• Journal of the Korea institute for structural maintenance and inspection
• /
• v.24 no.6
• /
• pp.206-216
• /
• 2020

As the number of aging railway bridges in Korea increases, maintenance costs due to aging are increasing and continuous management is becoming more important. However, while the number of old facilities to be managed increases, there is a shortage of professional personnel capable of inspecting and diagnosing these old facilities. To solve these problems, this study presents an improved model that can detect Local damage to structures using machine learning techniques of AI technology. To construct a damage detection machine learning model, an analysis model of the bridge was set by referring to the design drawing of a non-ballasted plate-girder railroad bridge. Static strain data according to the damage scenario was extracted with the analysis model, and the Local damage index based on the reliability of the bridge was presented using statistical techniques. Damage was performed in a three-step process of identifying the damage existence, the damage location, and the damage severity. In the estimation of the damage severity, a linear regression model was additionally considered to detect random damage. Finally, the random damage location was estimated and verified using a machine learning-based damage detection classification learning model and a regression model.

• Clean Technology
• /
• v.27 no.2
• /
• pp.168-173
• /
• 2021

The treatment of plated wastewater is subject to various and complex processes depending on the pH, heavy metal, and cyanide content of the wastewater. Alkali chlorine treatment using NaOCl is commonly used for cyanide treatment. However, if ammonia and cyanide are present simultaneously, NaOCl is consumed excessively to treat ammonia. To solve this problem, this study investigated 1) the consumption of NaOCl according to ammonia concentration in the alkaline chlorine method and 2) whether ferrate (VI) could selectively treat the cyanide. Experiments using simulated wastewater showed that the higher the ammonia concentration, the lower the cyanide removal rate, and the linear increase in NaOCl consumption according to the ammonia concentration. Removal of cyanide using ferrate (VI) confirmed the removal of cyanide regardless of ammonia concentration. Moreover, the removal rate of ammonia was low, so it was confirmed that the ferrate (VI) selectively eliminated the cyanide. The cyanide removal efficiency of ferrate (VI) was higher with lower pH and showed more than 99% regardless of the ferrate (VI) injection amount. The actual application to plated wastewater showed a high removal ratio of over 99% when the input mole ratio of ferrate (VI) and cyanide was 1:1, consistent with the molarity of the stoichiometry reaction method, which selectively removes cyanide from actual wastewater containing ammonia and other pollutants like the result of simulated wastewater.

• Journal of Korea Entertainment Industry Association
• /
• v.14 no.3
• /
• pp.343-352
• /
• 2020

The aim of this study was to investigate the relationship between level of participation in children's play, empathy ability and coping behavior in mothers of children with disabilities. The study included 94 mothers of children with disabilities and the study was conducted from May 2018 to August 2018 based on the questionnaire about the level of participation in children's play, empathy ability and coping behavior. According to the study, Mothers of children with disabilities actively participate in physical play with their children and have a higher emotional empathy compared to cognitive empathy, and have acted to actively solve the problems for their children. When children have been treated for more than 1 to 2 years, the level of participation in children's play and the coping behavior were the highest. The longer their children's treatment period, the higher the mothers' empathy. The correlation between level of participation in children's play and coping behavior were significant. And empathy and coping behavior had a strong positive linear relationship. These results suggest that there are differences in level of participation in children's play, empathy and coping behavior depending on mothers' of children with disabilities general characteristics and the higher level of mothers' of children with disabilities participation in children's play, the more empathetic they have. When individualized intervention is provided, we look forward to providing professional service that take into account the characteristics of mothers of children with disabilities.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.25 no.5
• /
• pp.173-181
• /
• 2021

