• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.7
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• pp.75-83
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• 2007

IEC code based decision nile have been widely applied to detect incipient faults in power transformers. However, this method has a drawback to achieve the diagnosis with accuracy without experienced experts. In order to resolve this problem, we propose an artificial diagnosis algorithm to detect faults of power transformers using Self-Organizing Feature Map(SOM). The proposed method has two stages such as model construction and diagnostic procedure. First, faulty model is constructed by feature maps obtained by unsupervised learning for training data. And then, diagnosis is performed by compare feature map with it obtained for test data. Also the proposed method usぉms the possibility and degree of aging as well as the fault occurred in transformer by clustering and distance measure schemes. To demonstrate the validity of proposed method, various experiments are unformed and their results are presented.

• Journal of Korean Society on Water Environment
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• v.38 no.6
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• pp.292-305
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• 2022

The Yeongsan River, one of the four largest rivers in South Korea, has been facing difficulties with water quality management with respect to algal bloom. The algal bloom menace has become bigger, especially after the construction of two weirs in the mainstream of the Yeongsan River. Therefore, the prediction and factor analysis of Chlorophyll-a (Chl-a) concentration is needed for effective water quality management. In this study, Chl-a prediction model was developed, and the performance evaluated using machine and deep learning methods, such as Deep Neural Network (DNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Moreover, the correlation analysis and the feature importance results were compared to identify the major factors affecting the concentration of Chl-a. All models showed high prediction performance with an R² value of 0.9 or higher. In particular, XGBoost showed the highest prediction accuracy of 0.95 in the test data.The results of feature importance suggested that Ammonia (NH₃-N) and Phosphate (PO₄-P) were common major factors for the three models to manage Chl-a concentration. From the results, it was confirmed that three machine learning methods, DNN, RF, and XGBoost are powerful methods for predicting water quality parameters. Also, the comparison between feature importance and correlation analysis would present a more accurate assessment of the important major factors.

• Journal of the Korea Organic Resources Recycling Association
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• v.32 no.3
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• pp.19-29
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• 2024

Total organic carbon (TOC) represents the total amount of organic carbon contained in water and is a key water quality parameter used, along with biochemical oxygen demand (BOD) and chemical oxygen demand (COD), to quantify the amount of organic matter in water. In this study, a model to predict TOC was developed using XGBoost (XGB), a representative ensemble machine learning algorithm. Independent variables for model construction included water temperature, pH, electrical conductivity, dissolved oxygen concentration, BOD, COD, suspended solids, total nitrogen, total phosphorus, and discharge. To quantitatively analyze the impact of various water quality parameters used in model construction, the feature importance of input variables was calculated. Based on the results of feature importance analysis, items with low importance were sequentially excluded to observe changes in model performance. When built by sequentially excluding items with low importance, the performance of the model showed a root mean squared error-observation standard deviation ratio (RSR) range of 0.53 to 0.55. The model that applied all input variables showed the best performance with an RSR value of 0.53. To enhance the model's field applicability, models using relatively easily measurable parameters were also built, and the performance changes were analyzed. The results showed that a model constructed using only the relatively easily measurable parameters of water temperature, electrical conductivity, pH, dissolved oxygen concentration, and suspended solids had an RSR of 0.72. This indicates that stable performance can be achieved using relatively easily measurable field water quality parameters.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.4
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• pp.437-444
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• 2020

A lunar rover's optical camera is used to provide navigation and terrain information in an exploration zone. However, due to the scant presence of atmosphere, the Moon has homogeneous terrain with dark soil. Also, in extreme environments, the rover has limited data storage with low computation capability. Thus, for successful exploration, it is required to examine feature detection and matching methods which are robust to lunar terrain and environmental characteristics. In this research, SIFT, SURF, BRISK, ORB, and AKAZE are comparatively analyzed with lunar terrain images from a lunar rover. Experimental results show that SIFT and AKAZE are most robust for lunar terrain characteristics. AKAZE detects less quantity of feature points than SIFT, but feature points are detected and matched with high precision and the least computational cost. AKAZE is adequate for fast and accurate navigation information. Although SIFT has the highest computational cost, the largest quantity of feature points are stably detected and matched. The rover periodically sends terrain images to Earth. Thus, SIFT is suitable for global 3D terrain map construction in that a large amount of terrain images can be processed on Earth. Study results are expected to provide a guideline to utilize feature detection and matching methods for future lunar exploration rovers.

