Radionuclide Identification via Digital Pulse Energy Estimation
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Solution Overview
Problem
Current methods for identifying radionuclides in materials, such as gamma spectrometry and neural networks, require long exposure times, high-resolution detectors, and calibrated energy spectra, making them inefficient for rapid identification, especially in mixtures with spread peaks and low activity sources.
Innovation Solution
A method that estimates the energy of digital pulses and calculates the activity of radionuclides using a succession of pulses, without the need for calibrated data, allowing for simultaneous or successive calculation of activities across multiple channels, and identifies radionuclides based on probability thresholds, enabling real-time identification without high-resolution detectors or calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gamma spectrometry is used to identify radionuclides by comparing energy spectra, then identification accuracy is improved, but measurement time increases significantly
Solution Approach 1:
The patent divides the continuous energy spectrum into discrete energy channels or bins. Instead of analyzing the complete continuous spectrum, the method segments it into manageable channels, allowing for faster processing and comparison while maintaining identification accuracy through probabilistic analysis of channel contents.
Solution Approach 2:
The patent transforms the problem from spectral shape comparison to parameter-based probability calculation. By changing the analysis parameter from continuous spectral distribution to discrete channel counts and applying Bayesian probability, the method achieves rapid identification without requiring full spectral resolution.
2Reliability
If high-resolution detectors are used to resolve energy peaks, then identification reliability is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces the need for high-resolution physical detectors with a computational approach. Instead of relying on detector hardware to resolve peaks, the method uses probability-based algorithms to identify radionuclides from lower-resolution data, substituting mechanical/detector complexity with information processing.
Solution Approach 2:
The patent introduces probabilistic analysis as an intermediary between the detector output and radionuclide identification. This intermediary layer allows reliable identification even when the detector itself has limited resolution, by using probability calculations to compensate for the lack of spectral detail.
3Measurement precision
If calibrated energy spectra are required for identification, then measurement accuracy is improved, but ease of operation deteriorates due to calibration requirements
Solution Approach 1:
The patent makes the identification method self-calibrating by using the measured channel contents directly in probability calculations. The Bayesian framework automatically normalizes and compares observed channel counts with expected distributions, eliminating the need for external calibration procedures while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary normalization and probability distribution setup in advance, creating lookup tables or pre-calculated probability models for different radionuclides. This preliminary action allows rapid identification during operation without requiring real-time calibration, improving ease of operation.
4Reliability
If complete energy spectra are constructed before analysis, then identification robustness is improved, but productivity decreases due to long processing time
Solution Approach 1:
The patent uses only the necessary portion of the spectral information - the channel contents - rather than analyzing the complete spectral shape. This partial action approach extracts sufficient information for robust identification while avoiding the computational burden of full spectral analysis, improving productivity.
Solution Approach 2:
The patent enables continuous real-time identification by processing data as it arrives in discrete channels, rather than waiting for complete spectral construction. The probabilistic method allows incremental updating of identification results, maintaining robustness while achieving continuous operation and higher productivity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rapid and accurate identification of radionuclides in real-time, even in mixtures, without the need for high-resolution detectors or calibration, and accounts for background noise, improving security gate response times and detection capabilities.
Implementation Method 1
The bodies to be identified are, for example, radionuclides which emit gamma radiation
Data Source
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AI summary
The invention relates to a method for determining the activity of N bodies CEk (k=1, 2, ..., N) provided in a material, from a series of digital pulses capable of characterising an activity of at least one of the N bodies, N being an integer no lower than 1. Said method is characterised in that it includes, for a digital pulse of order Q which follows a digital pulse of order Q-1, the following steps implemented by a computer: estimating a piece of data that characterises the energy of the digital pulse of order Q; identifying a channel Ci (i=1, 2, ..., M) to which said estimated piece of data belongs, each channel Ci being associated with a separate energy band; and, once the channel Ci to which said estimated piece of data belongs has been identified, calculating the activity Ak Q of the body CEk (k=1, 2, ..., N) for the digital pulse of order Q with the form: Formula (I), Formula (II) being an estimated probability that the digital pulse of order Q corresponds to the body CEk (k=1, 2, ..., N), so that: Formula (III), where ∙ Formula (IV) is an estimated probability of the presence of the body CEk in the material, so that: Formula (V) ∙ Wij (i=1, 2, ..., M; j = 1, 2, ..., N) is a synaptic weight associated with the body CEj for the channel Ci.