Regression for Radioactive Source Detection

Cordone, Guthrie; Brooks, Richard; Sen, Satyabrata; Rao, Nageswara S. V.; Wu, Chase Q.; Berry, Mark L.; Grieme, Kayla M.

doi:10.1109/nssmic.2017.8532726

Cited by 4 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 9 shows a linear regression model built using detector data and their inverse-squared distances to a source location estimate over a ten second time window. In [12], we use the source estimate from a linear regression model to successfully detect the presence of a moving source within a distributed detector field.…”

Section: Linear Regressionmentioning

confidence: 99%

Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data

Brooks

et al. 2018

Proceedings of International Symposium on Sensor Networks, Systems and Security

Self Cite

View full text Add to dashboard Cite

Many systems are partially stochastic in nature. We have derived datadriven approaches for extracting stochastic state machines (Markov models) directly from observed data. This chapter provides an overview of our approach with numerous practical applications. We have used this approach for inferring shipping patterns, exploiting computer system side-channel information, and detecting botnet activities. For contrast, we include a related data-driven statistical inferencing approach that detects and localizes radiation sources. IntroductionMarkov models have been widely used for detecting patterns [33,36,7,4,24,25,15,17]. The premise behind a Markov models is that the current state only depends on the previous state and that transition probabilities are stationary. This makes Markov models versatile, as this is a direct result of the causal world we live in. Often,

show abstract

Section: Linear Regressionmentioning

confidence: 99%

Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data

Brooks

et al. 2018

Proceedings of International Symposium on Sensor Networks, Systems and Security

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although there are devices such as Sodium Iodide (NaI) and other detectors, they may not function well if the material concentration is low or there are several isotopes mixed together. In recent years, there have been new developments in machine learning/deep learning that have great potential to enhance the detection and classification of nuclear materials with low concentration or mixtures [ 1 , 2 , 3 , 4 ]. However, it is still challenging for several reasons.…”

Section: Introductionmentioning

confidence: 99%

New Results on Radioactive Mixture Identification and Relative Count Contribution Estimation

Ayhan

Kwan

2021

Sensors

View full text Add to dashboard Cite

Detecting nuclear materials in mixtures is challenging due to low concentration, environmental factors, sensor noise, source-detector distance variations, and others. This paper presents new results on nuclear material identification and relative count contribution (also known as mixing ratio) estimation for mixtures of materials in which there are multiple isotopes present. Conventional and deep-learning-based machine learning algorithms were compared. Realistic simulated data using Gamma Detector Response and Analysis Software (GADRAS) were used in our comparative studies. It was observed that a deep learning approach is highly promising.

show abstract

Localizing unknown radiation sources by unscented particle filtering based on divide-and-conquer sampling

Liu

Xuan

et al. 2022

Journal of Nuclear Science and Technology

View full text Add to dashboard Cite

Regression for Radioactive Source Detection

Cited by 4 publications

References 3 publications

Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data

Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data

New Results on Radioactive Mixture Identification and Relative Count Contribution Estimation

Localizing unknown radiation sources by unscented particle filtering based on divide-and-conquer sampling

Contact Info

Product

Resources

About