• Speech Sciences
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• v.7 no.4
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• pp.11-26
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• 2000

In this paper, we propose the HMM with the MIN module. Because initial and re-estimated variance vectors are important elements for performance in HMM recognition systems, we propose a method which compensates for the mismatched statistical feature of training and test data. The MIN module function is a differentiable function similar to the sigmoid function. Unlike a continuous density function, it does not include variance vectors of the data set. The proposed hybrid HMM/MIN module is a unified network in which the observation probability in the HMM is replaced by the MIN module neural network. The parameters in the unified network are re-estimated by the gradient descent method for the Maximum Likelihood (ML) criterion. In estimating parameters, the variance vector is not estimated because there is no variance element in the MIN module function. The experiment was performed to compare the performance of the proposed HMM and the conventional HMM. The experiment measured an isolated number for speaker independent recognition.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.19-24
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• 2003

신경망은 적용 다양성과 제약조건의 최소성, 강력한 예측성, 범용성, 근사성 등 많은 장점을 지니고 있으나 초기 가중치의 할당에 따라 모델 생성의 Performance와 예측의 결과가 달라지게 되는 단점을 지니고 있다. 이런 신경망의 초기 가중치에 따른 단점을 보안하기 위해 통계적 알고리즘의 접목을 통해 Hybrid된 신경망 보완 알고리즘을 제시하고자 하였다. 논문을 위한 기본 가정으로 신경망의 가장 기본인 SLP 알고리즘을 바탕으로 활성함수에 가장 일반적으로 사용되는 Sigmoid 활성함수를 이용하였을 때, 초기 가중치로 기존의 임의 난수 생성 방식이 아닌 통계적 로지스틱 회귀분석의 계수값(mle)을 제시하여 이를 초기치로 사용한 경우와 그렇지 않은 경우의 예측 정확성과 수렴의 Performance정도를 비교하여 가장 효과적인 초기치 방법을 제시하고자 하였다.

• Journal of Environmental Science International
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• v.11 no.3
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• pp.201-207
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• 2002

This study was carried out to evaluate the artificial neural network algorithm for water quality forecasting in Chungju lake, north Chungcheong province. Multi-layer perceptron(MLP) was used to train artificial neural networks. MLP was composed of one input layer, two hidden layers and one output layer. Transfer functions of the hidden layer were sigmoid and linear function. The number of node in the hidden layer was decided by trial and error method. It showed that appropriate node number in the hidden layer is 10 for pH training, 15 for DO and BOD, respectively. Reliability index was used to verify for the forecasting power. Considering some outlying data, artificial neural network fitted well between actual water quality data and computed data by artificial neural networks.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.2
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• pp.319-324
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• 1996

Using random pulse stream, a number can be transformed to the pulse stream with the probability value. So the digital value are computed by simple digital gates. Thus it will be possible to build a small and strong noise immunity processing element. We propose a faster convergence algorithm using a new methods for better performance of Random Number Generator(RNG) an the nonlinear transfer function(Sigmoid function)in this paper. And a feedback circuit were fitted for pulse stream in this paper. We proposed method is simulated with C program language and conformed by circuit implementation. Finally a system for hand written number recognition is constructed by FPGA and its performance verified.

• KIEE International Transactions on Power Engineering
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• v.5A no.1
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• pp.40-45
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• 2005

Substation facilities have become extremely large and complex parts of electric power systems. The development of condition monitoring and diagnosis techniques has been a very significant factor in the improvement of substation transformer security. This paper presents a method to analyze the cause, the degree, and the aging process power transformers by the Self Organizing Map (SOM) method. Dissolved gas data were non-linearly transformed by the sigmoid function in SOM that works much the same way as the human decision making process. The potential for failure and the degree of aging of normal transformers are identified by using the proposed quantitative criterion. Furthermore, transformer aging is monitored by the proposed criterion for a set of transformers. To demonstrate the validity of the proposed method, a case study is performed and its results are presented.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2977-2979
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• 1999

본 논문에서는 MEBP(Modified Error Back-Propagation) 학습 규칙을 간단한 비선형 회로를 이용하여 구현하였다. 인공 신경 회로망(ANNs : Artificial Neural Networks)은 많은 수의 뉴런을 필요하기 때문에 표준 CMOS 기술을 이용하는 간단한 비선형 시냅스(synapse) 회로는 인공 신경 회로망 구현에 적합하다. 학습회로는 비선형 시냅스 회로. 시그모이드(sigmoid) 회로. 그리고 선형 곱셈기로 구성되어 있다. 학습 회로의 출력은 각 입력 패턴에 따라 유일한 값으로 결정되어진다. 제안한 학술회로를 $2{\times}2{\times}1$과 $2{\times}3{\times}1$ 다층 feedforward 신경 회로망 모델에 적용하였다. MEBP 하드웨어 구현은 HSPICE 회로 시뮬레이터를 이용하여 검증하였다. 제안한 학술 회로는 on-chip 학습회로를 포함한 대규모 신경회로망 구현에 매우 적합하리라 예상된다.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2932-2934
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• 1999

In this paper, a scheme for recognition of handwritten digits using a multilayer neural network trained with the back-propagation algorithm using generalized delta rule is proposed. The neural network is trained with hand written digit data of different writers and different styles. One of the purpose of the work with neural networks is the minimization of the mean square error(MSE) between actual output and desired one. The back-propagation algorithm is an efficient and very classical method. The back-propagation algorithm for training the weights in a multilayer net uses the steepest descent minimization procedure and the sigmoid threshold function. As an error rate is reduced, recognition rate is improved. Therefore we propose a method that is reduced an error rate.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.212-214
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• 1996

The growth of tree is affected by voltage, frequency, temperature, mechanical stress, etc.. This paper describes the effect of applied voltage on the growth of tree in DGEBA/MDA/GN(10 phr) system. As applied voltage increased, the time to breakdown of the system reduced. As applied time increased, the tree length of X-axis increased with sigmoid shape, however, the tree length of Y-axis increased sharply at the initial step and then were nearly constant. The phenomena of tree were complicated more and more, as applied time increased.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1422-1423
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• 2007

This study deals with various neural network algorithms for the on-site partial discharge pattern recognition. For the purpose, the pattern recognition has been carried out on partial discharge data for the typical artificial defect using 9 different neural network models. In order to enhance on-site applicability, artificial defects were installed in the insulation joint box of extra-high voltage xLPE cables and partial discharges were measured by use of the metal foil sensor and a HFCT as a sensor. As the result, it is found out that the accuracy of pattern recognition could be enhanced through the application of the Sigmoid function, the Momentum algorithm and the Genetic algorism on the artificial neural networks. Although Multilayer Perceptron (MLP) algorism showed the best result among 9 neural network algorisms, it is thought that more researches on others would be needed in consideration of on-site application.

• Structural Engineering and Mechanics
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• v.72 no.2
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• pp.257-273
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• 2019

In this paper, the mechanical behaviour of functionally graded material beams is studied using the 3D Saint-Venant's theory, in which the section is free to warp in and out of its plane (Poisson's effects and out-of-plane warpings). The material properties of the FGM beam are distributed continuously through the thickness by several distributions, such as power-law distribution, exponential distribution, Mori-Tanaka schema and sigmoid distribution. The proposed method has been applied to study a simply supported FGM beam. The numerical results obtained are compared to other models in the literature, which show a high performance of the 3D exact theory used to describe the stress and strain fields in FGM beams.