• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.131-138
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• 2022

Git's commit message is closely related to the project life cycle, and by this characteristic, it can greatly contribute to cost reduction and improvement of work efficiency by identifying risk factors and project status of project operation activities. Among these related fields, there are many studies that classify commit messages as types of software maintenance, and the maximum accuracy among the studies is 87%. In this paper, the purpose of using a solution using the commit classification model is to design and implement a complex classification model that combines several models to increase the accuracy of the previously published models and increase the reliability of the model. In this paper, a dataset was constructed by extracting automated labeling and source changes and trained using the DistillBERT model. As a result of verification, reliability was secured by obtaining an F1 score of 95%, which is 8% higher than the maximum of 87% reported in previous studies. Using the results of this study, it is expected that the reliability of the model will be increased and it will be possible to apply it to solutions such as software and project management.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.244-244
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• 2023

Both the frequency and the magnitude of hydrometeorological extreme events such as severe floods and droughts are increasing. In order to prevent a damage from the climatic disaster, hydrological models are often simulated under various meteorological conditions. While performing the simulations, a synthetic data generated through time series models which maintains the key statistical characteristics of the sample data are widely applied. However, the synthetic data can easily maintains both the average and the variance of the sample data, but the quantile is not maintained well. In this study, we proposes a data generation method which maintains the quantile of the sample data well. The equations of the former maintenance of variance extension (MOVE) are expanded to maintain quantile rather than the average or the variance of the sample data. The equations are derived and the coefficients are determined based on the characteristics of the sample data that we aim to preserve. Monte Carlo simulation is utilized to assess the performance of the proposed data generation method. A time series data (data length of 500) is regarded as the sample data and selected randomly from the sample data to create the data set (data length of 30) for simulation. Data length of the selected data set is expanded from 30 to 500 by using the proposed method. Then, the average, the variance, and the quantile difference between the sample data, and the expanded data are evaluated with relative root mean square error for each simulation. As a result of the simulation, each equation which is designed to maintain the characteristic of data performs well. Moreover, expanded data can preserve the quantile of sample data more precisely than that those expanded through the conventional time series model.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.1-10
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• 2023

The nuclear power plant(NPP)'s Instrumentation and Control(I&C) system periodically conducts integrity checks for the maintenance of self-diagnostic function during normal operation. Additionally, it performs functionality and performance checks during planned preventive maintenance periods. However, there is a need for technological development to diagnose failures and prevent accidents in advance. In this paper, we studied methods for estimating the reliability function by utilizing environmental data and self-diagnostic data of the I&C equipment. To obtain failure data, we assumed probability distributions for component features of the I&C equipment and generated virtual failure data. Using this failure data, we estimated the reliability function using representative artificial intelligence(AI) models used in survival analysis(DeepSurve, DeepHit). And we also estimated the reliability function through the Cox regression model of the traditional semi-parametric method. We confirmed the feasibility through the residual lifetime calculations based on environmental and diagnostic data.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.130-139
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• 2024

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.6 s.58
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• pp.148-155
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• 2009

A remarkable discrepancy of lateral deformation of shear wall structures for seismic loads due to a rigid diaphragm assumption without floor slab modelling asks a study how much effective the slab stiffness ratio is to the lateral behaviour. Typical shear wall type 15 stories structure is selected to analysis using MIDAS-ADS2008 commercial softwares modelling three types; 1) rigid diaphragm (RD model) 2) considered out-of plane slab flexural stiffness (DB model), and 3) considered in and out of plane slab flexural stiffness (SRC model). Based on National Code of KBC2005, the Equivalent Static and Response Spectrum seismic analysis are undertaken to compare each responses of the three models. The differences of lateral responses due to the three slab stiffness ratios applied on the models are compared and discussed.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.17-24
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• 2020

In this study, series of nonlinear seismic analysis were performed on a reinforced concrete intake tower surrounded by water. To consider the fluid effect around the structure, analysis models were composed using an added mass and CEL approach. At this time, the implicit method was used for the added mass model, and the explicit method was used for the fluid structure interaction model. The input motions were scaled to correspond to 500, 1000, and 2400 years return period of the same artificial earthquake. To estimate the counteractivity of the fluid coupled model, models without fluid effect were constructed and used as a reference. The material models of concrete and reinforcement were selected to consider the nonlinear behavior after yielding, and analysis were performed by ABAQUS. As results, in the acceleration response spectrum of the structure, it was found that the influence of the surrounding fluid reducing the peak frequency and magnitude corresponding to the fundamental frequency of the structure. However, the added mass model did not affect the peak value corresponding to the higher mode. The sectional moments were increased significantly in the case of the added mass model than those of the reference model. Especially, this amplification occurred largely for a small-sized earthquake response in which linear behavior is dominant. In the fluid structure interaction model, the sectional moment with a low frequency component amplifies compared to that of the reference model, but the sectional moment with a high requency component was not amplified. Based in these results, it was evaluated that the counteractivity of the additive mass model was greater than that of the fluid structure interaction model.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.30-39
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• 2023

