• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.579-582
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• 2001

Vector Transformation is a new method unified vector quantization and coding. So far, codebook generation applied to coding was LBG algorithm. But using the advantage of SOFM(Self-Organizing Feature Map) based on neural network can improve a system's performance. In this paper, we generated VTC(Vector Transformation Coding) codebook applied with SOFM algorithm and compare the result for several coding rates with LBG algorithm. The problem of Vector quantization is complicated calculation and codebook generation. So, to solve this problem, we used neural network approach method.

• Journal of Broadcast Engineering
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• v.8 no.2
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• pp.172-180
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• 2003

In this paper, we propose fast and precise extracting & tracking algorithm based on general camera and frame grabber for facial motion capture animation. Proposed algorithm consists of two steps. extracting and tracking. The former is to separate multiple markers from input image using region merging based on neural networks. The latter Is to track extracted multiple markers at each frame using tracking algorithm based on neural networks. In the experiment, we could remove noise and reduce processing time in the step of extraction. In addition, we could have good tracking results in the low frame rates.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.7
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• pp.20-25
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• 2005

In this paper, the short-term load forecasting of transformers is performed by artificial neural networks. Input parameters of the proposed algorithm are peak loads of pole-transformer of previous days and their maximum and minimum temperatures. The proposed algorithm is tested for one of transformers in Seoul, Korea. Test results show that the proposed algorithm improves the accuracy of the load forecasting of transformer compared with the conventional algorithm. The reposed algorithm can help to prevent some damages by over-loads of transformers.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.12 no.4
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• pp.619-627
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• 1999

다분야 통합 시스템의 설계문제는 다량의 설계변수와 구속조건으로 구성되며 다수의 공학적 현상으로 연관되어 있다. 다분야 통합 최적설계 문제를 효과적으로 다루기 위해서는 다양한 해석분야의 공학적 설계원리를 동시에 고려하여 균형 있고 유기적인 방법으로 최적의 설계를 결정하는 체계적인 설계자동화기술이 요구된다. 다분야 통합 설계문제를 위한 효율적인 설계방법론으로 분리기반 최적화 기법이 적용되는데 이 방법은 한 단위의 대규모 설계문제를 여러 개의 하부시스템으로 분리하여 독립적으로 최적화를 수행하고 각 하부 시스템으로부터의 설계해 사이의 중재 및 통합화를 거쳐 최종적으로 수렴된 최적설계를 찾는 방법이다. 본 논문에서는 분리기반 최적화기법을 다분야 통합최적 설계문제에 적용하는데 필요한 시스템분리기법을 유전알고리즘 및 다층 역전 파 신경회로망을 이용하여 정립하였다. 시스템분리기법을 검증하기 위해 최근 미국 Boeing사에서 개발중인 고속 민간항공기인 HSCT의 시뮬레이션기반 설계문제를 이용하였다. 대규모 설계시스템의 분리결과는 전체 설계문제의 특성을 파악하기 위한 자료로 활용되며 향후, 분리기반 최적화과정에서 최종적으로 통합된 최적설계를 탐색하는데 필요한 기반구조를 제공한다.

• The Journal of the Acoustical Society of Korea
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• v.13 no.1
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• pp.5-13
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• 1994

This paper presents a study on Neural Networks for Phoneme Recognition and performs the Phoneme Recognition using TDNN (Time Delay Neural Network). Also, this paper proposes training algorithm for speech recognition using neural nets that is a proper to large scale TDNN. Because Phoneme Recognition is indispensable for continuous speech recognition, this paper uses TDNN to get accurate recognition result of phonemes. And this paper proposes new training algorithm that can converge TDNN to an optimal state regardless of the number of phonemes to be recognized. The recognition experiment was performed with new training algorithm for TDNN that combines backpropagation and Cauchy algorithm using stochastic approach. The results of the recognition experiment for three phoneme classes for two speakers show the recognition rates of $98.1\%$. And this paper yielded that the proposed algorithm is an efficient method for higher performance recognition and more reduced convergence time than TDNN.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.74-81
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• 1997

