Automated Isotope Identification Algorithm Using Artificial Neural Networks

Kamuda, Mark; Stinnett, Jacob; Sullivan, Clair J.

doi:10.1109/tns.2017.2693152

Cited by 82 publications

(26 citation statements)

References 37 publications

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“…The first applications of neural networks for source identification were examined between the early 1990s and 2000s [2,[7][8][9][10], with networks that mapped input spectra to the relative amount of known background and sources contained within the spectrum. More recently, modern approaches to perform identification using neural networks have been developed [3,[11][12][13][14][15], using methods similar to those seen since the deep learning boom of the early 2010s (e.g., ref. [16]).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Data-driven methods for discerning between background radiation and anomalous radiological sources using spectroscopic gamma-ray measurements have long been used to meet these needs [1]. Artificial neural networks (ANNs) [2,3] are one class of methods previously introduced for gamma-ray source identification. To perform identification, ANNs, also referred to simply as neural networks, are used to determine a function which maps a given gamma-ray spectrum to the types of radionuclides, or lack thereof, that are observed in the spectrum.…”

Section: Introductionmentioning

confidence: 99%

“…For example, ref. [3] used a stationary detector configuration (i.e., constant background) in a laboratory, high signal-to-noise ratio (SNR) measurements, and a relatively high FAR of 5%. Knowing this, the intentions of this paper are:…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural Network Approaches for Mobile Spectroscopic Gamma-Ray Source Detection

Bilton

Joshi

Bandstra

et al. 2021

JNE

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Artificial neural networks (ANNs) for performing spectroscopic gamma-ray source identification have been previously introduced, primarily for applications in controlled laboratory settings. To understand the utility of these methods in scenarios and environments more relevant to nuclear safety and security, this work examines the use of ANNs for mobile detection, which involves highly variable gamma-ray background, low signal-to-noise ratio measurements, and low false alarm rates. Simulated data from a 2” × 4” × 16” NaI(Tl) detector are used in this work for demonstrating these concepts, and the minimum detectable activity (MDA) is used as a performance metric in assessing model performance.In addition to examining simultaneous detection and identification, binary spectral anomaly detection using autoencoders is introduced in this work, and benchmarked using detection methods based on Non-negative Matrix Factorization (NMF) and Principal Component Analysis (PCA). On average, the autoencoder provides a 12% and 23% improvement over NMF- and PCA-based detection methods, respectively. Additionally, source identification using ANNs is extended to leverage temporal dynamics by means of recurrent neural networks, and these time-dependent models outperform their time-independent counterparts by 17% for the analysis examined here. The paper concludes with a discussion on tradeoffs between the ANN-based approaches and the benchmark methods examined here.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neural Network Approaches for Mobile Spectroscopic Gamma-Ray Source Detection

Bilton

Joshi

Bandstra

et al. 2021

JNE

View full text Add to dashboard Cite

show abstract

“…In one of the earliest works, reported in 2001 [8], a neural network algorithm was employed in combination with a peak search procedure to reduce the network input data size, a limitation due to the reduced computational power at the time. Later works in 2008 [7] and 2009 [9] employed fully connected networks to perform nuclide identification, while a more recent work [10] claimed to have implemented a neural network algorithm that can determine the relative activities of radioisotopes in a large data set containing a mixture of multiple radioisotopes. All neural network techniques used for radioisotope identification are based on a variety of methods except for convolutional methods.…”

Section: Cnl Nuclear Reviewmentioning

confidence: 99%

“…The efficiency of this approach may depend on the resolution of data available from detectors, but the general approach will be applicable to this or other scanning regimes at borders or elsewhere. Machine learning for radioisotope identification has been attempted already based on neural networks [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Algorithm for Shielded Special Nuclear Materials Detection

Masala

Blomeley

2019

CNL Nuclear Review

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A machine-learning algorithm has been implemented by use of a neural network as a preliminary study on the applicability of this method to special nuclear materials detection. The algorithm predicts the presence of the 238U isotope when learning from a gamma spectrum data measured with a high-purity germanium detector from a sample of depleted uranium. In this work, both a fully connected neural network and a convolutional neural network have been implemented, and the performance of different configurations of the network has been studied. The use of convolutional network showed better performance over the fully connected network, with cost function and success rate values supporting a better prediction while avoiding overfitting. Furthermore, implemented network features such as filtering, max-pooling, dropout regularization, and momentum optimization also showed improved prediction performance.

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Survey of Machine Learning Approaches in Radiation Data Analytics Pertained to Nuclear Security

Alamaniotis

Heifetz

2021

Learning and Analytics in Intelligent Systems

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The increasing concerns over the use of nuclear materials for malevolent purposes (i.e., terrorist attacks) have fueled the interest in developing technologies that can detect hidden nuclear material before its use. The process of detecting and identifying nuclear materials for non-reported purposes is under the umbrella of nuclear security. Among several areas that contribute to the security and safeguards of nuclear materials, radiation data analytics has recently been marked as an area of high potential. Therefore, there is an increasing trend in applying machine learning for developing data analysis methods applied to radiation signals aiming at identifying patterns of interest associated with nuclear materials. This chapter aspires in providing a comprehensive survey and discussion of machine learning and data analytics methods pertained to nuclear security. The chapter will also discuss further trends and how data analytics can further enhance nuclear security by effectively analyzing radiation data.

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Automated Isotope Identification Algorithm Using Artificial Neural Networks

Cited by 82 publications

References 37 publications

Neural Network Approaches for Mobile Spectroscopic Gamma-Ray Source Detection

Neural Network Approaches for Mobile Spectroscopic Gamma-Ray Source Detection

Machine-Learning Algorithm for Shielded Special Nuclear Materials Detection

Survey of Machine Learning Approaches in Radiation Data Analytics Pertained to Nuclear Security

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