Principal Component Analysis of Gamma-Ray Spectra for Radiation Portal Monitors

Boardman, D.; Reinhard, Mark I.; Flynn, Allen J.

doi:10.1109/tns.2011.2179313

Cited by 32 publications

(8 citation statements)

References 10 publications

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“…PCA is a multivariate analysis method used in radiation detection for improving performance . In fact, PCA has been used to identify patterns in data and reduce the number of dimensions with little information loss.…”

Section: Methodsmentioning

confidence: 99%

Cherenkov radiation‐based three‐dimensional position‐sensitive PET detector: A Monte Carlo study

et al. 2018

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Section: Methodsmentioning

confidence: 99%

Cherenkov radiation‐based three‐dimensional position‐sensitive PET detector: A Monte Carlo study

et al. 2018

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“…Clustering algorithms may include K-means or Mahalanobis distance [26,27]. PCA has been applied to isotope identification using plastic scintillators [28] and anomaly detection using both plastic scintillators and NaI detectors [29]. Despite the progress of PCA in isotope identification, there has not been significant progress in applying PCA to separating mixtures of isotopes in gamma-ray spectra.…”

Section: Existing Methods For Automated Isotope Identification and Qumentioning

confidence: 99%

Automated Isotope Identification Algorithm Using Artificial Neural Networks

Kamuda

Stinnett

Sullivan

2017

IEEE Trans. Nucl. Sci.

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There is a need to develop an algorithm that can determine the relative activities of a mixture of many isotopes in a low-resolution gamma-ray spectrum. While techniques for this task exist, they require a human operator and are too slow to use on very large datasets of spectra. Pattern recognition algorithms such as neural networks are prime candidates for automated isotope identification using low-resolution gamma-ray spectra. While algorithms based on feature extraction such as peak finding or ROI algorithms work well for well calibrated high resolution detectors, for low-resolution detectors it may be more beneficial to use algorithms that incorporate more abstract features of the spectrum. This is especially true when analyzing a mixture of isotopes where peak overlap and Compton continuum effects occlude features of interest. To solve this, an artificial neural network (ANN) was trained to predict the presence and relative activities of isotopes from a mixture of many isotopes. The

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“…The projection of actual data by a matrix of eigenvectors reveals the PCs (principal components) Y. [3].…”

Section: Methodsmentioning

confidence: 99%

Recognizing Rainfall Pattern for Pakistan using Computational Intelligence

Aun¹,

Ghani²,

Azeem³

et al. 2017

ijacsa

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Abstract-Over the world, rainfall patterns and seasons are shifting in new directions due to global warming. In the case of Pakistan, unusual rainfall events may outcome with droughts, floods and other natural disasters along with disturbance of economy, so the scientific understating of rainfall patterns will be very helpful to water management and for the economy. In this paper, we have attempted to recognize rainfall patterns over selected regions of the Pakistan.

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Principal Component Analysis of Gamma-Ray Spectra for Radiation Portal Monitors

Cited by 32 publications

References 10 publications

Cherenkov radiation‐based three‐dimensional position‐sensitive PET detector: A Monte Carlo study

Cherenkov radiation‐based three‐dimensional position‐sensitive PET detector: A Monte Carlo study

Automated Isotope Identification Algorithm Using Artificial Neural Networks

Recognizing Rainfall Pattern for Pakistan using Computational Intelligence

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