AIMC Topic: Radiation Monitoring

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Identification and quantification of anomalies in environmental gamma dose rate time series using artificial intelligence.

Journal of environmental radioactivity
Gamma dose rate (GDR) monitors are the most widely used tool for continuous monitoring of environmental radioactivity. They are inexpensive to procure and operate, and generally require little maintenance. However, since no spectral information is av...

Design of Nuclear Radiation Monitoring System in Floor Exploration Based on Deep Learning.

Computational intelligence and neuroscience
Nuclear radiation environmental monitoring has become an important issue in floor surveys. From the perspective of regional environmental nuclear radiation monitoring, it is of great practical significance to establish a scientific and reliable wirel...

Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms.

Environmental pollution (Barking, Essex : 1987)
The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geog...

Classification of radioxenon spectra with deep learning algorithm.

Journal of environmental radioactivity
In this study, we propose for the first time a model of classification for Beta-Gamma coincidence radioxenon spectra using a deep learning approach through the convolution neural network (CNN) technique. We utilize the entire spectrum of actual data ...

What is the point of innovation in patient dose monitoring?

Annals of the ICRP
says that radiology is one of the fastest growing and developing areas of medicine, and therefore this might be the speciality in which we can expect to see the largest steps in development. So why do they think that, and does it apply to dose monit...

Automated anomalous behaviour detection in soil radon gas prior to earthquakes using computational intelligence techniques.

Journal of environmental radioactivity
In this article, three computational intelligence (CI) models were developed to automatically detect anomalous behaviour in soil radon gas (Rn) time series data. Data were obtained at a fault line and analysed using three machine learning techniques ...

Spatial interpolation and radiological mapping of ambient gamma dose rate by using artificial neural networks and fuzzy logic methods.

Journal of environmental radioactivity
The aim of this study was to determine spatial risk dispersion of ambient gamma dose rate (AGDR) by using both artificial neural network (ANN) and fuzzy logic (FL) methods, compare the performances of methods, make dose estimations for intermediate s...

Advantages of Synthetic Noise and Machine Learning for Analyzing Radioecological Data Sets.

PloS one
The ecological effects of accidental or malicious radioactive contamination are insufficiently understood because of the hazards and difficulties associated with conducting studies in radioactively-polluted areas. Data sets from severely contaminated...

Impact of robotics and a suspended lead suit on physician radiation exposure during percutaneous coronary intervention.

Cardiovascular revascularization medicine : including molecular interventions
BACKGROUND: Reports of left-sided brain malignancies among interventional cardiologists have heightened concerns regarding physician radiation exposure. This study evaluated the impact of a suspended lead suit and robotic system on physician radiatio...

U.S. Department of Defense Multiple-Parameter Biodosimetry Network.

Radiation protection dosimetry
The U.S. Department of Defense (USDOD) service members are at risk of exposure to ionizing radiation due to radiation accidents, terrorist attacks and national defense activities. The use of biodosimetry is a standard of care for the triage and treat...