• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.115-122
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• 2012

We present here a new class of activation functions for neural networks, which herein will be called CosGauss function. This function is a cosine-modulated gaussian function. In contrast to the sigmoidal-, hyperbolic tangent- and gaussian activation functions, more ridges can be obtained by the CosGauss function. It will be proven that this function can be used to aproximate polynomials and step functions. The CosGauss function was tested with a Cascade-Correlation-Network of the multilayer structure on the Tic-Tac-Toe game and iris plants problems, and results are compared with those obtained with other activation functions.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.4
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• pp.61-70
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• 2012

We propose a method that finds candidate targets based on morphology and detects a small target from them using modified gaussian distance function. The existing small target detection methods use predictive filters or morphology. The methods using predictive filters take long to approach least errors. The methods using morphology are weak at clutters and need to consider size of a small target when selecting size of structure elements. We propose a robust method for small target detection to complete the existing methods. First, the proposed method deletes clutters using a median filter. Next, it does closing and opening operation using various size of structure elements, and figures target candidate pixels with subtraction operation between the results of closing and opening operation. It detects an exact small target using a gaussian distance function from the candidates target areas. The proposed method is less sensitive to clutters, and shows a detection rate of 98%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.672-677
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• 2013

This paper have analyzed the change of breakdown voltage for channel dimension of double gate(DG) MOSFET. The breakdown voltage to have the small value among the short channel effects of DGMOSFET to be next-generation devices have to be precisely analyzed. The analytical solution of Poisson's equation have been used to analyze the breakdown voltage, and Gaussian function been used as carrier distribution to analyze closely for experimental results. The breakdown voltages have been analyzed for device parameters such as channel thickness and doping concentration, and projected range and standard projected deviation of Gaussian function. Since this potential model has been verified in the previous papers, we have used this model to analyze the breakdown voltage. As a result, we know the breakdown voltage is influenced on Gaussian function and device parameters for DGMOSFET.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.811-814
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• 2012

This paper have analyzed the change of breakdown voltage for channel dimension of double gate(DG) MOSFET. The breakdown voltage to have the small value among the short channel effects of DGMOSFET to be next-generation devices have to be precisely analyzed. The analytical solution of Poisson's equation have been used to analyze the breakdown voltage, and Gaussian function been used as carrier distribution to analyze closely for experimental results. The breakdown voltages have been analyzed for device parameters such as channel thickness and doping concentration, and projected range and standard projected deviation of Gaussian function. Since this potential model has been verified in the previous papers, we have used this model to analyze the breakdown voltage. Resultly, we know the breakdown voltage is influenced on Gaussian function and device parameters for DGMOSFET.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.753-756
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• 2013

This paper analyzed the change of subthreshold current for channel dimension of double gate(DG) MOSFET. The nano-structured DGMOSFET to reduce the short channel effect had to be preciously analyze. Poisson's equation had been used to analyze the potential distribution in channel, and Gaussian function had been used as carrier distribution. The subthreshold current had been analyzed for device parameters such as channel dimension, and projected range and standard projected deviation of Gaussian function. Since this potential model was verified in the previous papers, we used this model to analyze the subthreshold current. Resultly, we know the subthreshold current was influenced on parameters of Gaussian function and channel dimension for DGMOSFET.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.544-546
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• 2002

본 논문은 CDHMM(Continuous Density Hidden Markov Model)의 훈련하는 방법을 동적 다중 그룹 혼합 가중치(Dynamic Mutli-Group mixture weight)을 이용하여 재구성하는 방법을 제안한다. 음성은 Hidden 상태열에 의하여 특성화되고, 각 상태는 가중된 혼합 가우시안 밑도 함수에 의해 표현된다. 음성신호를 더욱더 정확하게 계산하려면 각 상태를 위한 가우시안 함수를 더욱더 많이 사용해야 하며 이것은 많은 계산량이 요구된다. 이러한 문제는 가우시안 분포 확률의 통계적인 평균을 이용하면 계산량을 줄일 수 있다. 그러나 이러한 기존의 방법들은 다양한 화자의 발화속도와 가중치의 적용이 적합하지 못하여 인식률을 저하시키는 단점을 가지고 있다. 이 문제를 다양한 화자의 발화속도에 적합하도록 화자의 화자의 발화속도에 따라 동적으로 5개의 그룹으로 구성하고 동적 다중 그룹 혼합 가중치를 적용하여 CDHMM 파라미터를 재구성함으로써 8.5%의 인식율이 증가되었다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.10 no.3
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• pp.130-134
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• 1985

