• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.2
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• pp.100-105
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• 2022

This paper applies the arc detection algorithm to prevent the false detection in photo voltaic series arc detection circuit, which is required not only to detect the series arc quickly, but also not falsely detect the arc for the non-arc noise. For this purpose, this study proposes a rapid and preventive false detection method of single peak noise and short noise signals. First, to prevent false detection by single peak noise, Discrete wavelet transform (DWT)-based characteristic parameters are applied to determine the shape and the amplitude of the noise. In addition, arc fault detection within a few milliseconds is performed with the DWT iterative algorithm to quickly prevent false detection for short noise signals, considering the continuity of serial arc noise. Thus, the method operates not only to detect series arc, but also to avoid false arc detection for peak and short noises. The proposed algorithm is applied to real-time serial arc detection circuit based on the TMS320F28335 DSP. The serial arc detection and peak noise filtering performances are verified in the built simulated arc test facility. Furthermore, the filtering performance of short noise generated through DC switch operation is confirmed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.749-754
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• 2003

In this paper, we introduce the measurement of the underwater noise with 32channel hydrophone array of Samsung CAvitation Tunnel (SCAT) and the detection technique of noise sources by using the beam-forming method. Measurement and way signal Processing under fluid flow are essential works for the underwater acoustics, especially for the detection of noise sources. As the acoustic impedance of the water is relatively high and the tunnel is an enclosed system, we have to consider the interaction between tunnel and water together with the reflection of noise in the beam-forming technique. Also, for a hydrophone array system that is fixed on one side of tunnel wall as done in SCAT is liable to suffer from some limitations in the detection of the noise sources with the array, we discuss these limitations particularly on the frequency range and spacing of noise sources.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.11
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• pp.138-147
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• 1997

The ultrasonic images are corrupted by the granular pattern noise - a speckle noise. The speckle exist in the type of coherent imaging systems, and the speckle is the signal independent and multiplicative noise. In this paepr, we derive two filters using the gradient and symmetry. One is a noise suppression filter which removes noise while preserves the edges. It is named the ASRF-GS (Adaptive Speckle Removal Filer - Gradient and Symmetry). And the other is a edge detection filter which obtains the thin edge map, called the EDUGS(Edge Detection Using Gradient and Symmetry). The performance of the proposed noise suppression filter is evaluated by the IMPV(SNR improvement) and the Speckle Index(SI), and the perforamnce of the edge detection is evaluated by the edge detection error rate. According to the evaluated method, The SI reduced about 0.035, The IMPV improved about 1.265(dB), and the edge detection error rate is about 17.5%.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.3
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• pp.173-178
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• 2008

In this paper, we present a new impulsive noise detection technique. To remove the impulse noise without detail loss, only corrupted pixels must be filtered. In order to identify the corrupted pixels, a new impulse detector based on rank and value estimations of the current pixel is proposed. Based on the rank and value estimations of the current pixel, the new proposed method provides excellent statistics for detecting an impulse noise while reducing the probability of detecting image details as impulses. The proposed detection is efficient and can be used with any noise removal filter. Simulation results show that the proposed method significantly outperforms many other well-known detection techniques in terms of image restoration and noise detection.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.3
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• pp.41-48
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• 2012

In this paper, we propose a method of edge detection for noisy image. The proposed method uses a progressive filter for noise reduction and a Sobel operator for edge detection. The progressive filter combines a median filter and a modified rational filter. The proposed method for noise reduction adjusts rational filter direction according to an edge in the image which is obtained by median filtering. Our method effectively attenuates the noise while preserving the image details. Edge detection is performed by a Sobel operator. This operator can be implemented by integer operation and is therefore relatively fast. Our proposed method not only preserves edge, but also reduces noise in uniform region. Thus, edge detection is well performed. Our proposed method could improve results using further developed Sobel operator. Experimental results show that our proposed method has better edge detection with correct positions than those by existing median and rational filtering methods for noisy image.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

• Smart Structures and Systems
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• v.18 no.5
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• pp.1063-1085
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• 2016

Taking advantage of the high sensitivity and long-distance detection capability of acoustic emission (AE) technique, this paper focuses on the crack detection in rail head, which is one of the most vulnerable parts of rail track. The AE source location and noise cancellation were studied on the basis of practical rail profile, material and operational noise. In order to simulate the actual AE events of rail head cracks, field tests were carried out to acquire the AE waves induced by pencil lead break (PLB) and operational noise of the railway system. Wavelet transform (WT) was first utilized to investigate the time-frequency characteristics and dispersion phenomena of AE waves. Here, the optimal mother wavelet was selected by minimizing the Shannon entropy of wavelet coefficients. Regarding the obvious dispersion of AE waves propagating along the rail head and the high operational noise, the wavelet transform-based modal analysis location (WTMAL) method was then proposed to locate the AE sources (i.e. simulated cracks) respectively for the PLB-induced AE signals with and without operational noise. For those AE signals inundated with operational noise, the Hilbert transform (HT)-based noise cancellation method was employed to improve the signal-to-noise ratio (SNR). Finally, the experimental results demonstrated that the proposed crack detection strategy could locate PLB-simulated AE sources effectively in the rail head even at high operational noise level, highlighting its potential for field application.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.11a
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• pp.179-181
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• 2009

In this paper, we propose an adaptive noise detection and modified Gaussian removal algorithm using local statistics for impulse noise. In order to determine constraints for noise detection, the local mean, variance, and maximum values are used. In addition, a modified Gaussian filter that integrates the tuning parameter to remove the detected noises. Experimental results show that our method is significantly better than a number of existing techniques in terms of image restoration and noise detection.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.856-859
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• 1997

In this paper we treat the application of fault detection method in DC motor having both model mismatch and noise problems. A fault detection method presented by Kwon et al. (1994) for SISO systems has been here experimented. The model mismatch includes here linearization error as well as undermodelling. Comparisons are made with the real plant, DC motor. The experimental result of robust fault detection method is shown to have good performance via with the alternative fault detection method which do not account noise.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.281-286
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• 2013

In this paper, we propose a noise estimation method using edge detection. It is a filter-based noise estimation method. Edge detection is to exclude structures and details which have an effect on the noise estimation. To detect edge, we use a modified rational filter which is robust to details of images. The proposed noise estimation method is more efficiently applied to noise estimation in various types of images and has better results than those of conventional filter-based noise estimation methods.