• 한국자동차공학회논문집
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• 제13권4호
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• pp.152-159
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• 2005

The method of induction noise reduction can be classified by using passive control or active control method. However, the passive control method has a demerit to reduce the effect of noise reduction to low frequency (below) 500Hz) range and to be limited in a space of the engine room. Whereas, the active control method can overcome the demerit of passive control method. The algorithm of active control is mostly used in LMS (Least-Mean-Square) algorithm because it can obtain the complex transfer function easily in real-time. Especially, Filtered-X LMS (FXLMS) algorithm is applied to an ANC system. However, the convergence performance of LMS algorithm could not match if the FXLMS algorithm is applied to an active control of the induction noise under rapidly accelerated driving conditions. So, in order to solve the problem in this study, the Moving Bandpass Filter(MBPF) was proposed and implemented. The ANC using MBPF for the reduction of the induction noise shows that more noise reduction as 4dB than without MBPF.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.737-742
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• 2007

The method of the reducing duct noise can be classified by passive and active control techniques. However, passive control has a limited effect of noise reduction at low frequencies (below 500Hz) and is limited by the space. On the other hand, active control can overcome these passive control limitations. The active control technique mostly uses the Least-Mean-Square (LMS) algorithm, because the LMS algorithm can easily obtain the complex transfer function in real-time particularly when the Filtered-X LMS (FXLMS) algorithm is applied to an active noise control (ANC) system. However, the convergence performance of the LMS algorithm decreases slightly so it may delay the convergence time when the FXLMS algorithm is applied to the active control of duct noise. Thus the Co-FXLMS algorithm was developed to improve the control performance in order to solve this problem. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Co-FXLMS algorithm is presented in comparison with the FXLMS algorithm. Simulation results show that active noise control using Co-FXLMS is effective in reducing duct noise.

• 한국BIM학회 논문집
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• 제10권4호
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• pp.32-39
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• 2020

In recent years, the construction industry is getting bigger and more complex, so it is becoming difficult to acquire point cloud data for construction equipments and workers. Point cloud data is measured using a drone and MMS(Mobile Mapping System), and the collected point cloud data is used to create a 3D digital map. In particular, the construction site is located at outdoors and there are many irregular terrains, making it difficult to collect point cloud data. For these reasons, adopting a noise reduction algorithm suitable for the characteristics of the construction industry can affect the improvement of the analysis accuracy of digital maps. This is related to various environments and variables of the construction site. Therefore, this study reviewed and analyzed the existing research and techniques on the noise reduction algorithm. And based on the results of literature review, performance evaluation of major noise reduction algorithms was conducted for digital maps of construction sites. As a result of the performance evaluation in this study, the voxel grid algorithm showed relatively less execution time than the statistical outlier removal algorithm. In addition, analysis results in slope, space, and earth walls of the construction site digital map showed that the voxel grid algorithm was relatively superior to the statistical outlier removal algorithm and that the noise removal performance of voxel grid algorithm was superior and the object preservation ability was also superior. In the future, based on the results reviewed through the performance evaluation of the noise reduction algorithm of this study, we will develop a noise reduction algorithm for 3D point cloud data that reflects the characteristics of the construction site.

• 대한음성학회지:말소리
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• 제48호
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• pp.155-173
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• 2003

This paper proposes a noise reduction-based speech detection method under telephone channel environments. We adopt the AURORA front-end noise reduction algorithm based on the two-stage mel-warped Wiener filter approach as a preprocessor for the frequency domain speech detector. The speech detector utilizes mel filter-bank based useful band energies as its feature parameters. The preprocessor firstly removes the adverse noise components on the incoming noisy speech signals and the speech detector at the next stage detects proper speech regions for the noise-reduced speech signals. Experimental results show that the proposed noise reduction-based speech detection method is very effective in improving not only the performance of the speech detector but also that of the subsequent speech recognizer.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2001년도 추계학술대회논문집 I
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• pp.127-132
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• 2001

A successful design approach for noise barriers should be multidisciplinary because noise reduction goals influence both acoustical and non-acoustical considerations, such as maintenance, safety, physical construction, cost, and visual impact. These various barrier design options are closely related with barrier dimensions. In this study, we have proposed an optimal design method of straight noise barriers using genetic algorithm, providing a barrier having the smallest dimension and achieving the specified noise reduction at a receiver region exposed to the industry and traffic noise, to help a successful barrier design.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 춘계학술대회 논문집
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• pp.326-329
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• 2002

