• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1463-1471
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• 2016

The indoor localization algorithm using the Difference Means based on Fingerprint (DMFPA) to improve the performance of indoor localization in moving Wi-Fi environment is proposed in this paper. In addition to this, the performance of the proposed algorithm is also compared with the Original Fingerprint Algorithm (OFPA) and the Gaussian Distribution Fingerprint Algorithm (GDFPA) by our developed indoor localization simulator. The performance metrics are defined as the accuracy of the average localization accuracy; the average/maximum cumulative distance of the occurred errors and the average measurement time in each reference point.

• Communications of the Korean Mathematical Society
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• v.9 no.2
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• pp.449-465
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• 1994

The two common numerical methods to approximate the solution of partial differential equations are the finite element method and the finite difference method. They both lead to solving large sparse linear systems. For many applications, it is not unusal that the order of matrix is greater than 10, 000. For this kind of problem, a direct method such as Gaussian elimination can not be used because of the prohibitive cost. To this end, many iterative methods with modest cost have been studied and proposed by numerical analysts.(omitted)

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.10
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• pp.130-138
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• 1996

In this paper, we propose a neural network which detect edges of different orentation and spatial frequency in arbitrary image data. We constructed the proposed neural network iwth two different types neural network. A diffusion network performs the gaussian operation efficiently by the diffusion process. And the spatial difference network has specially designed connections suitble to detect the contours of a specific oriention. Simulation results showed that the proposed neural network can extract the edges of selected orientation efficiently by applying the neural network to a test pattern and the real image.

• Honam Mathematical Journal
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• v.36 no.2
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• pp.291-303
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• 2014

In this paper, through a direct computation with subintervals partitioning [0, 1], we compute better a posteriori bounds for the average case error of the difference between the true value of $I(f)=\int_{0}^{1}f(x)dx$ with $f{\in}C^r$[0, 1] minus the composite trapezoidal rule and the composite trapezoidal rule minus the basic trapezoidal rule for $r{\geq}3$ by using zero mean-Gaussian.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2000.11a
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• pp.16-19
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• 2000

We propose a deterministic vector channel simulation model satisfying not only rigorous temporal correlation but also arbitrary spatial correlation using the method of Doppler phase difference sampling. The model is more efficient than the conventional PN filtered Gaussian model with coloring process in evaluating the laboratory performance of mobile communication systems employing adaptive way antennas or space diversity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.8
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• pp.1136-1145
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• 1993

The channel capacities of various lading channels are calculated and compared with that of Gaussian noise channel to find out the decrements of channel capacity according to each fading environment. As a result, it is confirmed that the channel capacities in Rician and m-distribution fading channels approach to that of Gaussian noise channel as direct-to-indirect power ratio in Rician fading channel and fading index m in m-distribution fading channel increases respectively. And the difference between two channel capacities of Gaussian noise channel and each fading channel which is dependent on carrier-to-noise power ratio (CNR) is found. Also the improvement of channel capacity of Rayleigh fading channel by introducing two-branch diversities is obtained. For diversity reception, predetection maximal-ratio and postdetection selective combining techniques are adopted. The results show that the improvement of channel capacity by predetection maximal-ratio combining diversity is superior to the postdetection selective combining diversitiy regardless of correlation coefficient between two diversity branches in Raylelgh fading channel. The best improvement is achieved when two branches are noncorrelative in both two diversify techniques and as correlation coefficient of two diversity branches is smaller, the improvement of channel capacity is greater.

• The Journal of the Acoustical Society of Korea
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• v.27 no.3
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• pp.119-128
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• 2008

This paper analyzes the relationship between two representative statistical methods for bandwidth extension (BWE): Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) ones, and compares their performances. The HMM method is a memory-based system which was developed to take advantage of the inter-frame dependency of speech signals. Therefore, it could be expected to estimate better the transitional information of the original spectra from frame to frame. To verify it, a dynamic measure that is an approximation of the 1st-order derivative of spectral function over time was introduced in addition to a static measure. The comparison result shows that the two methods are similar in the static measure, while, in the dynamic measure, the HMM method outperforms explicitly the GMM one. Moreover, this difference increases in proportion to the number of states of HMM model. This indicates that the HMM method would be more appropriate at least for the 'blind BWE' problem. On the other hand, nevertheless, the GMM method could be treated as a preferable alternative of the HMM one in some applications where the static performance and algorithm complexity are critical.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.87-92
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• 2023

With the continuous development of the speech recognition system, the recognition rate for speech has developed rapidly, but it has a disadvantage in that it cannot accurately recognize the voice due to the noise generated by mixing various voices with the noise in the use environment. In order to increase the vocabulary recognition rate when processing speech with environmental noise, noise must be removed. Even in the existing HMM, CHMM, GMM, and DNN applied with AI models, unexpected noise occurs or quantization noise is basically added to the digital signal. When this happens, the source signal is altered or corrupted, which lowers the recognition rate. To solve this problem, each voice In order to efficiently extract the features of the speech signal for the frame, the MFCC was improved and processed. To remove the noise from the speech signal, the noise removal method using the Gaussian model applied noise deviation estimation was improved and applied. The performance evaluation of the proposed model was processed using a cross-correlation coefficient to evaluate the accuracy of speech. As a result of evaluating the recognition rate of the proposed method, it was confirmed that the difference in the average value of the correlation coefficient was improved by 0.53 dB.

• Journal of the Korean Society for Advanced Composite Structures
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• v.1 no.2
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• pp.51-58
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• 2010

This work examines the identification of stiffness reduction in damaged reinforced concrete bridges under moving loads, and carries out the performance assessment after repairing using advanced composite materials. In particular, the change of stiffness in each element before and after repairing, based on the Microgenetic algorithm as an advanced inverse analysis, is described and discussed by using a modified bivariate Gaussian distribution function. The proposed method in the study is more feasible than the conventional element-based method from computation efficiency point of view. The validity of the technique is numerically verified using a set of dynamic data obtained from a simulation of the actual bridge modeled with a three-dimensional solid element. The numerical examples show that the proposed technique is a feasible and practical method which can inspect the complex distribution of deteriorated stiffness although there is a difference between actual bridge and numerical model as well as uncertain noise occurred in the measured data.

• Journal of the Optical Society of Korea
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• v.16 no.3
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• pp.305-312
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• 2012

We numerically demonstrate the performance of a plasmonic lens composed of an array of nanoslits perforated on thin metallic film with slanted cuts on the output surface. Embedding Kerr nonlinear material in nanoslits is employed to modulate the output beam. A two dimensional nonlinear-dispersive finite-difference time-domain (2D N-D-FDTD) method is utilized. The performance parameters of the proposed lens such as focal length, full-width half-maximum, depth of focus and the efficiency of focusing are investigated. The structure is illuminated by a TM-polarized plane wave and a Gaussian beam. The effect of the beam waist of the Gaussian beam and the incident light intensity on the focusing effect is explored. An exact formula is proposed to derive electric field E from electric flux density D in a Kerr-Dispersive medium. Surface plasmon (SPs) modes and Fabry-Perot (F-P) resonances are used to explain the physical origin of the light focusing phenomenon. Focused ion beam milling can be implemented to fabricate the proposed lens. It can find valuable potential applications in integrated optics and for tuning purposes.