• The Journal of the Acoustical Society of Korea
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• v.30 no.7
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• pp.396-401
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• 2011

In this paper, we propose a novel voice activity detection (VAD) algorithm to effectively distinguish speech from nonspeech in various noisy environments. The presented VAD utilizes the power spectral deviation (PSD) based on Teager energy (TE) instead of the conventional PSD scheme to improve the performance of decision for speech segments. In addition, the speech absence probability (SAP) is derived in each frequency subband to modify the PSD for further VAD. Performances of the proposed VAD algorithm are evaluated by objective test under various environments and better results compared with the conventional methods are obtained.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.547-560
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• 2008

It is well known that spectral density function determines auto-covariance function of stationary time-series data and smoothed periodogram is a consistent estimator of spectral density function. Recently, Kim and Park (2007) showed that smoothed- periodogram based distances performs very well for the classification. In this paper, we introduce classification methods with smoothed periodogram and apply the approaches to the monthly precipitation measurements obtained from January, 1987 through December, 2007 at 22 locations in South Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1188-1202
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• 2012

In this paper, we propose a new multi-scale connected coherence tree algorithm (MCCTA) by improving the connected coherence tree algorithm (CCTA). In contrast to many multi-scale image processing algorithms, MCCTA works on multiple scales space of an image and can adaptively change the parameters to capture the coarse and fine level details. Furthermore, we design a Multi-scale Connected Coherence Tree algorithm plus Spectral graph partitioning (MCCTSGP) by combining MCCTA and Spectral graph partitioning in to a new framework. Specifically, the graph nodes are the regions produced by CCTA and the image pixels, and the weights are the affinities between nodes. Then we run a spectral graph partitioning algorithm to partition on the graph which can consider the information both from pixels and regions to improve the quality of segments for providing image segmentation. The experimental results on Berkeley image database demonstrate the accuracy of our algorithm as compared to existing popular methods.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.886-893
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• 2022

Outlier detection refers to the task of detecting data that deviate significantly from the normal data distribution. Most outlier detection methods compute an outlier score which indicates the degree to which a data sample deviates from normal. However, setting a threshold for an outlier score to determine if a data sample is outlier or normal is not trivial. In this paper, we propose a binary prediction method for outlier detection based on spectral clustering and one-class SVM ensemble. Given training data consisting of normal data samples, a clustering method is performed to find clusters in the training data, and the ensemble of one-class SVM models trained on each cluster finds the boundaries of the normal data. We show how to obtain a threshold for transforming outlier scores computed from the ensemble of one-class SVM models into binary predictive values. Experimental results with high dimensional text data show that the proposed method can be effectively applied to high dimensional data, especially when the normal training data consists of different shapes and densities of clusters.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.637-639
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• 2003

As the number of spectral bands of high spectral resolution data increases, the capability to detect more detailed classes should also increase, and the classification accuracy should increase as well. Often, it is impossible to access enough training pixels for supervise classification. For this reason, the performance of traditional classification methods isn't useful. In this paper, we propose a new model for classification that operates based on decision fusion. In this classifier, learning is performed at two steps. In first step, only training samples are used and in second step, this classifier utilizes semilabeled samples in addition to original training samples. At the beginning of this method, spectral bands are categorized in several small groups. Information of each group is used as a new source and classified. Each of this primary classifier has special characteristics and discriminates the spectral space particularly. With using of the benefits of all primary classifiers, it is made sure that the results of the fused local decisions are accurate enough. In decision fusion center, some rules are used to determine the final class of pixels. This method is applied to real remote sensing data. Results show classification performance is improved, and this method may solve the limitation of training samples in the high dimensional data and the Hughes phenomenon may be mitigated.

• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.25-31
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• 2009

This paper introduces a spectrally formulated element method (SEM) for the beams treated with passive constrained layer damping (PCLD). The viscoelastic core of the beams has a complex modulus that varies with frequency. The SEM is formulated in the frequency domain using dynamic shape functions based on the exact displacement solutions from progressive wave methods, which implicitly account for the frequency-dependent complex modulus of the viscoelastic core. The frequency response function and dynamic responses obtained by the SEM and the conventional finite element method (CFEM) are compared to evaluate the validity and accuracy of the present spectral PCLD beam element model. The spectral PCLD beam element model is found to provide very reliable results when compared with the conventional finite element model.

• Communications of the Korean Mathematical Society
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• v.27 no.1
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• pp.207-215
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• 2012

In this paper, we provide comparison results of several types of the preconditioned Gauss-Seidel methods for solving a linear system whose coefficient matrix is a Z-matrix. Lastly, numerical results are presented to illustrate the theoretical results.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.511-526
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• 2020

Single-cell RNA-sequencing (scRNA-seq) data consists of each cell's RNA expression extracted from large populations of cells. One main purpose of using scRNA-seq data is to identify inter-cellular heterogeneity. However, scRNA-seq data pose statistical challenges when applying traditional clustering methods because they have many missing values and high level of noise due to technical and sampling issues. In this paper, motivated by analyzing scRNA-seq data, we propose a novel spectral-based clustering method by imposing different weights on genes when computing a similarity between cells. Assigning weights on genes and clustering cells are performed simultaneously in the proposed clustering framework. We solve the proposed non-convex optimization using an iterative algorithm. Both real data application and simulation study suggest that the proposed clustering method better identifies underlying clusters compared with existing clustering methods.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.13 no.9
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• pp.898-906
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• 2002

In this paper, we investigated the improvement degree of transmission distance of the various initial frequency chirped optical pulse with 5 dBm initial power dependence on the various bit rate and fiber dispersion coefficient, when MSSI(Mid-Span Spectral Inversion) with the optimal pump power condition is adopted for the compensation method for optical pulse distortion. And we analyzed the influence of mutual relation of optical pulse frequency chirp and Kerr effect on the MSSI methods for the long-haul optical transmission through the computer simulation. We found that the compensation degree of distorted optical pulse varies as a consequence of the variation of combined phase modulation of self phase modulation(Kerr effect) and initial frequency chirp parameter dependence on the fiber dispersion coefficient. And we found that, if the transmission bit rate is increased k times, the dispersion coefficient value of dispersion shift fiber is decreased $2^k$ times so as to be almost the same performance of the transmission system with k times lower bit rate.

• Journal of Power System Engineering
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• v.16 no.5
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• pp.40-46
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• 2012

For free surface flow problem, a high-order spectral/boundary element method is adapted as an efficient numerical tool. This method is one of the most efficient numerical methods by which the nonlinear gravity waves can be simulated and hydrodynamic forces also can be calculated in time domain. In this method, the velocity potential is expressed as the sum of surface potential and body potential. Then, surface potential is solved by using the high-order spectral method and body potential is solved by using the high-order boundary element method. Using the combination of these two methods, the free surface flow problems of a submerged moving body are solved in time domain. In the present study, lifting surface theory is added to the former work to include effects of lift force. Therefore, a new formulation for the basic mathematical theory is introduced to contain the lift body in calculation.