Yapay Sinir Ağları ve Makine Öğrenme Yöntemlerinin Nükleer Fizik Uygulamaları

Öz

Yapay zekâ ve makine öğrenimi yöntemlerindeki ilerlemeler, bilimsel araştırmalarda geniş uygulanabilirliği olan araçlar sağlamışlardır. Bu teknikler, nükleer teori, deneysel yöntemler, hızlandırıcı teknoloji ve nükleer verilerdeki konuları kapsayan çeşitli alanlarda uygulanmakta ve bilimsel keşifleri ve toplumsal uygulamaları kolaylaştıracak ilerlemeleri sağlamaktadır. Deneysel verilerin analizi ve nükleer sistemlerin teorik olarak modellemesi, fiziğin tüm alanlarında olduğu gibi, korelasyonlara dayalı tahmin yapmak ve etkileşimleri sağlamak amaçlar. Deneysel çalışmalar, her biri benzersiz operasyon, veri toplama ve analiz yöntemlerine sahip dünya çapında birçok laboratuvarı kullanır. Benzer şekilde, teorik nükleer fizikte yayılan odak ölçekleri, algoritma yöntemleri ve belirsizlik ölçümü için geniş ihtiyaçlara yol açar. Boyut ve enerji ölçeklerinde veri türleri dizilerini kullanan bu teorik çalışmalar, YSA/ML yöntemlerinin uygulamaları için mükemmel bir ortam yaratır. Ayrıca, bu yöntemlerin son on yılda daha pratik hale gelmesiyle, nükleer bilim ve teknolojide öğrenmeye dayalı yöntemlerin popülaritesinin artacağı öngörülmekte; sonuç olarak, bu tür metodolojileri uygulamanın yararlarını ve engellerini anlamak, daha iyi araştırma planları oluşturmaya ve proje risklerini ve fırsatlarını belirlemeye yardımcı olabilir. Bu çalışma, yapay zekâ ve makine öğrenmesi teknikleri ile yapılmış nükleer fizik araştırmaları ve nükleer tıp teknolojileri hakkında bilgi vermektedir.

Anahtar Kelimeler

• Nükleer bilimler
• makine öğrenimi
• yapay zeka
• ileri nükleer tıp teknolojileri

Applications of Artificial Neural Networks and Machine Learning Methods in Nuclear Physics

Öz

Advances in artificial intelligence/machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of covering topics in nuclear theory, experimental methods, accelerator technology, and nuclear data, leading to advances that will facilitate scientific discoveries and societal applications. The analysis of experimental data and the theoretical modeling of nuclear systems aims, as is the case in all fields of physics, at uncovering the basic laws of motion in order to make predictions and estimations, as well as finding correlations and causations for the strongly interacting matter. Experimental efforts utilize many laboratories worldwide, each with unique operation, data acquisition, and analysis methods. Similarly, the scales of focus spanned in theoretical nuclear physics lead to broad needs for algorithmic methods and uncertainty quantification. These efforts, utilizing arrays of data types across size and energy scales, create a perfect environment for applications of ANN/ML methods. Furthermore, as these methods have become more practical during the past decade, it is foreseen that the popularity of learning-based methods in nuclear science and technology will increase; consequently, understanding the benefits and barriers of implementing such methodologies can help create better research plans, and identify project risks and opportunities. This study gives information on nuclear physics research and nuclear medical technologies that have been done by artificial intelligence and machine learning techniques.

Anahtar Kelimeler

• Nuclear science
• machine learning
• artificial intelligence
• advanced nuclear medical technologies

