Kernel-based Gaussian process for anomaly detection in sparse gamma-ray data

Romanchek, Gregory R.; Liu, Zheng; Abbaszadeh, Shiva

doi:10.1371/journal.pone.0228048

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…This background set consists of 14077, 1 s spectra with an average of 40±16 counts spread across 1024 detector channels (binned from the 4096 native to the D3S). The source spectra were synthesized via a statistical model as in [ 19 ]. This process first computes how many source counts are detected and then distributes them into a source spectrum for each collection.…”

Section: Methodsmentioning

confidence: 99%

“…In Miller et al the background b in Eq 5 is assumed to be constant after recording a few seconds of data taken to be only background. We utilize the anomaly detection algorithm proposed in [ 19 ] to first detect source presence and then to compute estimated background and source counts after each step. This selection is a natural choice for background estimation as this anomaly detection scheme is designed for time-series, sparse gamma-ray data.…”

Section: Methodsmentioning

confidence: 99%

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Stopping criteria for ending autonomous, single detector radiological source searches

Romanchek

Abbaszadeh

2021

PLoS ONE

Self Cite

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While the localization of radiological sources has traditionally been handled with statistical algorithms, such a task can be augmented with advanced machine learning methodologies. The combination of deep and reinforcement learning has provided learning-based navigation to autonomous, single-detector, mobile systems. However, these approaches lacked the capacity to terminate a surveying/search task without outside influence of an operator or perfect knowledge of source location (defeating the purpose of such a system). Two stopping criteria are investigated in this work for a machine learning navigated system: one based upon Bayesian and maximum likelihood estimation (MLE) strategies commonly used in source localization, and a second providing the navigational machine learning network with a “stop search” action. A convolutional neural network was trained via reinforcement learning in a 10 m × 10 m simulated environment to navigate a randomly placed detector-agent to a randomly placed source of varied strength (stopping with perfect knowledge during training). The network agent could move in one of four directions (up, down, left, right) after taking a 1 s count measurement at the current location. During testing, the stopping criteria for this navigational algorithm was based upon a Bayesian likelihood estimation technique of source presence, updating this likelihood after each step, and terminating once the confidence of the source being in a single location exceeded 0.9. A second network was trained and tested with similar architecture as the previous but which contained a fifth action: for self-stopping. The accuracy and speed of localization with set detector and source initializations were compared over 50 trials of MLE-Bayesian approach and 1000 trials of the CNN with self-stopping. The statistical stopping condition yielded a median localization error of ~1.41 m and median localization speed of 12 steps. The machine learning stopping condition yielded a median localization error of 0 m and median localization speed of 17 steps. This work demonstrated two stopping criteria available to a machine learning guided, source localization system.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Stopping criteria for ending autonomous, single detector radiological source searches

Romanchek

Abbaszadeh

2021

PLoS ONE

Self Cite

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“…Others make use of specific energy windows within which the source and background count contributions are estimated using the spectrum outside the windows [13]. Additionally, some recent works utilize modern machine learning techniques, such as deep neural networks (DNN) [14], [15] and Gaussian processes (GP) [16].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Approach to Computationally Efficient Radiological Anomaly Detection and Isotope Identification

Lee

Cooper

Joshi

et al. 2022

IEEE Trans. Nucl. Sci.

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Radiological source search is a challenging task involving detection and identification of weak sources in a constantly changing radiological background. As of now, many radiological source detection algorithms have been proposed; however, their computational complexity, and hence reliance on power intensive processing units inhibit low-power applications of radiological source search systems. In this work, we introduce the anomaly filter (AF) algorithm; a computationally light, yet effective time-series source detection algorithm based on exponential weighted moving average (EWMA) and Poisson deviance statistics. Then, we demonstrate that the proposed algorithm can be used in ensemble with other more computationally intensive source detection and identification algorithms to achieve both increased detection performance and reduced power consumption. The proposed AF algorithm and the ensemble algorithms were thoroughly benchmarked against several existing source detection and identification algorithms. The results show that the AF algorithm outperforms existing conventional source detection algorithms, and the ensemble approach improves the overall performance of existing source detection and isotope identification algorithms. Furthermore, the AF algorithm and the Non-negative Matrix Factorization approach based source identification (NMF-ID) algorithm were combined and implemented on a singleboard microcontroller and the power consumption was measured. This ensemble algorithm reduced the power consumption of the NMF-ID algorithm almost by a factor of 100, while improving the detection performance of the overall system.

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Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network

Chaudhuri

Kleppinger

Karadavut

et al. 2022

J Mater Sci: Mater Electron

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Kernel-based Gaussian process for anomaly detection in sparse gamma-ray data

Cited by 5 publications

References 18 publications

Stopping criteria for ending autonomous, single detector radiological source searches

Stopping criteria for ending autonomous, single detector radiological source searches

An Ensemble Approach to Computationally Efficient Radiological Anomaly Detection and Isotope Identification

Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network

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