• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5930-5938
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• 2013

The sigmoid functionally graded mateiral(S-FGM) theory is reformulated using the nonlocal elatictiry of Erigen. The equation of equilibrium of the nonlocal elasticity are derived. This theory has ability to capture the both small scale effects and sigmoid function in terms of the volume fraction of the constituents for material properties through the plate thickness. Navier's method has been used to solve the governing equations for all edges simply supported boundary conditions. Numerical solutions of biaxial buckling of nano-scale plates are presented using this theory to illustrate the effects of nonlocal theory and power law index of sigmoid function on buckling load. The relations between nonlocal and local theories are discussed by numerical results. Further, effects of (i) power law index, (ii) length, (iii) nonlocal parameter, (iv) aspect ratio and (v) mode number on nondimensional biaxial buckling load are studied. To validate the present solutions, the reference solutions are discussed.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.6
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• pp.26-37
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• 2001

In order to improve the several problems of the general backpropagation, we propose a method using a fuzzy logic system for automatic tuning of the activation function gain in the backpropagation. First, we researched that the changing of the gain of sigmoid function is equivalent to changing the learning rate, the weights, and the biases. The inputs of the fuzzy logic system were the sensitivity of error respect to the last layer and the mean sensitivity of error respect to the hidden layer, and the output was the gain of the sigmoid function. In order to verify the effectiveness of the proposed method, we performed simulations on the parity problem, function approximation, and pattern recognition. The results show that the proposed method has considerably improved the performance compared to the general backpropagation.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.04a
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• pp.412-420
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• 1993

플라스틱(수지)의 활용이 급격하게 증가함에 따라 사출성형의 중요성이 높아지고 있는 가운데, 사출성형시의 원가절감이 큰 관심사가 되어가고 있다. 원가절감의 일환으로 적절한 수지를 선정하는 일이 대두되는데, 사출성형의 재료로서 수지를 선정할 때는 성형후 사용환경에서 일정기능을 유지하도록 수지의 물성을 고려하여야 한다. 본 연구에서는 여러물성을 동시에 만족시켜야 하는 수지선정의 특수성을 감안하여 수지선정문제를 가치함수(Value Function)를 사용한 다목적 의사결정문제(Multi-Attribute Decision Problem)로 정형화하였다. 수지는 물성의 어느 범위에서는 수치의 작은차이가 중요하지만, 어느 범위에서는 크게 중요하지 않으므로 필요한 물성의 수준을 고려하여 선택을 하여야한다. 본 연구에서는 가치함수를 이용하여 이를 해결하도록 하였다. 가치함수는 Sigmoid함수를 사용하였고, 함수의 상수(parameter)를 조절하여 수지가 갖는 물성과 물성과의 관계를 평가하고 수지가 갖는 특성을 보다 정확하게 고려하여 수지선정이 되도록 하였다.

• 전자공학회논문지 IE
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• v.44 no.3
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• pp.12-22
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• 2007

In this paper, we designed of the high speed digital neuron processor in order to digital neural networks. we designed of the MAC(Multiplier and Accumulator) operation unit used residue number system without carry propagation for the high speed operation. and we implemented sigmoid active function which make it difficult to design neuron processor. The Designed circuits are descripted by VHDL and synthesized by Compass tools. we designed of MAC operation unit and sigmoid processing unit are proved that it could run time 19.6 nsec on the simulation and decreased to hardware size about 50%, each order. Designed digital neuron processor can be implementation in parallel distributed processing system with desired real time processing, In this paper.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.2
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• pp.56-64
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• 1998

Multi-layered perceptrons that are a nonlinear neural network model, have been widely used for various applications mainly thanks to good function approximation capability for nonlinear fuctions. However, for digital hardware implementation of the multi-layere perceptrons, the quantization scheme using "look-up tables (LUTs)" is commonly employed to handle nonlinear signmoid activation functions in the neworks, and thus requires large amount of storage to prevent unacceptable quantization errors. This paper is concerned with a new effective methodology for digital hardware implementation of multi-layered perceptrons, and proposes a "piecewise affine approximation" method in which input domain is divided into (small number of) sub-intervals and nonlinear sigmoid function is linearly approximated within each sub-interval. Using the proposed method, we develop an expression and an error backpropagation type learning algorithm for a multi-layered perceptron, and compare the performance with the quantization method through Monte Carlo simulations on XOR problems. Simulation results show that, in terms of learning convergece, the proposed method with a small number of sub-intervals significantly outperforms the quantization method with a very large storage requirement. We expect from these results that the proposed method can be utilized in digital system implementation to significantly reduce the storage requirement, quantization error, and learning time of the quantization method.quantization method.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.649-653
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• 2005

To estimate for an error signal with sigmoid nonlinearity what reduced constellation applies closed eye pattern in the initial equalization, there can be improves problems of previous soft decision-directed algorithm that increasing estimate complexity and decreasing of convergence speed when substitute high-order constellation. The characteristic of sigmoid function is adjusted by a mean and a variance parameter, so it depends on adjustment of variance that what reduced constellation $values(\gamma)$ can have ranges between + $\gamma$ and - $\gamma$. In this paper, we proposed Variable Modulus Algorithm (VMA) that can be improving a performance of steady-state by adjustment of variance when equalization works normally and each cluster of constellation decrease.

