• 한국인터넷방송통신학회논문지
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• 제15권3호
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• pp.99-105
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• 2015

본 논문에서는 nonconstant modulus 신호를 대상으로 채널의 찌그러짐에 의한 부호간 간섭을 보상하기 위한 MMA (Multiple Modulus Algorithm) 적응 등화기를 가변 적응 스텝 크기를 적용하지 않고 2단의 직렬 연결 형태로 구현하여 수렴 속도를 개선할 수 있는 mMMA (modified MMA)에 대하여 다룬다. 적응 등화기는 유한 차수의 탭 지연선에 의한 단일 디지털 필터로 구현되므로, 논문에서는 이를 2단의 직렬 연결 필터로 구현한 후 각 단에서는 MMA와 동일한 알고리즘으로 오차 신호를 얻은 후 필터 계수를 갱신하게 된다. 따라서 첫단에는 빠른 수렴 속도를 결정하며, 두 번째단에서는 첫단의 출력에 포함되어 있는 잔류 isi양을 최소화시키도록 탭 계수를 갱신한다. 이때 1단 시스템과 2단 시스템은 동일한 차수의 필터가 되도록 조정하면서 적응 등화 성능을 비교하였으며, 성능 비교를 위한 지수로는 등화기 출력 신호 성상도, 수렴 특성을 나타내는 잔류 isi, 최대 찌그러짐과 MSE, 채널의 신호대 잡음비에 따른 SER을 사용하였다. 시뮬레이션 결과 2단의 FIR 구조를 갖는 mMMA가 1단의 기존 MMA보다 등화 잡음에 의한 성상도를 제외한 모든 성능 지수에서 우월하며, 수렴 속도는 1.5~1.8배 정도 개선됨을 확인하였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 춘계학술대회 학술발표 논문집 제14권 제1호
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• pp.139-143
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• 2004

Lee et al have proposed a framework for the linguistic map-based navigational planning of a mobile robot on dynamic environment and provided simulation results applied it to the static environment[1], In this paper, we extends the navigational planning of a mobile robot into dynamic environment. There are two kinds of dynamic obstacles： (1) Time-obstacles that change condition of obstacles with time. (2) Space-obstacles that move their position with time. We propose an algorithm which a mobile robot identifies and avoids the two kinds of dynamic obstacles. The proposed algorithm consists of two stages： (1) The fuzzy logic-based perception stage which identifies the dynamic obstacles around a mobile robot by using sensory data and fuzzy rules, (2) The planning stage which plans the path to goal by avoiding the dynamic obstacles[2-6]. We provide computer simulation results for a mobile robot in order to show the validity of the proposed algorithm.

• ETRI Journal
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• 제38권2호
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• pp.366-375
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• 2016

This paper describes an algorithm to suppress composite noise in a two-microphone speech enhancement system for robust hands-free speech communication. The proposed algorithm has four stages. The first stage estimates the power spectral density of the residual stationary noise, which is based on the detection of nonstationary signal-dominant time-frequency bins (TFBs) at the generalized sidelobe canceller output. Second, speech-dominant TFBs are identified among the previously detected nonstationary signal-dominant TFBs, and power spectral densities of speech and residual nonstationary noise are estimated. In the final stage, the bin-wise output signal-to-noise ratio is obtained with these power estimates and a Wiener post-filter is constructed to attenuate the residual noise. Compared to the conventional beamforming and post-filter algorithms, the proposed speech enhancement algorithm shows significant performance improvement in terms of perceptual evaluation of speech quality.

• 한국정보과학회논문지:소프트웨어및응용
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• 제35권9호
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• pp.547-553
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• 2008

본 논문에서는 2 단계 서류 레이아웃 분할 방법을 제안한다. 서류 분할의 1 차 단계는 top-down 계열의 영역 추출로서 모폴로지 기반의 거리 함수를 사용하여 주어진 영상 데이타를 사각형 영역들로 분할한다. 거리 사상 함수를 통한 예비 결과는 성능 개선을 위한 2 차 단계의 입력 변수로 작용한다. 서류 분할의 2차 단계로서 기계 학습 이론을 적용한다. 통계 모델을 따르는 RBF 신경망을 선택하였고, 은닉 층의 설계를 위해 코호넨 네트워크의 자기 조직화 성격을 활용한 데이타 군집화 기법을 기반으로 하였다. 본 논문에서는 300개의 영상에서 추출된 영역 데이타를 통해 학습된 신경망이 1차 단계에서 도출된 예비 결과를 개선함을 연구 결과로 제시하였다.

• The Journal of the Acoustical Society of Korea
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• 제16권2E호
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• pp.27-31
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• 1997

This paper proposes a new rejection algorithm which distinguishes unregistered spoken words(or non-keywords) from registered vocabulary. Two kinds of garbage models are employed in this design ; the original garbage model and a new word garbage model. The original garbage model collects all non-keyword patterns where the new word garbage model collects patterns classified by recognizing each non-keyword pattern with registered vocabulary. These two types of garbage models work together to make a robust reject decision. The first stage of processing is the classification of an input pattern through the original garbage model. In the event that the first stage of processing is ambiguous, the new word dependent garbage model is used to classify thye input pattern as either a registered or non-registered word. This paper shows the efficiency of the new word dependent garbage model. A Dynamic Multisection method is used to test the performance of the algorithm. Results of this experiment show that the proposed algorithm performs at a higher level than that of the original garbage model.

