• Nuclear Engineering and Technology
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• 제55권8호
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• pp.2747-2756
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• 2023

Reinforced Concrete (RC) shear walls are one of the civil structures in nuclear power plants to resist lateral loads such as earthquakes and wind loads effectively. Risk-informed and performance-based regulation in the nuclear industry requires considering possible accidents and determining desirable performance on structures. As a result, rather than predicting only the ultimate capacity of structures, the prediction of performances on structures depending on different damage states or various accident scenarios have increasingly needed. This study aims to develop machine-learning models predicting drifts of the RC shear walls according to the damage limit states. The damage limit states are divided into four categories: the onset of cracking, yielding of rebars, crushing of concrete, and structural failure. The data on the drift of shear walls at each damage state are collected from the existing studies, and four regression machine-learning models are used to train the datasets. In addition, the bagging ensemble method is applied to improve the accuracy of the individual machine-learning models. The developed models are to predict the drifts of shear walls consisting of various cross-sections based on designated damage limit states in advance and help to determine the repairing methods according to damage levels to shear walls.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3242-3265
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• 2023

Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.

• Communications for Statistical Applications and Methods
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• 제23권4호
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• pp.355-362
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• 2016

Ensemble methods often help increase prediction ability in various predictive models by combining multiple weak learners and reducing the variability of the final predictive model. In this work, we demonstrate that ensemble methods also enhance the accuracy of prediction under kernel ridge regression and kernel logistic regression classification. Here we apply bagging and random forests to two kernel-based predictive models; and present the procedure of how bagging and random forests can be embedded in kernel-based predictive models. Our proposals are tested under numerous synthetic and real datasets; subsequently, they are compared with plain kernel-based predictive models and their subsampling approach. Numerical studies demonstrate that ensemble approach outperforms plain kernel-based predictive models.

• Journal of the Korean Data and Information Science Society
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• 제25권3호
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• pp.677-684
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• 2014

Support vector quantile regression (SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. To improve the estimation performance of SVQR we propose to use SVQR ensemble with bagging (bootstrap aggregating), in which SVQRs are trained independently using the training data sets sampled randomly via a bootstrap method. Then, they are aggregated to obtain the estimator of the quantile regression function using the penalized objective function composed of check functions. Experimental results are then presented, which illustrate the performance of SVQR ensemble with bagging.

• 인터넷정보학회논문지
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• 제17권2호
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• pp.49-57
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• 2016

앙상블 학습 기법은 개별 모형보다 더 좋은 예측 성과를 얻기 위해 다수의 분류기를 결합하는 것으로 예측 성과를 향상시키는데에 매우 유용한 것으로 알려져 있다. 배깅은 단일 분류기의 예측 성과를 향상시키는 대표적인 앙상블 기법중의 하나이다. 배깅은 원 학습 데이터로부터 부트스트랩 샘플링 방법을 통해 서로 다른 학습 데이터를 추출하고, 각각의 부트스트랩 샘플에 대해 학습 알고리즘을 적용하여 서로 다른 다수의 기저 분류기들을 생성시키게 되며, 최종적으로 서로 다른 분류기로부터 나온 결과를 결합하게 된다. 배깅에서 부트스트랩 샘플은 원 학습 데이터로부터 램덤하게 추출한 샘플로 각각의 부트스트랩 샘플이 동일한 정보를 가지고 있지는 않으며 이로 인해 배깅 모형의 성과는 편차가 발생하게 된다. 본 논문에서는 이와 같은 부트스트랩 샘플을 최적화함으로써 표준 배깅 앙상블의 성과를 개선시키는 새로운 방법을 제안하였다. 제안한 모형에서는 앙상블 모형의 성과를 개선시키기 위해 부트스트랩 샘플링을 최적화하였으며 이를 위해 유전자 알고리즘이 활용되었다. 본 논문에서는 제안한 모형을 국내 부도 예측 문제에 적용해 보았으며, 실험 결과 제안한 모형이 우수한 성과를 보였다.

• 정보처리학회논문지B
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• 제14B권4호
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• pp.303-310
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• 2007

기계학습에서 분류기틀의 집합으로 구성된 앙상블 분류기는 단일 분류기에 비해 정확도가 높다는 것이 입증되어왔다. 본 논문에서는 새로운 앙상블 학습으로서 데이터의 지역 기반 분류기들의 앙상블 학습을 제시하여 기존의 앙상블 학습과의 비교를 통해 성능을 검증하고자 한다. 지역 기반 분류기의 앙상블 학습은 데이터의 분포가 지역에 따라 다르다는 점에 착안하여 학습 데이터를 분할하여 해당하는 지역에 기반을 둔 분류기들을 만들어 나간다. 이렇게 만들어진 분류기들로부터 지역에 따라 가중치를 둔 투표를 적용하여 앙상블 방법을 이끌어낸다. 본 논문에서 제시한 앙상블 분류기의 성능평가를 위해 단일 분류기와 기존의 앙상블 분류기인 배깅과 부스팅 등을 UCI Machine Learning Repository에 있는 11개의 데이터 셋으로 정확도 비교를 하였다. 그 결과 새로운 앙상블 방법이 기본 분류기로 나이브 베이즈와 SVM을 사용했을 때 다른 방법보다 좋은 성능을 보이는 것을 알 수 있었다.

