes. The energy dependent optical model potential parameters are searched based on the recent experimental data and applied up to 20 MeV. Optical model, full featured Hauser-Feshbach model and multistep direct and multistep compound model are used in the calculation. The direct-semidirect capture model and the direct coupled-channels contribution to discrete levels are introduced to improve the capture and inelastic scattering cross sections. The theoretically calculated cross sections are compared with the experimental data and the evaluated files. The model-calculated total and capture cross sections are in good agreement with the reference experimental data. The evaluated cross section results are compiled to ENDF-6 format and are expected to improve the ENDF/B-Vl.

• Nuclear Engineering and Technology
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• v.34 no.2
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• pp.154-164
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• 2002

Neutron cross section data on Dy-160, Dy-161, Dy-162, Dy-163 and Dy-164 were calculated and evaluated in the energy range of 1 keV to 20 MeV using a spherical optical model, statistical model and pre-equilibrium model. The energy dependent optical model potential parameters were obtained based on the recent experimental data. The width fluctuation correction in Hauser-Feshbach particle decay and the quantum mechanical approach in pre-equilibrium analysis were introduced and gave a better cross section calculation in EMPIRE-II. The total, elastic scattering and threshold reaction cross sections were evaluated and compared with the evaluated files. The model calculated (n, tot), (n, ${\gamma}$) and (n, p) cross sections were in good agreement with the experimental data in the measured energy range. The results will be applied to ENDF/B-VI for data improvement.

• Nuclear Engineering and Technology
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• v.34 no.4
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• pp.370-381
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• 2002

The neutron induced nuclear data for Pr-141, Nd-143, Nd-145, Sm-147 and Sm-149 were calculated and evaluated from 10 keV to 20 MeV. The energy dependent optical model potential parameters were extracted based on the recent experimental data and applied up to 20 MeV. The s-wave strength function was calculated. Spherical optical model , statistical model in equilibrium energy, multistep direct and multistep compound model in pre-equilibrium energy and direct capture model were introduced in Empire calculation. The theoretically calculated cross sections were compared with the experimental data and the evaluated files. The model calculated total and capture cross sections were in good agreement with the reference experimental data. The capture cross sections in pre-equilibrium were enhanced in recent released Empire version. The evaluated cross section results were compiled to ENDF-6 format and will improve the ENDF/B-Vl.

• Asia Marketing Journal
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• v.22 no.2
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• pp.1-18
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• 2020

We estimate a choice-based aggregate demand model accounting for consumers' consideration sets, and study its implications on market structure. In contrast to past research, we model and estimate consumer demand using aggregate-level consumer browsing data in addition to aggregate-level choice data. The use of consumer browsing data allows us to study consumer demand in a realistic setting in which consumers choose from a subset of products. We calibrate the proposed model on both data sets, avoid biases in parameter estimates, and compute the price elasticity measures. As an empirical application, we estimate consumer demand in the camcorder category and study its implications on market structure. The proposed model predicts a limited consumer price response and offers a more discriminating competitive landscape from the one assuming universal consideration set.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.105-108
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• 1997

Previous studies in stock market predictions using artificial intelligence techniques such as artificial neural networks and case-based reasoning, have focused mainly on spot market prediction. Korea launched trading in index futures market (KOSPI 200) on May 3, 1996, then more people became attracted to this market. Thus, this research intends to predict the daily up/down fluctuant direction of the price for KOSPI 200 index futures to meet this recent surge of interest. The forecasting methodologies employed in this research are the integration of genetic algorithm and artificial neural network (GAANN) and the integration of genetic algorithm and case-based reasoning (GACBR). Genetic algorithm was mainly used to select relevant input variables. This study adopts the categorical data preprocessing based on expert's knowledge as well as traditional data preprocessing. The experimental results of each forecasting method with each data preprocessing method are compared and statistically tested. Artificial neural network and case-based reasoning methods with best performance are integrated. Out-of-the Model Integration and In-Model Integration are presented as the integration methodology. The research outcomes are as follows; First, genetic algorithms are useful and effective method to select input variables for Al techniques. Second, the results of the experiment with categorical data preprocessing significantly outperform that with traditional data preprocessing in forecasting up/down fluctuant direction of index futures price. Third, the integration of genetic algorithm and case-based reasoning (GACBR) outperforms the integration of genetic algorithm and artificial neural network (GAANN). Forth, the integration of genetic algorithm, case-based reasoning and artificial neural network (GAANN-GACBR, GACBRNN and GANNCBR) provide worse results than GACBR.