• 전자공학회논문지S
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• 제34S권9호
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• pp.109-117
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• 1997

A new relaxation algorithm based on distribution of matched errors and possibility is proposed to solve efficiently correspondence problem. This algorithm can be applied to various method, such as BMA, feature-, and region-based matching methods, by modifying its smoothness function. It consists of two stages which are transformation and iteration process. In transformation stage, the errors obtained by any matching algorithm are transformed to possibility values according to these statistical distribution. Each grade of possility is updated by some constraints which are defined as smoothness, uniqueness, and discontinuity factor in iteration stage. The discontinuity factor is used to reserve discontinuity of disparity. In conventional methods, it is difficult to find proper weights and stop condition, because only two factors, smoothness and uniqueness, have been used. However, in the proposed mthod, the more smoothing is not ocurred because of discontinuity factor. And it is efective to the various image, even if the image has a severe noise and repeating patterns. In addition, it is shown that the convergence rate and the quality of output are improved.

• 한국통신학회논문지
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• 제21권4호
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• pp.972-983
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• 1996

This paper proposes a new technique for frequency offset correction for OFDM systems on a frequency selective fading channel. Frequency offset in OFDM introduces interchannel interference among the multiple subcarriers of OFDM signal. To compensate the interference, this paper describes an algorithm with two stages:acquisition and tracking. At both stages, the proposed algorithm oversamples the received OFDM signal to obtain a couple of demodulated symbol sets. At acquisition stage the frequency offset is reduced to half or less of the intercarrier spacings by matching the sign pattern of each element of the sets. Next, at tracking stage the frequency offset is corrected with a frequency detector which is controlled by the correlation of the two sets. It is shown that the proposed algorithm can correct the frequency offset in the event of uncertainty in the initial offset that exceeds one half of the intercarrier spacing. In addition, the proposed algorithm is robust to transmitted symbols and channel characteristics by using oversampled symbol sets.

• Journal of the Optical Society of Korea
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• 제5권3호
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• pp.93-98
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• 2001

In order to verify the performance of the two-stage iterative Fourier transform algorithm[Hankook Kwanghak Hoeji 11, 47 (2000)], a number of phase-only diffractive pattern elements which produce simple 16x16-pixel intensity patterns useful in the field of optical information processing have been designed and their performance has been compared with that from the nonlinear least-squares algorithm[Appl. Opt. 36, 7297(1977)] which is computationally intensive. for all intensity patterns, elements designed by the former algorithm show better overall signal-to noise ratio and uniformity, although they show essentially the same diffraction efficiency. In the case of continuous phase elements, they show far superior uniformity. Computationally,. the former algorithm is far more efficient than the latter.

• 한국통신학회논문지
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• 제24권9A호
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• pp.1392-1400
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• 1999

본 논문에서는 전역 탐색 알고리듬 (full search algorithm; FSA)과 동일한 성능을 나타내면서도 고속으로 움직임을 추정할 수 있는 블록 움직임 추정을 위한 2단계 고속 전역 탐색 알고리듬을 제안하였다. 제안한 방법에서는 첫 번째 단계에서 9:1로 부표본화된 탐색점에 대하여 블록 정합을 행하여, 여기서 얻어지는 최소 평균 절대치 오차 (mean absolute error, MAE)를 기준 MAE로 설정한다. 두 번째 단계에서는 첫 번째 단계에서 블록 정합을 행하지 않은 탐색점에 대하여 각 탐색점에서 가질 수 있는 MAE의 최소 범위를 구한 뒤, 이 값이 기준 MAE보다 작은 탐색점에 대해여서만 블록 정합을 행하였다. 이때, MAE의 최소 범위는 첫 번째 단계에서 블록 정합을 통하여 얻은 MAE들과 현재 블록 내의 화소들의 이웃 화소간의 화소 값의 차를 이용하여 구하였다. 그러므로, 제안한 방법에서는 MAE의 최소 범위를 이용하여 블록 정합이 필요한 블록에 대하여서만 정합을 행함으로써 FAS와 동일한 움직임 추정 성능을 유지하면서도 움직임 벡터의 추정을 위한 계산량을 줄일 수 있었다.

• ETRI Journal
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• 제31권3호
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• pp.282-291
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• 2009

An online mean-shift object tracking algorithm, which consists of a learning stage and an estimation stage, is proposed in this work. The learning stage selects the features for tracking, and the estimation stage composes a likelihood image and applies the mean shift algorithm to it to track an object. The tracking performance depends on the quality of the likelihood image. We propose two schemes to generate and integrate likelihood images: one based on the discrete AdaBoost (DAB) and the other based on the real AdaBoost (RAB). The DAB scheme uses tuned feature values, whereas RAB estimates class probabilities, to select the features and generate the likelihood images. Experiment results show that the proposed algorithm provides more accurate and reliable tracking results than the conventional mean shift tracking algorithms.