Analysis on Influence of Vacuum Chuck on Machined Surface of KDP Crystal Using Power Spectral Density

2011 ◽  
Vol 48 (9) ◽  
pp. 092201
Author(s):  
陈珊珊 Chen Shanshan ◽  
徐敏 Xu Min
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Machined Surface ◽  
Kdp Crystal ◽  
Power Spectral

Evaluation of WEDM Surface Quality

2010 ◽  
Vol 97-101 ◽  
pp. 4080-4083 ◽  
Author(s):  
Lei Geng ◽  
Hua Yan Zhong
Keyword(s):  
Surface Roughness ◽  
Fractal Dimension ◽  
Spectral Density ◽  
Surface Topography ◽  
Surface Quality ◽  
Power Spectral Density ◽  
Evaluation Method ◽  
Fractal Theory ◽  
Machined Surface ◽  
Power Spectral

The formation of WEDM surface is a complicated process. There are many factors which make machined surface topography have the characteristics of complex and irregular, and impact using performance of parts. The work investigated microscopic features of the WEDM surface topography based on power spectral density and fractal theory, and proposed power spectral density evaluation method of the WEDM surface. The fractal dimension of the WEDM surface was calculated by structure function method. The physical meaning of the fractal dimension of the WEDM surface was described. The result shows that topography of the WEDM surface exhibits strong fractal characteristics within a certain scale. The processing parameters and pulse power performance will affect the fractal dimension D. The fractal dimension D has a certain relationship with the surface roughness Ra. It is more reasonable to use the fractal dimension D as well as the surface roughness Ra together to evaluate WEDM surface quality.

Fuzzy Modeling of Multi-Degree-of-Freedom Power Spectral Density Systems

2009 ◽  
Vol 2 (1) ◽  
pp. 40-47
Author(s):  
Montasser Tahat ◽  
Hussien Al-Wedyan ◽  
Kudret Demirli ◽  
Saad Mutasher
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Fuzzy Modeling ◽  
Degree Of Freedom ◽  
Power Spectral

scholarly journals Noise power spectral density scaled SNR response estimation with restricted range search for sound source localisation using unmanned aerial vehicles

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Benjamin Yen ◽  
Yusuke Hioka
Keyword(s):  
Spectral Density ◽  
Noise Reduction ◽  
Power Spectral Density ◽  
Sound Source ◽  
Processing Algorithm ◽  
Post Processing ◽  
Time Frequency ◽  
Distance Error ◽  
Rotor Noise ◽  
Power Spectral

Abstract A method to locate sound sources using an audio recording system mounted on an unmanned aerial vehicle (UAV) is proposed. The method introduces extension algorithms to apply on top of a baseline approach, which performs localisation by estimating the peak signal-to-noise ratio (SNR) response in the time-frequency and angular spectra with the time difference of arrival information. The proposed extensions include a noise reduction and a post-processing algorithm to address the challenges in a UAV setting. The noise reduction algorithm reduces influences of UAV rotor noise on localisation performance, by scaling the SNR response using power spectral density of the UAV rotor noise, estimated using a denoising autoencoder. For the source tracking problem, an angular spectral range restricted peak search and link post-processing algorithm is also proposed to filter out incorrect location estimates along the localisation path. Experimental results show the proposed extensions yielded improvements in locating the target sound source correctly, with a 0.0064–0.175 decrease in mean haversine distance error across various UAV operating scenarios. The proposed method also shows a reduction in unexpected location estimations, with a 0.0037–0.185 decrease in the 0.75 quartile haversine distance error.

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