• 한국컴퓨터정보학회논문지
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• 제23권4호
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• pp.65-70
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• 2018

This paper describes a effective information compressor algorithm for the fourth industrial technology. One of the difficult problems for outdoor is to obtain effective updating process of background images. Because input images generally contain the shadows of buildings, trees, moving clouds and other objects, they are changed by lapse of time and variation of illumination. They provide the lowering of performance for surveillance system under outdoor. In this paper, a effective information algorithm for variable environment variable under outdoor is proposed, which apply the Kalman Estimation Modeling and adaptive threshold on pixel level to separate foreground and background images from current input image. In results, the better SNR of about 3dB~5dB and about 10%~25% noise distribution rate in the proposed method. Furthermore, it was showed that the moving objects can be detected on various shadows under outdoor and better result Information.

• 전기학회논문지
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• 제57권9호
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• pp.1636-1641
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• 2008

In this study, we introduce a new approach to Parallel Genetic Algorithms (PGA) which combines AHFCGA with crossover operator. As to crossover operators, we use three types of the crossover operators such as modified simple crossover(MSX), arithmetic crossover(AX), and Unimodal Normal Distribution Crossover(UNDX) for real coding. The AHFC model is given as an extended and adaptive version of HFC for parameter optimization. The migration topology of AHFC is composed of sub-populations(demes), the admission threshold levels, and admission buffer for the deme of each threshold level through succesive evolution process. In particular, UNDX is mean-centric crossover operator using multiple parents, and generates offsprings obeying a normal distribution around the center of parents. By using test functions having multimodality and/or epistasis, which are commonly used in the study of function parameter optimization, Experimental results show that AHFCGA can produce more preferable output performance result when compared to HFCGA and RCGA.

• 한국멀티미디어학회논문지
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• 제17권9호
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• pp.1064-1069
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• 2014

In this paper, we propose a novel approach to improve the performance of a voice activity detection(VAD) which is based on the adaptive band-partitioning with the likelihood ratio(LR). The previous method based on the adaptive band-partitioning use the weights that are derived from the variance of the spectral. In our VAD algorithm, the weights are derived from LR, and then the weights are incorporated with the entropy. The proposed algorithm discriminates the voice activity by comparing the weighted entropy with the adaptive threshold. Experimental results show that the proposed algorithm yields better results compared to the conventional VAD algorithms. Especially, the proposed algorithm shows superior improvement in non-stationary noise environments.

• 한국음향학회지
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• 제21권4호
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• pp.178-178
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• 2002

In this paper, we propose an adaptive wavelet method for car interior noise cancellation. For this purpose, we use a node dependent threshold which minimizes the Bayesian risk. We propose a noise estimation method based on spectral entropy using histogram of intensity and a candidate best basis instead of Donoho's best bases. And we modify the hard threshold function. Experimental results show that the proposed algorithm is more efficient, especially to heavy noisy signal than conventional one.

• 제어로봇시스템학회논문지
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• 제17권6호
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• pp.587-595
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• 2011

In this paper, we focus on the issue of automatic selection for multi-level threshold, and we greatly improve the efficiency of Otsu's method for image segmentation based on genetic algorithm. We have investigated and evaluated the performance of the Otsu and Valley-emphasis threshold methods. Based on this observation we propose a method for automatic threshold method that segments an image into more than two regions with high performance and processing in real-time. Our paper introduced new peak detection, combines with evolution algorithm using MAGA (Modified Adaptive Genetic Algorithm) and HCA (Hill Climbing Algorithm), to find the best threshold automatically, accurately, and quickly. The experimental results show that the proposed evolutionary algorithm achieves a satisfactory segmentation effect and that the processing time can be greatly reduced when the number of thresholds increases.

• Journal of Information Processing Systems
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• 제14권2호
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• pp.539-551
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• 2018

Images are unavoidably contaminated with different types of noise during the processes of image acquisition and transmission. The main forms of noise are impulse noise (is also called salt and pepper noise) and Gaussian noise. In this paper, an effective method of removing mixed noise from images is proposed. In general, different types of denoising methods are designed for different types of noise; for example, the median filter displays good performance in removing impulse noise, and the wavelet denoising algorithm displays good performance in removing Gaussian noise. However, images are affected by more than one type of noise in many cases. To reduce both impulse noise and Gaussian noise, this paper proposes a denoising method that combines adaptive median filtering (AMF) based on impulse noise detection with the wavelet threshold denoising method based on a Gaussian mixture model (GMM). The simulation results show that the proposed method achieves much better denoising performance than the median filter or the wavelet denoising method for images contaminated with mixed noise.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 1998년도 춘계학술발표논문집
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• pp.143-147
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• 1998

