• Geomechanics and Engineering
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• 제29권5호
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• pp.523-533
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• 2022

Several prediction model of penetration rate (PR) of tunnel boring machines (TBMs) have been focused on applying to design stage. In construction stage, however, the expected PR and its trends are changed during tunneling owing to TBM excavation skills and the gap between the investigated and actual geological conditions. Monitoring the PR during tunneling is crucial to rescheduling the excavation plan in real-time. This study proposes a sequential prediction method applicable in the construction stage. Geological and TBM operating data are collected from Gunpo cable tunnel in Korea, and preprocessed through normalization and augmentation. The results show that the sequential prediction for 1 ring unit prediction distance (UPD) is R²≥0.79; whereas, a one-step prediction is R²≤0.30. In modeling algorithm, a gradient boosted regression tree (GBRT) outperformed a least square-based linear regression in sequential prediction method. For practical use, a simple equation between the R² and UPD is proposed. When UPD increases R² decreases exponentially; In particular, UPD at R²=0.60 is calculated as 28 rings using the equation. Such a time interval will provide enough time for decision-making. Evidently, the UPD can be adjusted depending on other project and the R² value targeted by an operator. Therefore, a calculation process for the equation between the R² and UPD is addressed.

• 한국측량학회지
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• 제39권6호
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• pp.351-361
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• 2021

화재는 막대한 재산과 인명피해를 초래하고 있으며 크고 작은 화재가 지속해서 발생하고 있다. 따라서 본 연구는 화재 유형별로 화재에 영향을 미치는 각종 위험요인을 예측하고자 한다. 전국에서 화재 발생 건수가 가장 많은 경기도를 대상으로 화재발생위험요인 예측분석을 실시하였다. 또한, 머신러닝 방법인 SVM, RF, GBRT를 활용하여 각 모형의 정확성을 MAE,RMSE를 통해 적합도가 높은 모형을 제시하였으며 이를 토대로 경기도 화재발생요인 예측분석을 실시하였다. 머신러닝 방법 3가지를 비교분석한 결과 RF가 MAE 1.517, RMSE 1.820으로 나타났으며 MAE, RMSE 검증데이터 및 시험데이터의 경우 MAE값 0.024, RMSE값 0.12의 차이로 매우 유사하게 나타나 가장 우수한 예측력으로 나타났다. RF기법을 적용하여 분석한 결과 공통적으로 발화장소가 화재발생에 가장 큰 영향을 주는 위험요인으로 나타났다. 이러한 연구 결과는 화재발생에 영향을 주는 요인들의 위험순서를 파악하여 화재안전관리의 유용한 자료로 활용될 것으로 예상된다.

• 한국멀티미디어학회논문지
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• 제21권5호
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• pp.617-625
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• Journal of Multimedia Information System
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• 제5권2호
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• pp.99-104
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.