• Journal of Magnetics
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• 제22권1호
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• pp.34-42
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• 2017

Material defects in the Steam Generator Tubes (SGT) of sodium cooled fast breeder reactor (PFBR) can lead to leakage of water into sodium. The water and sodium reaction will lead to major accidents. Therefore, the examination of steam generator tubes for the early detection of defects is an important requirement for safety and economic considerations. In this work, the Magnetic Flux Leakage (MFL) based Non Destructive Testing (NDT) technique is used to perform the defect detection process. The rectangular notch defects on the outer surface of steam generator tubes are modeled using COMSOL multiphysics 4.3a software. The obtained MFL images are de-noised to improve the integrity of flaw related information. Grey Level Co-occurrence Matrix (GLCM) features are extracted from MFL images and taken as input parameter to train the neural network. A comparative study on characterization have been carried out using feed-forward back propagation (FFBP) and cascade-forward back propagation (CFBP) algorithms. The results of both algorithms are evaluated with Mean Square Error (MSE) as a prediction performance measure. The average percentage error for length, depth and width are also computed. The result shows that the feed-forward back propagation network model performs better in characterizing the defects.

• Computers and Concrete
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• 제5권5호
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• pp.461-473
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• 2008

The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

• 한국지구물리탐사학회:학술대회논문집
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• 한국지구물리탐사학회 2007년도 공동학술대회 논문집
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• pp.81-86
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• 2007

Spectral features of the seismic wave propagation from Odaesan Earthquake were evaluated based on the commonly treated random error between the observed data and the prediction values by the stochastic point-source ground-motion spectral model regarding the source, path and site effects. Radiation pattern of the error according to azimuth angle was found to be similar to the theoretical estimate. It was also observed that the spatial distribution of the errors was correlated with the geological map and the Q0 map which are indicatives of seismic boundaries.

• Water Engineering Research
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• 제1권2호
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• pp.129-136
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• 2000

Predicting water levels is a difficult task because a lot of uncertainties are included. Therefore the neural network which is appropriate to such a problem, is introduced. One day ahead forecasting of river stage in the Bocheong River is carried out by using the neural network model. Historical water levels at Snagye gauging point which is located at the downstream of the Bocheong River and average rainfall of the Bocheong River basin are selected as training data sets. With these data sets, the training process has been done by using back propagation algorithm. Then waters levels in 1997 and 1998 are predicted with the trained algorithm. To improve the accuracy, a filtering method is introduced as predicting scheme. It is shown that predicted results are in a good agreement with observed water levels and that a filtering method can overcome the lack of training patterns.

• 방송공학회논문지
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• 제16권5호
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• pp.824-833
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• 2011

In this paper, using the Rec. ITU-R P.1546 explaining a propagation prediction model in the VHF and UHF bands, we propose the analytical methodology for calculating the service distance and field strength for analogue and digital TV receivers. From the derived formulation of the receiver field strength, some computation results are presented and discussed in terms of the equivalent level of service caused by analogue to digital TV conversion. The suggested method is also applicable to the analysis of frequency coordination or compatibility from unwanted signal in VHF and UHF bands.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 춘계학술대회논문집
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• pp.825-830
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• 2000

In this article, the underwater radiated noise of the submerged cylindrical shell model is investigated using hull transfer functions which were defined in accordance with structureborne and airborne noise propagation paths. This method is very useful tool as the prediction of radiated noise from submerged structures in design stage. This approach is verified by experimental model and its measurement results.