• Tunnel and Underground Space
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• v.30 no.3
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• pp.214-225
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• 2020

Uniaxial compressive strength (UCS) of rock is one of the important factors to determine the advance speed during shield TBM tunnel excavation. UCS can be obtained through the Geotechnical Data Report (GDR), and it is difficult to measure UCS for all tunneling alignment. Therefore, the purpose of this study is to predict UCS by utilizing TBM machine driving data and machine learning technique. Several machine learning techniques were compared to predict UCS, and it was confirmed the stacking model has the most successful prediction performance. TBM machine data and UCS used in the analysis were obtained from the excavation of rock strata with slurry shield TBMs. The data were divided into 8:2 for training and test and pre-processed including feature selection, scaling, and outlier removal. After completing the hyper-parameter tuning, the stacking model was evaluated with the root-mean-square error (RMSE) and the determination coefficient (R²), and it was found to be 5.556 and 0.943, respectively. Based on the results, the sacking models are considered useful in predicting rock strength with TBM excavation data.

• Journal of Cadastre & Land InformatiX
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• v.53 no.1
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• pp.47-63
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• 2023

The Seoul Metropolitan Government is building and operating digital twin-based urban spatial information to solve various problems in the city and provide public services. Two essential factors to ensure the stable utilization of spatial information for the implementation of such a digital twin city are the latest and quality of the data. However, it is time-consuming and costly to maintain continuous updating of high-quality urban spatial information. To overcome this problem, we studied efficient urban spatial information construction technology and the operation, management, and update procedures of construction data. First, we demonstrated and applied automatic 3D building construction technology centered on point clouds using the latest hybrid sensors, confirmed that it is possible to automatically construct high-quality building models using high-density airborne lidar results, and established an efficient data management plan. By applying differentiated production methods by region, supporting detection of urban change areas through Seoul spatial feature identifiers, and producing international standard data by level, we strengthened the utilization of urban spatial information. We believe that this study can serve as a good precedent for local governments and related organizations that are considering activating urban spatial information based on digital twins, and we expect that discussions on the construction and management of spatial information as infrastructure information for city-level digital twin implementation will continue.

• International Journal of Highway Engineering
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• v.17 no.4
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• pp.127-133
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• 2015

PURPOSES: The purpose of this study is to develop a methodology to prioritize sidewalk construction on rural national highways. METHODS : In order to determine an appropriate prioritization for sidewalk construction, we developed a specific methodology. The proposed methodology includes three main steps: 1) Analytic Hierarchy Process (AHP) methods, 2) Subjective evaluation of relevant road agencies for the candidate sidewalks along rural national highways, and 3) Field study conduction. Each step has four phases. The primary feature of this methodology is the addition of expert consultation and survey data, as well as a field study. In addition, the method could guarantee flexibility in selection for evaluation criteria. As a result, the proposed methodology could be used as a general procedure for application to other roadway classifications when considering sidewalk construction. RESULTS: In order to demonstrate the reasonableness of the proposed methodology, a case study was performed for exactly 100 candidate sites for sidewalk construction on rural national highways. All required evaluation scores were properly produced for each candidate site. By doing so, decision-makers can determine the priority for sidewalk construction at these sites by reviewing quantitatively and qualitatively considered data. CONCLUSIONS: The results of the case study can be applied to a long-term fundamental plan for sidewalk construction on rural national highways. Furthermore, this methodology could be employed to prioritize a small-scale SOC project(e. g. bicycle or pedestrian roads).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.257-261
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• 2022

Additive manufacturing (AM, also known as 3D printing) applied to the construction industry is implemented and verified for various effects since advantages such as high design freedom, improving worker safety, and predictable construction period. However, due to the low maturity compared to the existing technology, studies are underway to solve new problems that occur in the overall of AM technology. In this paper, we confirm the effect of low construction surface flatness on the stacked features in the process of on-site AM construction. In particular, unstable AM features are determined through quantitative analysis by laser scanning, and a construction strategy is proposed for the surface flattening.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.63-66
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• 2007

In recent years, the concrete cracks in most construction field have been widely presented and developed. Also it is well known the repair cost of the concrete cracks have been rapidly increased in Korea. So the concrete crack expert system was developed to minimize the repair cost and to support the field engineer. The feature of this web based system for diagnosing the causes of concrete cracks is comprised of comfortable user application.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.09a
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• pp.53-58
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• 2000

The KOESWall system that minimizes the horizontal deformation of reinforced wall effectively was developed bt E&S Eng. Co., Ltd. in 1999. Due to its systematical feature i.e. isolated construction method. KOESWall system is able to use as temporary structures more economically without the facing block. In this report, it is shown that the case history of KOESWall as a temporary soil retaining structure and the field measuremnets.