• Structural Engineering and Mechanics
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• v.32 no.5
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• pp.667-683
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• 2009

FE model-based dynamic analysis has been widely used to predict the dynamic characteristics of civil structures. In a physical point of view, an FE model is unavoidably different from the actual structure as being formulated based on extremely idealized engineering drawings and design data. The conventional model updating methods such as direct method and sensitivity-based parameter estimation are not flexible for model updating of complex and large structures. Thus, it is needed to develop a model updating method applicable to complex structures without restriction. The main objective of this paper is to present the model updating method based on the hybrid genetic algorithm (HGA) by combining the genetic algorithm as global optimization method and modified Nelder-Mead's Simplex method as local optimization method. This FE model updating method using HGA does not need the derivation of derivative function related to parameters and without application of complicated inverse analysis methods. In order to allow its application on diversified and complex structures, a commercial FEA tool is adopted to exploit previously developed element library and analysis algorithms. Moreover, an output-level objective function making use of measurement and analytical results is also presented to update simultaneously the stiffness and mass of the analysis model. The numerical examples demonstrated that the proposed method based on HGA is effective for the updating of the FE model of bridge structures.

• Research in Plant Disease
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• v.30 no.3
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• pp.256-267
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• 2024

We have developed FBcastS (Fire Blight Forecasting System), a cloud-based information system that leverages the K-Maryblyt forecasting model. The FBcastS provides an optimal timing for spraying antibiotics to prevent flower infection caused by Erwinia amylovora and forecasts the onset of disease symptoms to assist in scheduling field scouting activities. FBcastS comprises four discrete subsystems tailored to specific functionalities: meteorological data acquisition and processing, execution of the K-Maryblyt model, distribution of web-based information, and dissemination of spray timing notifications. The meteorological data acquisition subsystem gathers both observed and forecasted weather data from 1,583 sites across South Korea, including 761 apple or pear orchards where automated weather stations are installed for fire blight forecast. This subsystem also performs post-processing tasks such as quality control and data conversion. The model execution subsystem operates the K-Maryblyt model and stores its results in a database. The web-based service subsystem offers an array of internet-based services, including weather monitoring, mobile services for forecasting fire blight infection and symptoms, and nationwide fire blight monitoring. The final subsystem issues timely notifications of fire blight spray timing alert to growers based on forecasts from the K-Maryblyt model, blossom status, pesticide types, and field conditions, following guidelines set by the Rural Development Administration. FBcastS epitomizes a smart agriculture internet of things (IoT) by utilizing densely collected data with a spatial resolution of approximately 4.25 km to improve the accuracy of fire blight forecasts. The system's internet-based services ensure high accessibility and utility, making it a vital tool in data-driven smart agricultural practices.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.183-183
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• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.21-28
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• 2021

This study proposes a deep learning-based flat foot classification methodology using transfer learning. We used a transfer learning with VGG16 pre-trained model and a data augmentation technique to generate a model with high predictive accuracy from a total of 176 image data consisting of 88 flat feet and 88 normal feet. To evaluate the performance of the proposed model, we performed an experiment comparing the prediction accuracy of the basic CNN-based model and the prediction model derived through the proposed methodology. In the case of the basic CNN model, the training accuracy was 77.27%, the validation accuracy was 61.36%, and the test accuracy was 59.09%. Meanwhile, in the case of our proposed model, the training accuracy was 94.32%, the validation accuracy was 86.36%, and the test accuracy was 84.09%, indicating that the accuracy of our model was significantly higher than that of the basic CNN model.

• Computers and Concrete
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• v.31 no.4
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• pp.349-358
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• 2023

This paper describes an in-depth analysis on flexural strength of a girder-deck system experiencing a strand debonding damage with various strengthening systems, based on finite element software ABAQUS. A detailed finite element analysis (FEA) model was developed and verified against the relevant experimental data performed by other researchers. The proposed analytical model showed a good agreement with experimental data. Based on the verified FE model, over a hundred girder-deck systems were investigated with the consideration of following variables: 1) debonding level, 2) span-to-depth ratio (L/d), 3) strengthening type, 4) strengthening material thickness. Based on the data above, a new detailed analytical model was developed and proposed for estimating residual flexural strength of the strand-debonding damaged girder-deck system with strengthening systems. It was demonstrated that both finite element model and analysis model could be used to predict flexural behaviors for debonding damaged prestressed girder-deck systems. Since the strands are debonding from surrounding concrete over a certain zone over the length of the beam, the increase of strain in strands can be linked with a ratio ψ, which is L_p/c. The analytical model was proposed and developed regarding the ratio ψ. By conducting procedure of calculating ψ, the ψ value varies from 9.3 to 70.1. Multiple nonlinear regression analysis was performed in Software IBM SPSS Statistics 27.0.1 to derive equation of ψ. ψ equation was curved to be an exponential function, and the independent variable (X) is a linear function in terms of three variables of debonding level (λ), span length (L), and amount of strengthening material (A_s). The coefficient of determinate (R²) for curve fitting in nonlinear regression analysis is 0.8768. The developed analytical model was compared to the ultimate capacities computed by FEA model.