A detection method for the random noise failure of MEMS gyros on orbit

2021 ◽  
Vol 64 (10) ◽  
pp. 66-78
Author(s):  
Mu Xia ◽  
et al.
Keyword(s):  
Detection Method ◽  
Random Noise

Disturbance Detection Method of Power Quality Based on Mathematical Morphology and Lifting Wavelet

2014 ◽  
Vol 644-650 ◽  
pp. 1205-1208 ◽  
Author(s):  
Xiao Fei Wang ◽  
Xia Chen ◽  
Ning Xia Yang ◽  
Xiao Jing Zhang ◽  
Ya Chao Hu
Keyword(s):  
Power Quality ◽  
Mathematical Morphology ◽  
Impulse Noise ◽  
Detection Method ◽  
Random Noise ◽  
Quality Detection ◽  
Lifting Wavelet ◽  
Simulation Results ◽  
Disturbance Detection ◽  
Detection Principle

Considering in the progress of the power quality detection for disturbance signal, detected signals are often has the problem of random noise and impulse noise, in this paper, by using traditional wavelet de-noising, put forward a detection method of disturbance signal based on mathematical morphology and lifting wavelet, discussed its detection principle and implementation methods. Simulation results show that the method de-noising effect is obvious, can effectively improve the accuracy of the disturbance signal judgment.

Single Frame Infrared Image Targets Detection Based on Multi-Mixture Filters Model

2012 ◽  
Vol 468-471 ◽  
pp. 1389-1392
Author(s):  
Yang Li ◽  
Jia Bao Wang ◽  
Jian Jiang Lu ◽  
Zhuang Miao ◽  
Peng Fei Fang
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Detection Efficiency ◽  
Random Noise ◽  
Infrared Image ◽  
Background Model ◽  
Gaussian Filter ◽  
Target Shape ◽  
Target Model ◽  
Single Frame

To detect the targets in single frame infrared image, a target detection method based on multi-mixture filters model were proposed. The multi-mixture filters model involves four modules. Firstly, smooth filter is used to restrain and eliminate random noise. Secondly background Gaussian filter is used to construct a rough background model. Thirdly target Gaussian filter is used to construct an accurate target model. Finally morphologic filter is used to optimize the final target shape. Experiments show that this method can achieve a higher detection efficiency and accuracy.

Solid-phase immunoelectron microscopy for rapid diagnosis of enteroviruses

1984 ◽  
Vol 42 ◽  
pp. 226-227 ◽  
Author(s):  
K. Pegg-Feige ◽  
F. W. Doane
Keyword(s):  
Electron Microscopy ◽  
Cell Culture ◽  
Immunoelectron Microscopy ◽  
Detection Method ◽  
Solid Phase ◽  
Protein A ◽  
Plant Viruses ◽  
Rapid Diagnosis ◽  
Virus Diagnosis ◽  
Antiviral Antibody

Immunoelectron microscopy (IEM) applied to rapid virus diagnosis offers a more sensitive detection method than direct electron microscopy (DEM), and can also be used to serotype viruses. One of several IEM techniques is that introduced by Derrick in 1972, in which antiviral antibody is attached to the support film of an EM specimen grid. Originally developed for plant viruses, it has recently been applied to several animal viruses, especially rotaviruses. We have investigated the use of this solid phase IEM technique (SPIEM) in detecting and identifying enteroviruses (in the form of crude cell culture isolates), and have compared it with a modified “SPIEM-SPA” method in which grids are coated with protein A from Staphylococcus aureus prior to exposure to antiserum.

A method for direct measurement of specimen noise in HREM images

1993 ◽  
Vol 51 ◽  
pp. 458-459
Author(s):  
Sidnei Paciornik ◽  
Roar Kilaas ◽  
Ulrich Dahmen ◽  
Michael Adrian O'Keefe
Keyword(s):  
Random Noise ◽  
Image Interpretation ◽  
Thin Foil ◽  
High Resolution Electron Microscopy ◽  
Black Dot ◽  
Atomic Column ◽  
Amorphous Oxide ◽  
Resolution Electron ◽  
Imaging Conditions ◽  
Image Simulations

High resolution electron microscopy (HREM) is a primary tool for studying the atomic structure of defects in crystals. However, the quantitative analysis of defect structures is often seriously limited by specimen noise due to contamination or oxide layers on the surfaces of a thin foil.For simple monatomic structures such as fcc or bcc metals observed in directions where the crystal projects into well-separated atomic columns, HREM image interpretation is relatively simple: under weak phase object, Scherzer imaging conditions, each atomic column is imaged as a black dot. Variations in intensity and position of individual image dots can be due to variations in composition or location of atomic columns. Unfortunately, both types of variation may also arise from random noise superimposed on the periodic image due to an amorphous oxide or contamination film on the surfaces of the thin foil. For example, image simulations have shown that a layer of amorphous oxide (random noise) on the surfaces of a thin foil of perfect crystalline Si can lead to significant shifts in image intensities and centroid positions for individual atomic columns.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

Sign in / Sign up

Export Citation Format

Share Document