• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.4
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• pp.555-560
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• 2013

This paper first proposes a classification algorithm which detects a voiced, unvoiced, and silence signal using distance measure, logarithm power and root mean square methods at each frame, then a spectrum subtraction algorithm based on a subband filter. The proposed algorithm subtracts spectrums of white noise and street noise from noisy signal based on the subband filter at each frame. In this experiment, experimental results of the proposed spectrum subtraction algorithm demonstrate using the speech and noise data of Aurora-2 database. Based on measuring the speech-to-noise ratio (SNR), experiments confirm that the proposed algorithm is effective for the speech by contaminated the noise. From the experiments, the improvement in the output SNR values was approximately 2.1 dB and 1.91 dB better for white noise and street noise, respectively.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2335-2337
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• 2004

Kalman filter를 필터링에 적용할 때에 센서의 아날로그 신호에 들어오는 측정값의 잡음은 Gaussian 확률분포를 갖는다고 가정한다. 그러나 Kalman filter를 digital 컴퓨터에 적용할 경우에는 analog-to-digital converter에서 측정값의 잡음이외에도 quantization 잡음이 존재하며 본 논문에서는 이러한 경우에 quantization 영향이 Kalman filter 알고리듬에 미치는 영향을 수치적으로 분석하여 quantization을 Kalman filter 구현에 고려해야 될 사항으로 분류하고자 한다.

• Journal of Information Technology Application
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• v.3 no.2
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• pp.133-147
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• 2001

This paper proposed a new algorithm for binarization and removal of noises in color images with characters and pictures. Binarization was performed by threshold which had computed with color-relationship relative to the number of pixel in background and character candidates and pre-threshold for dividing of background and character candidates in input images. The pre-threshold has been computed by the histogram of R, G, B In respect of the images, while background and character candidates of input images are divided by the above pre-threshold. As it is possible that threshold can be dynamically decided by the quantity of the noises, and the character images are maintained and the noises are removed to the maximum. And, in this study, we made the noise pattern table as a result of analysis in noise pattern included in the various color images aiming at removal of the noises from the Images. Noises included in the images can figure out Distribution by way of the noise pattern table and pattern matching itself. And then this Distribution classified difficulty of noises included in the images into the three categories. As removal of noises in the images is processed through different procedure according to the its classified difficulties, time required for process was reduced and efficiency of noise removal was improved. As a result of recognition experiments in respect of extracted characters in color images by way of the proposed algorithm, we conformed that the proposed algorithm is useful in a sense that it obtained the recognition rate in general documents without colors and pictures to the same level.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2265-2271
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• 2014

In modern society digital equipments that are related with various hardware and software are popularized, and digital images are widely applied in the field of production and scientific research. In general, however, images are degraded by the noise in the process of transmission and storage. In this paper, to reduce the influence of mixed noises, the algorithm in which noises in the space area are classified into impulse noise and Gaussian noise and this is processed by applying weighted value, while that is processed by modified nonlinear filter is proposed. And the excellence of the proposed algorithm is judged by PSNR(peak signal to noise ratio).

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.48-55
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• 2001

A novel method to reduce additive non-stationary noise is proposed. The method requires neither the information about noise nor the estimate of the noise statistics from any pause regions. The enhancement is performed on a band-by-band basis for each time frame. Based on both the decision on whether a particular band in a frame is speech or noise dominant and the masking property of the human auditory system, an appropriate amount of noise is reduced using spectral subtraction. The proposed method was tested on various noisy conditions (car noise, Fl6 noise, white Gaussian noise, pink noise, tank noise and babble noise) and on the basis of comparing segmental SNR with spectral subtraction method and visually inspecting the enhanced spectrograms and listening to the enhanced speech, the method was able to effectively reduce various noise while minimizing distortion to speech.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.512-514
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• 2018

최근 보안 버그의 중요성이 증가함에 따라, 버그 리포트 중 보안과 관련된 리포트를 빠르게 분류하는 기술이 필요하다. 기존 기술들은 버그 리포트의 단어들을 가지고 기계학습을 위한 훈련 데이터를 생성한다. 이 때 기계학습에 잡음이 발생하면 성능을 떨어뜨릴 수 있다. 이를 보완하기 위해 본 연구에서는 감정 단어를 활용하여 잡음을 줄인 보안 버그리포트를 자동으로 식별하는 기계학습기반 기술을 제안한다. 제안 기술은 기계학습 시 사용되는 훈련 데이터의 품질을 높이기 위해 감정 단어를 활용한다. 실험 결과 감정 단어를 활용했을 때 기존 기술 대비 보안 버그를 분류하는 정확도가 3.03% 향상되었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.261-263
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• 2009

