New method of substance detection and identification using the substance emission frequency up-conversion in the THz frequency range

2018 ◽  
Author(s):  
Vyacheslav A. Trofimov ◽  
Irina Zakharova ◽  
Svetlana Varentsova
Keyword(s):  
New Method ◽  
Frequency Range ◽  
Emission Frequency ◽  
Detection And Identification

AFMAG—AIRBORNE AND GROUND

Geophysics ◽  
1959 ◽  
Vol 24 (4) ◽  
pp. 761-787 ◽  
Author(s):  
S. H. Ward
Keyword(s):  
Magnetic Fields ◽  
Broad Band ◽  
Primary Source ◽  
New Method ◽  
Mineral Deposits ◽  
Geophysical Prospecting ◽  
Frequency Range ◽  
Time Variance ◽  
Measuring Period ◽  
New Development

The existence of natural magnetic fields in the audio and subaudio frequency range has been known for some time. The primary source of energy for these fields is usually considered to be distant and local thunderstorms. Because of this origin, the fields are quasi‐random with both amplitudes and directions changing drastically over short periods of time. Hence, use of these fields in geophysical prospecting has been extremely limited. A new development, AFMAG, however, essentially eliminates the time variance in recording these fields without any sacrifice of the intelligence of their space variance. Since the space variance can be correlated with geologic features, AFMAG provides a new method of exploration with particular application to prospecting for conductive mineral deposits. Instrumentation of the AFMAG method currently is available for both ground and airborne operation; the tilt of the plane of polarization of the natural magnetic fields is recorded simultaneously at two frequencies. Examples drawn from airborne and ground surveys show that the method has a much greater depth of exploration than its conventional cousin, the induction electromagnetic method. Numerous other advantages, such as the possibility of choosing discrete operating frequencies over a broad band from 1 cps to 1,000 cps, are discussed. The chief disadvantage of the method lies in a sometimes restricted daily measuring period during which the fields are of an amplitude too low to permit measurement with current instrumentation; this is not a serious problem and is being minimized as the technology improves.

A new method for automated detection and identification of neurons in microscopic images of brain slices

2012 ◽  
Vol 22 (4) ◽  
pp. 558-569 ◽  
Author(s):  
I. B. Gurevich ◽  
A. A. Myagkov ◽  
Yu. A. Sidorov ◽  
Yu. O. Trusova ◽  
V. V. Yashina
Keyword(s):  
Brain Slices ◽  
Automated Detection ◽  
New Method ◽  
Microscopic Images ◽  
Detection And Identification

scholarly journals A Digital Heterodyne 2-150 kHz Measurement Method

2021 ◽  
Author(s):  
Paul Wright ◽  
Deborah Ritzmann
Keyword(s):  
Measurement Method ◽  
Power Grid ◽  
New Method ◽  
Digital Method ◽  
Frequency Range ◽  
Multi Resolution Analysis ◽  
Resolution Analysis ◽  
Conducted Emissions

This paper describes a new method for measurements of signals in the 2–150 kHz frequency range, as required to support the regulation of conducted emissions on the power grid. The digital method is based on heterodyning, decimation and multi resolution analysis.<div><br></div>

The instrument fault detection and identification based on kernel principal component analysis and coupling analysis in process industry

2020 ◽  
pp. 014233122096024
Author(s):  
Yanjie Liang ◽  
Zhiyong Gao ◽  
Jianmin Gao ◽  
Guangnan Xu ◽  
Rongxi Wang
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Principal Component ◽  
Process Industry ◽  
New Method ◽  
Kernel Principal Component Analysis ◽  
Fault Identification ◽  
Coupling Analysis ◽  
Detection And Identification ◽  
Fault Detection And Identification

This paper investigates the fault detection problem of instruments in process industry. Considering the difficulty of fault identification and the problems of multivariable and large computation complexity based on traditional kernel principal component analysis (KPCA), this paper presents a new method for fault detection and identification, which combines the coupling analysis with kernel principal component for multivariable fault detection and employed the local outlier factor (LOF) for multivariable fault identification. The new method consists of three parts. Firstly, according to nonlinear correlation of multivariable, coupling analysis and module division of variables based on detrended cross-correlation analysis (DCCA) are considered to reduce false alarm rate (FAR) and missed detection rate (MDR) in fault detection and identification. Secondly, KPCA is employed to detect fault in each sub-module of variables. Finally, for the sub-module which has the fault detected in second step, the LOF is adopted to calculate abnormal contribution of each variable in sub-modules to realize fault identification. To prove that the new method has the better capability of processing multivariable fault detection and the more accuracy rate on fault detection and identification than the conventional methods of KPCA, a case study on Tennessee process is carried out at the end.

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