Reinforced concrete structures are exposed to various environments, resulting in reinforcement corrosion due to moisture and ions penetration. Reinforced concrete corrosion causes a decrease in the durability performance of reinforced concrete structures. One solution to mitigate such issues is using FRP rebars, which offer several advantages such as high tensile strength, corrosion resistance, and light-weight than conventional rebars, in reinforced concrete instead of conventional steel rebars. The FRP rebar used should be examined at the limit state because FRP reinforced concrete has linear behavior until its fracture and can generate excessive deflection due to the low elastic modulus. It should be considered while designing FRP reinforced concrete for flexure. In the ultimate limit state, the flexural strength of FRP reinforced concrete as per ACI 440.1R is significantly lower than the flexural strength by applying both the environmental reduction and strength reduction factors accounting for the material uncertainty of FRP rebar. Therefore, in this study, the experimental results were compared with the deflection of the proposed effective moment of inertia referring to the local and international standards. The experimental results of GFRP and BFRP reinforced concrete were compared with the flexural strength as determined by ACI 440.1R and Fib bulletin 40. The flexural strength obtained by the experimental results was more similar to that obtained by Fib bulletin 40 than ACI 440.1R. The flexural strength of ACI 440.1R was conservatively evaluated in the tension-controlled section.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.3
• /
• pp.193-200
• /
• 2022

In this study, the finite-failure NHPP software reliability model was applied to the software development model based on the Weibull lifetime distribution (Goel-Okumoto, Rayleigh, Type-2 Gumbe), which is widely used in the software reliability field, and then the cost attributes were compared and evaluated. For this study, failure time data detected during normal operation of the software system were collected and used, the most-likelihood estimation (MLE) method was applied to the parameter estimation of the proposed model, and the calculation of the nonlinear equation was solved using the binary method. As a result, first, in the software development model, when the cost of testing per unit time and the cost of removing a single defect increased, the cost increased but the release time did not change, and when the cost of repairing failures detected during normal system operation increased, the cost increased and the release time was also delayed. Second, as a result of comprehensive comparative analysis of the proposed models, it was found that the Type-2 Gumble model was the most efficient model because the development cost was lower and the release time point was relatively faster than the Rayleigh model and the Goel-Okumoto basic model. Third, through this study, the development cost properties of the Weibull distribution model were newly evaluated, and the analyzed data is expected to be utilized as design data that enables software developers to explore the attributes of development cost and release time.

• Journal of Intelligence and Information Systems
• /
• v.29 no.3
• /
• pp.419-437
• /
• 2023

As the number of thin filers in Korea surpasses 12 million, there is a growing interest in enhancing the accuracy of assessing their credit default risk to generate additional revenue. Specifically, researchers are actively pursuing the development of default prediction models using machine learning and deep learning algorithms, in contrast to traditional statistical default prediction methods, which struggle to capture nonlinearity. Among these efforts, Graph Neural Network (GNN) architecture is noteworthy for predicting default in situations with limited data on thin filers. This is due to their ability to incorporate network information between borrowers alongside conventional credit-related data. However, prior research employing graph neural networks has faced limitations in effectively handling diverse categorical variables present in credit information. In this study, we introduce the Transformer embedded Graph Convolutional Network (TeGCN), which aims to address these limitations and enable effective default prediction for thin filers. TeGCN combines the TabTransformer, capable of extracting contextual information from categorical variables, with the Graph Convolutional Network, which captures network information between borrowers. Our TeGCN model surpasses the baseline model's performance across both the general borrower dataset and the thin filer dataset. Specially, our model performs outstanding results in thin filer default prediction. This study achieves high default prediction accuracy by a model structure tailored to characteristics of credit information containing numerous categorical variables, especially in the context of thin filers with limited data. Our study can contribute to resolving the financial exclusion issues faced by thin filers and facilitate additional revenue within the financial industry.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.36 no.1
• /
• pp.9-18
• /
• 2023

