Presentation Type
Poster
Location
Portland State University
Start Date
5-2-2018 11:00 AM
End Date
5-2-2018 1:00 PM
Subjects
Electromagnetic waves, Neural networks (Computer science), Acoustic localization, Algorithms and computation in mathematics, Radioisotopes
Abstract
The most difficult part of the radiation localization is that we cannot use the traditional acoustic localization method to determine where the radiation source is. It’s mainly because the electromagnetic waves are totally different with the sound wave. From the expression of the radioactive intensity, we can tell that the intensity of radiation not only depend on the distance from the radiation but also related to the type of the nuclide. In general, the relationship between the intensity and the distance satisfy the inverse-square law, which is a non-linear relationship. In other words, if we can use the measurement and dynamic parameters of the moving sensor to train a neural network. The trained network can predict the location and the intensity of the source based on any movement of the sensor.
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Persistent Identifier
http://archives.pdx.edu/ds/psu/25025
Radiation Source Localization by using Backpropagation Neural Network
Portland State University
The most difficult part of the radiation localization is that we cannot use the traditional acoustic localization method to determine where the radiation source is. It’s mainly because the electromagnetic waves are totally different with the sound wave. From the expression of the radioactive intensity, we can tell that the intensity of radiation not only depend on the distance from the radiation but also related to the type of the nuclide. In general, the relationship between the intensity and the distance satisfy the inverse-square law, which is a non-linear relationship. In other words, if we can use the measurement and dynamic parameters of the moving sensor to train a neural network. The trained network can predict the location and the intensity of the source based on any movement of the sensor.