AI based Scintillation Detector Calibration (original) (raw)
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Journal of Signal Processing Systems, 2021
Scintillator detector's electronics is recalibrated against the datasheet given by the manufacturer. Optimal and mutual dependent values of (a) high voltage at PMT (Photomultiplier Tube), (b) amplifier gain, (c) average time to count the radiation particles (set by operator), and (d) number of instances/sample number are estimated. Total 5: two versions of Central Limit Theorem (CLT), (3) industry preferred Pulse Width Saturation, (4) calibration based on MPPC coupled Gamma-ray detector and (5) gross method are used. It is shown that CLT method is the most optimal method to calibrate detector and its respective electronics couple. An inverse modeling-based Computerized Tomography method is used for verification. It is shown that statistically averaging results are more accurate and precise data than mode and median, if the data is not skewed and random number of samples are used during the calibration process. It is also shown that the average time to count the radiation particle is the most important parameter affecting the optimal calibration setting for precision and accurate measurements of gamma radiation.
Artificial neural network modelling of uncertainty in gamma-ray spectrometry
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2005
An artificial neural network (ANN) model for the prediction of measuring uncertainties in gamma-ray spectrometry was developed and optimized. A three-layer feed-forward ANN with back-propagation learning algorithm was used to model uncertainties of measurement of activity levels of eight radionuclides ( 226 Ra, 238 U, 235 U, 40 K, 232 Th, 134 Cs, 137 Cs and 7 Be) in soil samples as a function of measurement time. It was shown that the neural network provides useful data even from small experimental databases. The performance of the optimized neural network was found to be very good, with correlation coefficients (R 2 ) between measured and predicted uncertainties ranging from 0.9050 to 0.9915. The correlation coefficients did not significantly deteriorate when the network was tested on samples with greatly different uranium-to-thorium ( 238 U/ 232 Th) ratios. The differences between measured and predicted uncertainties were not influenced by the absolute values of uncertainties of measured radionuclide activities. Once the ANN is trained, it could be employed in analyzing soil samples regardless of the 238 U/ 232 Th ratio. It was concluded that a considerable saving in time could be obtained using the trained neural network model for predicting the measurement times needed to attain the desired statistical accuracy. r
Calibration of positron emission tomograph detector modules using new neural method
Electronics Letters, 2004
In this paper we describe a neural network-based method aimed at automatically calibrating the detector module contained in a scanner for a highresolution positron emission tomography (PET) system for small animals. The detector module is composed of crystal elements, arranged in a regular matrix and sensitive to gamma rays emitted by a radioactive source. The crystal matrix is optically coupled to a position-sensitive photo-multiplier tube, which reconstructs the original image. Calibration, required to cope with spatial distortions introduced by the optical system, consists of a segmentation process of the image produced after the photo-multiplier tube into a fixed number of areas. The purpose of this segmentation is to map each pixel of the perceived image onto the pertinent crystal, which was actually struck by the gamma ray emitted by the radioactive source.
Radiation Research, 2008
A new method for calibration was developed that takes the energy deposited by radioisotope sources into account; it was validated with a plastic scintillation plate using a 207 Bi source. The energy deposits of the conversion electrons in the 207 Bi source led to an energy difference of a few keV for the 976 keV monoenergetic peak. Calibration with high accuracy is essential to estimate radiation doses as well as to evaluate detector performance. The method will be available not only for use in scintillation detectors but also for other detectors.
2014
A major advantage of Sodium Iodide scintillator, NaI(Tl), is its high efficiency in gamma ray detection. In this research, Using experimental values of Full Width at Half Maximum (FWHM), the response functions of a 4 in × Ø 4 in NaI(Tl) detector were investigated by MCNP4C code using GEB option as a special treatment for tallies. Computational results were compared with measured data by using standard gamma ray sources to check their accuracy. Then, the intrinsic efficiency was calculated for several source-detector distances and various sizes of NaI(Tl) detectors. Calculations were performed analytically as well as Monte Carlo method. It was found that the intrinsic efficiency has a minimum at d/R=0.8 (d is source-detector distance and R is the detector radius), independent of gamma energy. This minimum is justified by using the Dirac theory for the mean chord of photons in the detector.