카메라 영상에 의한 자동차 번호판 인식시스템은 영상 획득, 번호판 추출, 전처리, 문자 분리, 문자 인식 등 크게 5자기의 핵심 부분으로 구성된다. 따라서 자동차 번호판 인식시스템의 성능을 향상시키기 위해서는 이들 부분들 각각의 성능의 최적화가 필요하다. 본 연구는 자동차 번호판 인식시스템의 여러 단계 중 전처리에 해당하는 번호판 영역의 이진화에 관한 연구로서, 기존의 단일 임계치 방법과 다중 임계치 방법이 해결하지 못했던 부분을 보완하는 새로운 다중 임계치 방법을 제안한다. 본 논문에서 제안하는 다중 임계치 알고리즘(Adaptive Multi-threshold Algorithm)을 사용함으로써 gray-level 번호판 영상에 대해서 보다 깨끗한 이진 영상을 얻을 수 있었으며, 또한 이 알고리즘은 번호판 영역의 밝기값이 고르지 않은 영상에 대해서도 효율적인 알고리즘 임을 알 수 있었다.

• Journal of Power Electronics
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• 제16권3호
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• pp.1012-1024
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• 2016

This study deals with an on-line software fault detection, localization, and system reconfiguration method for electrical system drives composed of three-phase AC/DC/AC converters and three-phase permanent magnet synchronous machine (PMSM) drives. Current sensor failure (outage), speed/position sensor loss (disconnection), and damaged DC-link voltage sensor are considered faults. The occurrence of these faults in PMSM drive systems degrades system performance and affects the safety, maintenance, and service continuity of the electrical system drives. The proposed method is based on the monitoring signals of "abc" currents, DC-link voltage, and rotor speed/position using a measurement chain. The listed signals are analyzed and evaluated with the generated residuals and threshold values obtained from a Sliding Mode Current-Speed-DC-link Voltage Observer (SMCSVO) to acquire an on-line fault decision. The novelty of the method is the faults diagnosis algorithm that combines the use of SMCSVO and adaptive thresholds; thus, the number of false alarms is reduced, and the reliability and robustness of the fault detection system are guaranteed. Furthermore, the proposed algorithm's performance is experimentally analyzed and tested in real time using a dSPACE DS 1104 digital signal processor board.

• 한국정보처리학회논문지
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• 제4권1호
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• pp.224-236
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• 1997

ATM 망의 트래픽 흐름을 조절하고 망 자원의 사용을 최적화하기 위해서는 폭주로 인한 성능 저하를 막거나 폭주 발생 시 대처할 수 있는 적절한 제어가 필요하다. 기 존의 UPC 알고리즘이 망의 상황과는 관계없이 매우 불안정한 예방적 기능만을 제공하 고 버퍼로 인해 셀 지연를 가중시키는 문제점이 있다. 그래서 본 논문에서는 OAM 셀 이 주는 정보를 바탕으로 망 내의 상황을 판단한 다음, 사용자가 요구한 서비스의 질 을 고려하여 리키율을 제어하고 버퍼에 임계값을 두어 조절함으로써 망 내의혼잡을 피하기 위한 적응적 UPC 알고리즘을 제안 하였다. 트래픽의 종류가 다양해지고 망 내 의 전송 속도가 증가함에 따라 제안한 적응적 UPC-BT 알고리즘은 그 활용 범위를 넓 혀가는 ATM 망에 매우 유용하게 사용될 수 있다. 본 논문에서는 제안한 알고리즘의 효율성을 음성과 고속 데이타를 중심으로 시뮬레이션을 통하여 입증하고, 시뮬레이션 결과를 기존의 UPC 알고리즘과 비교 분석하여, 제안한 적응적 UPC-BT 알고리즘을 이 용하면 다양한 트래픽에 따라 사용자가 요구하는 서비스의 질을 유지하면서, 동시에 망의 자원을 효율적으로 사용할 수 있다는 것을 보인다.

• 한국통신학회논문지
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• 제35권7C호
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• pp.618-627
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• 2010

이 논문에서는 다중 송수신 안테나 시스템에서 탐색 단계별 반경들의 차이를 이용하여 후보 심볼수 K를 변화시키는 적응 복호화 알고리즘을 제안하고, 기존의 복호화 알고리즘과 비트 오류율 및 평균 연산량 측면에서의 성능을 비교한다. 제안한 알고리즘은 반경의 차이가 기준값보다 큰 심볼은 후보에서 제외하는 방법으로 단계마다 적응적으로 다른 K를 사용하며, 기준값으로는 단계별 반경 차이들의 최대값과 평균값으로 적용한다. 제안한 복호화 알고리즘은 K를 고정한 K-best 알고리즘에 비해 오차 전달 현상에 의한 비트 오류율 성능 저하를 줄이고 복잡도면에서 좀 더 효율적인 성능을 보이며, 기존의 적응 K-best 알고리즘과 비교하면 비슷한 비트 오류율 성능을 보이면서 평균 복잡도를 줄일 수 있음을 모의실험으로 보인다.