• Journal of the Korean Mathematical Society
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• v.51 no.1
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• pp.203-224
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• 2014

We propose and analyze a spectral Jacobi-collocation approximation for fractional order integro-differential equations of Fredholm-Volterra type. The fractional derivative is described in the Caputo sense. We provide a rigorous error analysis for the collection method, which shows that the errors of the approximate solution decay exponentially in $L^{\infty}$ norm and weighted $L^2$-norm. The numerical examples are given to illustrate the theoretical results.

• Journal of information and communication convergence engineering
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• v.10 no.2
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• pp.162-167
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• 2012

This paper proposes a speech processing system based on a model of the human auditory system and a noise reduction neural network with fast Fourier transform (FFT) amplitude and phase spectrums for noise reduction under background noise environments. The proposed system reduces noise signals by using the proposed neural network based on FFT amplitude spectrums and phase spectrums, then implements auditory processing frame by frame after detecting voiced and transitional sections for each frame. The results of the proposed system are compared with the results of a conventional spectral subtraction method and minimum mean-square error log-spectral amplitude estimator at different noise levels. The effectiveness of the proposed system is experimentally confirmed based on measuring the signal-to-noise ratio (SNR). In this experiment, the maximal improvement in the output SNR values with the proposed method is approximately 11.5 dB better for car noise, and 11.0 dB better for street noise, when compared with a conventional spectral subtraction method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.68-86
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• 2015

Future wireless network aims to integrate different radio access networks (RANs) to provide a seamless access and service continuity. In this paper, a new resource denotation method is proposed in the WLAN and LTE heterogeneous networks based on a concept of spectral bandwidth mapping. This method simplifies the denotation of system resources and makes it possible to calculate system residual capacity, upon which an economic model-based network selection algorithm is designed in both under-loaded and over-loaded scenarios in the heterogeneous networks. The simulation results show that this algorithm achieves better performance than the utility function-based access selection (UFAS) method proposed in [12] in increasing system capacity and system revenue, achieving load balancing and reducing the new call blocking probability in the heterogeneous networks.

• Proceedings of the KSPS conference
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• 2006.11a
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• pp.131-134
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• 2006

Quality of narrowband speech $(0{\sim}4kHz)$ can be enhanced by the bandwidth expansion technique, by which the high- band components are estimated. This paper proposes the bandwidth expansion method using the spline codebook based spectral folding. For the performance evaluation, the PESQ(Perceptual Evaluation of Speech Quality) scores are measured as the objective measurement In addition, the MOS (Mean Opinion Score) and the preference tests are performed as the subjective measurement. The results show our proposed method outperforms the existing spline based one.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.6
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• pp.108-113
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• 1999

For cylindrical shells, the closed-form solutions are confined to the specific boundary and/or loading conditions. Though the finite element method is certainly a powerful solution approach for the structural dynamics problems, it has been well known to provide the solution reliable only in the low frequency region due to the inherent high sensitivities of structual and numerical modeling errors. Instead, the spectral element method has been proved to provide accurate dynamic characteristics of a structure even at the ultrasonic frequency region. Since the wave characteristic of a cylindrical shell becomes identical to that fo a flat plate as the frequency increases, an equivalent plate model (EPM) representing the high-frequency dynamic characteristics of the cylindrical shell is introduced herein. The EPM-based spectral element analysis solutions are compared with the known analytical solutions for the cylindrical shells to confirm the validity of the present modeling approach.

• Journal of Biomedical Engineering Research
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• v.12 no.4
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• pp.249-254
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• 1991

In the study on the quantitative diagnosis using ultrasound, the stability and precision of tissue characterized parameters are important for the clinical application. We estimate attenuation coefficient introducing homomorphlc process Into the modified spectral differnce method about silicon-madu phantom. We compare the results with those estimated uslng the method used for obtaining the attenuation map image before. Homomorphic process has the effect smoothing the reflected echo signal spectrum, therefore eliminat os the random pattern of the signal spectrum generated by the scatterers. As a result, it Is shown that the stability is enhanced

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.9 s.252
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• pp.1154-1159
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• 2006

In this paper, time-frequency analysis and multi-dimensional spectral analysis methods are applied to source identification and diagnostic of non-stationary sound vibration signals. By checking the coherences for concerned time, this simulation is very well coincident to expected results. The proposed method analyzes the signal instantaneously in both time and frequency domains. The MDSA (Multiple Dimensional Spectral Analysis) analyzes the signal in the plane of instantaneous time and instantaneous frequency at the same time. And it was verified by using the 1500cc passenger car which is accelerated from 70Hz to 95Hz in 4 seconds, the proposed method is effective in determining the vehicle diagnostic problems.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.727-739
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• 2018

This study evaluates the viewpoints of user focus incidents using microblog sentiment analysis, which has been actively researched in academia. Most existing works have adopted traditional supervised machine learning methods to analyze emotions in microblogs; however, these approaches may not be suitable in Chinese due to linguistic differences. This paper proposes a new microblog sentiment analysis method that mines associated microblog emotions based on a popular microblog through user-building combined with spectral clustering to analyze microblog content. Experimental results for a public microblog benchmark corpus show that the proposed method can improve identification accuracy and save manually labeled time compared to existing methods.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.183-190
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• 1999

In this paper, a dynamic modeling approach is introduced to identify the dynamic characteristics of the structural/mechanical joints within an one-dimensional structure. A structural joint is represented by the four-pole parameters and the four-pole parameters are determined from the measured frequency response functions by using the spectral element method. As the illustrative examples, a cantilevered beam a clamped-clamped beam, both consist of two beams connected by a bolted joint, are investigated to evaluate the present modeling approach. It is found that the dynamic responses predicted by using the identified for-pole parameters for the bolted joint are well agreed with the measured dynamic responses measured

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.693-703
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• 2010

High spectral resolution of hyperspectral data enables analysis of complex natural phenomena that is reflected on the data nonlinearly. Although many manifold learning methods have been developed for such problems, most methods do not consider the spatial correlation between samples that is inherent and useful in remote sensing data. We propose a manifold learning method which directly combines the spatial proximity and the spectral similarity through kernel PCA framework. A gain factor caused by spatial proximity is first modelled with a heat kernel, and is added to the original similarity computed from the spectral values of a pair of samples. Parameters are tuned with intelligent grid search (IGS) method for the derived manifold coordinates to achieve optimal classification accuracies. Of particular interest is its performance with small training size, because labelled samples are usually scarce due to its high acquisition cost. The proposed spatial kernel PCA (KPCA) is compared with PCA in terms of classification accuracy with the nearest-neighbourhood classification method.