N. Egel and R. L. Hines, Chinese views on nuclear weapons: Evidence from an online survey, Res. Polit., vol. 8, no. 3, p. 20531680211032840, 2021.
FCCC, Report of the Conference of the Parties to the United Nations Framework Convention on Climate Change (21st Session, 2015: Paris). Geneva, Switzerland: FCCC, 2015.
F. H. Zhu, Y. S. Wang, Z. Xu, J. Z. Li, Y. H. Dong, H. Li, X. L. Li, Y. Hu, X. L. Sun, and Y. Ding, Research on the development path of carbon peak and carbon neutrality in China’s power industry, (in Chinese), Electric Power Technol. Environ. Prot., vol. 37, no. 3, pp. 9-16, 2021.
R. J. Woods and A. K. Pikaev, Applied Radiation Chemistry: Radiation Processing. New York, NY, USA: John Wiley & Sons, 1993.
Z. Zimek, G. Przybytniak, and I. Kaluska, Radiation processing of polymers and semiconductors at the institute of nuclear chemistry and technology, Nukleonika, vol. 51, no. S1, pp. S129-S132, 2006.
P. E. Christian and K. M. Waterstram-Rich, Nuclear Medicine and PET/CT: Technology and Techniques. St. Louis, MO, USA: Mosby, 2007.
W. W. Lie, B. Jiang, and W. J. Zhao, Obstetric imaging diagnostic platform based on cloud computing technology under the background of smart medical big data and deep learning, IEEE Access, vol. 8, pp. 78265-78278, 2020.
C. Aktolun, Artificial intelligence and radiomics in nuclear medicine: Potentials and challenges, Eur. J. Nucl. Med. Mol. Imaging, vol. 46, no. 13, pp. 2731-2736, 2019.
S. Russell, Artificial intelligence: The future is superintelligent, Nature, vol. 548, no. 7669, pp. 520-521, 2017.
X. Q. Yang, J. Guo, H. Tang, and Z. Liao, Application prospect analysis of big data and artificial intelligence in the field of nuclear industry, (in Chinese), Inf. Commun., vol. 2, pp. 266-268, 2020.
Z. Y. Kong, Development trend of robot application in nuclear industry, (in Chinese), At. Energy Sci. Technol., vol. 14, no. 5, pp. 645-649&625, 1980.
K. Zhang, J. B. Ni, K. Yang, X. H. Liang, J. Ren, and X. S. Shen, Security and privacy in smart city applications: Challenges and solutions, IEEE Commun. Mag., vol. 55, no. 1, pp. 122-129, 2017.
J. Mabrouki, M. Azrour, D. Dhiba, Y. Farhaoui, and S. El Hajjaji, IoT-based data logger for weather monitoring using arduino-based wireless sensor networks with remote graphical application and alerts, Big Data Mining and Analytics, vol. 4, no. 1, pp. 25-32, 2021.
A. Guezzaz, Y. Asimi, M. Azrour, and A. Asimi, Mathematical validation of proposed machine learning classifier for heterogeneous traffic and anomaly detection, Big Data Mining and Analytics, vol. 4, no. 1, pp. 18-24, 2021.
J. P. Zhang, F. Y. Wang, K. F. Wang, W. H. Lin, X. Xu, and C. Chen, Data-driven intelligent transportation systems: A survey, IEEE Trans. Intell. Transp. Syst., vol. 12, no. 4, pp. 1624-1639, 2011.
L. Figueiredo, I. Jesus, J. A. T. Machado, J. R. Ferreira, and J. L. M. De Carvalho, Towards the development of intelligent transportation systems, in Proc. ITSC 2001. 2001 IEEE Intelligent Transportation Systems, Oakland, CA, USA, 2001, pp. 1206-1211.
Y. N. Malek, M. Najib, M. Bakhouya, and M. Essaaidi, Multivariate deep learning approach for electric vehicle speed forecasting, Big Data Mining and Analytics, vol. 4, no. 1, pp. 56-64, 2021.
J. H. Kamdar, J. J. Praba, and J. J. Georrge, Artificial intelligence in medical diagnosis: Methods, algorithms and applications, in Machine Learning with Health Care Perspective, V. Jain, J. M. Chatterjee, eds. Springer, 2020, pp. 27-37.
