• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2006년도 춘계학술대회 논문집
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• pp.61-62
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• 2006

A new neural network classifier is proposed for the automatic real-time surface inspection of high-speed cold steel strips having 11 different types of defects. 46 geometrical and gray-level features are extracted for the defect classification. 3241 samples of Posco's Kwangyang steel factory are used for training and testing the neural network classifier. The developed classifier produces plausible 15% error rate which is much better than 20-30% error rate of human vision inspection adopted in most of domestic steel factories.

• 한국정밀공학회지
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• 제24권4호
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• pp.76-83
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• 2007

A new neural network classifier is proposed for the automatic real-time surface inspection of high-speed cold steel strips having 11 different types of defects. 46 geometrical and gray-level features are extracted for the defect classification. 3241 samples of Posco's Kwangyang steel factory are used for training and testing the neural network classifier. The developed classifier produces plausible 15% error rate which is much better than 20-30% error rate of human vision inspection adopted in most of domestic steel factories.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.255-262
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• 1998

This paper presents a translation, rotation and scale invariant methodology for the recognition of closed planar shape images using the bispectrum of a contour sequence and the learning vector quantization(LVQ) neural classifier. The contour sequences obtained from the closed planar images represent the Euclidean distance between the centroid and all boundary pixels of the shape, and are related to the overall shape of the images. The higher order spectra based on third order cumulants is applied to tihs contour sample to extract fifteen bispectral feature vectors for each planar image. There feature vector, which are invariant to shape translation, rotation and scale transformation, can be used to represent two0dimensional planar images and are fed into a neural network classifier. The LVQ architecture is chosen as a neural classifier because the network is easy and fast to train, the structure is relatively simple. The experimental recognition processes with eight different hapes of aircraft images are presented to illustrate the high performance of this proposed method even the target images are significantly corrupted by noise.

• 대한기계학회논문집A
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• 제20권4호
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• pp.1309-1319
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• 1996

The research for the classification of the natural defects in welding zone is performd using the neuro-pattern recognition technology. The signal pattern recognition package including the user's defined function is developed to perform the digital signal processing, feature extraction, feature selection and classifier selection, The neural network classifier and the statistical classifiers such as the linear discriminant function classifier and the empirical Bayesian calssifier are compared and discussed. The neuro-pattern recognition technique is applied to the classificaiton of such natural defects as root crack, incomplete penetration, lack of fusion, slag inclusion, porosity, etc. If appropriately learned, the neural network classifier is concluded to be better than the statistical classifiers in the classification of the natural welding defects.

• 전자공학회논문지B
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• 제31B권5호
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• pp.141-148
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• 1994

Both the neural network classifier utilizing multi-layer perceptron and the linear tree classifier composed of hierarchically structured linear discriminating functions can form arbitrarily complex decision boundaries in the feature space and have very similar decision making processes. In this paper, a new method for automatically choosing the number of neurons in the hidden layers and for initalzing the connection weights between the layres and its supporting theory are presented by mapping the sequential structure of the linear tree classifier to the parallel structure of the neural networks having one or two hidden layers. Experimental results on the real data obtained from the military ship images show that this method is effective, and that three exists no siginificant difference in the classification acuracy of both classifiers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권9호
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• pp.3243-3257
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• 2021

Deep neural networks provide excellent performance in pattern recognition, audio classification, and image recognition. It is important that they accurately recognize input data, particularly when they are used in autonomous vehicles or for medical services. In this study, we propose a data correction method for increasing the accuracy of an unknown classifier by modifying the input data without changing the classifier. This method modifies the input data slightly so that the unknown classifier will correctly recognize the input data. It is an ensemble method that has the characteristic of transferability to an unknown classifier by generating corrected data that are correctly recognized by several classifiers that are known in advance. We tested our method using MNIST and CIFAR-10 as experimental data. The experimental results exhibit that the accuracy of the unknown classifier is a 100% correct recognition rate owing to the data correction generated by the proposed method, which minimizes data distortion to maintain the data's recognizability by humans.

