Sunrise effect on atmospherics and its relation to the direction of the night noise source

1982 ◽  
Vol 26 (3) ◽  
pp. 281-294 ◽  
Author(s):  
Vojtěch Letfus ◽  
Emil M. Apostolov ◽  
Jan Klimeš ◽  
J. Laštovička
Keyword(s):  
Noise Source ◽  
Sunrise Effect

SPATIAL DISTRIBUTION OF 1/f NOISE SOURCE

1988 ◽  
Vol 49 (C4) ◽  
pp. C4-157-C4-160 ◽  
Author(s):  
L.K.J. VANDAMME
Keyword(s):  
Spatial Distribution ◽  
Noise Source

Uncertainty of a Millimeter-Wave Variable Noise Source using a Variable Attenuator

2011 ◽  
Vol 131 (4) ◽  
pp. 302-303
Author(s):  
Hitoshi Iida ◽  
Takayuki Inaba ◽  
Yozo Shimada ◽  
Koji Komiyama
Keyword(s):  
Millimeter Wave ◽  
Noise Source ◽  
Wave Variable ◽  
Variable Attenuator

Physics-informed Broadband Noise Source Identification and Prediction of an Ideally Twisted Rotor

2021 ◽  
Author(s):  
Christopher Thurman ◽  
Nikolas S. Zawodny ◽  
Nicole A. Pettingill ◽  
Leonard V. Lopes ◽  
James D. Baeder
Keyword(s):  
Source Identification ◽  
Noise Source ◽  
Broadband Noise ◽  
Noise Source Identification

Noise source location and scaling of subsonic upper-surface blowing

2020 ◽  
Vol 19 (3-5) ◽  
pp. 191-206
Author(s):  
Trae L Jennette ◽  
Krish K Ahuja
Keyword(s):  
Strouhal Number ◽  
Noise Source ◽  
Low Frequency ◽  
Directivity Pattern ◽  
Source Location ◽  
Dipole Source ◽  
Frequency Noise ◽  
Noise Sources ◽  
Trailing Edge ◽  
Trailing Edge Noise

This paper deals with the topic of upper surface blowing noise. Using a model-scale rectangular nozzle of an aspect ratio of 10 and a sharp trailing edge, detailed noise contours were acquired with and without a subsonic jet blowing over a flat surface to determine the noise source location as a function of frequency. Additionally, velocity scaling of the upper surface blowing noise was carried out. It was found that the upper surface blowing increases the noise significantly. This is a result of both the trailing edge noise and turbulence downstream of the trailing edge, referred to as wake noise in the paper. It was found that low-frequency noise with a peak Strouhal number of 0.02 originates from the trailing edge whereas the high-frequency noise with the peak in the vicinity of Strouhal number of 0.2 originates near the nozzle exit. Low frequency (low Strouhal number) follows a velocity scaling corresponding to a dipole source where as the high Strouhal numbers as quadrupole sources. The culmination of these two effects is a cardioid-shaped directivity pattern. On the shielded side, the most dominant noise sources were at the trailing edge and in the near wake. The trailing edge mounting geometry also created anomalous acoustic diffraction indicating that not only is the geometry of the edge itself important, but also all geometry near the trailing edge.

scholarly journals New Results on Radioactive Mixture Identification and Relative Count Contribution Estimation

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4155
Author(s):  
Bulent Ayhan ◽  
Chiman Kwan
Keyword(s):  
Deep Learning ◽  
Noise Source ◽  
Simulated Data ◽  
Nuclear Material ◽  
Machine Learning Algorithms ◽  
Detector Response ◽  
Nuclear Materials ◽  
Ratio Estimation ◽  
Analysis Software ◽  
Detector Distance

Detecting nuclear materials in mixtures is challenging due to low concentration, environmental factors, sensor noise, source-detector distance variations, and others. This paper presents new results on nuclear material identification and relative count contribution (also known as mixing ratio) estimation for mixtures of materials in which there are multiple isotopes present. Conventional and deep-learning-based machine learning algorithms were compared. Realistic simulated data using Gamma Detector Response and Analysis Software (GADRAS) were used in our comparative studies. It was observed that a deep learning approach is highly promising.

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