This paper proposes an evolutionary learning algorithm to discipline the projection neural nctworks (PNNs) with special type of hidden nodes which can activate radial basis functions as well as sigmoid functions. The proposed algorithm not only trains the parameters and the connection weights hut also c~ptimizes the network structure. Through the structure optimization, the number of hidden node:; necessary to represent a given target function is determined and the role of each hidden node is decided whether it activates a radial basis function or a sigmoid function. To apply the algorithm, PNN is realized by a self-organizing genotype representation with a linked list data structure. Simulations show that the algorithm can build the PNN with less hidden nodes than thc existing learning algorithm using error hack propagation(EE3P) and network growing strategy.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.7
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• pp.81-89
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• 1999

Image obtained by incomplete communication always include noise, blur and distortion, etc. In this paper, we propose and apply the new spatial filter algorithm, called an optimal adaptive generalized stack filter(AGSF), which optimizes adaptive generalized stack filter(AGSF) using neural network weight learning algorithm of back-propagation learning algorithm for improving noise removal and edge preservation rate. AGSF divides into two parts: generalized stack filter(GSF) and adaptive multistage median filter(AMMF), GSF improves the ability of stack filter algorithm and AMMF proposes the improved algorithm for reserving the sharp edge. Applied to neural network theory, the proposed algorithm improves the performance of the AGSF using two weight learning algorithms, such as the least mean absolute(LAM) and least mean square (LMS) algorithms. Simulation results of the proposed filter algorithm are presented and discussed.

• Proceedings of the Korea Multimedia Society Conference
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• 1998.04a
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• pp.411-416
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• 1998

본 논문은 Wavelet 변환 영역에서 특징 벡터를 추출하여 ART2 신경회로망으로 실장 PCB 패턴을 인식하는 알고리즘을 제안한다. PCB 형태 정보는 Wavelet에 의해 주파수 영역으로 변환되고, 이들 계수 행렬로부터 특징 벡터로서 추출된다. ART2 신경회로망은 이러한 특징 벡터들을 입력벡터로 사용하여 인식한다. 실장 PCB 영상 55장을 사용하여 실험한 결고, 학습된 입력패턴은 물론 비학습 입력패턴에 대해서도 약 99%의 인식율을 얻었다. 또한 제안된 방법은 Wavelet 변환 영역사에서 수직, 수평, 대각선 정보만으로 특징 벡터를 구축함으로써 특징 추출 과정이 비교적 간단하고 특징 벡터의 수도 줄일 수 있어, 효과적인 특징벡터의 추출이 가능함을 보였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.511-516
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• 2002

The neural networks may have problem such that the amount of calculation for the network learning goes too big according to the dimension of the dimension. To overcome this problem, the wavelet neural networks(WNN) which use the orthogonal basis function in the hidden node are proposed. One can compose wavelet functions as activation functions in the WNN by determining the scale and center of wavelet function. In this paper, when we compose the WNN using wavelet functions, we set a single scale function as a node function together. We intend that one scale function approximates the target function roughly, the other wavelet functions approximate it finely During the determination of the parameters, the wavelet functions can be determined by the global search for solutions suitable for the suggested problem using the genetic algorithm and finally, we use the back-propagation algorithm in the learning of the weights.

• Journal of the Korea Concrete Institute
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• v.17 no.3 s.87
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• pp.369-374
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• 2005

This study proposes a technique for the recognition of crack patterns, which includes horizontal, vertical, diagonal($-45^{\circ}$), diagonal($+45^{\circ}$), and random cracks, based on image processing technique and artificial neural network. A MATLAB code was developed for the proposed image processing algorithm and artificial neural network. Features were determined using total projection technique, and the structure(no. of layers and hidden neurons) and weight of artificial neural network were determined by learning from artificial crack images. In this process, we adopted Bayesian regularization technique as a generalization method to eliminate overfitting Problem. Numerical tests were performed on thirty-eight crack images to examine validity of the algorithm. Within the limited tests in the present study, the proposed algorithm was revealed as accurately recognizing the crack patterns when compared to those classified by a human expert.