• Journal of Korean Society of Disaster and Security
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• v.16 no.3
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• pp.45-56
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• 2023

In this study, the application of ANC (Active Noise Control) technology to address inter-floor noise was explored. To achieve this, an ANC system was developed to manage the heavy impact sound within the frequency range of 40 to 500 Hz. The ANC system utilized an adaptive filter employing a feedforward approach based on the Fx-LMS algorithm. To set up the ANC system, a comprehensive analysis of various variables within the system was performed using computational simulations. This process enabled the identification of optimal filter settings and system configuration arrangements. In addition, the ANC system was implemented in the inter-floor noise test room at the Korea Conformity Laboratories (KCL). Through a certified standard testing procedure, it was confirmed that the ANC system led to a 4 dB reduction in inter-floor noise when the system was activated compared to when it was turned off. The results of this study indicate that the developed ANC system has an effect significant enough to elevate the rating criteria by one level for heavy impact sound.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3242-3265
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• 2023

Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.975-981
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• 2007

In this paper, we propose a spatially adaptive image restoration algorithm using local and statistics and estimated noise. The ratio of local mean, variance, and maximum values with different window size is used to constrain the solution space, and these parameters are computed at each iteration step using partially restored image. In addition, the additive noise estimated from partially restored image and the local constraints are used to determine a parameter for controlling the degree of local smoothness on the solution. The resulting iterative algorithm exhibits increased convergence speed when compared to the non-adaptive algorithm. In addition, a smooth solution with a controlled degree of smoothness is obtained without a prior knowledge about the noise. Experimental results demonstrate that the proposed algorithm requires the similar iteration number to converge, but there is the improvement of SNR more than 0.2 dB comparing to the previous approach.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.949-958
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• 2018

A voice recognition control system is a system for controlling a peripheral device by recognizing a voice. Recently, a voice recognition control system have been applied not only to smart devices but also to various environments ranging from IoT(: Internet of Things), robots, and vehicles. In such a voice recognition control system, the recognition rate is lowered due to the ambient noise in addition to the voice of the user. In this paper, we propose a dual channel acoustic beamforming hardware architecture for MEMS(: Microelectromechanical Systems) microphones to eliminate ambient noise in addition to user's voice. And the proposed hardware architecture is designed as ASIC(: Application-Specific Integrated Circuit) using TowerJazz $0.18{\mu}m$ CMOS(: Complementary Metal-Oxide Semiconductor) technology. The designed dual channel acoustic beamforming ASIC has a die size of $48mm^2$, and the directivity index of the user's voice were measured to be 4.233㏈.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.41-46
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• 2023

It is very important to meet the maximum detection range in a radar system. In order to meet the maximum detection Range, the sensitivity of the received signal of the radar system must be high. In addition, the dynamic range should be wide in the radar signal processing board. To meet these requirements, the signal processing board must be designed to be robust against external and internal noise. In particular, a design is required to minimize the effect of noise generated by various switching circuits inside the board on the received radar signal. In this paper, we derive the requirements of the signal processor board to meet the radar system performance and describe the design to meet the derived requirements. In addition, the EMC design to minimize the influence of noise input from the outside or generated from the inside is described. Confirm the secured performance through the test of the manufactured board.

• International Journal of Molecular Medicine
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• v.44 no.4
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• pp.1473-1483
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• 2019