Predicting the compressive strength of high-performance concrete (HPC) is challenging because of the use of additional cementitious materials; thus, the development of improved predictive models is essential. The purpose of this study was to develop an HPC compressive-strength prediction model using an ensemble machine-learning method of combined bagging and stacking techniques. The result is a new ensemble technique that integrates the existing ensemble methods of bagging and stacking to solve the problems of a single machine-learning model and improve the prediction performance of the model. The nonlinear regression, support vector machine, artificial neural network, and Gaussian process regression approaches were used as single machine-learning methods and bagging and stacking techniques as ensemble machine-learning methods. As a result, the model of the proposed method showed improved accuracy results compared with single machine-learning models, an individual bagging technique model, and a stacking technique model. This was confirmed through a comparison of four representative performance indicators, verifying the effectiveness of the method.

• Nuclear Engineering and Technology
• /
• v.27 no.6
• /
• pp.877-887
• /
• 1995

YGN 3 is the first nuclear power plant to use the Core Protection Calculator (CPC) as the core protection system and the Core Operating Limit Supervisory System (COLSS) as the core monitor-ing system in Korea. The CPC is designed to provide on-line calculations of Departure from Nucleate Boiling Ratio (DNBR) and Local Power Density (LPD) and to initiate reactor trip if the core conditions exceed the DNBR or LPD design limit. The COLSS is designed to assist the operator in implementing the Limiting Conditions for Operation (LCOs) in Technical Specifications for DNBR/Linear Heat Rate (LHR) margin, azimuthal tilt, and axial shape index and to provide alarm when the LCOs are reached. During YGN 3 initial startup testing, extensive CPC/COLSS related tests ore peformed to ver-ify the CPC/COLSS performance and to obtain optimum CPC/COLSS calibration constants at var, -ious core conditions. Most of test results met their specific acceptance criteria. In the case of missing the acceptance criteria, the test results ore analyzed, evaluated, and justified. Through the analysis and evaluation of each of the CPC/COLSS related test results, it can be concluded that the CPC/COLSS are successfully Implemented as designed at YGN 3.

• Journal of Cadastre & Land InformatiX
• /
• v.52 no.2
• /
• pp.189-210
• /
• 2022

Cities around the world have paid attention to public transportation as an alternative to reducing traffic congestion caused by automobile usage, excessive energy consumption, and environmental pollution. This study measures accessibility to subway stations in Seoul using a supply-demand-based accessibility technique. Then, the impacts were analyzed through land prices by use and segment. As a result of analysis using the multilevel hedonic price models, accessibility considering both supply and demand for the subway had a positive effect on both residential and non-residential land prices. The effect was stronger for residential than for non-residential. Further, among the accessibility measured by the three functions, the accessibility by the Exponential function was most suitable for the residential land price, and the accessibility measured by the Power function for the non-residential land price had the highest explanatory power. Also, looking at the impacts by land price segments, it was found that higher access to metro stations had the greatest positive impacts on the most expensive segment of residential and non-residential land prices. The results of this study can be applied not only to identify the impacts of public investment on neighborhoods, but also to support real estate valuation.

• Journal of Technology Innovation
• /
• v.30 no.1
• /
• pp.57-104
• /
• 2022

The government has supported the innovation of private firms by intervening the market for various purposes, such as preventing market failure, alleviating information asymmetry, and allocating resources efficiently. Although the government's R&D budget increased rapidly in the 2000s, it is not clear whether the government intervention has made desirable impact on the market. To address this, the current study attempts to explore this issue by doing a systematic literature review on foreign and domestic papers in an integrated way. In total, 168 studies are analyzed using contents analysis approach and various lens, such as policy additionality, policy tools, firm size, unit of analysis, data and method, are adopted for analysis. Overlapping policy target, time lag between government intervention and policy effects, non-linearity of financial supports, interference between different polices, and out-dated R&D tax incentive system are reported as factors hampering the effect of the government intervention. Many policy prescriptions, such as program evaluation indices reflecting behavioral additionality, an introduction of policy mix and evidence-based policy using machine learning, are suggested to improve these hurdles.