Study of radioactive particle tracking using MCNP-X code and artificial neural network
Brazilian Journal of Radiation Sciences, 2021
Agitators or mixers are highly used in the chemical, food, pharmaceutical and cosmetic industries. During the fabrication process, the equipment may fail and compromise the appropriate stirring or mixing procedure. Besides that, it is also important to determine the right point of homogeneity of the mixture. Thus, it is very important to have a diagnosis tool for these industrial units to assure the quality of the product and to keep the market competitiveness. The radioactive particle tracking (RPT) technique is widely used in the nuclear field. In this paper, a method based on the principles of RPT is presented. Counts obtained by an array of detectors properly positioned around the unit will be correlated to predict the instantaneous positions occupied by the radioactive particle by means of an appropriate mathematical search location algorithm. Detection geometry developed employs eight NaI(Tl) scintillator detectors and a Cs-137 (662 keV) source with isotropic emission of gamma...
Virtual Calibration of Cosmic Ray Sensor: Using Supervised Ensemble Machine Learning
International Journal of Advanced Computer Science and Applications, 2013
In this paper an ensemble of supervised machine learning methods has been investigated to virtually and dynamically calibrate the cosmic ray sensors measuring area wise bulk soil moisture. Main focus of this study was to find an alternative to the currently available field calibration method; based on expensive and time consuming soil sample collection methodology. Data from the Australian Water Availability Project (AWAP) database was used as independent soil moisture ground truth and results were compared against the conventionally estimated soil moisture using a Hydroinnova CRS-1000 cosmic ray probe deployed in Tullochgorum,
A new application of radioactive particle tracking using MCNPX code and artificial neural network
Applied Radiation and Isotopes, 2019
Stirrers and mixers are highly used in chemical, food, pharmaceutical, cosmetic, concrete industries and others. During the fabrication process, the equipment may fail to appropriately stir or mix the solution. Besides that, it is also important to determine when the right homogeneity of the mixture is attained. Thus, it is very important to have a diagnosis tool for these industrial units to assure the quality of the product and maintain market competitiveness. Nuclear techniques, such as gamma densitometry, are widely used in industry to overcome a sort of difficulties, as they are minimally non-invasive techniques. This paper presents a method based on the principles of the radioactive particle tracking technique to predict the instantaneous position of a radioactive particle to monitor a concrete mixture inside an industrial unit by means of Monte Carlo method and artificial neural network. Counts obtained by an array of detectors properly positioned around the mixing canister will be correlated to each other, by means of an appropriate mathematical search location algorithm, in order to predict the instantaneous positions occupied by an inserted radioactive particle. The simulation consists of a detection geometry of eight NaI(Tl) scintillator detectors, a 662 keV 137 Cs point source with isotropic emission of gamma-rays and a polyvinyl chloride tank. At first, the tank is air filled and, afterwards, filled with concrete made with Portland cement. The modeling of the detection system is performed using the MCNPX code. For both medium, the correlation coefficient was 0.99 for all coordinates, which indicates that this methodology could be a good tool to evaluate industrial mixers. Keywords: gamma densitometry; MCNPX code; artificial neural network; NaI(Tl) scintillator detector; radioactive particle tracking. 1. INTRODUCTION Stirrers and mixers are highly used in the industry for processes such as dispersion and homogenization. Several industrial segments need this type equipment, such as chemical,
Sustainability
Nuclear power is a sustainable energy source, but radiation management is required for its safe use. Radiation-detection technology has been developed for the safe management of radioactive materials in nuclear facilities but its performance may vary depending on the size and complexity of the structure of nuclear facilities. In this study, a nuclear monitoring system using a multi-sensor network was designed to monitor radioactive materials in a large nuclear facility. Additionally, an artificial-intelligence-based localization algorithm was developed to accurately locate radioactive materials. The system parameters were optimized using the Geant4 Application for Tomographic emission (GATE) toolkit, and the localization algorithm was developed based on the performance evaluation of the Artificial Neural Network (ANN) and Decision Tree (D-Tree) models. In this article, we present the feasibility of the proposed monitoring system by converging the radiation detection system and artif...
Scientific Reports, 2021
In this paper, six types of air pollutant concentrations are taken as the research object, and the data monitored by the micro air quality detector are calibrated by the national control point measurement data. We use correlation analysis to find out the main factors affecting air quality, and then build a stepwise regression model for six types of pollutants based on 8 months of data. Taking the stepwise regression fitting value and the data monitored by the miniature air quality detector as input variables, combined with the multilayer perceptron neural network, the SRA-MLP model was obtained to correct the pollutant data. We compared the stepwise regression model, the standard multilayer perceptron neural network and the SRA-MLP model by three indicators. Whether it is root mean square error, average absolute error or average relative error, SRA-MLP model is the best model. Using the SRA-MLP model to correct the data can increase the accuracy of the self-built point data by 42.5%...