• The Korean Journal of Financial Management
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• v.12 no.2
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• pp.1-23
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• 1995

본 연구의 목적은 특정 금융기관의 주거래기업들에 대한 부실예측을 위해 주거래기업들을 잠식, 도산, 그리고 건전기업과 같이 세집단으로 구분하여 예측하고자 하며, 기업부실 예측력에 영향을 미치는 세 가지 요인으로서 표본구성, 투입 변수, 분석 기법의 관점에서 다음을 살펴보는 것이다. 첫째, 기업부실예측에서 전통적인 delta learning rule과 sigmoid함수를 사용한 역전파학습(신경망 I)과 이들의 변형형태인 normalized cumulative delta learning rule과 hyperbolic tangent함수를 사용한 역전파 학습(신경망 II)과의 예측력의 차이를 살펴보고 또한 이러한 두가지 신경망기법의 예측력을 MDA(다변량판별분석) 결과와 비교하여 신경망기법에 대한 예측력의 유용성을 살펴보고자 한다. 둘째, 세집단분류문제에서는 잠식, 도산, 건전기업의 구성비율이 위의 세가지 예측기법의 결과에 어떠한 영향을 미치는지를 살펴보고자 한다. 세째, 투입 변수선정은 기존연구 또는 이론을 바탕으로 연구자의 판단에 의해 선택하는 방법과 다수의 변수를 가지고 통계적기법에 의해 좋은 판별변수의 집합을 찾는 것이다. 본 연구에서는 이러한 방법들에 의해 선정된 투입변수들이 세가지 예측기법의 결과에 어떠한 영향을 미치는지를 살펴보고자 한다. 이러한 관점에서 본 연구의 실증분석 결과를 요약하면 다음과 같다. 1) 신경망기법이 두집단에서와 같이 세집단 분류문제에서도 MDA보다는 더 높은 예측력을 보였다. 2) 잠식과 도산기업의 수는 비슷하게 그리고 건전기업의 수는 잠식과 도산기업을 합한 수와 비슷하게 표본을 구성하는 것이 예측력을 향상하는데 도움이 된다고 할 수 있다. 3) 속성별로 고르게 투입변수로 선정한 경우가 그렇지 않은 경우보다 더 높은 예측력을 보였다. 4) 전통적인 delta learning rule과 sigmoid함수를 사용한 역전파학습 보다는 normalized cumulative delta learning rule과 hyperbolic tangent함수를 사용한 역전파 학습이 더 높은 예측력을 보였다. 이러한 현상은 두집단문제에서 보다 세집단문제에서 더 큰 차이를 나타내고 있다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.611-616
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• 2023

ECG(electrocardiogram) is a test used to measure the rate and regularity of heartbeats, as well as the size and position of the chambers, the presence of any damage to the heart, and the cause of all heart diseases can be found. Because the ECG signal obtained using the ECG-KIT includes noise in the ECG signal, noise must be removed from the ECG signal to apply to the deep learning. In this paper, the noise of the ECG signal was removed using the digital IIR Butterworth low-pass filter. When the performance evaluation of the three activation functions, sigmoid(), ReLU(), and tanh() functions, was compared using the deep learning model of LSTM, it was confirmed that the activation function with the smallest error was the tanh() function. Also, When the performance evaluation and elapsed time were compared for LSTM and GRU models, it was confirmed that the GRU model was superior to the LSTM model.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.563-568
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• 2023

ECG(electrocardiogram) is used to measure the rate and regularity of heartbeats, as well as the size and position of the chambers, the presence of any damage to the heart, and the cause of all heart diseases can be found. Because the ECG signal obtained using the ECG-KIT includes noise in the ECG signal, noise must be removed from the ECG signal to apply to the deep learning. In this paper, Noise included in the ECG signal was removed by using a lowpass filter of the Digital FIR Hamming window function. When the performance evaluation of the three activation functions, sigmoid(), ReLU(), and tanh() functions, which was confirmed that the activation function with the smallest error was the tanh() function, the elapsed time was longer when the batch size was small than large. Also, it was confirmed that result of the performance evaluation for the GRU model was superior to that of the LSTM model.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.3
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• pp.115-122
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• 1994

The problems in back-propagation(BP) generally are learning speed and misclassification due to lacal minimum. In this paper, to solve these problems, the classical modified methods of BP are reviewed and an extension of the BP to compensate the sigmoide-prime function around the extremity where the actual output of a unit is close to zero or one is proposed. The proposed method is not onlu faster than the conventional methods in learning speed but has an advantage of setting variables easily because it shows good classification results over the vast and uncharted space about the variations of learning rate, etc.. And it is simple for hardware implementation.