One of the primary theories of the pathogenesis of tinnitus involves maladaptive auditory-somatosensory plasticity in the dorsal cochlear nucleus (DCN), which is assumed to be due to axonal sprouting. Although a disrupted balance between auditory and somatosensory inputs may occur following hearing damage and may induce tinnitus, examination of this phenomenon employed a model of hearing damage that does not account for the causal relationship between these changes and tinnitus. The present study aimed to investigate changes in auditory-somatosensory innervation and the role that axonal sprouting serves in this process by comparing results between animals with and without tinnitus. Rats were exposed to a noise-inducing temporary threshold shift and were subsequently divided into tinnitus and non-tinnitus groups based on the results of gap prepulse inhibition of the acoustic startle reflex. DCNs were collected from rats divided into three sub-groups according to the number of weeks (1, 2 or 3) following noise exposure, and the protein levels of vesicular glutamate transporter 1 (VGLUT1), which is associated with auditory input to the DCN, and VGLUT2, which is in turn primarily associated with somatosensory inputs, were assessed. In addition, factors related to axonal sprouting, including growth-associated protein 43 (GAP43), postsynaptic density protein 95, synaptophysin, α-thalassemia/mental retardation syndrome X-linked homolog (ATRX), growth differentiation factor 10 (GDF10), and leucine-rich repeat and immunoglobulin domain-containing 1, were measured by western blot analyses. Compared to the non-tinnitus group, the tinnitus group exhibited a significant decrease in VGLUT1 at 1 week and a significant increase in VGLUT2 at 3 weeks post-exposure. In addition, rats in the tinnitus group exhibited significant increases in GAP43 and GDF10 protein expression levels in their DCN at 3 weeks following noise exposure. Results from the present study provided further evidence that changes in the neural input distribution to the DCN may cause tinnitus and that axonal sprouting underlies these alterations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.728-735
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• 2017

With the proliferation of digital devices, the devices have generated considerable additive white Gaussian noise while acquiring digital images. The most well-known denoising methods focused on eliminating the noise, so detailed components that include image information were removed proportionally while eliminating the image noise. The proposed algorithm provides a method that preserves the details and effectively removes the noise. In this proposed method, the goal is to separate meaningful detail information in image noise environment using the edge strength and edge connectivity. Consequently, even as the noise level increases, it shows denoising results better than the other benchmark methods because proposed method extracts the connected detail component information. In addition, the proposed method effectively eliminated the noise for various noise levels; compared to the benchmark algorithms, the proposed algorithm shows a highly structural similarity index(SSIM) value and peak signal-to-noise ratio(PSNR) value, respectively. As shown the result of high SSIMs, it was confirmed that the SSIMs of the denoising results includes a human visual system(HVS).

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.763-768
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• 2011

Image denoising is a lively research field. Today the researches are focus on the wavelet domain especially using wavelet threshold method. We proposed an adaptive threshold method which considering the characteristic of different sub-band, the method is adaptive to each sub-band. Experiment results show that the proposed method extracts white Gaussian noise from original signals in each step scale and eliminates the noise effectively. In addition, the method also preserves the detail information of the original image, obtaining superior quality image with higher peak signal to noise ratio(PSNR).

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.49-56
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• 2004

Model updating is a very active research field, in which significant efforts has been invested in recent years. Model updating methodologies are invariably successful when used on noise-free simulated data, but tend to be unpredictable when presented with real experimental data that are-unavoidably-corrupted with uncorrected noise content. In this paper, Reanalysis using frequency response functions for correlating and updating dynamic systems is presented. A transformation matrix is obtained from the relationship between the complex and the normal frequency response functions of a structure. The transformation matrix is employed to calculate the modified damping matrix of the system. The modified mass and stiffness matrices are identified from the normal frequency response functions by using the least squares method. One simulated system is employed to illustrate the applicability of the proposed method. The result indicate that the damping matrix of correlated finite element model can be identified accurately by the proposed method. In addition, the robustness of the new approach uniformly distributed measurement noise Is also addressed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.87-94
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• 2016

In this paper, we discuss a gait representation based on the width of silhouette in terms of discriminative power and robustness against the noise in silhouette image for gait recognition. Its sensitivity to the noise in silhouette image are rigorously analyzed using probabilistic noisy silhouette model. In addition, we develop a gait recognition system using width representation and identify subjects using the decision level fusion based on majority voting. Experiments on CASIA gait dataset A and the SOTON gait database demonstrate the recognition performance with respect to the noise level added to the silhouette image.