• 한국인터넷방송통신학회논문지
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• 제10권3호
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• pp.97-102
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• 2010

의료 데이터 마이닝의 목적은 효율적인 알고리즘 및 기법을 통하여 각종 질병을 예측 분류하고 신뢰도를 높이는데 있다. 기존의 연구로 단일모델을 기반으로 하는 알고리즘이 존재하며 나아가 모델의 더 좋은 예측과 분류 정확도를 위하여 다중모델을 기반으로 하는 앙상블 기법을 적용한 연구도 진행되고 있다. 본 논문에서는 의료데이터의 보다 높은 예측의 신뢰도를 위하여 기존의 앙상블 기법에 사분위간 범위를 적용한 I-ENSEMBLE을 제안한다. 갑상선 기능 저하증 진단을 위한 데이터를 통해 실험 적용한 결과 앙상블의 대표적인 기법인 Bagging, Boosting, Stacking기법 모두 기존에 비해 현저하게 향상된 정확도를 나타내었다. 또한 기존 단일모델 기법과 비교하여 다중모델인 앙상블 기법에 사분위간 범위를 적용했을 때 더 뚜렷한 효과를 나타냄을 확인하였다.

• Journal of Electrical Engineering and Technology
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• 제12권4호
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• pp.1406-1416
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• 2017

In complex and large-scale industries, properly designed fault detection and identification (FDI) systems considerably improve safety, reliability and availability of target processes. In thermal power plants (TPPs), generating units operate under very dangerous conditions; system failures can cause severe loss of life and property. In this paper, we propose a bagged auto-associative kernel regression (AAKR)-based FDI approach for steam boilers in TPPs. AAKR estimates new query vectors by online local modeling, and is suitable for TPPs operating under various load levels. By combining the bagging method, more stable and reliable estimations can be achieved, since the effects of random fluctuations decrease because of ensemble averaging. To validate performance, the proposed method and comparison methods (i.e., a clustering-based method and principal component analysis) are applied to failure data due to water wall tube leakage gathered from a 250 MW coal-fired TPP. Experimental results show that the proposed method fulfills reasonable false alarm rates and, at the same time, achieves better fault detection performance than the comparison methods. After performing fault detection, contribution analysis is carried out to identify fault variables; this helps operators to confirm the types of faults and efficiently take preventive actions.

• 한국정밀공학회지
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• 제17권9호
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• pp.81-86
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• 2000

This paper was carried out to investigate the characteristics of interlaminar fracture toughness of foam core sandwich structures under opening loading mode by using the double cantilever beam (DCB) specimens in Carbon/Epoxy and foam core composites. instead of using symmetric geometry of DCB specimen non-symmetric DCB specimen was used to calculate the interlaminar fracture toughness. Three approaches for calculating the energy release rate({{{{ {G }_{IC } }}}}) were compared. Fracture toughness of foam core sandwich structures by autoclave vacuum bagging and hotpress were compared and analyzed. Experiment nonlinear beam bending FEM method were performed. Suggested bonding surface compensation and equivalent area inertia moment was used to calculate the energy release rate in nonlinear analytical results. The conclusions among experimental nonlinear analytical and FEM results was observed. The vacuum bagging method was shown to be able to substitute method in stead of autoclave without serious loss of Mode I energy release rate({{{{ {G }_{IC }}}}}) to be able to substitute method in stead of autoclave without serious loss of Mode I energy release rate({{{{ {G }_{IC }}}}}).

• 한국음향학회:학술대회논문집
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• 한국음향학회 2000년도 하계학술발표대회 논문집 제19권 1호
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• pp.39-42
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• 2000

VQ 모델로 구성된 화자인식 시스템의 성능 향상을 위해 Bootstrap 방식을 적용하였다. Bootstrap 및 aggregating방식은 unstable한 모델에서 그 성능이 유효하므로 이의 적용을 위해 먼저 VQ 모델의 bias와 variance를 계산하여 unstable함을 보였다. 화자인식 실험은 TIMIT Database를 사용하여 수행하였고 실험결과 높은 인식율 향상을 확인하였다. 또한 적은 훈련 데이터 환경에서도 좋은 인식율을 갖는 것으로 나타났다.