• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2522-2524
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• 2003

본 논문은 다수의 가우시안(Gaussian) 함수를 가중치 함수로 이용하여 퍼지 소속 함수의 효율적인 동정기법을 제안한다. 먼저 데이터를 가장 잘 구분하는 특징 변수를 선정하고, 이에 대한 기본 소속 함수를 가우시안 함수로 설정한 후, 다수의 가우시안 함수를 곱하여 소속 함수를 동정한다. 해당 특징 변수에 대한 소속 함수의 동정 후, 다음 우선 순위의 특징 변수를 퍼지 규칙에 첨가하여 가장 높은 정확도를 획득할 때까지 반복적으로 소속 함수를 동정한다. 이러한 방법은 데이터의 분포 성향을 소속 함수에 반영시킬 수 있을 뿐만아니라, 알고리듬의 고속 연산도 가능하다. 제안한 방법의 성능을 검증하기 위해 iris 데이터에 적용하여 모의실험의 예를 보인다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.167-176
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• 2005

This paper describes how the state tying model based on the decision tree which is one of Acoustic models used for speech recognition optimizes the model by reducing the number of mixture Gaussians of the output probability distribution. The state tying modeling uses a finite set of questions which is possible to include the phonological knowledge and the likelihood based decision criteria. And the recognition rate can be improved by increasing the number of mixture Gaussians of the output probability distribution. In this paper, we'll reduce the number of mixture Gaussians at the highest point of recognition rate by clustering the Gaussians. Bhattacharyya and Euclidean method will be used for the distance measure needed when clustering. And after calculating the mean and variance between the pair of lowest distance, the new Gaussians are created. The parameters for the new Gaussians are derived from the parameters of the Gaussians from which it is born. Experiments have been performed using the STOCKNAME (1,680) databases. And the test results show that the proposed method using Bhattacharyya distance measure maintains their recognition rate at $97.2\%$ and reduces the ratio of the number of mixture Gaussians by $1.0\%$. And the method using Euclidean distance measure shows that it maintains the recognition rate at $96.9\%$ and reduces the ratio of the number of mixture Gaussians by $1.0\%$. Then the methods can optimize the state tying model.

• Korean Journal of Optics and Photonics
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• v.35 no.4
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• pp.150-156
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• 2024

One of the key factors in research regarding long-distance laser beam propagation, as in free-space optical communication or laser power transmission, is the transmission efficiency of the laser beam. As a way to improve efficiency, we perform extensive numerical simulations of the effect of modifying the laser beam's profile, especially replacing the fundamental Gaussian beam with a super-Gaussian beam. Numerical simulations of the transmitted power in the ideal diffraction-limited beam diameter determined by the optical system of the transmitter, after about 1-km propagation, reveal that the second-order super-Gaussian beam can yield superior performance to that of the fundamental Gaussian beam, in both single-channel and coherently combined multi-channel laser transmitters. The improvement of the transmission efficiency for a 1-km propagation distance when using a second-order super-Gaussian beam, in comparison with a fundamental Gaussian beam, is estimated at over 1.2% in the singlechannel laser transmitter, and over 4.2% and over 4.6% in coherently combined 3- and 7-channel laser transmitters, respectively. For a range of the propagation distance varying from 750 to 1,250 m, the improvement in transmission efficiency by use of the second-order super-Gaussian beam is estimated at over 1.2% in the single-channel laser transmitter, and over 4.1% and over 4.0% in the coherently combined 3- and 7-channel laser transmitters, respectively. These simulation results will pave the way for future advances in the generation of higher-order super-Gaussian beams and the development of long-distance optical energy-transfer technology.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.623-625
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• 2012

The restoration of an image corrupted by Gaussian noise is an important task in image processing. There are many kinds of filters are proposed to remove Gaussian noise such as Gaussian filter, mean filter, weighted filter, etc. However, they perform not good enough for denoising and edge preservation. Hence, in this paper we proposed an adaptive weighted filter which considers spatial distance and the estimated variance of noise. We also compared the proposed method with existing methods through the simulation and used MSE(mean squared error) as the standard of judgement of improvement effect.

• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.112-117
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• 1999

In case of spectral subtraction for noise robust speech recognition system, this method often makes loss of speech signal. In this study, we propose a method that variation and determination of Gaussian function at semi-continuous HMM(Hidden Markov Model) is made on the basis of SNR criteria function, in which SNR means signal to noise ratio between estimation noise and subtracted signal per frame. For proving effectiveness of this method, we show the estimation error to be related with the magnitude of estimated noise through signal waveform. For this reason, Gaussian function is varied and determined by SNR. When we test recognition rate by computer simulation under the noise environment of driving car over the speed of 80㎞/h, the proposed Gaussian decision method by SNR turns out to get more improved recognition rate compared with the frequency subtracted and non-subtracted cases.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.5
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• pp.967-974
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• 2012

In this paper, a vehicle detection algorithm with HD CCTV camera images using GMM(Gaussian Mixture Model) algorithm and mathematical morphological processing is proposed. At the first stage, background could be estimated using GMM from CCTV input image signal and then object could be separated from difference image of the input image and background image. At the second stage, candidated object were reformed by using mathematical morphological processing. Finally, vehicle object could be detected using vehicle size informations depend on distance and vehicle type in tunnel. Through real experiments in tunnel, it is shown that the proposed system works well.

• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.85-93
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• 2011

Previous existing vocabulary recognition programs calculate general vector values from a database, so they can not process phonemes that form during a search. And because they can not create a model for phoneme data, the accuracy of the Gaussian model can not secure. Therefore, in this paper, we recommend use of the Bhattacharyya distance measurement method based on the features of the phoneme-thus allowing us to improve the recognition rate by picking up accurate phonemes and minimizing recognition of similar and erroneous phonemes. We test the Gaussian model optimization through share continuous probability distribution, and we confirm the heighten recognition rate. The Bhattacharyya distance measurement method suggest in this paper reflect an average 1.9% improvement in performance compare to previous methods, and it has average 2.9% improvement based on reliability in recognition rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.6
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• pp.1409-1413
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• 2013

This paper analyzed the change of subthreshold current for channel doping concentration of double gate(DG) MOSFET. Poisson's equation had been used to analyze the potential distribution in channel, and Gaussian function had been used as carrier distribution. The potential distribution was obtained as the analytical function of channel dimension, using the boundary condition. The subthreshold current had been analyzed for channel doping concentration, and projected range and standard projected deviation of Gaussian function. Since this analytical potential model was verified in the previous papers, we used this model to analyze the subthreshold current. As a result, we know the subthreshold current was influenced on parameters of Gaussian function and channel doping concentration for DGMOSFET.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.407-409
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• 2015

In this paper, a hardware implementation of MOD(Moving Object Detection) algorithm is described, which is based GMM(Gaussian Mixture Model) and background subtraction. The EGML(Effective Gaussian Mixture Learning) is used to model and update background. Some approximations of EGML calculations are applied to reduce hardware complexity, and pipelining technique is used to improve operating speed. Gaussian parameters are adjustable according to various environment conditions to achieve better MOD performance. MOD processor is verified by using FPGA-in-the-loop verification, and it can operate with 109 MHz clock frequency on XC5VSX95T FPGA device.

• Korean Journal of Optics and Photonics
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• v.15 no.3
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• pp.200-213
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• 2004

The realization of partially coherent Gaussian Schell-model beams with isotropic cross section and anisotropic degree of coherence function is investigated theoretically. An optical system is devised to transform diffused light generated by passing the Gaussian beam of the He-Ne laser thorough a rotating holographic diffuser to the partially coherent Gaussian Schell-model beam with isotropic cross section and anisotropic degree of coherence function. Analytic design equations are formulated and design examples are presented.