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Radiation Monitoring

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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...

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...

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 ...

IMPROVEMENT OF DOSE ESTIMATION PROCESS USING ARTIFICIAL NEURAL NETWORKS.

Radiation protection dosimetry
We present here for the first time a fast and reliable automatic algorithm based on artificial neural networks for the anomaly detection of a thermoluminescence dosemeter (TLD) glow curves (GCs), and compare its performance with formerly developed su...

LOCALIZATION OF IONIZING RADIATION SOURCES VIA AN AUTONOMOUS ROBOTIC SYSTEM.

Radiation protection dosimetry
The article discusses an autonomous and flexible robotic system for radiation monitoring. The detection part of the system comprises two NaI(Tl) scintillation detectors: one of these is collimated to allow directionally sensitive measurements and the...

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...

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 ...

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...

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...

DEVELOPMENT OF A ROBOT FOR THE MEASUREMENT OF RADIOACTIVE CONTAMINATION AND FERTILITY OF THE SOIL IN FARMLAND.

Radiation protection dosimetry
A tractor-based robot with the capability of real-time assessing and visualizing the radioactive material density and fertility distribution of farmlands has been developed to accelerate the recovery process of the farmlands suffered by the accident ...