• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.435-440
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• 2006

In order to understand changes of water temperature for thermal discharge of Hadong power plant in Gwangyang and Jinju Bay, it was analyzed for temperature data of representative season by MEM(Maximum entropy method) that is one of the spectral analysises. And due to understand effect of thermal discharge at each point, analyzed spectral data showed reactive energy rate of reference point by calculating energy from 24 time period to height frequency zone. As a result of spectral analysis, it showed that there were 9 points which are largely effected, 7 points which will be estimated, 6 points which is difficult to estimate, 14 points which rarely effected by thermal discharge.

• Journal of information and communication convergence engineering
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• v.1 no.4
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• pp.205-208
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• 2003

Voice activity detection is a important problem in the speech recognition and speech communication. This paper introduces new feature parameter which are reconstructed by spectral entropy of information theory for robust voice activity detection in the noise environment, then analyzes and compares it with energy method of voice activity detection and performance. In experiments, we confirmed that spectral entropy and its reconstructed parameter are superior than the energy method for robust voice activity detection in the various noise environment.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.8
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• pp.589-597
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• 2002

In this paper the nonstationary response of accelerating vehicle is firstly obtained by using nonstationary road roughness model in time domain. To get the result of nonstationary response in frequency domain, the maximum entropy method is used for Processing nonstationary response of vehicle in frequency domain. The three-dimensional transient maximum entropy spectrum (MES) of response is given.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.211-216
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• 2006

The Constrained Linear Spectral Unmixing(CLSU) is investigated for sub-pixel image processing, Its result is the abundance map which mean fractions of endmember existing in a mixed pixel. Compared to the Linear Spectral Unmixing using least square method, CLSU uses the NNLS (Non-Negative Least Square) algorithm to guarantee that the estimated fractions are constrained. But, CLSU gets Into difficulty in image processing due to select endmember at a user's disposition. In this study, endmember selection optimization method using entropy in the error-image analysis is proposed. In experiments which is used hyperion image, it is shown that our method can select endmember number than CLSU based on unsupervised endemeber selection.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.3
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• pp.107-114
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• 1985

This paper presents the iterative algorithm for obtaining the ME PSE(Maximum Entropy Power Spectral Estimation) of 2-dimensional signals. This problem involves a correction matching power spectral estimate that can be represented as the reciprocal of the spectral of 2-dimensional signals. This requires two matrix inversion every iterations. Thus, we compensate the matrix to be constantly positive definite with relaxational parameters. Using Row/Column decomposition Discrete Fourier Transform, we can decrease a calculation quantity. Using Lincoln data and white noise, this paper examines ME PSE algorithms. Finally, the results output at the graphic display device. The 2-dimensional data have the 3-dimensional axis components, and, this paper develops 3-dimensional graphic output algorithms using 2-dimensional DGL(Device Independent Graphic Library) which is prepared for HP-1000 F-series computer.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.169-174
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• 2010

This paper explains end-point detection method for better speech recognition rates. The proposed method determines speech and non-speech region with the entropy and the harmonic detection of speech. The end-point detection using entropy on the speech spectral energy has good performance at the high SNR(SNR 15dB) environments. At the low SNR environment(SNR 0dB), however, the threshold level of speech and noise varies, so the precise end-point detection is difficult. Therefore, this paper introduces the end-point detection methods which uses speech spectral entropy and harmonics. Experiment shows better performance than the conventional entropy methods.

• Journal of Biomedical Engineering Research
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• v.8 no.1
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• pp.69-74
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• 1987

The power spectrum of background EEG is estimated by the Plsarenko Harmonic Decomposition with the stochastic process whlch consists of the nonhamonic sinus Bid and the white nosie. The estimation results are examined and compared with the results from the maximum entropy spectral extimation, and the optimal order of this from the maximum entropy spectral extimation, and the optimal order of this model can be determined from the eigen value's fluctuation of autocorrelation of background EEG. From the comparing results, this method is possible to estimate the power spectrum of background EEG.

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 1993.07a
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• pp.125-129
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• 1993

학문의 어느 분야든, 어느 분야의 어느 한 가지든 그 출발점으로 거슬러 올라 가기란 결코 쉬운 일이 아니다. 불규칙 자료의 스펙트럼분석이야 고전적인 방법이지만 그 분석방법중 Burg(1967)에 의해 제안된 엔트로피(entropy) 개념을 이용한 방법은 그 출발점을 명확하게 이해하기가 손쉽지 않다. 차제에 최대엔트로피방법(Maximum Entropy Method： MIM)을 복습하고, 그것이 어떻게 스펙트럼 추정에 응용되는가를 정리함은 나름대로 의의가 있을 것이다. (중략)

• Journal of The Korean Astronomical Society
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• v.38 no.4
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• pp.445-462
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• 2005

In a companion paper, we have presented so-called Spatio-Spectral Maximum Entropy Method (SSMEM) particularly designed for Fourier-Transform imaging over a wide spectral range. The SSMEM allows simultaneous acquisition of both spectral and spatial information and we consider it most suitable for imaging spectroscopy of solar microwave emission. In this paper, we run the SSMEM for a realistic model of solar microwave radiation and a model array resembling the Owens Valley Solar Array in order to identify and resolve possible issues in the application of the SSMEM to solar microwave imaging spectroscopy. We mainly concern ourselves with issues as to how the frequency dependent noise in the data and frequency-dependent variations of source size and background flux will affect the result of imaging spectroscopy under the SSMEM. We also test the capability of the SSMEM against other conventional techniques, CLEAN and MEM.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.05
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• pp.47-50
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• 1990

In this study, the degree of spectral transfer to lower frequency caused by accumulation of Latic acid inside the muscle is estimated the convintional dip analysis, zero-crossing method and FFT method have intrinsic errors and estimation problems in case of severe noise. The new spectral analysis method using "$2^{nd}$ order Maximum Entropy Method" was applied to estimate mean frequency and we confirmed that this new method yields fast and reliable estimation over the FFT method.