Kaynakça

1. [1]. IAEA-TECDOC-1389, Managing Modernization of Nuclear Power Plant Instrumentation and Control Systems, Technical Report International Atomic Energy Agency, 2004.
2. [2]. Parasuraman, R., Riley, V., Humans and Automation: Use, Misuse, Disuse, Abuse, Hum. Factors, 1997, 39(2), 230-253.
3. [3]. Dave, V.S., Dutta, K,.Neural Network Based Models for Software Effort Estimation: A Review, Artif. Intell. Rev., 2014, 42(2), 295e307.
4. [4]. LeCun, Y., Bengio, Y., Hinton, G., Deep Learning. Nature, 2015, 521, 436–444.
5. [5]. Gomez-Fernandez, M., Higleya, K., Tokuhiroc, A., Welterd, K., Wongb, W. K., Yanga, H., Status of Research and Development of Learning-Based Approaches in Nuclear Science and Engineering: A Review, Nuclear Engineering and Design, 2020, 359, 110479. [6]. Buettner, W., Advanced Computerized Operator Support Systems in The FRG. IAEA Bull., 1985, 27, 13–17.
6. [7]. Olmos, P., Diaz, J.C., Perez, J.M., Gomez, P., Rodellar, V., Aguayo, P., Bru, A., GarciaBelmonte, G., de Pablos, J.L., A New Aapproach to Automatic Aadiation Spectrum Analysis. IEEE Trans. Nucl. Sci., 1991, 38(4), 971–975.
7. [8]. Fagan, D. K, Robinson S. M., Runkle, R. C., Statistical Methods Applied to Gamma Ray Spectroscopy Algorithms in Nuclear Security Missions, Appl. Radiat. Isot., 2012, 70(10), 2428-2439.
8. [9]. Breiman, L., Friedman, J., Stone, C., Olshen, R., Classification and Regression Tree, The Wadsworth and Brooks-Cole statistics-probability series, Taylor & Francis, 1984.

