Technology Description
This algorithm is applicable to radiation detection and counting, for example, in Gamma spectroscopy. The purpose of this algorithm is to eliminate the need for a priori information when counting radioactive material and instead using machine learning techniques to solve the optimal counting time real-time. Data are arranged into a matrix, which can then be used to create training material for classification. Once the desired outcome is achieved (i.e., radiation source is identified, radiation dose reaches a specific threshold, or the radiation applied suffices to support the desired application), the radiation source is turned off.
Features & Benefits
Applications
Background of Invention
There are a number of systems that exist which are used to detect radiation or determine the content of radioactive material. They are applicable across various nuclear science applications. Most of the current solutions on the market require a radiation detector, process signal electronics and an analytical approach to determining the amount of radioactive material existing. This approach typically involves collecting detector data over a finite period of time and post processing. This algorithm outlines a method in which a counting architecture called Frieder Counting is used to collect raw count data from a counting system. Then machine learning techniques are used to optimize the counting time of radiation or radionuclides present in a material. The advantage of this solution is that it can be applied in real time whereas the current solutions cannot.
Status
Patent pending; seeking development partners