• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.627-629
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• 1998

We propose a robust blind equalization algorithm based on quadrant-partitioned constellations for QAM demodulation. The algorithm divides the received M-QAM constellations into simple four quadrant. The channel equalization for symbols in each quadrant can be accomplished fast and reliably using the Constant Modulus Algorithm(CMA) and the Stop-and-Go Algorithm(SGA). Test results confirm that the proposed algorithm with lower complexity outperforms both the CMA and the SGA in reducing the SER as well as the MSE at the equalizer output.

• Structural Engineering and Mechanics
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• v.62 no.2
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• pp.139-142
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• 2017

For the conventional computational methods for structural reliability analysis, the common limitations are long computational time, large number of iteration and low accuracy. Thus, a new novel method for structural reliability analysis has been proposed in this paper based on response surface method incorporated with an improved genetic algorithm. The genetic algorithm is first improved from the conventional genetic algorithm. Then, it is used to produce the response surface and the structural reliability is finally computed using the proposed method. The proposed method can be used to compute structural reliability easily whether the limit state function is explicit or implicit. It has been verified by two practical engineering cases that the algorithm is simple, robust, high accuracy and fast computation.

• The Journal of the Acoustical Society of Korea
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• v.26 no.1E
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• pp.21-26
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• 2007

In this paper, we propose a modified Viterbi algorithm to compensate for endpoint detection error during the decoding phase of an isolated word recognition task. Since the conventional Viterbi algorithm explores only the search space whose boundaries are fixed to the endpoints of the segmented utterance by the endpoint detector, the recognition performance is highly dependent on the accuracy level of endpoint detection. Inaccurately segmented word boundaries lead directly to recognition error. In order to relax the degradation of recognition accuracy due to endpoint detection error, we describe an unconstrained search of word boundaries and present an algorithm to explore the search space with efficiency. The proposed algorithm was evaluated by performing a variety of simulated endpoint detection error cases on an isolated word recognition task. The proposed algorithm reduced the Word Error Rate (WER) considerably, from 84.4% to 10.6%, while consuming only a little more computation power.

• Korean Journal of Computational Design and Engineering
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• v.11 no.5
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• pp.375-383
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• 2006

Presented in this paper is a new fast and robust algorithm generating NC tool path for 2D pockets with islands. The input shapes are composed of line segments and circular arcs. The algorithm has two steps: creation of successive offset loops and linking the loops to a tool path. A modified pairwise technique is developed in order to speed up and stabilize the offset process, and the linking algorithm is focused on avoiding thin-wall cutting and minimizing tool retractions. The proposed algorithm has been implemented in C++ and some illustrative examples are presented to show the practical strength of the algorithm.

• Journal of Sensor Science and Technology
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• v.25 no.6
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• pp.418-422
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• 2016

A filtering algorithm based on the decentralized moving average Kalman filter with uncertainties is proposed in this paper. The proposed filtering algorithm presented combines the Kalman filter with the moving average strategy. A decentralized fusion algorithm with the weighted sum structure is applied to the local moving average Kalman filters (LMAKFs) of different window lengths. The proposed algorithm has a parallel structure and allows parallel processing of observations. Hence, it is more reliable than the centralized algorithm when some sensors become faulty. Moreover, the choice of the moving average strategy makes the proposed algorithm robust against linear discrete-time dynamic model uncertainties. The derivation of the error cross-covariances between the LMAKFs is the key idea of studied. The application of the proposed decentralized fusion filter to dynamic systems within a multisensor environment demonstrates its high accuracy and computational efficiency.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.10
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• pp.1441-1447
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• 2014

A robust new algorithm for R wave detection named for Multiscale-based Peak Detection(MSPD) is assessed in this paper using MIT/BIH Arrhythmia Database. MSPD algorithm is based on a matrix composed of local maximum and find R peaks using result of standard deviation in the matrix. Furthermore, By reducing needless procedure of proposed algorithm, improve algorithm ability to detect R peak efficiently. And algorithm performance is assessed according to detection rates about various arrhythmia database.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.344-358
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• 2019

Surveillance cameras have installed in many places because security and safety is becoming important in modern society. Through surveillance cameras installed, we can deal with troubles and prevent accidents. However, watching surveillance videos and judging the accidental situations is very labor-intensive. So now, the need for research to analyze surveillance videos is growing. This study proposes an algorithm to track multiple persons using SURF and background subtraction. While the SURF algorithm, as a person-tracking algorithm, is robust to scaling, rotating and different viewpoints, SURF makes tracking errors with sudden changes in videos. To resolve such tracking errors, we combined SURF with a background subtraction algorithm and showed that the proposed approach increased the tracking accuracy. In addition, the background subtraction algorithm can detect persons in videos, and SURF can initialize tracking targets with these detected persons, and thus the proposed algorithm can automatically detect the enter/exit of persons.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.697-702
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• 2009

Filtered-x LMS algorithm maybe the most popular control algorithm used in DSP implementations of active noise and vibration control system. The algorithm converges on a timescale comparable to the response time of the system to be controlled, and is found to be very robust. If the pure tone reference signal is synchronously sampled, it is found that the behavior of the adaptive system can be completely described by a matrix of linear, time invariant, transfer functions. This is used to explain the behavior observed in simulations of a simplified single input, single output adaptive system, which retains many of the properties of the multichannel algorithm.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.73-78
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• 2022

Based on experimental measurements conducted on many different practical wireless communication systems, ambient noise has been shown to be decidedly non-Gaussian owing to impulsive phenomena. However, most multiuser detection techniques proposed thus far have considered Gaussian noise only. They may therefore suffer from a considerable performance loss in the presence of impulsive ambient noise. In this paper, we consider a large-scale multiuser multiple-input multiple-output system in the presence of non-Gaussian noise and propose a genetic algorithm (GA) based detector for large-dimensional multiuser signal detection. The proposed algorithm is more robust than linear multi-user detectors for non-Gaussian noise because it uses a multi-directional search to manipulate and maintain a population of potential solutions. Meanwhile, the proposed GA-based algorithm has a comparable complexity because it does not require any complicated computations (e.g., a matrix inverse or derivation). The simulation results show that the GA offers a performance gain over the linear minimum mean square error algorithm for both non-Gaussian and Gaussian noise.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.2
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• pp.63-68
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• 2015

This paper proposes a robust multiple audio watermarking algorithm using improved QIM(quantization index modulation) with adaptive stepsize for different signal power and SVM(support vector machine) decoding model. The proposed algorithm embeds watermarks into both frequency magnitude response and frequency phase response using QIM. This multiple embedding method can achieve a complementary robustness. The SVM decoding model can improve detection rate when it is not sure whether the extracted data are the watermarks or not. To evaluate robustness, 11 attacks are employed. Consequently, the proposed algorithm outperforms previous multiple watermarking algorithm, which is identical to the proposed one but without SVM decoding model, in PSNR and BER. It is noticeable that the proposed algorithm achieves improvements of maximum PSNR 7dB and BER 10%.