• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2577-2579
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• 2004

중성자를 사용한 비파괴검사는 X선을 사용하는 것에 비해 상대적으로 뛰어난 투과력을 가지고 있다. 하지만 중성자와 원자핵의 반응에 의한 scattering 효과와 중성자 빔의 uniformity부족 등으로 인한 영상의 왜곡이 발생한다. 본 논문에서는 이런 중성자 영상의 왜곡을 보정하기 위한 영상처리 알고리즘을 연구하고 연구된 알고리즘을 토대로 영상처리 소프트웨어를 구현하였다. 먼저 히스토그램 연산을 이용하여 영상의 밝기와 대비를 조절하여 영상의 가시성을 높였고, 필터링 기법을 통하여 영상이 가지는 임펄스 잡음과 가우시안 잡음을 순차적으로 제거하였다. 마지막으로 가우시안 잡음 제거시 부가적으로 발생한 영상의 흐려짐을 보완하여 보다 향상된 질의 영상을 얻게 되었다. 또한 Visual C++을 사용하여 위의 알고리즘들을 GUI 환경의 프로그램으로 구현하였다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.11 no.5
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• pp.723-730
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• 2000

In this paper, we perform a performance analysis of serial acquisition scheme using AWGN rejection filter in Rayleigh fading channel and propose robust acquisition scheme using Reference filter, which is utilized to vary threshold at fading rate, in Rayleigh fading channel. AWGN rejection filter is utilized to evaluate running average for compensating channel gain. The JAKE model, which a channel model, is used for the analysis. The simulation result shows that the mean acquisition time of the proposed acquisition system is minimized than acquisition system using AWGN rejection filter and serial-search acquisition system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.10
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• pp.2261-2268
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• 2010

The information captured by imaging devices such as CCD or CIS may contain external noises through the processes of passing signals or storing images. In this paper, we propose a Modified Sigma Filter (MSF) algorithm to reduce such noises. In experiment, we verified that our MSF algorithm showed better performance in PSNR and 1D plot of simulation results compared with Gaussian Filter (GF), Local Sigma Filter (LSF). Tested images include random Gaussian Noises.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.8
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• pp.2001-2007
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• 2014

Image signal related technology has been developing via various display equipment development and popularization of contents. However, errors occur in these image contents due to addition of excess noise from several cause during the process of general image signal data processing, transmission and storage. In terms of noise added to the image content, there are various types in accordance with cause of occurrence and form, and it is typically impulse noise, gaussian noise and complex noise which is composed of two types of overlapping noise. In this paper, complex algorithm is suggested in order to lessen the effect of mixed noise added to the image content by putting it through noise judgement process and categorizing each into impulse and gaussian noise and processing them separately. And in order to demonstrate the superiority of the suggested algorithm, PSIN(peak signal to noise ratio) was used as the standard of judgement.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.759-770
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• 2009

A problem of separating signals from noises is considered, when they are randomly mixed in the observation. It is assumed that the noise follows a Gaussian distribution and the signal follows a Gamma distribution, thus the underlying distribution of an observation will be a mixture of Gaussian and Gamma distributions. The parameters of the mixture model will be estimated from the EM algorithm. Then the signals and noises will be classified by a fixed threshold approach based on multiple testing using positive false discovery rate and Bayes error. The proposed method is applied to a real optical emission spectroscopy data for the quantitative analysis of inclusions. A simulation is carried out to compare the performance with the existing method using 3 sigma rule.

• Journal of Advanced Navigation Technology
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• v.13 no.4
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• pp.482-489
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• 2009

This paper proposes a CW(Continuous-Wave) bio-radar applying a new adaptive noise canceller based on ALE(Adaptive Line Enhancer) which can remove the Gaussian noise and system noise. Recently the research works on this CW bio-radar which can be used to detect heartbeat and respiration are advanced by the university and research facility. Although the researches describe CW bio-radar not only is vulnerable for the Gaussian noise but also has a disadvantage of decreasing the heart-rate accuracy due to the noise, the researches do not demonstrate the effective method for removing the noise component in a baseband signal. In this paper, a CW bio-radar applying the new adaptive noise canceller based on ALE which can remove the noise component is proposed. This paper compares and analyzes the performance for increasing the heart-rate accuracy according to removing the Gaussian noise and system noise in the baseband signal through the quadrature receiver which can alleviate the demodulation sensitivity to target position.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.727-734
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• 2014

Digital processing technologies are being studied in various areas of image compression, recognition and recovery. However, image deterioration still occurs due to the noises in the process of image acquisition, storage and transmission. Generally in the typical noises which are included in the images, there are Gaussian noise and the mixed noise where the Gaussian noise and impulse noise are overlapped and in order to remove these noises, various researches are being executed. In order to preserve the edge and effectively remove mixed noises, image recovery filter algorithm was suggested in this study which sets and processes the adaptive weight using the median values and average values after noise judgment. Additionally, existing methods were compared through simulations and PSNR(peak signal to noise ratio) was used as a judgment standard.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1047-1054
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• 2015

This paper presents an acoustic model transform method using untranscribed speech database for improved speech recognition. In the presented model transform method, an adapted GMM is obtained by employing the conventional adaptation method, and the most similar Gaussian component is selected from the adapted GMM. The bias vector between the mean vectors of the clean GMM and the adapted GMM is used for updating the mean vector of HMM. The presented GAMT combined with MAP or MLLR brings improved speech recognition performance in car noise and speech babble conditions, compared to singly-used MAP or MLLR respectively. The experimental results show that the presented model transform method effectively utilizes untranscribed speech database for acoustic model adaptation in order to increase speech recognition accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3A
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• pp.272-280
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• 2009

Measurement of the background radio noise is very important process being used in survey of radio noise environment, calculating the threshold level for the frequency occupancy measurement and so forth. First step of background radio noise measurement is to select the sample sub-band which is mostly dominated by the background white Gaussian noise (WGN) within the target band. The second step is to carry out the main measurement of radio noise on this selected sample sub-band for the representative value of the noise power. In this paper, a method for selection of sample sub-band for the effective background radio noise measurement using SVD is proposed under the assumption that background radio noise is WGN. The performance of the proposed method is compared with that of the APD method which is widely used for the same purpose. Simulation results are shown to demonstrate the high performance of the proposed method in comparison with the existing APD method.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.87-92
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• 2023

With the continuous development of the speech recognition system, the recognition rate for speech has developed rapidly, but it has a disadvantage in that it cannot accurately recognize the voice due to the noise generated by mixing various voices with the noise in the use environment. In order to increase the vocabulary recognition rate when processing speech with environmental noise, noise must be removed. Even in the existing HMM, CHMM, GMM, and DNN applied with AI models, unexpected noise occurs or quantization noise is basically added to the digital signal. When this happens, the source signal is altered or corrupted, which lowers the recognition rate. To solve this problem, each voice In order to efficiently extract the features of the speech signal for the frame, the MFCC was improved and processed. To remove the noise from the speech signal, the noise removal method using the Gaussian model applied noise deviation estimation was improved and applied. The performance evaluation of the proposed model was processed using a cross-correlation coefficient to evaluate the accuracy of speech. As a result of evaluating the recognition rate of the proposed method, it was confirmed that the difference in the average value of the correlation coefficient was improved by 0.53 dB.