W. L. Bi, A. Hosny, M. B. Schabath, M. L. Giger, N. J. Birkbak, A. Mehrtash, T. Allison, O. Arnaout, C. Abbosh, I. F. Dunn, et al., Artificial intelligence in cancer imaging: Clinical challenges and applications, CA: Cancer J. Clin., vol. 69, no. 2, pp. 127-157, 2019.
K. K. Singh and A. Singh, Diagnosis of COVID-19 from chest X-ray images using wavelets-based depthwise convolution network, Big Data Mining and Analytics, vol. 4, no. 2, pp. 84-93, 2021.
R. Y. Zhong, X. Xu, E. Klotz, and S. T. Newman, Intelligent manufacturing in the context of industry 4.0: A review, Engineering, vol. 3, no. 5, pp. 616-630, 2017.
G. Lampropoulos, K. Siakas, and T. Anastasiadis, Internet of things (IoT) in industry: Contemporary application domains, innovative technologies and intelligent manufacturing, Int. J. Adv. Sci. Res. Eng., vol. 4, no. 10, pp. 109-118, 2018.
J. Mabrouki, M. Azrour, G. Fattah, D. Dhiba, and S. El Hajjaji, Intelligent monitoring system for biogas detection based on the internet of things: Mohammedia, morocco city landfill case, Big Data Mining and Analytics, vol. 4, no. 1, pp. 10-17, 2021.
J. W. Veile, D. Kiel, J. M. Müller, and K. I. Voigt, Lessons learned from industry 4.0 implementation in the German manufacturing industry, J. Manuf. Technol. Manage., vol. 31, no. 5, pp. 977-997, 2019.
J. Habánik, A. Grencíková, and K. Krajco, The impact of industry 4.0 on the selected macroeconomic indicators in Slovak republic, Germany, the USA and Japan, J. Int. Stud., vol. 14, no. 2, pp. 26-37, 2021.
J. M. Müller and K. I. Voigt, Sustainable industrial value creation in SMEs: A comparison between industry 4.0 and made in China 2025, Int. J. Precis. Eng. Manuf.-Green Technol., vol. 5, no. 5, pp. 659-670, 2018.
L. Jay, X. Li, Y. M. Xu, S. J. Yang, and K. Y. Sun, Recent advances and prospects in industrial AI and applications, (in Chinese), Acta Automat. Sin., vol. 46, no. 10, pp. 2031-2044, 2020.
T. Y. Chai, Development directions of industrial artificial intelligence, (in Chinese), Acta Automat. Sin., vol. 46, no. 10, pp. 2005-2012, 2020.
Y. Yuan, Y. Zhang, and H. Ding, Research on key technology of industrial artificial intelligence and its application in predictive maintenance, Acta Automat. Sin., vol. 46, no. 10, pp. 2013-2030, 2020.
Y. Yu, M. Li, L. L. Liu, Y. H. Li, and J. X. Wang, Clinical big data and deep learning: Applications, challenges, and future outlooks, Big Data Mining and Analytics, vol. 2, no. 4, pp. 288-305, 2019.
J. C. Lv, M. Ye, and Z. Yi, Convergence analysis for Oja+ MCA learning algorithm, in Proc. Int. Symp. on Neural Networks, Dalian, China, 2004, pp. 810-814.
J. C. Lv and Z. Yi, Convergence analysis of Chauvin’s PCA learning algorithm with a constant learning rate, Chaos, Solitons Fractals, vol. 32, no. 4, pp. 1562-1571, 2007.
G. E. Hinton and R. R. Salakhutdinov, Reducing the dimensionality of data with neural networks, Science, vol. 313, no. 5786, pp. 504-507, 2006.
Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol. 521, no. 7553, pp. 436-444, 2015.
S. Moazemi, Z. Khurshid, A. Erle, S. Lütje, M. Essler, T. Schultz, and R. A. Bundschuh, Machine learning facilitates hotspot classification in PSMA-PET/CT with nuclear medicine specialist accuracy, Diagnostics, vol. 10, no. 9, p. 622, 2020.