• 전자공학회논문지 IE
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• 제46권1호
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• pp.31-36
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• 2009

본 논문에서는 송 수신 차량이 편도 2차선의 일반국도를 80[km/h]의 속도로 주행하고, 곡선 반경은 교통사고율이 높은 통계자료를 근거하여 280[m], 직선도로의 길이는 정지시거를 고려하여 140[m], 곡선의 길이는 90[m], 곡선도로를 11.25[m] 간격으로 8개 지점을 선정하여 시뮬레이션 하였다. 그 결과 송 수신 차량간 거리가 111[m] 이상이 될 경우에는 좌, 우측 반사체에 의해 이루어지는 반사파의 전파경로 보다는 인접한 차량들에 의해 이루어지는 반사파의 전파 경로가 반복 반사수가 증가함으로 더 갈어지게 된다. 송 수신차량간 거리가 111[m] 미만인 경우에는 수신차량에 전파가 도달하기 위한 반복 반사는 $1{\sim}2$[회]정도 이었으며 송 수신 차량이 위치한 차선에 관계없이 인접한 차량에 의해 발생하는 반사파 보다는 좌, 우측 반사체를 경유하여 수신하게 되는 반사파의 전파경로가 $1{\sim}1.5[m]$정도 더 큼을 알 수 있었다.

• 터널과지하공간
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• 제13권6호
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• pp.434-443
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• 2003

본 연구에서는 비서형 모델에 적용 가능한 역전파 알고리즘을 이용하여 도로터널에서 발생하는 오염물질을 예측하기 위한 인공신경망을 개발하였다. 도로 터널에서 중요시되는 오염인자는 CO농도와 가시도이므로, 인공신경망의 구성을 각각의 독립적인 네트워크로서 구성하였다. 사용한 입력데이터는 영동고속도로에 위치한 종류식 환기 방식을 채택한 일방향 2차선 도로 터널 2개소에서 실측한 데이터를 사용하였다. 예측치와 실측치를 비교할 때 인공신경망의 학습도는 약 95%의 정확성을 보이는 것으로 나타났다. 분석결과 개발된 인공신경망에 의한 결과는 PIARC 방식에 의한 계산치 보다 약 5배 정도의 정확성을 보였다. 특히 주행속도가 낮을 경우 더 높은 정확도를 나타낼 것으로 기대 되었다.

• Advances in Computational Design
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• 제3권3호
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• pp.289-302
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• 2018

The use of optimum content of supplementary cementing materials (SCMs) such as limestone filler (LF) to blend with Portland cement has been resulted in many environmental and technical advantages, such as increase in physical properties, enhancement of sustainability in concrete industry and reducing $CO_2$ emission are well known. Artificial neural networks (ANNs) have been already applied in civil engineering to solve a wide variety of problems such as the prediction of concrete compressive strength. The feed forward back propagation (FFBP) algorithm and Tan-sigmoid transfer function were used for the ANNs training in this study. The training, testing and validation of data during the backpropagation training process yielded good correlations exceeding 97%. A parametric study was conducted to study the sensitivity of the developed model to certain essential parameters affecting the compressive strength of concrete. The effects and benefits of limestone filler on hardened properties of the concrete such as compressive strength were well established endorsing previous results in the literature. The results of this study revealed that the proposed ANNs model showed a high performance as a feasible and highly efficient tool for simulating the LF concrete compressive strength prediction.

• Smart Structures and Systems
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• 제13권6호
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• pp.901-925
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• 2014

The reliability of a Bridge management System depends on the quality of visual inspection and the reliable estimation of bridge condition rating. However, the current practices of visual inspection have been identified with several limitations, such as: they are time-consuming, provide incomplete information, and their reliance on inspectors' experience. To overcome such limitations, this paper presents an approach of automating the prediction of condition rating for bridges based on digital image analysis. The proposed methodology encompasses image acquisition, development of 3D visualization model, image processing, and condition rating model. Under this method, scaling defect in concrete bridge components is considered as a candidate defect and the guidelines in the Ontario Structure Inspection Manual (OSIM) have been adopted for developing and testing the proposed method. The automated algorithms for scaling depth prediction and mapping of condition ratings are based on training of back propagation neural networks. The result of developed models showed better prediction capability of condition rating over the existing methods such as, Naïve Bayes Classifiers and Bagged Decision Tree.