• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.178-178
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• 2002

In this paper, we propose an adaptive wavelet method for car interior noise cancellation. For this purpose, we use a node dependent threshold which minimizes the Bayesian risk. We propose a noise estimation method based on spectral entropy using histogram of intensity and a candidate best basis instead of Donoho's best bases. And we modify the hard threshold function. Experimental results show that the proposed algorithm is more efficient, especially to heavy noisy signal than conventional one.

• Steel and Composite Structures
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• v.19 no.3
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• pp.569-583
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• 2015

Recent years, explosive welding structures have been widely used in many engineering fields. The bonding state detection of explosive welding structures is significant to prevent unscheduled failures and even catastrophic accidents. However, this task still faces challenges due to the complexity of the bonding interface. In this paper, a new method called dual-tree complex wavelet transform based permutation entropy (DTCWT-PE) is proposed to detect bonding state of such structures. Benefiting from the complex analytical wavelet function, the dual-tree complex wavelet transform (DTCWT) has better shift invariance and reduced spectral aliasing compared with the traditional wavelet transform. All those characters are good for characterizing the vibration response signals. Furthermore, as a statistical measure, permutation entropy (PE) quantifies the complexity of non-stationary signals through phase space reconstruction, and thus it can be used as a viable tool to detect the change of bonding state. In order to more accurate identification and detection of bonding state, PE values derived from DTCWT coefficients are proposed to extract the state information from the vibration response signal of explosive welding structure, and then the extracted PE values serve as input vectors of support vector machine (SVM) to identify the bonding state of the structure. The experiments on bonding state detection of explosive welding pipes are presented to illustrate the feasibility and effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1271-1275
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• 1987

With the stochastic process which consists of the harmonic sinusoid and the white nosie, the power spectrum of background EEG is estimated by the Pisarenko Harmonic Decomposition. The estimating results are examined and compared with the results from the maximum entropy spectral estimation, 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 paper ensures that this method is possible to analyze the power spectrum of background EEG.

• Journal of Biomedical Engineering Research
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• v.11 no.2
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• pp.243-248
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• 1990

EMG (eleltromyographic) signals are generated by contracting muscle and detected in and out side of muscle in the form of random signals. In the measurement of muscle fatigue, the mean frequency of EMG signals using spectrum analysis is an important parameter in diagonosis of muscle disease and in sports medicine fields. In this study, the degree of spectral transfer to lower frequency caused by accumulation of Latic acid inside the muscle is estimated. The new spectral analysis method using 2"d order hAaximum Entropy Method was applied to estimate the mean frequency and we confirmed that this new method yields fast and reliable estimation.tion.

• Journal of Biomedical Engineering Research
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• v.9 no.1
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• pp.101-108
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• 1988

In this paper the power spectrum of background EEG is estimated by the LMS PHD based on least mean square. At the power spectrum estimatiom, the stocastic process of background EEG is assumed to consist of the nonharmonic sinusoid and the white noise. In the LMS PHD the model parameters are obtained by the least mean square at optimal order which is obtained from the fact that the eigenvalue's fluctuation of autocorrelation matrix of the normal back-ground EEG is smaller at some order than at other order when the power spectrum of background EEG is esitmated by PHD. The optimal order of this model is the 6-th order when the eigenvalue's fluctuation of autocorrelation matrix of background EEG is considered. The estimation results are with compared the results from the Maximum Entropy Spectral Estimation and Pisarenko Harmonic Decomposition. From the comparison results. The LMS PHD is possible to estimate the power spectrum of background EEG.

• Journal of Biomedical Engineering Research
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• v.37 no.2
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• pp.61-67
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• 2016

This paper presented a method for random forest based the arrhythmia classification using both heart rate (HR) and heart rate variability (HRV) features. We analyzed the MIT-BIH arrhythmia database which contains half-hour ECG recorded from 48 subjects. This study included not only the linear features but also non-linear features for the improvement of classification performance. We classified abnormal ECG using mean_NN (mean of heart rate), SD1/SD2 (geometrical feature of poincare HRV plot), SE (spectral entropy), pNN100 (percentage of a heart rate longer than 100 ms) affecting accurate classification among combined of linear and nonlinear features. We compared our proposed method with Neural Networks to evaluate the accuracy of the algorithm. When we used the features extracted from the HRV as an input variable for classifier, random forest used only the most contributed variable for classification unlike the neural networks. The characteristics of random forest enable the dimensionality reduction of the input variables, increase a efficiency of classifier and can be obtained faster, 11.1% higher accuracy than the neural networks.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.4
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• pp.421-430
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• 2010

