• International journal of advanced smart convergence
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• v.8 no.2
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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.

• Journal of Space Technology and Applications
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• v.3 no.3
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• pp.229-238
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• 2023

One of the important ways to improve the performance of magnetometers in satellite exploration is to reduce magnetic noise from satellites. One of the methods to decrease magnetic noise is by extending the satellite boom. However, this approach is often not preferred due to its high cost and operational considerations. Therefore, in many cases, removing interference from the satellite platform in the measured dataset is widely utilized after data acquisition. In this study, we would like to introduce an algorithm for removing magnetic noise observed from magnetometers installed on two solar panels and one main body without a boom.

• Journal of information and communication convergence engineering
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• v.9 no.3
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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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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• v.17 no.6
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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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.3
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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.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.2
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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.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.277-282
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• 2012

The speech recognition system can not quickly adapt to varied environmental noise factors that degrade the performance of recognition. In this paper, the echo noise robust HMM learning model using average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise HMM learning model consists of the recognition performance is evaluated. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 3.1dB, recognition rate improved as 3.9%.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1999.03a
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1C
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• pp.17-23
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• 2010

In this paper, we present an adaptive noise removal method using local statistics and generalized Gaussian filter. we propose a generalized Gaussian filter for removing noise effectively and detecting noise adaptively using local statistics based human visual system. The simulation results show the objective and subjective capabilities of the proposed algorithm.