• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.22-24
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• 2021

This paper is a proposal for edge weight prediction using neural networks to graph configurations of nodes and edges. Brand is one of the components of society. and one of the brand's most important strategies is geographical location strategy. This paper is focus on that strategy. In This paper propose two things: 1) Graph Configuration. node consists of brand store, edge consists of store-to-store relationships and edge weight consists of actual walk and drive distance values. 2) numbering edges and training neural networks to predict next store distance values. It is expected to be useful in analyzing successful brand geographical location strategies.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.4 no.4
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• pp.50-60
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• 1995

Recently advantages joining dissimiliar materials and light weight material techniques have led to increasing use of structural adhesives in the various industries. Stress singulartiy occurs at the interface edges of adhesively bonded dissimilar materials. So it is required to analyze its stress singularity at the interface edges of adhesively bonded joints indissimilar materials. In this paper, the analysis method of stress singularity is studied in detail. Also, effects of the stress singularity at the interface edge of adhesively bonded scarf joints in combinations of dissimilar materials are investigated by using 2-dimensional elastic program of boundary element method. As the results, the strength evaluation method of adhesively bonded dissimilar materials using the stress singularity factor, $\Gamma$,is very useful. The fracture criterion, method of strength evaluation and prediction of fracture strength by the stress singularity factor on the adhesively bonded dissimilar materials are proposed.

• Journal of Korea Water Resources Association
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• v.54 no.2
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• pp.121-133
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• 2021

Suspended Solids (SS) generated in rivers are mainly introduced from non-point pollutants or appear naturally in the water body, and are an important water quality factor that may cause long-term water pollution by being deposited. However, the conventional method of measuring the concentration of suspended solids is labor-intensive, and it is difficult to obtain a vast amount of data via point measurement. Therefore, in this study, a model for measuring the concentration of suspended solids based on remote sensing in the Nakdong River was developed using Sentinel-2 data that provides high-resolution multi-spectral satellite images. The proposed model considers the spectral bands and band ratios of various wavelength bands using a machine learning model, Support Vector Regression (SVR), to overcome the limitation of the existing remote sensing-based regression equations. The optimal combination of variables was derived using the Recursive Feature Elimination (RFE) and weight coefficients for each variable of SVR. The results show that the 705nm band belonging to the red-edge wavelength band was estimated as the most important spectral band, and the proposed SVR model produced the most accurate measurement compared with the previous regression equations. By using the RFE, the SVR model developed in this study reduces the variable dependence compared to the existing regression equations based on the single spectral band or band ratio and provides more accurate prediction of spatial distribution of suspended solids concentration.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.316-328
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• 2021

In this study, the leaf Na content prediction model for spring potato was established using 400-1000 nm hyperspectral sensor to develop the multispectral sensor for the salinity monitoring in reclaimed land. The irrigation conditions were standard, drought, and salinity (2, 4, 8 dS/m), and the irrigation amount was calculated based on the amount of evaporation. The leaves' Na contents were measured 1st and 2nd weeks after starting irrigation in the vegetative, tuber formative, and tuber growing periods, respectively. The reflectance of the leaves was converted from 5 nm to 10 nm, 25 nm, and 50 nm of FWHM (full width at half maximum) based on the 10 nm wavelength intervals. Using the variance importance in projections of partial least square regression(PLSR-VIP), ten band ratios were selected as the variables to predict salinity damage levels with Na content of spring potato leaves. The MLR(Multiple linear regression) models were estimated by removing the band ratios one by one in the order of the lowest weight among the ten band ratios. The performance of models was compared by not only R2, MAPE but also the number of band ratios, optimal FWHM to develop the compact multispectral sensor. It was an advantage to use 25 nm of FWHM to predict the amount of Na in leaves for spring potatoes during the 1st and 2nd weeks vegetative and tuber formative periods and 2 weeks tuber growing periods. The selected bandpass filters were 15 bands and mainly in red and red-edge regions such as 430/440, 490/500, 500/510, 550/560, 570/580, 590/600, 640/650, 650/660, 670/680, 680/690, 690/700, 700/710, 710/720, 720/730, 730/740 nm.

• International Journal of Highway Engineering
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• v.19 no.6
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• pp.67-74
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• 2017

PURPOSES : In this study, a three-dimensional nonlinear finite element analysis (FEA) model for airport concrete pavement was developed using the commercial program ABAQUS. Users can select an analysis method and set the range of input parameters to reflect actual conditions such as environmental loading. METHODS : The geometrical shape of the FEA model was chosen by considering the concrete pavement located in the third-stage construction site of Incheon International Airport. Incompatible eight-node elements were used for the FEA model. Laboratory test results for the concrete specimens fabricated at the construction site were used as material properties of the concrete slab. The material properties of the cement-treated base suggested by the Federal Aviation Administration(FAA) manual were used as those of the lean concrete subbase. In addition, preceding studies and pavement evaluation reports of Incheon International Airport were referred for the material properties of asphalt base and subgrade. The kinetic friction coefficient between the concrete slab and asphalt base acquired from a preceding study was used for the friction coefficient between the layers. A nonlinear temperature gradient according to slab depth was used as an input parameter of environmental loading, and a quasistatic method was used to analyze traffic loading. The average load transfer efficiency obtained from an Heavy falling Weight Deflectomete(HWD) test was converted to a spring constant between adjacent slabs to be used as an input parameter. The reliability of the FEA model developed in this study was verified by comparing its analysis results to those of the FEAFAA model. RESULTS : A series of analyses were performed for environmental loading, traffic loading, and combined loading by using both the model developed in this study and the FEAFAA model under the same conditions. The stresses of the concrete slab obtained by both analysis models were almost the same. An HWD test was simulated and analyzed using the FEA model developed in this study. As a result, the actual deflections at the center, mid-edge, and corner of the slab caused by the HWD loading were similar to those obtained by the analysis. CONCLUSIONS : The FEA model developed in this study was judged to be utilized sufficiently in the prediction of behavior of airport concrete pavement.