Image segmentation technique is becoming increasingly important in the field of remote sensing image analysis in areas such as object oriented image classification to extract object regions of interest within images. This paper presents a new method for image segmentation in High Resolution Remote Sensing Image based on Improved Seeded Region Growing (ISRG) and Region merging. Firstly, multi-spectral edge detection was done using an entropy operator in pan-sharpened QuickBird imagery. Then, the initial seeds were automatically selected from the obtained multi-spectral edge map. After automatic selection of significant seeds, an initial segmentation was achieved by applying ISRG to consider spectral and edge information. Finally the region merging process, integrating region texture and spectral information, was carried out to get the final segmentation result. The accuracy assesment was done using the unsupervised objective evaluation method for evaluating the effectiveness of the proposed method. Experimental results demonstrated that the proposed method has good potential for application in the segmentation of high resolution satellite images.

• The Journal of the Acoustical Society of Korea
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• v.27 no.1
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• pp.18-25
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• 2008

In this paper, new lossless coding method of spectral coefficients for audio codec is proposed. Conventional lossless coder uses Huffman coding utilizing the statistical characteristics of spectral coefficients, but does not provide the high coding efficiency due to its simple structure. To solve this limitation, new lossless coding scheme with better performance is proposed that consists of bit-plane transform and run-length coding. In the proposed scheme, the spectral coefficients are first transformed by bit-plane into 1-D bit-stream with better correlative properties, which is then coded intorun-length and is finally Huffman coded. In addition, the coding performance is further increased by applying the proposed bit-plane coding selectively to each group, after the entire frequency is divided into 3 groups. The performance of proposed coding scheme is measured in terms of theoretical number of bits based on the entropy, and shows at most 6% enhancement compared to that of conventional lossless coder used in AAC audio codec.

• Journal of The Korean Astronomical Society
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• v.36 no.spc1
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• pp.29-36
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• 2003

Quiescent solar radiation, at microwave spectral regime, is dominated by gyroresonant and thermal Bremsstrahlung radiations from hot electrons residing in solar active region corona. These radiations are known to provide excellent diagnostics on the coronal temperature, density, and magnetic field, provided that spatially resolved spectra are available from observations. In this paper we present an imaging spectroscopy implemented for a bipolar active region, AR 7912, using the multifrequency interferometric data from the Owens Valley Solar Array (OVSA), as processed with a new imaging technique, so-called Spatio-Spectral Maximum Entropy Method (SSMEM). From the microwave maps at 26 frequencies in the range of 1.2-12.4 GHz at both right- and left-circular polarizations, we construct spatially resolved brightness spectra in every reconstructed pixel of about 2 arcsec interval. These spectra allowed us to determine 2-D distribution of electron temperature, magnetic field of coronal base, and emission measure at the coronal base above the active region. We briefly compare the present result with existing studies of the coronal active regions.

• Bulletin of the Korean Chemical Society
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• v.6 no.1
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• pp.23-29
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• 1985

The charge-transfer complexing properties of 1-methyl nicotinamide (MNA), an acceptor, and adenine, a donor, were investigated in water and SDS micellar solutions in relation to the intramolecular interaction in nicotinamide adenine dinucleotide ($NAD^+$). The spectral and thermodynamic parameters of MNA-indole and methyl viologen-adenine complex formations were determined, and the data were utilized to evaluate the charge-transfer abilities of MNA and adenine. The electron affinity of nicotinamide was estimated to be 0.28 eV from charge-transfer energy $of{\sim}300$ nm for MNA-indole. The large enhancement of MNA-indole complexation in SDS solutions by entropy effect was attributed to hydrophobic nature of indole. The complex between adenine and methyl viologen showed an absorption band peaked near 360 nm. The ionization potential of adenine was evaluated to be 8.28 eV from this. The much smaller enhancement of charge-transfer interaction involving adenine than that of indole in SDS solutions was attributed to weaker hydrophobic nature of the donor. The charge-transfer energy of 4.41 eV (280 nm) was estimated for nicotinamide-adenine complex. The spectral behaviors of $NAD^+$ were accounted to the presence of intramolecular interaction in $NAD^+$, which is only slightly enhanced in SDS solutions. The replacement of nicotinamide-adenine interaction in $NAD^+$ by intermolecular nicotinamide-indole interaction in enzyme bound $NAD^+$, and guiding role of adenine moiety in $NAD^+$ were discussed.