• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.271-276
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• 2003

In this study, we compared backpropagation neural network(BPNN) with probabilistic neural network(PNN) as shape recognition algorithm of welding flaws. For this purpose, variables are applied the same to two algorithm. Here, feature variable is composed of time domain signal itself and frequency domain signal itself, Through this process, we comfirmed advantages/disadvantages of two algorithms and identified application methods of two algorithms.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.3
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• pp.229-236
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• 2003

Artificial neural network has been used for damage assessment by many researchers, but there are still some barriers that must be overcome to improve its accuracy and efficiency. The major problems associated with the conventional artificial neural network, especially the Back Propagation Neural Network(BPNN), are on the need of many training patterns and on the ambiguous relationship between neural network architecture and the convergence of solution. Therefore, the number of hidden layers and nodes in one hidden layer would be determined by trial and error. Also, it takes a lot of time to prepare many training patterns and to determine the optimum architecture of neural network. To overcome these drawbacks, the PNN can be used as a pattern classifier. In this paper, the PNN is used numerically to detect damage in a plate girder railway bridge. Also, the comparison between mode shapes and natural frequencies of the structure is investigated to select the appropriate training pattern for the damage detection in the railway bridge.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.3
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• pp.39-44
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• 2006

In this study, we nodestructive test based on ultrasonic test as inspection method and compared backpropagation neural network(BPNN) with probabilistic neural network(PNN) as pattern recognition algorithm of welding flasw. For this purpose, variables are applied the same to two algorithms. Where, feature variables are zooming flaw signals of reflected whole signals from welding flaws in time domain. Through this process, we confirmed advantages/disadvantages of two algorithms and identified application methods of two algorithms.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.433-434
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• 2012

• Journal of the Korean Society for Nondestructive Testing
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• v.17 no.3
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• pp.162-173
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• 1997

Automatic on-line surface inspection systems have been applied for monitoring a quality of steel strip surfaces. One of the important issues in this application is the performance of on-line defect classifiers. Rule-based classification table methods which are conventionally used for this purpose have been suffered from their low performances. In this work, probabilistic neural networks and the enhanced classification tables which are newly proposed here are applied as alternative on-line classifiers to identify types of surface defects on cold rolled strips. Probabilistic neural networks have shown very excellent performance for classification of surface defects.

• Journal of the Korean Geotechnical Society
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• v.23 no.7
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• pp.65-76
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• 2007

Recently, there have been many construction projects on soft ground with growth of industry and various construction problems concerning soft soil behavior also have been reported. Especially, foundation piles of abutments and (or) buildings which were constructed on the soft ground have been suffering from a lot of stability problems of inordinary displacement due to lateral flow of soft ground. Although many researches for this phenomena have been carried out, it is still difficult to assess the mechanism of lateral flow on soft ground quantitatively. And reliable design method for judgement of lateral flow occurrence is not established yet. In this study, PNN (probabilistic neural network) and CNN (committee neural network) theories were applied for judgment of lateral flow occurrence based on eat data compiled from Korea and Japan. Predictions of PNN and CNN models for new data which were not used during model development are compared with those predicted by conventional empirical methods. It was found that the developed PNN and CNN models can predict more precise and reliable judgment of lateral flow occurrence than conventional empirical methods.

• The Journal of the Acoustical Society of Korea
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• v.12 no.6
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• pp.21-27
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• 1993

계층형 신경망은 패턴 분류를 위해 사용되어 왔다. 이것은 주어진 교사패턴들의 학습으로 원하는 입력-출력 간의 매핑을 할 수 있기 때문이다. 신경망은 타겟ㅌ트 패턴이 입력 패턴의 카테고리에 일치할 때 타겟트 패턴을 학습하므로서 사후 확률을 근사화할 수 있다. 그리고 입력 공간을 부분 공간으로 나누어 학습 데이터들의 비율로서 만든 타겟트 벡터들로 학습한 신경망은 확률밀도 함수를 나타낼 수 있다. 본 연구에서는 역전파 학습법을 이용한 계층형 NN 과 코드북으로서 사후 확률과 확률밀도함수의 측정방법을 제안하였다. VQ 로 추정한 사후확률고 확률밀도함수를 이용하여 학습이 필요없는 RBF network 의 일종인 PNN으로 모음 인식을 수행 하였다. 인식 실험에서 PNN 의 결과는 역전파 학습법을 이용항 3층 신경망과 VQ 의 평균 인식율과 비교되었다. VQ-PNN의 인식율이 다른 것보다 우수하게 나타났다.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.65-72
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• 2007

In this study, we used nondestructive test based on ultrasonic test as inspection method and compared backpropagation neural network(BPNN) with probabilistic neural network(PNN) as pattern recognition algorithm of weld flaws. For this purpose, variables are applied the same to two algorithms. Where, feature variables are zooming flaw signals of reflected whole signals from weld flaws in time domain. Through this process, we compared advantages/ disadvantages of two algorithms and confirmed application methods of two algorithms.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1C
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• pp.1-8
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• 2008

Recently, there have been many construction projects on soft ground with growth of industry and economy. Therefore foundation piles of abutments and(or) buildings had been suffering from a lot of stability problems of inordinary displacement due to lateral flow of soft ground. Although many researches about lateral flow have been carried out, it is still difficult to assess the mechanism of lateral flow in soft ground quantitatively. And reasonable design method for judgement of lateral flow occurrence in soft ground is not established yet. In this study, six PNN (Probabilistic Neural Network) models were developed according to input variables and database compiled from Korea and Japan for the judgment of lateral flow occurrence. PNN models were compared with present empirical methods. It was found that the developed PNN models can give more precise and reliable judgment of lateral flow occurrence than empirical methods.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.1 s.94
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• pp.53-62
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• 2005

This study presents the NDE (non-destructive evaluation) technique for detecting the loosened bolts on joint steel structures on the basis of TOF (time of flight) and amplitudes of Lamb waves. Probabilistic neural network (PNN) technique which is an effective tool for pattern classification problem was applied to the damage estimation using PZT induced Lamb waves. Two kinds of damages were introduced by dominant damages (DD) which mean loosened bolts within the Lamb waves beam width and minor damages (MD) which mean loosened bolts out of the Lamb waves beam width. They were investigated for the establishment of the optimal decision boundaries which divide each damage class's region including the intact class. In this study, the applicability of the probabilistic neural networks was identified through the test results for the damage cases within and out of wave beam path. It has been found that the present methods are very efficient and reasonable in predicting the loosened bolts on the joint steel structures probabilistically.