• International journal of advanced smart convergence
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• 제8권2호
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• pp.199-203
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• 2019

In this paper, we propose the efficient impulsive noise mitigation scheme for power line communication (PLC) systems in smart grid applications. The proposed scheme estimates the channel impulsive noise information of receiver by applying machine learning. Then, the estimated impulsive noise is updated in data base. In the modulator, the impulsive noise which reduces the PLC performance is effectively mitigated through proposed technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the conventional model. As a result, the proposed noise mitigation improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC systems for smart grid.

• 우주기술과 응용
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• 제3권3호
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• pp.229-238
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• 2023

위성에서 나오는 자기잡음(magnetic noise)을 줄이는 것은 위성탐사에서 자력계의 성능을 향상시키는 중요한 방법 중의 하나이다. 자기잡음(magnetic noise)를 줄이는 방법 중의 하나가 위성에서 붐(boom)을 길게 뽑아내는 것이나, 이것은 높은 비용과 위성 운용 난이도 측면에서 선호하지 않는 방법이다. 그래서 많은 경우, 자기장 데이터 산출 후에 측정 데이터 세트에서 위성 플랫폼의 자기 간섭을 제거하는 것이 널리 사용된다. 본 연구에서는 붐 없이 태양전지판에 2개 그리고 본체 1개씩 각각 설치된 자력계(magnetometer)에서 관측한 자기잡음(magnetic noise)을 제거하는 알고리즘을 소개하고자 한다.

• Journal of information and communication convergence engineering
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• 제9권3호
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• pp.336-340
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• 2011

Many approaches to image restoration are aimed at removing either gauss or impulse noise. This is because both types of degradation processes are distinct in nature, and hence they are easier to manage when considered separately. Nevertheless, it is possible to find them operating on the same image, which produces a hard damage. This happens when an image, already contaminated by Gaussian noise in the image acquisition procedure, undergoes impulsive corruption during its digital transmission. Here we proposed an algorithm first judge the type of the noise according to the difference values of pixel's neighborhood region and impulse noise's characteristic. Then removes the gauss noise by modified weighted mean filter and removes the impulse noise by modified nonlinear filter. The result of computer simulation on test images indicates that the proposed method is superior to traditional filtering algorithms. The proposed method can not only remove mixed noise effectively, but also preserve image details.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2007년도 추계종합학술대회
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• pp.367-370
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• 2007

Both impluse noise and AWGN (additive white Gaussian noise) are easily corrupted into images, during signal transmission and acquisition. Thus, an algorithm for removing both noises is represented in this paper. An impulse noise detection step can effectively separate impulse noise with AWGN, then in the noise filtering step, by using several parameters, not only impulse noise but also AWGN can be reduced. The value of those parameters are automatically changeable when the standard deviation of AWGN, the impulse noise density, and the spatial distances between pixels are different. Results of computer simulations show that the proposed approach performs better than other conventional filters.

• Journal of Welding and Joining
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• 제17권6호
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• pp.100-107
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• 1999

In this paper, we deal with the speckle noise reduction and parameter estimation of ultrasonic NDT(non-destructive test) signals obtained during weld inspection of piping. The overall approach consists of three major steps, namely, speckle noise analysis, proposition of wavelet domain AR(autoregressive) model and flaw detection by proposed model parameter. The data are first processed whereby signals obtained using vertical and angle beam transducer. Correlation properties of speckle noise are then analyzed using multiresolution analysis in wavelet domain. The parameter estimation curve obtained using the proposed model is classified a flaw in weld region where is contaminated by severe speckle noise and also clear flaw signal is obtained through CA-CFAR threshold estimator that is a nonlinear post-processing method for removing the noise from reconstructed ultrasonic signal.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권3호
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• pp.166-173
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• 2005

Preserving image features, edges and details in the process of impulsive noise filtering is an important problem. To avoid image blurring, only corrupted pixels must be filtered. In this paper, we propose an effective impulse noise detection method using Self-Organizing Neural Network(SONN) which applies median filter selectively for removing random-valued impulse noises while preserving image features, edges and details. Using a $3\times3$ window, we obtain useful local features with which impulse noise patterns are classified. SONN is trained with sample image patterns and each pixel pattern is classified by its local information in the image. The results of the experiments with various images which are the noise range of $5-15\%$ show that our method performs better than other methods which use multiple threshold values for impulse noise detection.

• 융합신호처리학회논문지
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• 제13권2호
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• pp.63-69
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• 2012

Noise is regarded as an unwanted component of the image because it significantly reduces image quality. And image is often corrupted by mixed noise. In this paper an efficient modified weighted filter algorithm which combines spatial weight and intensity weight is proposed for removing mixed noise. In the proposed method, the filtering mask is separated into the four sub-windows and the parameters of the weights are confirmed by calculating local standard deviation and the mean of four sub-windows' standard deviations. Considering the spatial information and intensity information, the proposed method has good performance on not only noise elimination but also preservation of details. Simulation results demonstrate that the proposed method performs better than conventional algorithms.

• 디지털융복합연구
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• 제10권10호
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• pp.277-282
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• 2012

음성 인식 시스템은 다양하게 변화하는 환경 잡음에 빠르게 적응할 수 없어서 인식 성능을 저하시키는 요인이 된다. 본 논문에서는 평균 예측 LMS 알고리즘을 이용하여 반향 잡음에 강인하게 하는 방법으로 HMM 학습 모델을 구성하는 방법을 제안하였으며, 변화하는 반향 잡음에 적응하도록 HMM 학습 모델을 구성하여 인식 성능을 평가하였다. 실험 결과 변화하는 환경 잡음을 제거하여 얻은 음성의 SNR은 평균 3.1dB이 향상되었고 인식률은 3.9% 향상되었다.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 1999년도 춘계학술발표논문집 논문집
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• pp.39-44
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• 1999

The purpose of this study was to extract emotional dimensions from Korean adjectives relating to apartment noise. Noise-related 296 Korean adjectives were extracted from a dictionary and three evaluators selected 96 adjectives from those by removing very similar ones in meaning. Two types of 96 7-point scales were conducted to college students for evaluation, whether each adjective describes apartment noise appropriately. From this evaluation, 28 adjectives having above 4.5 points were selected. Again, 8 different types of 7-point scales on 378 adjective pairs(28 x 27/2) were administrated to separate college students to evaluate the degree of similarity between 28 adjectives. Based upon this evaluation, 14 adjectives were finally selected and scores on similarity sere analyzed through two different statistical analyses (Multi-dimensional scale and Cluster analysis). The results showed that three dimensions (displeasure, sensitivity and perceived loudness) exist in peoples' emotional response state to apartment noise. The previous studies have treated annoyance and sensitivity as separate measures to noise. However, this study showed that these two factors were on the same emotional dimension labeled as 'sensitivity' In addition, new dimension, labeled as 'displeasure', was found.

• 한국통신학회논문지
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• 제35권1C호
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• pp.17-23
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• 2010

본 논문에서는 국부 통계 및 일반화된 Gaussian 필터를 이용한 적응 노이즈 제거 방식으로, 인간 시각 시스템 기반의 국부 통계 특성을 이용하여 적응적으로 노이즈 검출하는 기법과 검출된 노이즈를 효과적으로 제거하기 위한 일반화된 Gaussian 필터 기법에 대해 제안한다. 제안방식의 성능을 기존 방식과 비교하여 객관적, 주관적 성능이 우수함을 확인할 수 있었다.