본문 바로가기
KERD
논문KCI 등재

TinyML Gamma Radiation Classifier

Altayeb Moez|Zennaro Marco|Pietrosemoli Ermanno
한국원자력학회 (Korean Nuclear Society)|2023.02|Nuclear Engineering and Technology|제55권 제2호|pp. 443-451|DOI 10.1016/j.net.2022.09.032

2

조회

0

다운로드

1

인용

0

참고문헌

키워드

IoTEdge computingAugmentationTinyMLTiny radiation sensorEdge impulse
주제분류원자력공학

초록

Machine Learning has introduced many solutions in data science, but its application in IoT faces significant challenges, due to the limitations in memory size and processing capability of constrained devices. In this paper we design an automatic gamma radiation detection and identification embedded system that exploits the power of TinyML in a SiPM micro radiation sensor leveraging the Edge Impulse platform. The model is trained using real gamma source data enhanced by software augmentation algorithms. Tests show high accuracy in real time processing. This design has promising applications in general-purpose radiation detection and identification, nuclear safety, medical diagnosis and it is also amenable for deployment in small satellites

Abstract

Machine Learning has introduced many solutions in data science, but its application in IoT faces significant challenges, due to the limitations in memory size and processing capability of constrained devices. In this paper we design an automatic gamma radiation detection and identification embedded system that exploits the power of TinyML in a SiPM micro radiation sensor leveraging the Edge Impulse platform. The model is trained using real gamma source data enhanced by software augmentation algorithms. Tests show high accuracy in real time processing. This design has promising applications in general-purpose radiation detection and identification, nuclear safety, medical diagnosis and it is also amenable for deployment in small satellites

참고문헌 (34)

  1. "IEEE Technol. Soc. Mag", 2018, 13~14
  2. Robert David, "Tensorflow lite micro: embedded machine learning on tinyml systems", CoRR (2020), 08678 abs, 2020 (2010)
  3. "Edge Impulse"[URL]
  4. V. Saveliev, "Silicon avalanche photodiodes on the base of metalresistor-semiconductor (mrs) structures", Nucl. Inst. Methods Phys. Res. Sect. A : Accel. Spectrom. Detect. Assoc. Equip, 442(1), pp.223~229 (2000)
  5. Fabio Acerbi, "Understanding and simulating sipms. Nuclear instruments and methods in Physics research section A: accelerators, spectrometers, detectors and associated equipment", Silicon Photomultipliers Technol. Char. Appl, 926, pp.16~35 (2019)
  6. Giulia Cozzi, "Development of a sipm-based detection module for prompt gamma imaging in proton therapy", IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS, RTSD), 2016, pp.2016[DOI]
  7. Odille F. Karcher, "Fully digital pet is unaffected by any deterioration in tof resolution and tof image quality in the wide range of routine pet count rates", EJNMMI Phys, 8(1) (2021)
  8. "Silicon phomultiplier sensors"[URL]
  10. Zhenhua Lin, "The design of a scintillation system based on SiPMs integrated with gain correction functionality", Nuclear Engineering and Technology, 52(1), pp.164~169 (2020), 한국원자력학회
  11. 이민주, "Pulse pileup correction method for gamma-ray spectroscopy in high radiation fi elds", Nuclear Engineering and Technology, 52(5), pp.1029~1035 (2020), 한국원자력학회[DOI]
  12. G.F. Knoll, "Radiation Detection and Measurement" (2010), WILEY
  16. K. J. Bilton, "Non-negative matrix factorization of gamma-ray spectra for background modeling, detection, and source identification", IEEE Trans. Nucl. Sci, 66(5), pp.827~837 (2019)
  17. Deborah K. Fagan, "Statistical methods applied to gamma-ray spectroscopy algorithms in nuclear security missions", Appl. Radiat. Isot, 70(10), pp.2428~2439 (2012)
  18. David Michael P fund, "Examination of count-starved gamma spectra using the method of spectral comparison ratios", IEEE Trans. Nucl. Sci, 54(4), pp.1232~1238 (2007)
  19. C. J. Sullivan, "Wavelet analysis of sodium iodide spectra", IEEE Nucl. Sci. Symp. Conf. Rec, 1, pp.302~306 (2005)
  20. David Boardman, "Principal component analysis of gamma-ray spectra for radiation portal monitors", IEEE Trans. Nucl. Sci, 59(1), pp.154~160 (2012)
  21. Kyle J. Bilton, "Neural network approaches for mobile spectroscopic gamma-ray source detection", J. Nucl. Eng, 2(2), pp.190~206 (2021)
  22. S.M. Galib, "A comparative study of machine learning methods for automated identifi cation of radioisotopes using NaI gamma-ray spectra", Nuclear Engineering and Technology, 53(12), pp.4072~4079 (2021), 한국원자력학회[DOI]
  23. Andrew D. Nicholson, "Generation of synthetic data for a radiation detection algorithm competition", IEEE Trans. Nucl. Sci, 67(8), pp.1968~1975 (2020)
  24. Jianping He, "Rapid radionuclide identification algorithm based on the discrete cosine transform and bp neural network", Ann. Nucl. Energy, 112, pp.1~8 (2018)
  25. Sebastien C. Wong, "Understanding Data Augmentation for Classification: When to Warp?", 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp.1~6 (2016), IEEE
  26. C. Shorten, "A survey on image data augmentation for deep learning", J. Big Data (2019)
  27. Alexandre Szenicer, "Seismic savanna: machine learning for classifying wildlife and behaviours using ground based vibration field recordings", Remote Sens. Ecol. Conserv, 8(2) (2021)
  28. Francisco J. Moreno-Barea, "Forward noise adjustment scheme for data augmentation", IEEE Symposium Series on Computational Intelligence (SSCI), pp.728~734 (2018)
  31. Kai Vogelgesang, "Uplink transmission probability functions for lora-based direct-to-satellite iot: a case study", IEEE Glob. Commun. Conf. (GLOBECOM) (2021), pp.01~06 (2021)
  32. Doroshkin Alexander, "Laboratory testing of lora modulation for cubesat radio communications", MATEC Web Conf, 158, pp.01008~ (2018)
  33. Inyong Kwon, "Preprocessing Energy Intervals on Spectrum for Real-Time Radionuclide Identification", IEEE Transactions on Nuclear Science, 68(8), pp.2202~2209 (2021), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
  34. G. Daniel, "Automatic and Real-Time Identification of Radionuclides in Gamma-Ray Spectra: A New Method Based on Convolutional Neural Network Trained With Synthetic Data Set", IEEE Transactions on Nuclear Science, 67(4), pp.644~653 (2020), Institute of Electrical and Electronics Engineers (IEEE)[DOI]