본 논문에서는 퍼지 기법을 이용하여 구름의 종류를 분석하는 방법을 제안한다. 제안된 방법은 각각 영상에 대해 R채널의 임계치를 적용하여 잡음을 제거하며, 잡음 영역이 제거된 각각의 근적외 영상과 가시 영상의 반사 특성 및 근적외 영상과 적외 영상의 방출 특성의 특징을 구한 후, 각각의 임계치를 적용하여 1차적으로 구름을 판별한다. 1차적으로 구름 판별에서 제외된 영역에 대해서는 가시 및 적외 영상의 R 채널 값을 퍼지 기법에 적용하여 2차적으로 구름의 종류를 판별한다. 1차적으로 판별된 구름 영역과 2차적으로 판별된 구름 영역을 합성하여 최종 구름 영역을 도출한다. 제안된 방법을 실험한 결과, 기존의 구름 분류 방법보다 제안된 방법이 구름 분류의 성능이 개선된 것을 확인하였다.

• Journal of IKEEE
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• v.22 no.4
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• pp.1140-1146
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• 2018

Recently, a methodology for analyzing complex bio-signals using a deep learning model has emerged among studies that recognize human emotions. At this time, the accuracy of emotion classification may be changed depending on the evaluation method and reliability depending on the kind of data to be learned. In the case of biological signals, the reliability of data is determined according to the noise ratio, so that the noise detection method is as important as that. Also, according to the methodology for defining emotions, appropriate emotional evaluation methods will be needed. In this paper, we propose a wavelet -based noise threshold setting algorithm for verifying the reliability of data for multimodal bio-signal data labeled Valence and Arousal and a method for improving the emotion recognition rate by weighting the evaluation data. After extracting the wavelet component of the signal using the wavelet transform, the distortion and kurtosis of the component are obtained, the noise is detected at the threshold calculated by the hampel identifier, and the training data is selected considering the noise ratio of the original signal. In addition, weighting is applied to the overall evaluation of the emotion recognition rate using the euclidean distance from the median value of the Valence-Arousal plane when classifying emotional data. To verify the proposed algorithm, we use ASCERTAIN data set to observe the degree of emotion recognition rate improvement.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.187-196
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• 2011

Induction motors play a vital role in aeronautical and automotive industries so that many researchers have studied on developing a fault detection and classification system of an induction motor to minimize economical damage caused by its fault. With this reason, this paper extracts robust feature vectors from the normal/abnormal vibration signals of the induction motor in noise circumstance: partial autocorrelation (PARCOR) coefficient, log spectrum powers (LSP), cepstrum coefficients mean (CCM), and mel-frequency cepstrum coefficient (MFCC). Then, we classified different types of faults of the induction motor by using the extracted feature vectors as inputs of a neural network. To find optimal feature vectors, this paper evaluated classification performance with 2 to 20 different feature vectors. Experimental results showed that five to six features were good enough to give almost 100% classification accuracy except features by CCM. Furthermore, we considered that vibration signals could include noise components caused by surroundings. Thus, we added white Gaussian noise to original vibration signals, and then evaluated classification performance. The evaluation results yielded that LSP was the most robust in noise circumstance, then PARCOR and MFCC followed by LSP, respectively.

• The Journal of the Acoustical Society of Korea
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• v.33 no.1
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• pp.75-80
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• 2014

The quality of real-time Voice over Internet Protocol (VoIP) network is affected by network impariments such as delays, jitters, and packet loss. This paper proposes a packet loss concealment algorithm based on voice classification for enhancing VoIP speech quality. In the proposed method, arriving packets are classified by an adaptive thresholding approach based on the analysis of multiple features of short signal segments. The excellent classification results are used in the packet loss concealment. Additionally, linear prediction-based packet loss concealment delivers high voice quality by alleviating the metallic artifacts due to concealing consecutive packet loss or recovering lost packet.