• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.3
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• pp.257-264
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• 1999

The lens distortions were corrected directly using the high-order polynomial which was offered in camera calibration data for the forward transformation and the root of Newton-Raphson's $2\times{2}$ nonlinear system for the backward transformation. The 0.04~0.08 pixels increase in accuracy was indicated through the use of direct correction of lens distortions instead of least square methods of commercial software. The least square adjustment method of high-order polynomial requires many control points which has a same weight. But this suggested method which is unnecessary to determine control points was developed and applied. The algorithm showed improved efficacy.

• International Journal of Naval Architecture and Ocean Engineering
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• v.4 no.2
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• pp.112-122
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• 2012

The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correla-tion coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.85-94
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• 2021

Infrastructure capacities are essential elements and one of the sustainable lines to drive economic growth. Infrastructure development, both physical and social, is vital to sector-wise economic development. However, there is limited evidence of how infrastructure development in certain sectors benefits the economy as a whole. This study explains the relationships between infrastructure and economic growth in selected middle-income Asian countries, highlighting the essential criteria to benefit from both physical and social infrastructure, as well as sectoral (agriculture, industry, and services) economic output. The research uses the data from 1990 to 2020 for empirical estimations. The study used Levin, Lin, & Chu test, ADF- Fischer chi- Square, and PP- Fischer Chi-Square to test unit root and to observe the stationary nature of the panel. Padroni and Kao cointegration is applied to check the cointegration among different panes. A Fully Modified OLS was employed for checking the association between physical and social infrastructure and economic growth. Results show that physical and social infrastructure negatively impact sectoral output in Asia's middle-income countries. Apart from infrastructure the per capita GDP growth, tax to GDP ratio, and population growth shows a simultaneous relation between infrastructure and sectoral economic growth.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.157-164
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• 2021

The aim of this study is to provide empirical evaluation of the accuracy of data-model fit using growing levels of invariance models. Overall model accuracy of factor solutions was evaluated by the examination of the order for testing three levels of measurement invariance (MIV) starting with configural invariance (model 0). Model testing was evaluated by the Chi-square difference test (∆𝛘²) between two groups, and root mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker-Lewis index (TLI) were used to evaluate the all-model fits. Factorial invariance result revealed that stability of the models was varying over increasing levels of measurement as a function of variable-to-factor ratio (VTF), subject-to-variable ratio (STV), and their interactions. There were invariant factor loadings and invariant intercepts among the groups indicating that measurement invariance was achieved. For VTF ratio (3:1, 6:1, and 9:1), the models started to show accuracy over levels of measurement when STV ratio was 6:1. Yet, the frequency of stability models over 1000 replications increased (from 69% to 89%) as STV ratio increased. The models showed more accuracy at or above 39:1 STV.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.87-94
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• 2023

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.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.28-28
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• 2022

본 연구는 용담댐유역을 포함한 금강 유역 상류 지역을 대상으로 Sentinel-1 SAR (Synthetic Aperture Radar) 위성영상을 기반으로 한 토양수분 산정을 목적으로 하였다. Sentinel-1 영상은 2019년에 대해 12일 간격으로 수집하였고, 영상의 전처리는 SNAP (SentiNel Application Platform)을 활용하여 기하 보정, 방사 보정 및 Speckle 보정을 수행하여 VH (Vertical transmit-Horizontal receive) 및 VV (Vertical transmit-Vertical receive) 편파 후방산란계수로 변환하였다. 토양수분 산정에는 Water Cloud Model (WCM)이 활용되었으며, 모형의 식생 서술자(Vegetation descriptor)는 RVI (Radar Vegetation Index)와 NDVI (Normalized Difference Vegetation Index)를 활용하였다. RVI는 Sentinel-1 영상의 VH 및 VV 편파자료를 이용해 산정하였으며, NDVI는 동기간에 대해 10일 간격으로 수집된 Sentinel-2 MSI (MultiSpectral Instrument) 위성영상을 활용하여 산정하였다. WCM의 검정 및 보정은 한국수자원공사에서 제공하는 10 cm 깊이의 TDR (Time Domain Reflectometry) 센서에서 실측된 6개 지점의 토양수분 자료를 수집하여 수행하였으며, 매개변수의 최적화는 비선형 최소제곱(Non-linear least square) 및 PSO (Particle Swarm Optimization) 알고리즘을 활용하였다. WCM을 통해 산정된 토양수분은 피어슨 상관계수(Pearson's correlation coefficient)와 평균제곱근오차(Root mean square error)를 활용하여 검증을 수행할 예정이다.

• Journal of Korean Association for Spatial Structures
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• v.23 no.4
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• Journal of Korean Association for Spatial Structures
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• v.24 no.2
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• pp.83-90
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• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• Geotechnical Engineering
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• v.11 no.3
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• pp.55-68
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• 1995

This paper is the analysis of the relationship between RQD and decay constant, blasting vi bration constant of cube root scaling and square root scaling, through experimental blast ins test in subway construction for excavation of shaft hole by bottom blasting. The magnitude of particle velocity is largely effected by the distance from blasting source, the maximum charge per delay and the properties of ground. In order to verify the effects of ground properties on blast-induced vibration, the relation-ship between magnitude of blasting vibration and Rock Quality Disignation which stands for joint property was studied. The results of test are verified that blasting vibration constant "K" and the absolute value("n") of decay constant relatively increse as RQD increased. According to the result, it can be predict the particle velocity by the blast -induced vibration in bottom blasting pattern.om blasting pattern.

• Journal of Korean Dental Science
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• v.2 no.2
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• pp.24-30
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• 2009

Two component orthodontic C-implants have been introduced as intermaxillary fixation (IMF) screws in cases of periodontal problems with bone loss, severely damaged teeth, or short roots. This retrospective research sought to investigate the complications and risk factors associated with the failure of two-part C-implants for IMF cases and to show the possible indications compared to one-component mini-implants. The study sample consisted of 46 randomly selected patients who had a total of 203 implants. Pearson chi-square tests of independence were used to test for associations among categorical variables. At least 19 of the total 203 implants failed (9.3%). There was no significant difference in implant failure due to gender, oral hygiene, and placement, although a significant difference due to soft tissue characteristics and root contact was observed. The two-component design of the mini-implant is reliable for difficult IMF cases. Note, however, that the factors influencing implant failure were found to be age, root damage, and condition of soft tissues.