K. M. He, X. Y. Zhang, S. Q. Ren, and J. Sun, Deep residual learning for image recognition, in Proc. 2016 IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016, pp. 770-778.
R. Girshick, Fast R-CNN, in Proc. IEEE Int. Conf. Computer Vision, Santiago, Chile, 2015, pp. 1440-1448.
T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient estimation of word representations in vector space, arXiv preprint arXiv: 1301.3781, 2013.
I. Sutskever, O. Vinyals, and Q. V. Le, Sequence to sequence learning with neural networks, in Proc. 27th Int. Conf. Neural Information Processing Systems, Montreal, Canada, 2014, pp. 3104-3112.
F. Seide, G. Li, and D. Yu, Conversational speech transcription using context-dependent deep neural networks, in Proc. of Twelfth Annual Conference of the International Speech Communication Association, Florence, Italy, 2011, pp. 437-440.
G. E. Dahl, D. Yu, L. Deng, and A. Acero, Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition, IEEE Trans. Audio, Speech, Lang. Process., vol. 20, no. 1, pp. 30-42, 2012.
K. Xu, J. L. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhutdinov, R. S. Zemel, and Y. Bengio, Show, attend and tell: Neural image caption generation with visual attention, in Proc. 32nd Int. Conf. Int. Conf. Machine Learning, Lille, France, 2015, pp. 2048-2057.
J. C. Lv, Z. Yi, and K. K. Tan, Global convergence of GHA learning algorithm with nonzero-approaching adaptive learning rates, IEEE Trans. Neural Netw., vol. 18, no. 6, pp. 1557-1571, 2007.
N. Srivastava and R. Salakhutdinov, Multimodal learning with deep Boltzmann machines, in Proc. 25th Int. Conf. Neural Information Processing Systems, Lake Tahoe, NV, USA, 2012, pp. 2222-2230.
W. Zhong, N. Yu, and C. Y. Ai, Applying big data based deep learning system to intrusion detection, Big Data Mining and Analytics, vol. 3, no. 3, pp. 181-195, 2020.
C. W. Tang, J. C. Lv, Y. Chen, and J. X. Guo, An angle-based method for measuring the semantic similarity between visual and textual features, Soft Comput., vol. 23, no. 12, pp. 4041-4050, 2019.
S. Suman, Artificial intelligence in nuclear industry: Chimera or solution? J. Clean. Prod., vol. 278, p. 124022, 2021.
H. A. Saeed, M. J. Peng, H. Wang, and B. W. Zhang, Novel fault diagnosis scheme utilizing deep learning networks, Prog. Nucl. Energy, vol. 118, p. 103066, 2020.
J. L. Lang, C. W. Tang, Y. Gao, and J. C. Lv, Knowledge distillation method for surface defect detection, in Proc. 28th Int. Conf. Neural Information Processing, Bali, Indonesia, 2021, pp. 644-655.
J. Li, Contemporary China: Nuclear Industry, (in Chinese), Hong Kong, China: Contemporary China Press, 2009.
S. Mandal, B. Santhi, S. Sridhar, K. Vinolia, and P. Swaminathan, Nuclear power plant thermocouple sensor-fault detection and classification using deep learning and generalized likelihood ratio test, IEEE Trans. Nucl. Sci., vol. 64, no. 6, pp. 1526-1534, 2017.
F. C. Chen and M. R. Jahanshahi, NB-CNN: Deep learning-based crack detection using convolutional neural network and Naïve Bayes data fusion, IEEE Trans. Ind. Electron., vol. 65, no. 5, pp. 4392-4400, 2017.
V. H. C. Pinheiro, M. C. Dos Santos, F. S. M. Do Desterro, R. Schirru, and C. M. D. N. A. Pereira, Nuclear power plant accident identification system with “don’t know” response capability: Novel deep learning-based approaches, Ann. Nucl. Energy, vol. 137, p. 107111, 2020.
D. C. Chen, Z. Wang, D. Guo, V. Orekhov, and X. B. Qu, Review and prospect: Deep learning in nuclear magnetic resonance spectroscopy, arXiv preprint arXiv: 2001.04813, 2020.
