Event Identification from Georeferenced Images

2014 ◽  
pp. 73-88 ◽  
Author(s):  
Yeran Sun ◽  
Hongchao Fan
Keyword(s):  
Event Identification

Regional Seismic Event Identification and Improved Locations with Small Arrays and Networks.

1995 ◽  
Author(s):  
Frank L. Vernon ◽  
J. B. Minster ◽  
John A. Orcutt
Keyword(s):  
Seismic Event ◽  
Event Identification

Bulk and surface event identification in p-type germanium detectors

2018 ◽  
Vol 886 ◽  
pp. 13-23 ◽  
Author(s):  
L.T. Yang ◽  
H.B. Li ◽  
H.T. Wong ◽  
M. Agartioglu ◽  
J.H. Chen ◽  
...  
Keyword(s):  
Germanium Detectors ◽  
Event Identification ◽  
P Type

Neutron, proton and gamma-ray event identification with a HPGe detector through pulse shape analysis

1991 ◽  
Vol 38 (2) ◽  
pp. 200-208 ◽  
Author(s):  
G.J. Bamford ◽  
A.C. Rester ◽  
R.L. Coldwell ◽  
C.M. Castaneda
Keyword(s):  
Shape Analysis ◽  
Hpge Detector ◽  
Pulse Shape ◽  
Gamma Ray ◽  
Pulse Shape Analysis ◽  
Event Identification

scholarly journals A multiscale approach to network event identification using geolocated twitter data

Computing ◽  
2013 ◽  
Vol 96 (1) ◽  
pp. 3-13 ◽  
Author(s):  
Chao Yang ◽  
Ian Jensen ◽  
Paul Rosen
Keyword(s):  
Multiscale Approach ◽  
Event Identification ◽  
Twitter Data ◽  
Network Event

scholarly journals Complementary Feature Extractions for Event Identification in Power Systems Using Multi-Channel Convolutional Neural Network

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4446
Author(s):  
Do-In Kim
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Convolutional Neural Network ◽  
Time Window ◽  
Transient Signal ◽  
Measurement Unit ◽  
Transient Phenomena ◽  
Event Identification ◽  
Starting Point ◽  
Fast Decision

This paper presents an event identification process in complementary feature extractions via convolutional neural network (CNN)-based event classification. The CNN is a suitable deep learning technique for addressing the two-dimensional power system data as it directly derives information from a measurement signal database instead of modeling transient phenomena, where the measured synchrophasor data in the power systems are allocated by time and space domains. The dynamic signatures in phasor measurement unit (PMU) signals are analyzed based on the starting point of the subtransient signals, as well as the fluctuation signature in the transient signal. For fast decision and protective operations, the use of narrow band time window is recommended to reduce the acquisition delay, where a wide time window provides high accuracy due to the use of large amounts of data. In this study, two separate data preprocessing methods and multichannel CNN structures are constructed to provide validation, as well as the fast decision in successive event conditions. The decision result includes information pertaining to various event types and locations based on various time delays for the protective operation. Finally, this work verifies the event identification method through a case study and analyzes the effects of successive events in addition to classification accuracy.

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