Noise Component Method for Airframe Noise

Abstract. Noise studies of planar thin-film Ni-P resistors made in/on Printed Circuit Boards, both covered with two different types of cladding or uncladded have been described. The resistors have been made of the resistive-conductive-material (Ohmega-Ply©) of 100 Ώ/sq. Noise of the selected pairs of samples has been measured in the DC resistance bridge with a transformer as the first stage in a signal path. 1/f noise caused by resistance fluctuations has been found to be the main noise component. Parameters describing noise properties of the resistors have been calculated and then compared with the parameters of other previously studied thin- and thick-film resistive materials.

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Intervention Modeling

10.1093/oso/9780190661557.003.0005 ◽

2017 ◽

Author(s):

Richard McCleary ◽

David McDowall ◽

Bradley J. Bartos

Keyword(s):

Time Series ◽

Autocorrelation Function ◽

Moving Average ◽

Intervention Model ◽

Noise Component ◽

Autoregressive Integrated Moving Average ◽

Noise Function ◽

Modeling Strategy ◽

The Impact ◽

True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

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Modified GSC Method to Reduce the Distortion of the Enhanced Speech Signal Using Cross-Correlation and Sidelobe Neutralization

Applied Sciences ◽

10.3390/app11146288 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6288

Author(s):

Hang Su ◽

Chang-Myung Lee

Keyword(s):

Speech Signal ◽

Output Signal ◽

Cross Correlation ◽

Acoustic Noise ◽

Audio Signal ◽

Noise Signal ◽

Lms Algorithm ◽

Least Mean Square ◽

Experiment Data ◽

Noise Component

The generalized sidelobe canceller (GSC) method is a common algorithm to enhance audio signals using a microphone array. Distortion of the enhanced audio signal consists of two parts: the residual acoustic noise and the distortion of the desired audio signal, which means that the desired audio signal is damaged. This paper proposes a modified GSC method to reduce both kinds of distortion when the desired audio signal is a non-stationary speech signal. First, the cross-correlation coefficient between the canceling signal and the error signal of the least mean square (LMS) algorithm was added to the adaptive process of the GSC method to reduce the distortion of the enhanced signal while the energy of the desired signal frame was increased suddenly. The sidelobe pattern of beamforming was then presented to estimate the noise signal in the beamforming output signal of the GSC method. The noise component of the beamforming output signal was decreased by subtracting the estimated noise signal to improve the denoising performance of the GSC method. Finally, the GSC-SN-MCC method was proposed by merging the above two methods. The experiment was performed in an anechoic chamber to validate the proposed method in various SNR conditions. Furthermore, the simulated calculation with inaccurate noise directions was conducted based on the experiment data to inspect the robustness of the proposed method to the error of the estimated noise direction. The experiment data and calculation results indicated that the proposed method could reduce the distortion effectively under various SNR conditions and would not cause more distortion if the estimated noise direction is far from the actual noise direction.

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Advanced multivariate cumulative sum control charts based on principal component method with application

Quality and Reliability Engineering International ◽

10.1002/qre.2889 ◽

2021 ◽

Author(s):

Muhammad Riaz ◽

Babar Zaman ◽

Rashid Mehmood ◽

Nasir Abbas ◽

Mu'azu Abujiya

Keyword(s):

Control Charts ◽

Principal Component ◽

Cumulative Sum ◽

Principal Component Method ◽

Component Method ◽

Cumulative Sum Control

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Interior Sound Quality Prediction of Pure Electric Vehicles Based on Transfer Path Synthesis

Applied Sciences ◽

10.3390/app11104385 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4385

Author(s):

Kun Qian ◽

Zhichao Hou ◽

Jie Liang ◽

Ruixue Liu ◽

Dengke Sun

Keyword(s):

Electric Vehicles ◽

Sound Quality ◽

Interior Noise ◽

Experimental Comparison ◽

Noise Component ◽

Acoustic Environment ◽

Evaluation Indexes ◽

Transfer Path ◽

New Ideas ◽

Path Synthesis

The interior sound quality (SQ) of pure electric vehicles (PEVs) has become an important consideration for users purchasing vehicles. At present, it is insufficient to take the sound pressure level as the interior acoustics design index of PEVs. Transfer path analysis (TPA) and transfer path synthesis (TPS) that take the SQ of interior noise as the improvement target remains in the preliminary exploration stage. In this paper, objective psychoacoustic parameters of SQ were taken as evaluation indexes of interior PEV noise. A virtual interior SQ synthesis model was designed on the basis of TPA and TPS, which combines experimentation and simulation. The SQ synthesis model demonstrates each noise component contribution in a PEV by new SQ separation technology. First, the interior noise transfer path and noise source of the PEV were determined in a synthesis analysis method of the interior PEV noise. Second, on the basis of the composition mechanism of interior noise and the basic principle of TPA, the excitation signal and transfer function of each interior noise path in the PEV were tested. On the basis of TPS, the interior SQ synthesis model of PEV was then established. Finally, the accuracy of the prediction model was verified in simulation and experimental comparison studies on the psychoacoustic objective parameters of SQ. The SQ objective parameter value of each transfer path was quantified by using contribution analysis. The results are expected to improve the comfort of the interior acoustic environment and enhance the competitiveness of vehicle products. They also provide an effective reference and new ideas for the development of interior SQ in PEVs.

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