In two-dimensional discrete cosine transform(DCT) coding, the measurements of the distributions of the transform coefficients are important because a better approximation yields a smaller mean square distorition. This paper presents the results of distribution tests which indicate that the statistics of the AC coefficients are well approximated to a generalized Gaussian distribution whose shape parameter is 0.6. Furthermore, from a simulation of the DCT coding, it was shown that the above approximation yields a higher experimental SNR and a better agreement between theory and simulation than the Gaussian or Laplacian assumptions.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1117-1120
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• 2004

경제적인 공정분석과 최적화를 위해서는 컴퓨터를 이용한 플라즈마 예측모델이 요구되고 있다. 본 연구에서는 일반화된 회귀 신경망 (GRNN)을 이용하여 플라즈마 증착공정 모델을 개발한다. GRNN의 예측성능은 패턴층 뉴런의 가우시안 함수를 구성하는 학습인자, 즉 spread에 의존한다. 종래의 모델에서는 모든 가우시안 함수의 spread가 동일한 값에서 최적화되었으며, 이로 인해 모델의 예측성능을 향상시키는 데에는 한계가 있었다. 본 연구에서는 유전자 알고리즘 (GA)를 이용하여 다변수 spread를 최적화하는 기법을 개발하였으며, 그 성능을 PECVD 공정에 의해 증착된 SiN 박막의 증착률에 적용하여 평가하였다. $2^{6-1}$ 부분인자 실험계획법에 의해 수집된 데이터를 이용하여 신경망을 학습하였고, 모델적합성 점검을 위해 별도의 12번의 실험을 수행하였다. 가우시안 함수의 spread는 0.2에서 2.0까지 0.2간격으로 증가시켰으며, 최적화한 GA-GRNN모델의 예측성능은 6.6 ${\AA}/min$이었다. 이는 종래의 방식으로 최적화한 모델의 예측성능 (13.5 ${\AA}/min$)과 비교하여 50.7% 향상된 예측성능이며, 이러한 향상은 제안한 GA-GRNN 모델이 플라즈마 공정 모델의 예측성능을 증진하는데 매우 효과적임을 보여준다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.10
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• pp.2403-2408
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• 2013

This paper has analyzed the change of forward and backward current for channel doping concentration to analyze off-current of double gate(DG) MOSFET. The Gaussian function as channel doping distribution has been used to compare with experimental ones, and the two dimensional analytical potential distribution model derived from Poisson's equation has been used to analyze the off-current. The off-current has been analyzed for the change of projected range and standard projected range of Gaussian function with device parameters such as channel length, channel thickness, gate oxide thickness and channel doping concentration. As a result, this research shows the off-current has greatly influenced on forward and backward current for device parameters, especially for the shape of Gaussian function for channel doping concentration.

• Proceedings of the Optical Society of Korea Conference
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• 2002.07a
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• pp.30-31
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• 2002

하나의 구동기를 작동하여 거울을 변형시킬 때, 변형된 거울면의 형태를 영향 함수(influence function)라고 정의하며, 이러한 영향 함수를 이용하여 적응 광학계의 주요한 광학 요소인 변형 거울을 효과적으로 모형화하고 설계할 수 있다. 본 논문에서는 유한요소해석을 이용하여 계산된 변형 거울의 실제 영향 함수를 가우시안 함수(Gaussian function) 형태로 단순화하고, 추가로 구동기들 사이의 영향을 고려한 커플링 계수(coupling coefficient)를 도입하여, 주어진 구동기 배열에 대한 영향 함수를 결정하였다. (중략)