• The Journal of the Acoustical Society of Korea
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• v.28 no.3
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• pp.268-272
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• 2009

In this paper, we propose a Gaussian Mixture Model (GMM) based incremental robust adaptation with a forgetting factor for the speaker verification. Speaker recognition system uses a speaker model adaptation method with small amounts of data in order to obtain a good performance. However, a conventional adaptation method has vulnerable to the outlier from the irregular utterance variations and the presence noise, which results in inaccurate speaker model. As time goes by, a rate in which new data are adapted to a model is reduced. The proposed algorithm uses an incremental robust adaptation in order to reduce effect of outlier and use forgetting factor in order to maintain adaptive rate of new data on GMM based speaker model. The incremental robust adaptation uses a method which registers small amount of data in a speaker recognition model and adapts a model to new data to be tested. Experimental results from the data set gathered over seven months show that the proposed algorithm is robust against outliers and maintains adaptive rate of new data.

• Journal of the Optical Society of Korea
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• v.20 no.6
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• pp.745-751
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• 2016

A detection algorithm, based on the combined local-global (CLG) optical-flow model and Gaussian pyramid for a moving target appearing against a dynamic background, can compensate for the inadaptability of the classic Horn-Schunck algorithm to illumination changes and reduce the number of needed calculations. Incorporating the hypothesis of gradient conservation into the traditional CLG optical-flow model and combining structure and texture decomposition enable this algorithm to minimize the impact of illumination changes on optical-flow estimates. Further, calculating optical-flow with the Gaussian pyramid by layers and computing optical-flow at other points using an optical-flow iterative with higher gray-level points together reduce the number of calculations required to improve detection efficiency. Finally, this proposed method achieves the detection of a moving target against a dynamic background, according to the background motion vector determined by the displacement and magnitude of the optical-flow. Simulation results indicate that this algorithm, in comparison to the traditional Horn-Schunck optical-flow algorithm, accurately detects a moving target undergoing illumination changes against a dynamic background and simultaneously demonstrates a significant reduction in the number of computations needed to improve detection efficiency.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.387-391
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• 2008

For the detection of moving objects, background subtraction methods are widely used. In case the background has variation, we need to update the background in real-time for the reliable detection of foreground objects. Gaussian mixture model (GMM) combined with probabilistic learning is one of the most popular methods for the real-time update of the background. However, it does not work well in the complex and dynamic backgrounds with high traffic regions. In this paper, we propose a new method for modelling and updating more reliably the complex and dynamic backgrounds based on the probabilistic learning and the layered processing.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.6
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• pp.537-543
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• 2016

A new approach to systematically model aerodynamic coefficients using an adaptive sampling based wind tunnel testing considering cost is proposed. The Latin Hypercube design is used for selecting initial test points. The Gaussian Process (GP) is iteratively used during the experiment to determine additional experimental points that minimizes the uncertainty reduction per incremental cost. A numerical simulation based experiment was conducted using the static aerodynamic coefficient database a fighter aircraft, which demonstrated the validity of the proposed method.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.499-505
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• 2018

Time delay estimation between two acoustic sensors is widely used in room acoustics and sonar for target position estimation, tracking and synchronization. A cross-correlation based method is representative for the time delay estimation. However, this method does not have enough consideration for the noise added to the receiving acoustic sensors. This paper proposes a new time delay estimation method considering the added noise on the receiver acoustic sensors. From comparing with the existing GCC (Generalized Cross Correlation) method, and adaptive eigen decomposition method, we show that the proposed method outperforms other methods for a colored signal source in the white Gaussian noise condition.

• Structural Engineering and Mechanics
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• v.83 no.1
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• pp.93-108
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• 2022

Real-time hybrid simulation (RTHS) was applied to investigate the train-bridge interaction of a high-speed railway system, where the railway bridge was selected as the numerical substructure, and the train was physically tested. The interaction between the two substructures was reproduced by a servo-hydraulic shaking table. To accurately reproduce the high-frequency interaction responses ranging from 10-25Hz using the hydraulic shaking table with an inherent delay of 6-50ms, an adaptive time series (ATS) compensation algorithm combined with the linear quadratic Gaussian (LQG) was proposed and implemented in the RTHS. Testing cases considering different train speeds, track irregularities, bridge girder cross-sections, and track settlements featuring a wide range of frequency contents were conducted. The performance of the proposed ATS+LQG delay compensation method was compared to the ATS method and RTHS without any compensation in terms of residual time delays and root mean square errors between commands and responses. The effectiveness of the ATS+LQG method to compensate time delay in RTHS with high-frequency responses was demonstrated and the proposed ATS+LQG method outperformed the ATS method in yielding more accurate responses with less residual time delays.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3B
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• pp.259-266
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• 2002

In the CDMA system, the Multiple access interference(MAI) causes system performance and capacity degradation. One of the MAI cancel method is a feedback detector that orthogonal decision-feedback detector(ODFD) and Deocrrelating decision-feedback detector(DDFD) have been studied by now. In this paper, we propose a blind adaptive ODFD(BA-ODFD) with blind equalizer combined ODFD, Also we simulate the operation of BA-ODFD in AWGN channel and Rayleigh (tiding channel. and compare blind adaptive ODFD with blind adaptive DDFD.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.321-325
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• 2004

Retinopathy of Prematurity (ROP) is a common retinal neovascular disorder of premature infants. It can be characterized by inappropriate and disorganized vessel. This paper present a method for blood vessel detection on infant retinal images. The algorithm is designed to detect the retinal vessels. The proposed method applies a Lapalacian of Gaussian as a step-edge detector based on the second-order directional derivative to identify locations of the edge of vessels with zero crossings. The procedure allows parameters computation in a fixed number of operations independent of kernel size. This method is composed of four steps : grayscale conversion, edge detection based on LOG, noise removal by adaptive Wiener filter & median filter, and Otsu's global thresholding. The algorithm has been tested on twenty infant retinal images. In cooperation with the Digital Imaging Research Centre, Kingston University, London and Department of Opthalmology, Imperial College London who supplied all the images used in this project. The algorithm has done well to detect small thin vessels, which are of interest in clinical practice.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.205-208
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• 2007

Due to the development and wide use of digital multimedia broadcasting (DMB) and Wireless Broadband Internet (WiBro), the digital contents industry using images has been progressed. Therefore, the image processing has been applied in a variety of fields and in order to transmit and conserve accurate information, the degradation phenomenon for images should be removed. As a representative cause of the degradation phenonenon, noise has become known and Gaussian noise occurs in the process of transmission. Diverse researches for Gaussian noise removal have been implemented and a great number of algorithms have been proposed until now. In this paper, for mage restoration an algorithm using the adaptive threshold value is proposed in Gaussian noise environment and the threshold value is established by using the histogram of edge image. And from simulation results, the noise removal performance of the proposed method is proven using mean square error (MSE) and peak signal to noise ratio (PSNR).

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.815-816
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• 2008

In this paper, we evaluate depth image preprocessing for 3D data service based on DIBR over T-DMB. We evaluate two preprocessing methods of depth images. These are gaussian smoothing and adaptive smoothing. The results show that adaptive smoothing is more suitable for images with sharp transition of depth.