A Focus Detection Method for Ground-Based Solar Telescope

2017 ◽  
Vol 37 (7) ◽  
pp. 0711003
Author(s):  
方玉亮 Fang Yuliang ◽  
柳光乾 Liu Guangqian ◽  
金振宇 Jin Zhenyu ◽  
李鹏飞 Li Pengfei ◽  
刘 忠 Liu Zhong
Keyword(s):  
Detection Method ◽  
Solar Telescope ◽  
Focus Detection

A Non-Contact Displacement Sensor with Diffraction Grating Metrology System for Profile Measurement

2007 ◽  
Vol 364-366 ◽  
pp. 74-79
Author(s):  
Yu Rong Chen ◽  
Xu Dong Yang ◽  
Tie Bang Xie
Keyword(s):  
Diffraction Grating ◽  
Measurement Accuracy ◽  
Detection Method ◽  
Voice Coil Motor ◽  
Displacement Sensor ◽  
Profile Measurement ◽  
Focus Detection ◽  
Focusing Lens ◽  
Metrology System ◽  
Contact Displacement

Focus detection method is one of non-contact profile measurement methods. However, the measurement accuracy of current focus detection method is limited by voice coil motor adopted by it. In this paper, based on an improved Foucault focus detection method, a new non-contact displacement sensor with diffraction grating metrology system is presented. Driven by a piezoelectric actuator instead of a voice coil motor, and a diffraction grating metrology system being with it, the sensor has high measurement accuracy. During surface profile sampling, according to focusing deviation signal, the focusing lens was driven to move vertically by the piezoelectric actuator so that its focus was always located on the workpiece surface, synchronously the vertical displacement of the focusing lens was obtained by the diffraction grating metrology system as the profile height of sampling points. The displacements of all sampling points gave the whole profile of the measured surface, which can be processed by a characterization software to obtain the measurement result. The resolution of the non-contact displacement sensor was 10 nm.

scholarly journals Application of Artificial Intelligence in Human Diagnosis and Public Health Management

2021 ◽  
Author(s):  
Weiwei Wang ◽  
Xinjie Zhao ◽  
Yanshu Jia
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Detection Rate ◽  
Detection Method ◽  
Health Management ◽  
Diagnosis Rate ◽  
Missed Diagnosis ◽  
Focus Detection ◽  
Deep Learning Model ◽  
Public Health Management

Abstract To improve the diagnostic efficiency and accuracy of corona virus disease 2019 (COVID-19), and to study the application of artificial intelligence (AI) in COVID-19 diagnosis and public health management, the computer tomography (CT) image data of 200 COVID-19 patients are collected, and the image is input into the AI auxiliary diagnosis software based on the deep learning model, "uAI the COVID-19 intelligent auxiliary analysis system", for focus detection. The software automatically carries on the pneumonia focus identification and the mark in batches, and automatically calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes, and in density, the most common shadow is the ground glass opacity. The detection rate of manual detection method is 95.30%, misdiagnosis rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of AI software focus detection method based on deep learning model is 99.76%, the misdiagnosis rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, it can effectively identify COVID-19 focus and provide relevant data information of focus to provide objective data support for COVID-19 diagnosis and public health management.

Automatic laser-scanning focus detection method using printed focus pattern

1995 ◽  
Author(s):  
Kyoichi Suwa ◽  
Hiroki Tateno ◽  
Nobuyuki Irie ◽  
Shigeru Hirukawa
Keyword(s):  
Laser Scanning ◽  
Detection Method ◽  
Focus Pattern ◽  
Focus Detection

scholarly journals Infectious Cryptosporidium parvum Oocysts in Final Reclaimed Effluent

2003 ◽  
Vol 69 (8) ◽  
pp. 4983-4984 ◽  
Author(s):  
Angela L. Gennaccaro ◽  
Molly R. McLaughlin ◽  
Walter Quintero-Betancourt ◽  
Debra E. Huffman ◽  
Joan B. Rose
Keyword(s):  
Cell Culture ◽  
Cryptosporidium Parvum ◽  
Detection Method ◽  
Reclaimed Water ◽  
Culture Technique ◽  
Most Probable Number ◽  
Probable Number ◽  
Cryptosporidium Parvum Oocysts ◽  
Focus Detection ◽  
Cell Culture Technique

ABSTRACT Water samples collected throughout several reclamation facilities were analyzed for the presence of infectious Cryptosporidium parvum by the focus detection method-most-probable-number cell culture technique. Results revealed the presence of infectious C. parvum oocysts in 40% of the final disinfected effluent samples. Sampled effluent contained on average seven infectious oocysts per 100 liters. Thus, reclaimed water is not pathogen free but contains infectious C. parvum.

Introduction to the Solar Space Telescope

2000 ◽  
Vol 179 ◽  
pp. 141-147
Author(s):  
G. Ai ◽  
S. Jin ◽  
S. Wang ◽  
B. Ye ◽  
S. Yang
Keyword(s):  
Solar Telescope ◽  
Basic Aspect ◽  
Space Telescope ◽  
Space Solar Telescope

AbstractThe design of the space solar telescope (SST) (phase B) has been completed. The manufacturing is under development. At the end of 2000, it will be assembled. The basic aspect will be introduced in this paper.

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 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