• Nuclear Engineering and Technology
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• 제51권7호
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• pp.1784-1790
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• 2019

Accurate and consistent characterization of defects in steam generator tubes (SGT) in nuclear power plants is one of the key issues in the field of nondestructive testing since the large number of signals to be analyzed in a time-limited in-service inspection causes a serious problem in practice. This paper presents an effective approach to this difficult task of automated classification of motorized rotating pancake coil (MRPC) eddy current flaw acquired from tube specimens with deliberated defects using deep neural networks (DNN). This approach consists of five steps, namely, the data acquisition using the MRPC probe in the tube, the signal preprocessing to make data more suitable for training DNN, the data augmentation for boosting a training performance, the training of DNN, and finally demonstration of the trained DNN for discriminating the axial and circumferential defects. The high performance obtained in this study shows that DNN is useful for classification of defects in tubes from the MRPC eddy current signals even though the number of signals is very large.

• 정보처리학회논문지B
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• 제12B권6호
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• pp.715-720
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• 2005

본 논문에서는 영상의 1차 모멘트와 단층신경망에 기반을 둔 주요성분분석을 이용한 얼굴인식 기법을 제안하였다. 여기서 1차 모멘트는 입력되는 얼굴영상의 중심이동을 위한 것으로 차원을 감소시켜 얼굴인식에 불필요한 배경을 배제시키기 위함이다. 또한 단층신경망을 이용한 주요성분분석은 수치적 기법의 대안으로 Foldiak 학습알고리즘을 이용하며, 차원을 감소시켜 얼굴영상의 특징추출을 위한 정규직교기저를 얻기 위함이다. 제안된 기법을 64$\ast$64 픽셀의 48개(12명$\ast$4장) 학습자 얼굴영상을 대상으로 city-block, Euclidean, 그리고 negative angle의 각 거리 척도를 분류척도로 이용하여 실험하였다. 실험결과, 제안된 기법은 우수한 인식성능이 있음을 확인하였다. 특히 negative angle를 이용하는 것이 city-block이나 Euclidean을 이용하는 것보다 상대적으로 정확하게 유사성을 측정할 수 있었다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.125-125
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• 2020

European Space Agency's Sentinel-1 has an improved spatial and temporal resolution, as compared to previous satellite data such as Envisat Advanced SAR (ASAR) or Advanced Scatterometer (ASCAT). Thus, the assumption used for low-resolution retrieval algorithms used by ENVISAT ASAR or ASCAT is not applicable to Sentinel-1, because a higher degree of land surface heterogeneity should be considered for retrieval. The assumption of homogeneity over land surface is not valid any more. In this study, considering that soil roughness is one of the key parameters sensitive to soil moisture retrievals, various approaches are discussed. First, soil roughness is spatially inverted from Sentinel-1 backscattering over Yanco sites in Australia. Based upon this, Artificial Neural Networks data (feedforward multiplayer perception, MLP, Levenberg-Marquadt algorithm) are compared with Fractal approach (brownian fractal, Hurst exponent of 0.5). When using ANNs, training data are achieved from theoretical forward scattering models, Integral Equation Model (IEM). and Sentinel-1 measurements. The network is trained by 20 neurons and one hidden layer, and one input layer. On the other hand, fractal surface roughness is generated by fitting 1D power spectrum model with roughness spectra. Fractal roughness profile is produced by a stochastic process describing probability between two points, and Hurst exponent, as well as rms heights (a standard deviation of surface height). Main interest of this study is to estimate a spatial variability of roughness without the need of local measurements. This non-local approach is significant, because we operationally have to be independent from local stations, due to its few spatial coverage at the global level. More fundamentally, SAR roughness is much different from local measurements, Remote sensing data are influenced by incidence angle, large scale topography, or a mixing regime of sensors, although probe deployed in the field indicate point data. Finally, demerit and merit of these approaches will be discussed.

• 비파괴검사학회지
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• 제28권3호
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• pp.263-272
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• 2008

Many technical methods are used for nondestructive testing field for solid materials. Among those, ultrasonic inspection methods are widely used and one of the popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature space. This paper describes an approach which uses LMS method to determine the coordinates of the ultrasonic probe followed by the use of SAFT with centroid technique to estimate the location of the ultrasonic reflector. The method is employed for classifying UT-NDE signals from the steam generator tubes in a nuclear power plant. The classification results are presented for the ultrasonic signals from cracks and deposits within steam generator tubes.