• 융합신호처리학회논문지
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• 제2권1호
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• pp.44-52
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• 2001

본 논문은 수정된 카오틱 신경망(MCNN)을 이용하여 완전 무제약 서체 숫자 인식을 다루고 있다. 카오틱 신경망(CNN)의 동적 특성과 학습과정을 강화함으로써 복잡한 패턴인식 문제를 해결할 수 있는 유용한 신경망으로 수정하였다. MCNN은 신경망 구조와 뉴런 자체가 높은 차수의 비선형 동적특성을 갖고 있으므로 복잡한 서체 숫자를 분류할 수 있는 적합한 신경망이다. 숫자 확인은 원래의 숫자 이미지로부터 특징을 추출하고 MCNN에 근거한 분류기를 이용하여 숫자를 인식한다. MCNN 분류기의 성능은 Canada, Montreal의 Concordia 대학의 숫자 데이터 베이스로 평가하였다. 인식성능의 상대적인 비교를 위해 MCNN 분류기는 리커런트 신경망(RNN) 분류기와 비교하였다. 실험결과에 의하면 인식율은 98.0%이었으며, 이는 MCNN 분류기가 같은 데이터 베이스에 대해 발표되었던 다른 분류기와 RNN 분류기보다 성능이 우수함을 나타낸다.

• 지능정보연구
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• 제11권1호
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• pp.17-33
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• 2005

자기공명영상과 계층적 신경망을 이용하여 간경변증을 단계별로 분류하고자 하였다. 내원한 231명의 데이터를 분석하였으며, 각 단계별 분류는 정상,1, 2, 3단계로 분류하였다. TI강조 자기공명 간 영상으로부터 정상 간 실질과 간 경변 결절을 추출하고, 간 경화증의 단계를 객관적으로 해석 분류하였다. 간 경변 분류기 구현은 계층적 신경망을 이용하였고, 명암도 분석과 간 결절 특성을 통하여 정상간과 3단계의 간 경변으로 구분하였다. 제안한 신경망 분류기는 오류 역전파 알고리듬을 이용하였다. 분류결과 인식율이 정상군은 $100\%$, 1 단계는 $82.8\%$, 2 단계는 $87.1\%$, 3 단계는 $84.2\%$의 분류율을 나타내었다. 신경망 분류 결과와 전문의 판독 결과를 서로 비교한 결과 인식률은 매우 높게 나타났다. 만일 더욱더 충분한 데이터나 파라미터를 가지고 지속적으로 수행한다면 간 경변 환자들에게 임상적으로 지원하는 도구뿐만 아니라 의료전문 신경망으로도 기대된다.

• 전기학회논문지
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• 제61권1호
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• pp.135-142
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• 2012

In this paper, we proposed a new architecture called radial basis function-based polynomial neural networks classifier that consists of heterogeneous neural networks such as radial basis function neural networks and polynomial neural networks. The underlying architecture of the proposed model equals to polynomial neural networks(PNNs) while polynomial neurons in PNNs are composed of Fuzzy-c means-based radial basis function neural networks(FCM-based RBFNNs) instead of the conventional polynomial function. We consider PNNs to find the optimal local models and use RBFNNs to cover the high dimensionality problems. Also, in the hidden layer of RBFNNs, FCM algorithm is used to produce some clusters based on the similarity of given dataset. The proposed model depends on some parameters such as the number of input variables in PNNs, the number of clusters and fuzzification coefficient in FCM and polynomial type in RBFNNs. A multiobjective particle swarm optimization using crowding distance (MoPSO-CD) is exploited in order to carry out both structural and parametric optimization of the proposed networks. MoPSO is introduced for not only the performance of model but also complexity and interpretability. The usefulness of the proposed model as a classifier is evaluated with the aid of some benchmark datasets such as iris and liver.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 춘계학술대회
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• pp.290-294
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• 1997

Chromosomes, as the genetic vehicles, provide the basic material for a large proportion of genetic investigations. The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis has been carried out, some of which produced commercial systems. However, there still remains much room for improving the accuracy of chromosome classification. In this paper, we propose an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of two-step multi-layer neural network(TMANN). We are employed three morphological feature parameters ; centromeric index(C.I.), relative length ratio(R.L.), and relative area ratio(R.A.), as input in neural network by preprocessing twenty human chromosome images. The results of our experiments show that our TMANN classifier is much more useful in neural network learning and successful in chromosome classification than the other classification methods.