Engine noise is one of the major causes of the interior noise, and so has been studied in various ways in recent days. Recently Intake noise has been extensively studied to reduce the engine noise. Conventional method to reduce the noise is adding several resonators to the induction system. However this causes a reduction of engine output power and an increase of fuel consumption. In this study, the prototype of Active Intake Noise Control System is developed by using the Filtered-x LMS algorithm to reduce the Intake noise during acceleration. Intake noise is more excessively increased when the engine is rapidly accelerated. So, Normalized LMS algorithm is applied to improve the control performance under the rapid acceleration.

• The Journal of the Acoustical Society of Korea
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• 제26권3E호
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• pp.87-96
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• 2007

In this paper we propose a new noise reduction algorithm for stationary and nonstationary noisy environments. Our algorithm classifies the speech and noise signal contributions in time-frequency bins, and is not based on a spectral algorithm or a minimum statistics approach. It relies on calculating the ratio of the standard deviation of the noisy power spectrum in time-frequency bins to its normalized time-frequency average. We show that good quality can be achieved for enhancement speech signal by choosing appropriate values for ${\delta}_t\;and\;{\delta}_f$. The proposed method greatly reduces the noise while providing enhanced speech with lower residual noise and somewhat higher mean opinion score (MOS), background intrusiveness (BAK) and signal distortion (SIG) scores than conventional methods.

• 한국정보통신학회논문지
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• 제12권4호
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• pp.603-610
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• 2008

본 논문에서는 디지털 홀로그램의 획득 및 전송과정에서 발생하는 잡음을 효율적으로 제거하는 알고리듬을 제안한다. 제안한 알고리듬은 디지털 홀로그램을 DCT(Discrete Cosine Transform)하여 주파수영역으로 변환한 후 객체영역과 배경영역으로 분리한다. 이 후, 객체영역은 히스토그램 변환기법을 적용하고 배경영역은 '0'으로 치환하였다. 제안한 알고리듬을 적용한 결과 PSNR이 6dB이상 향상되었다.

• Journal of information and communication convergence engineering
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• 제15권3호
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• pp.175-181
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• 2017

This paper describes the upgrade of an existing wave information retrieval algorithm by employing noise reduction in the pixel domain. Several algorithms for collecting wave information parameters from X-band radar image sequences including the wind field and current velocity have been developed over the past three decades. Using these algorithms, a band-pass filter (BPF) is applied to remove the non-wave contribution from the image spectra after the sea surface current velocity has been computed. However, such BPF designs have been both complex and insufficient in removing undesired components in X-band radar images. For this study, to improve the performance of wave information retrieval, an efficient noise reduction algorithm is incorporated into a regular wave information retrieval process. That is, the proposed algorithm was designed for operation in a more proper manner by effectively removing the undesired components in the pixel domain. Experiment results demonstrate that the proposed algorithm produces very close estimates to the buoy data records under undesirable noise conditions.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 추계학술대회논문집
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• pp.551-554
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• 2005

The method of the reduction of the automotive induction noise can be classified by the method of passive control and the method of active control. However, the passive control method has a demerit to reduce the effect of noise reduction at low frequency (below 500Hz) range and to be limited by a space of the engine room. Whereas, the active control method can overcome the demerit of passive control method. The algorithm of active control is mostly used the LMS (Least-Mean-Square) algorithm because the LMS algorithm can easily obtain the complex transfer function in real-time. Especially, When the Filtered-X LMS (FXLMS) algorithm is applied to an ANC system. However, the convergence performance of LMS algorithm goes bad when the FXLMS algorithm is applied to an active control of the induction noise under rapidly accelerated driving conditions. Thus Normalized FXLMS algorithm was developed to improve the control performance under the rapid acceleration. The advantage of Normalized FXLMS algorithm is that the step size is no longer constant. Instead, it varies with time. But there is one additional practical difficulty that can arise when a nonstationary input is used. If the input is zero for consecutive samples, then the step size becomes unbounded. So, in order to solve this problem. the Correlation FXLMS algorithm was developed. The Correlation FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Correlation FXLMS Is presented in comparison with that of the other FXLMS algorithms based on computer simulations.