• The Journal of the Acoustical Society of Korea
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• v.22 no.2E
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• pp.62-68
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• 2003

This paper addresses a novel noise-compensation scheme to solve the mismatch problem between training and testing condition for the automatic speech recognition (ASR) system, specifically in car environment. The conventional spectral subtraction schemes rely on the signal-to-noise ratio (SNR) such that attenuation is imposed on that part of the spectrum that appears to have low SNR, and accentuation is made on that part of high SNR. However, these schemes are based on the postulation that the power spectrum of noise is in general at the lower level in magnitude than that of speech. Therefore, while such postulation is adequate for high SNR environment, it is grossly inadequate for low SNR scenarios such as that of car environment. This paper proposes an efficient spectral subtraction scheme focused specifically to low SNR noisy environment by extracting harmonics distinctively in speech spectrum. Representative experiments confirm the superior performance of the proposed method over conventional methods. The experiments are conducted using car noise-corrupted utterances of Aurora2 corpus.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.6
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• pp.1073-1080
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• 2011

In this study, a target detection algorithm was proposed for using hyperspectral imagery. The proposed algorithm is designed to have minimal processing time, low false alarm rate, and flexible threshold selection. The target detection procedure can be divided into two steps. Initially, candidates of target pixel are extracted using matching ratio of spectral pattern that can be calculated by spectral derivation. Secondly, spectral distance is computed only for those candidates using Euclidean distance. The proposed two-step method showed lower false alarm rate than the Euclidean distance detector applied over the whole image. It also showed much lower processing time as compared to the Mahalanobis distance detector.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.20-24
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• 1988

This paper proposed the phonemic segmentation method that employed two types of Likelihood Ratio that measures the change of spectral structure. By this method, isolated digits and words of VCV form are segmented into phoneme-unit and especially, first-burst part in an aspirated bilabial plosive is divided.

• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1287-1300
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• 2017

Based on the observation that ignoring the angle dependency of multigroup resonance cross sections within a fuel pellet would result in nontrivial underestimation of the spatial self-shielding of flux, a parametrized spectral superhomogenization (SPH) factor library (PSSL) method is developed as a practical means of resolving the problem. Region-wise spectral SPH factors are calculated by the normal and transport corrected SPH iterations after ultrafine group slowing down calculations over various light water reactor pin-cell configurations. The parametrization is done with fuel temperature, U-238 number density, fuel radius, moderator source represented by ${\Sigma}_{mod}V_{mod}$, and the number density ratio of resonance nuclides to that of U-238 in a form of resonance interference correction factors. The parametrization is successful in that the root mean square errors of the interpolated SPH factors over the fuel regions of various pin-cells are within 0.1%. The improvement in reactivity error of the PSSL method is shown to be superior to that by the original SPH method in that the reactivity bias of -200 pcm to -300 pcm vanishes almost completely. It is demonstrated that the environment effect takes only about 4% in the reactivity improvement so that the pin-cell based PSSL method is effective in the assembly problems.

• Bulletin of the Korean Chemical Society
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• v.22 no.1
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• pp.37-42
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• 2001

Ridge regression is compared with multiple linear regression (MLR) for determination of Research Octane Number (RON) when the baseline and signal-to-noise ratio are varied. MLR analysis of near-infrared (NIR) spectroscopic data usually encounters a collinearity problem, which adversely affects long-term prediction performance. The collinearity problem can be eliminated or greatly improved by using ridge regression, which is a biased estimation method. To evaluate the robustness of each calibration, the calibration models developed by both calibration methods were used to predict RONs of gasoline spectra in which the baseline and signal-to-noise ratio were varied. The prediction results of a ridge calibration model showed more stable prediction performance as compared to that of MLR, especially when the spectral baselines were varied. . In conclusion, ridge regression is shown to be a viable method for calibration of RON with the NIR data when only a few wavelengths are available such as hand-carry device using a few diodes.

• Journal of Korea Multimedia Society
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• v.4 no.4
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• pp.306-315
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• 2001

In noise reduction method from noisy speech for speech recognition in noisy environments, conventional spectral subtraction method has a disadvantage which distinction of noise and speech is difficult, and characteristic of noise can't be estimated accurately. Also, noise reduction method in the wavelet transform domain has a disadvantage which loss of signal is generated in the high frequency domain. In order to compensate theme disadvantage, this paper propose spectral subtraction method in continuous wavelet transform domain which speech and non- speech intervals is distinguished by standard deviation of wavelet coefficient, and signal is divided three scales at different scale. The proposed method extract accurately characteristic of noise in order to apply spectral subtraction method by end detection and band division. The proposed method shows better performance than noise reduction method using conventional spectral subtraction and wavelet transform from viewpoint signal to noise ratio and Itakura-Saito distance by experimental.

• Wind and Structures
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• v.19 no.4
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• pp.353-370
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• 2014

A new method is proposed in this study for estimating the damping ratio of a super tall building under strong wind loads with short-time measured acceleration signals. This method incorporates two main steps. Firstly, the power spectral density of wind-induced acceleration response is obtained by the wavelet transform, then the dynamic characteristics including the natural frequency and damping ratio for the first vibration mode are estimated by a nonlinear regression analysis on the power spectral density. A numerical simulation illustrated that the damping ratios identified by the wavelet spectrum are superior in precision and stability to those values obtained from Welch's periodogram spectrum. To verify the efficiency of the proposed method, wind-induced acceleration responses of the Guangzhou West Tower (GZWT) measured in the field during Typhoon Usagi, which affected this building on September 22, 2013, were used. The damping ratios identified varied from 0.38% to 0.61% in direction 1 and from 0.22% to 0.59% in direction 2. This information is expected to be of considerable interest and practical use for engineers and researchers involved in the wind-resistant design of super-tall buildings.

• 한국지구물리탐사학회:학술대회논문집
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• 2009.10a
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• pp.60-61
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• 2009

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.3
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• pp.443-449
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• 2001

This paper is simulation research to improve speech recognition rates under the noisy environment. We examines recognition ratio based on frequency-weighted HMM together with spectral subtraction. As results, frequency-weighted HMM with scaling coefficients is trained as a minimum error classification criterion, and is presents a higher recognition rates in noisy condition than a conventional method. Furthermore, spectral subtraction method gives 11 to 28% improvements for this frequency-weighted HMM in low SNR, and gives recognition rates of 81.7% at 6dB SNR of noisy speech.

• Journal of the Korean Graphic Arts Communication Society
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• v.18 no.2
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• pp.1-12
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• 2000

This paper proposes a new algorithm classifing same hues in order toe estimate the spectral reflectance of object from 3 band color image information. To estimate the spectral reflectance of object, the conventional estimation methods are required of 5 or 9 band digital color values. The 5 or 9 band image acquisition systems are required of 5 or 3 times same work for color image acquisition process. To solve the above problems, we propose a new method that can be estimated spectra reflectance estimation of object. The proposed method is to classify same hues corresponding a color stimulus, by using hue angle and chroma vector of a color stimulus. The classified same hues are used as the population corresponding a color stimulus. The range of same hue is estimated by the cumulative proportional ration according to the number of basis function.