관련 논문AI추천

Gamma/neutron classification with SiPM CLYC detectors using frequency-domain analysis for embedded real-time applicationsBogovac Mladen, Carrato Sergio, Cicuttin Andres, Crespo Maria Liz, Kanaki Kalliopi, Morales Iván René한국원자력학회 · Nuclear Engineering and Technology2024 · 제56권 제2호Using machine learning for anomaly detection on a system-on-chip under gamma radiationEhsan Shoaib, Kasap Server, Kolozali Şefki, McDonald-Maier Klaus D., Wächter Eduardo Weber, Zhai Xiaojun한국원자력학회 · Nuclear Engineering and Technology2022 · 제54권 제11호Towards energy-insensitive and robust neutron/gamma classification: A learning-based frequency-domain parametric approachAi Pengcheng, Qin Hongtao, Shang Kaiwen, Sun Xiangming한국원자력학회 · Nuclear Engineering and Technology2025 · 제57권 제9호Time-of-flight based one-dimensional position estimation of radioactive sources using artificial neural network model김승현, 김진홍, 박재형, 송시원, 이봉수, 이상준, 조승현, Cheolhaeng Huh한국원자력학회 · Nuclear Engineering and Technology2025 · 제57권 제9호Radioisotope identification using sparse representation with dictionary learning approach for an environmental radiation monitoring system김기윤, 김원구, 김준혁, 김진환, 이대희, 조규성, 황지성한국원자력학회 · Nuclear Engineering and Technology2022 · 제54권 제3호Edge Impulse Machine Learning for Embedded System Design홍선학(사)디지털산업정보학회 · (사)디지털산업정보학회 논문지2021 · 제17권 제3호

이 간행물의 다른 논문 (39건)

Separation and purification of elements from alkaline and carbonate nuclear waste solutionsSafiulina Alfiya M., Kostikova Galina V., Zhilov Valeriy I., Tsivadze Aslan Yu, Boyarintsev Alexander V., Stepanov Sergei I.pp. 391-407Beryllium oxide utilized in nuclear reactors: Part II: A systematic review of the neutron irradiation effectsZhou Xiang-wen, Hou Ming-dong, Liu Bingpp. 408-420Retrieval methodology for similar NPP LCO cases based on domain specific NLP성풍현, 이재희, 이종범, 성노규pp. 421-431Developing an interface strength technique using the laser shock methodSmith James A., Benefiel Bradley C., Scott Clark L.pp. 432-442The optimization study of core power control based on meta-heuristic algorithm for China initiative accelerator driven subcritical systemLin Cong, Wang Yong-Quan, Zhou Xing-Chen, Lin Huan, Li Jin-Yang, Zhang You-Peng, Gu Long, Du Jun-Liangpp. 452-459
원문 보기