• 비파괴검사학회지
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• 제25권3호
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• pp.189-200
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• 2005

Ultrasonic inspection methods are widely used for detecting flaws in materials. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature space. This paper describes an approach which uses LMS method to determine the coordinates of the ultrasonic probe followed by the use of SAFT to estimate the location of the ultrasonic reflector The method is employed for classifying NDE signals from the steam generator tubes in a nuclear power plant. The classification results using this scheme for the ultrasonic signals from cracks and deposits within steam generator tubes are presented.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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• pp.203-205
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• 1997

In this paper, we present a useful method of estimating earth resistivity using BP algorithm in Neural-Networks. From this method, equivalent earth resistivity(EER) can be obtained directly using training data composed of field-measured apparent resistivity and probe distance. This approach can reduce errors which is conventionally raised from manual operation of calculating EER. To evaluate its accuracy and convenience, the result of proposed method is compared to that of conventional methods, graphical($\rho$-a graph) and numerical(CDEGS program), respectively.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.31-34
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• 1997

A semiconductor microelectrode array has been successfully used in obtaining single unit recordings from medial giant nerve of clay fish, rat saphenous nerve and abdominal ganglia of aplysia. The recording device fabricated using silicon microfabrication techniques is a depth-probe type and, previously, has been mostly used to record from central nerve system of vertebrates. From invertebrates, and also from peripheral nerves of vertebrates, however, the quality of the recorded signal depends heavily on the recording conditions, such as the proximity of the electrode site to the nerve cells and the size of the neuron. We have modeled the signal to noise ratio as unctions of these parameters and compared the experimental data with the calculated values thus obtained.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2744-2746
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• 2000

일반화된 회귀 신경망을 이용하여 반도체 공정 최적화를 위한 플라즈마를 모델링한다. 플라즈마는 Box-W린son 실험계획표에 의해 특성화되었으며, 여기에서 변화시킨 인자로는 소스전력, 압력, 척지지대의 위치, 그리고 염소의 유량이다. 총 24회의 실험이 수행이 되었으며, 플라즈마 변수는 Langmuir Probe를 이용하여 측정하였다. 측정된 주요 플라즈마 변수로는 전자밀도, 전자온도, 그리고 플라즈마 전위이다. 폭변수를 점진적으로 증가시켜 회귀신경망을 최적화하였으며. 최적화된 모델은 통계적인 반응표면모델과 비교하였다. 비교 결과, 회귀신경망은 반응표면모델에 상응하는 예측능력을 보이고 있음을 알 수 있었다.

• 한국공작기계학회논문집
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• 제16권6호
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• pp.37-42
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• 2007

The purpose of this research is stability estimation of plant structure through classification and recognition about welding flaw in SWP(Spiral Welding Pipe). And, In this research, we used nondestructive test based on ultrasonic test as inspection method, and made up 2-axes inspection robot in order to control of ultrasonic probe on the SWP surface, and programmed to image processing and probabilistic neural network(PNN) classifying code by MATLAB programming. Through this process, we proved efficiency on the system of SWP stability Estimation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2663-2665
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• 2001

역전파 신경망은 반도체 공정 모델링에 효과적으로 응용이 되고 있으며, 최근 선형뉴런을 비선형 함수 대신 출력층에 이용하여 모델의 예측정확도를 향상 시킨 바 있다. 본 연구에서는 그 원인을 규명하기 위한 모델의 평가지표로서의 유클리디언 웨이트 거리(Euclidean Weight Distance)를 제안한다. 이 지표를 이용하여 신경망의 입력층과 은닉층, 그리고 은닉층과 출력층의 웨이트를 감시하였으며, 그 결과 예측정확도의 향상이 이 지표의 감소에 기인하고 있음을 알았다. 모델링에 이용한 실험데이터는 다중 유도결합형 플라즈마 장비로부터 Langmuir Probe 진단 시스템을 이용하여 수집하였다.