Large dams, which are critical infrastructures for disaster prevention, are exposed to various risks such as aging, floods, and earthquakes. Better dam safety inspection and diagnosis using digital transformation technologies are needed. Traditional visual inspection methods by human inspectors have several limitations, including many inaccessible areas, danger of working at heights, and know-how based subjective inspections. In this study, drone photogrammetry was performed on two large dams to evaluate the applicability of digital data-based dam safety inspection and propose a data management methodology for continuous use. High-quality 3D digital models with GSD (ground sampling distance) within 2.5 cm/pixel were generated by flat double grid missions and manual photography methods, despite reservoir water surface and electromagnetic interferences, and severe altitude differences ranging from 42 m to 99.9 m of dam heights. Geometry profiles of the as-built conditions were easily extracted from the generated 3D mesh models, orthomosaic images, and digital surface models. The effectiveness of monitoring dam deformation by photogrammetry was confirmed. Cracks and deterioration of dam concrete structures, such as spillways and intake towers, were detected and visualized efficiently using the digital 3D models. This can be used for safe inspection of inaccessible areas and avoiding risky tasks at heights. Furthermore, a methodology for mapping the inspection result onto the 3D digital model and structuring a relational database for managing deterioration information history was proposed. As a result of measuring the labor and time required for safety inspection at the SYG Dam spillway, the drone photogrammetry method was found to have a 48% productivity improvement effect compared to the conventional manpower visual inspection method. The drone photogrammetry-based dam safety inspection is considered very effective in improving work productivity and data reliability.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.7 no.1
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• pp.91-107
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• 1995

In this paper, previously proposed mechanisms of generation and maintenance of rip currents are grouped into three broad categories; (1) prismatic topography models, (2) non-prismatic topography models and (3) structural controls by natural and/or constructed features, such as headlands, piers. groins, jetties. etc. The prismatic models can explain the occurrence of a rip current on a planar beach, while non-prismatic model needs undulatory topography inside the surf zone to generate and maintain a rip current. Yet more detailed and thorough studies need to be conducted to include all relevant variables and to clarify the mechanism(s) governing rip current. Next a simple model is presented to predict mean longshore currents behind a longshore bar (or submerged breakwaters) by considering mass transport over the bar and the bar morphology. This hydrodynamic model could be extended to include the sedimentary feedback mechanism.

• Journal of Wetlands Research
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• v.18 no.4
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• pp.448-455
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• 2016

Diffuse pollution management in Korea initiated by the Ministry of Environment (MOE) resulted to the construction of pilot facilities termed Best Management Practices (BMPs). Twelve BMPs installed for the diffuse pollution management in the Kyung-An Stream were monitored since 2006. Data on the mass loading, removal efficiency, maintenance activities, etc. were gathered and utilized to conduct the evaluation of long-term performance of BMPs. The financial data such as actual construction, design and maintenance cost were also collected to evaluate the lifecycle cost (LCC) of BMPs. In this study, most of the maintenance activity was focused in the aesthetic maintenance that resulted to the annual maintenance cost of the four BMP types was closely similar ranging from 8,483 $/yr for retention pond to 8,888 $/yr infiltration system. The highest LCC were observed in constructed wetland ($418,324) while vegetated system had the lowest LCC ($210,418). LCC of BMPs was not so high as compared with the conventional treatment facility and sewage treatment plant. On the other hand, the relationship of removal efficiency on unit cost for TSS and TN was significant. This study will be used to design the cost effective BMP for diffuse pollution management and become models for LCC analysis.

• Journal of the Korean Society for Railway
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• v.20 no.2
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• pp.275-287
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• 2017

During the feasibility study of new rail lines, maintenance cost of railway infrastructure has mostly been estimated based on the track length and on simplified parameters; however, the estimation reliability can be improved by employing the correction factor from UIC 715, as well as the line classification in UIC 714. The correlations between maintenance cost and various parameters such as weighted track length based on line classification, radius of curvature, gradient and worn -out rate have been analyzed according to the case studies. Prediction of the maintenance cost has been carried out using the cost data, which were representative of the whole cost data; as a result, it was demonstrated that a cost model based on the line classification and the correction factor was more reliable than the existing models. Furthermore, possibilities of using data from both the track measurement vehicle and from the maintenance information system, which are under development, have been investigated and, based on this investigation, a combined cost model using line classification, radius of curvature, gradient and worn-out rate, among other factors, will be proposed in the near future.