• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.10
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• pp.68-77
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• 1990

In this paper, a neural patten recognition method for the automatic circuit diagram reading system is proposed. The proposed procedure to recognize a deformed logic symbols is composed of three stages: feature detection, log mapping, and pattern classification. In the feature detection stage, a modified competitive learning algorithm where each pattern has the inhibition weight as well as the activation weight is developed. The global information of hand-written logic symbols is obtained by the feature detection neural network having both the inhibition and activation weights. The obtained global data is then transformed into a log space by the conformal mapping where according to the Schwartz's theory about the human visual signal process-ing, the degree of rotation and the scale change are mapped into the translation change. Logic symbols are finally classified by a three layer perceptron trained by the error back propagation algorithm. The computer simulation demonstrates that the proposed multistage neural network system can recognize well the deformed patterns of hand-written logic circuit diagrams.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.12
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• pp.2418-2425
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• 1994

Although the error backpropagation(EBP) algorithm based on the gradient descent method is a widely-used learning algorithm of neural networks, learning sometimes takes a long time to acquire accuracy. This paper develops a novel learning method to alleviate the problems of EBP algorithm such as local minima, slow speed, and size of structure and thus to improve performance by adopting other new networks. Gaussian Potential Function networks(GPFN), in parallel with multilayer neural networks. Empirical simulations show the efficacy of the proposed algorithm in function approximation, which enables us to train networks faster with the better generalization capabilities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.258-261
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• 2007

This paper presents techniques to recognize music using back propagation network, one of the neural network algorithms, and to preprocess technique for music image. Music symbols and music notes are segmented by preprocessing such as binarization, slope correction, staff line removing, etc. Segmented music symbols and music notes are recognized by music note recognizing network and non-music note recognizing network. We proved correctness of proposed music recognition algorithm through experiments and analysis with various kind of musics.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2974-2976
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• 1999

본 논문에서는 단일패턴과 다중패턴 학습이 가능한 FNNs(Feedforward Neural Networks)을 하드웨어로 구현하는데 필요한 학습방안을 제안한다. 제안된 학습방안은 기존의 하드웨어 구현에 이용되는 방식과는 전혀 다른 방식이며, 오히려 기존의 소프트웨어 학습방식과 유사하다. 기존의 하드웨어 구현에서 사용되는 방법은 오프라인 학습이나 단일패턴 온 칩(on-chip) 학습방식인데 반해, 제안된 학습방식은 단일/다중패턴은 칩 학습방식으로 다층 FNNs 회로와 학습회로 사이에 스위칭 회로를 넣어 구현되었으며, FNNs의 학습회로는 선형 시냅스 회로와 선형 곱셈기 회로를 사용하여MEBP(Modified Error Back-Propagation) 학습규칙을 구현하였다. 제안된 방식은 기존의 CMOS 공정으로 구현되었고 HSPICE 회로 시뮬레이터로 그 동작을 검증하였다 구현된 FNNs은 어떤 학습패턴 쌍에 의해 유일하게 결정되는 출력 전압을 생성한다. 제안된 학습방안은 향후 학습 가능한 대용량 신경망의 구현에 매우 적합하리라 예상된다.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.2
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• pp.170-177
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• 2009

The objective of this study is to evaluate an acoustic emission (AE) source characterization and fracture behavior of the SM45C steel by using back-propagation neural network (BPN). In previous research Ref. [8] about k-nearest neighbor classifier (k-NNC) continuity, we used K-means clustering method as an unsupervised learning method for obtaining multi-variate AE main data sets, such as AE counts, energy, amplitude, risetime, duration and counts to peak. Similarly, we applied k-NNC and BPN as a supervised learning method for obtaining multi-variate AE working data sets. According to the error of convergence for determinant criterion Wilk's ${\lambda}$, heuristic criteria D&B(Rij) and Tou values are discussed. As a result, in k-NNC before fracture signal is detected or when fracture signal is detected, showed that produce some empty classes in BPN. And we confirmed that could save trouble in AE signal processing if suitable error of convergence or acceptable encoding error give to BPN.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.310-314
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• 2000

This paper proposes a new parallel architecture for extracting the object from binarized images using recurrent linear threshold neural networks. Binary functions are initially obtained from the existing iterative thinning algorithms, and the linear threshold neural threshold neural networks are then synthesized using the MSP term grouping algorithm. Experimental results show that the proposed architectures can be implemented easier than with other existing methods.

• Journal of the Korean Geotechnical Society
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• v.20 no.8
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• pp.77-87
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• 2004

In this paper a back-propagation neural network model is developed to estimate the preconsolidation pressure of Korean soft soils based on 176 oedometer tests and 63 piezocone test results, which were compiled from 11 sites - western and southern parts of Korea. Only 147 data were used for the training of the neural network and 29 data, which were not used during the training phase, were used for the verification of trained network. Empirical and theoretical models were compared with the developed neural network model. A simple 4-4-9-1 multi-layered neural network has been developed. The cone tip resistance $q_T$ penetration pore pressure $u_2$, total overburden pressure $\sigma_{vo}$ and effective overburden pressure $\sigma'_{vo}$ were selected as input variables. The developed neural network model was validated by comparing the prediction results of the proposed neural network model for the new data which were not used for the training of the model with the measured preconsolidation pressures. It can also predict more precise and reliable preconsolidation pressures than the analytical and empirical model. Furthermore, it can be carefully concluded that neural network model can be used as a generalized model for prediction of preconsolidation pressure throughout Korea since developed model shows good performance for the new data which were not used in both training and testing data.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.8
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• pp.995-1000
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• 1999

This paper presents a optic system and a visual inspection algorithm looking for solder joint defects of J-lead chip which are more integrate and smaller than ones with Gull-wing on PCBs(Printed Circuit Boards). The visual inspection system is composed of three sections : host PC, imaging and driving parts. The host PC part controls the inspection devices and executes the inspection algorithm. The imaging part acquires and processes image data. And the driving part controls XY-table for automatic inspection. In this paper, the most important five features are extracted from input images to categorize four classes of solder joint defects in the case of J-lead chip and utilized to a back-propagation network for classification. Consequently, good accuracy of classification performance and effectiveness of chosen five features are examined by experiment using proposed inspection algorithm.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.231-235
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• 1992

A new algorithm is proposed to identify the structure and the parameters of the nonlinear discrete-time plant with only the unknown dynamics and the weak informations about its structure. The proposed algorithm is constructed with the compensation method of weghing values using its previous derivatives and with the efficient technique updating self-learning coefficients. The result in this application is thought to prove the effectiveness of the algorithm proposed in this paper and its superiority to the conventional ones.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.953-955
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• 1995

Studied on identification of nonlinear system with unknown variables and adaptive control were successful. We need a mathmatical model when control a dynamic system using adaptive control technique, but it is very difficult due to its nonlinearity. In this paper, we described about performance improvement of error back-propagation algorithm and learning algorithm of non-linear dynamic system. We examined the proposed back-propagation learn algorithm for through an experiment.