Measurement of uranium enrichment in radiowastes by using a deep learning algorithm and a low resolution detector

• Main Authors: PARK CHAN JUN, PARK JUNG SUK, RYU JI CHANG
• Format: Patent
• Language: English; Korean
• Published: 07.12.2022
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; MEASUREMENT OF NUCLEAR OR X-RADIATION; MEASURING; PHYSICS; TESTING

The present invention relates to a method for measuring uranium enrichment in radioactive waste using a deep learning algorithm and a low-resolution detector. Unlike existing algorithms that require long measurement times, deep learning algorithms and low-resolution detectors are used for effectively analyzing uranium enrichment with a gamma ray spectrum measured over a short period of time. According to the present invention, an algorithm capable of obtaining a real-time measurement spectrum of uranium enrichment in low-level or ultra-low-level radioactive waste is provided. The method for measuring uranium enrichment in radioactive waste using a deep learning algorithm and a low-resolution detector includes steps of: constructing a neural network and receiving a gamma ray spectrum as an input; and extracting uranium enrichment information from the gamma ray spectrum while passing through a layer composed of several nodes to analyze uranium enrichment. 본 발명은 딥러닝 알고리즘과 저분해능 검출기를 이용한 방사성폐기물 내의 우라늄 농축도 측정 방법에 관한 것으로, 긴 측정시간을 요구하는 기존 알고리즘과 달리 딥러닝 알고리즘과 저분해능 검출기를 이용하여 짧은 시간동안 측정한 감마선 스펙트럼으로 우라늄 농축도를 효과적으로 분석할 수 있는 방법에 관한 것이다. 본 발명에 따르면, 저준위 또는 극저준위 수준의 방사성폐기물 내의 우라늄 농축도를 실시간 측정스펙트럼으로 얻을 수 있는 알고리즘을 제공한다.