9. [10]. Brownlee, J., Machine Learning Mastery, https://machinelearningmastery.com/implement-decision-tree-algorithm-scratch-python/ (Erişim Tarihi: 10.03.2022)
10. [11]. Ağyar, Z., Yapay Sinir Ağlarının Kullanım Alanları ve Bir Uygulama, MMO, Mühendis ve Makine, 2015, (56)662, 22-23.
11. [12]. Ma, J., Jiang, J., Applications of Fault Detection and Diagnosis Methods in Nuclear Power Plants: A Review Prog. Nucl. Energy, 2011, 53 (3), 255-266.
12. [13]. Khan, M., Ding, Q., Perrizo, W., K-nearest Neighbor Classification on Spatial Data Streams Using P-trees, Advances in Knowledge Discovery and Data Mining, 2022, 2336, 517-528.
13. [14]. Vapnik, V.N., The Nature of Statistical Learning Theory, Springer-Verlag, 2022, New York.
14. [15]. Dimitoglou, G., Adams, J.A., Jim, C.M., Comparison of the C4.5 and a Naïve Bayes Classifier for The Prediction of Lung Cancer Survivability, Arxiv, 2012, 1121.
15. [16]. Beyer T., Townsend D.W., Brun T., Kinahan P.E., Charron M., Roddy R., et al. A Combined PET/CT Scanner for Clinical Oncology, J Nucl Med. 2000, 41:1369, 79.
16. [17]. Berg E., Cherry S.R., Innovations in Instrumentation for Positron Emission Tomography, Semin Nucl Med, 2018, 48:311, 31.
17. [18]. Hosny A., Parmar C., Quackenbush J., Schwartz L.H., Aerts H.J.W.L., Artificial Intelligence in Radiology, Nat Rev Cancer, 2018, 18:500, 10.
18. [19]. Seifert, R., Weber, R., Kocakavuk, E., Rischpler, C., Kersting, D., Artificial Intelligence and Machine Learning in Nuclear Medicine, Future Perspectives, Seminars in Nuclear Medicine, 2021, (51)2, 170-177.
19. [20]. Otuka, N., vd., Towards a More Complete and Accurate Experimental Nuclear Reaction Data Library (EXFOR): International Collaboration Between Nuclear Reaction Data Centres (NRDC), Nuclear Data Sheets, 2014, 120, 272-276.
20. [21]. Evaluated Nuclear Data File (ENDF), https://www-nds.iaea.org/exfor/endf.htm.
21. [22]. Koning, A.J., Hilaire S., Duijvestijn, M.C., TALYS-1.0, Proceedings of the International Conference on Nuclear Data for Science and Technology, April 22-27, 2007, Nice, France, EDP Sciences, 2008, 211-214.
22. [23]. Herman, M., Capote,R., Carlson, B.V., Oblozinsky, P., Sin, M., Trkov, A., Wienke, H., Zerkin, V., EMPIRE: Nuclear Reaction Model Code System for Data Evaluation, Nucl. Data Sheets, 2007, 108, 2655-2715.
23. [24]. Vicente-Valdez, P., Bernstein, L., Fratoni, M., Nuclear Data Evaluation Augmented by Machine Learning, Annals of Nuclear Energy, 2021, 163, 108596.
24. [25]. Rising, M.E., Brown, F.B., Salazar, J.R., Sweezy, J.E., Overview of the MCNP6 SQA Plan & Requirements, 2020, LA-UR-20-26666.
25. [26]. Grechanuk, P.A., Rising, M.E., Palmer, T.S., Application of Machine Learning Algorithms to Identify Problematic Nuclear Data, 2021, (195)12, 1265-1278.
26. [27]. Denœux, T., Masson, M., Dubuisson B., Advanced Pattern Recognition Techniques for System Monitoring and Diagnosis: A Ssurvey, Journal Européen des Systémes Automatisés, 1997, 31, 1509-1540.
27. [28]. Boring, R.L., Thomas, K.D., Ulrich, T.A, Lew R.T., Computerized Operator Support Systems to Aid Decision Making in Nuclear Power Plants Proc. Manuf., 2015, 3, 5261-5268.
28. [29]. Chai, J., Sisk, D.R., Bond, L.J., Jarrell, D.B., Hatley, D.D., Meador, R.J., Koehler, T.M., Watkins, K.S., Kim, W., On-line Intelligent Self-diagnostic Monitoring System for Next Generation Nuclear Power Plants, United States. Dept. of Energy, 2003.
29. [30]. Patra, S.R., Rajeswari, S., Satyamurthy, S.A.V., Artificial Neural Network Model for Intermediate Heat Exchanger of Nuclear Reactor, Int. J. Comput. Appl., 2010, 1(26), 65-72.
30. [31]. MONTES, José Luis, et al., Local Power Peaking Factor Estimation in Nuclear Fuel by Artificial Neural Networks, Annals of Nuclear Energy, 2009, 36.1: 121-130.
31. [32]. Calivá, F., et al., A Deep Learning Approach to Anomaly Detection in Nuclear Reactors, International Joint Conference on Neural Networks (IJCNN) (July 2018), 1-8.
32. [33]. Agarwal, V., Alamaniotis, M., Tsoukalas, L.H., Predictive based monitoring of nuclear plant component degradation using support vector regression. In: Conference: 9. International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human Machine Interface Technologies. Idaho National Lab., 2015.
33. [34]. Ma, J., Jiang, J., Applications of Fault Detection and Diagnosis Methods in Nuclear Power Plants: A review. Progress in Nuclear Energy, 2011, 53.3: 255-266.
34. [35]. Zio, E., George E., Pedroni, N., Quantitative Functional Failure Analysis of a Thermal–Hydraulic Passive System by Means of Bootstrapped Artificial Neural Networks. Annals of Nuclear Energy, 2010, 37.5, 639-649.
35. [36]. Yang, Z., Ji, H., Huang, Z., Wang, B., Li, H., Application of Convolution Neural Network to Flow Pattern Identification of Gas-Liquid Two-Phase Flow in Small-Size Pipe, Chinese Automation Congress (CAC), 2017, 1389–1393.
36. [37]. Kanevski, M., Parkin, R., Pozdnukhov, A., Timonin, V., Maignan, M., Demyanov, V., Canu, S., Environmental Data Mining and Modeling Based on Machine Learning Algorithms and Geostatistics, Environ. Modell. Software, 2004, 19(9), 845-855.
37. [38]. Liu, Y., Li, M., Xie, C., Peng, M., Xie, F., Path-Planning Research in Radioactive Environment Based on Particle Swarm Algorithm, Prog. Nucl. Energy, 2014, 74, 184-192.
38. [39]. Yeşilkanat, C.M., Kobya, Y., Taşkin, H., Çevik, U., Spatial Interpolation and Radiological Mapping of Ambient Gamma Dose Rate by Using Artificial Neural Networks and Fuzzy Logic Methods, J. Environ. Radioact., 2017, 175, 78-93.
39. [40]. Einian, M.R., Aghamiri, S.M.R., Ghaderi, R., Evaluation of the Suitability of Neural Network Method for Prediction of Uranium Activity Ratio in Environmental Alpha Spectra, Appl. Radiat. Isot., 2015, 105, 225-232.