J. P. Langstrand, H. T. Nguyen, and R. McDonald, Applying deep learning to solve alarm flooding in digital nuclear power plant control rooms, in Proc. Int. Conf. Applied Human Factors and Ergonomics, San Diego, CA, USA, 2020, pp. 521-527.
H. J. Zhao, H. Tang, B. Xiao, J. Zheng, Y. W. Li, and X. Q. Yang, Application research of artificial intelligence and big data in the field of nuclear power, (in Chinese), China Nucl. Power, vol. 12, no. 3, pp. 247-251, 2019.
X. M. Xiao and R. Sha, Application of artificial intelligence in nuclear energy industry, (in Chinese), China Informatization, vol. 12, pp. 10-12, 2017.
M. Schneider and A. Froggatt, The world nuclear industry status report 2019, in World Scientific Encyclopedia of Climate Change: Case Studies of Climate Risk, Action, and Opportunity Volume 2, J. W. Dash, ed. Singapore: World Scientific, 2021, pp. 203-209.
S. L. Zhang and Q. Li, The development and scale of the nuclear power in China under the restraint of low carbon, (in Chinese), China Popul., Resour. Environ., vol. 25, no. 6, pp. 47-52, 2015.
F. M. Salman and S. S. Abu-Naser, Expert system for castor diseases and diagnosis, Int. J. Eng. Inf. Syst., vol. 3, no. 3, pp. 1-10, 2019.
R. P. Martin and B. Nassersharif, Deep knowledge expert system for diagnosis of multiple-failure severe transients in nuclear power plant, in Artificial Intelligence and Other Innovative Computer Applications in the Nuclear Industry, M. C. Majumdar, D. Majumdar, and J. I. Sackett, eds. Boston, MA, USA: Springer, 1988, pp. 281-288.
M. A. Al-Garadi, A. Mohamed, A. K. Al-Ali, X. J. Du, I. Ali, and M. Guizani, A survey of machine and deep learning methods for internet of things (IoT) security, IEEE Commun. Surv. Tut., vol. 22, no. 3, pp. 1646-1685, 2020.
A. Hedayat, Developing a futuristic multi-objective optimization of the fuel management problems for the nuclear research reactors, Kerntechnik, vol. 85, no. 1, pp. 26-37, 2021.
R. Seifert, M. Weber, E. Kocakavuk, C. Rischpler, and D. Kersting, Artificial intelligence and machine learning in nuclear medicine: Future perspectives, Semin. Nucl. Med., vol. 51, no. 2, pp. 170-177, 2021.
O. Tokatli, P. Das, R. Nath, L. Pangione, A. Altobelli, G. Burroughes, E. T. Jonasson, M. F. Turner, and R. Skilton, Robot-assisted glovebox teleoperation for nuclear industry, Robotics, vol. 10, no. 3, p. 85, 2021.
D. Lee and J. Kim, Autonomous algorithm for safety systems of the nuclear power plant by using the deep learning, in Proc. Int. Conf. Applied Human Factors and Ergonomics, Los Angeles, CA, USA, 2017, pp. 72-82.
M. Elsisi, M. Q. Tran, K. Mahmoud, M. Lehtonen, and M. M. F. Darwish, Deep learning-based industry 4.0 and internet of things towards effective energy management for smart buildings, Sensors, vol. 21, no. 4, p. 1038, 2021.
P. Ongsulee, Artificial intelligence, machine learning and deep learning, in Proc. 2017 15th Int. Conf. ICT and Knowledge Engineering (ICT&KE), Bangkok, Thailand, 2017, pp. 1-6.
G. E. Hinton, Deep belief networks, Scholarpedia, vol. 4, no. 5, p. 5947, 2009.
L. R. Medsker and L. C. Jain, Recurrent Neural Networks: Design and Applications. Washington, DC, USA: CRC Press, 2001, pp. 64-67.
S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Comput., vol. 9, no. 8, pp. 1735-1780, 1997.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative adversarial nets, in Proc. 27th Int. Conf. Neural Information Processing Systems, Montreal, Canada, 2014, pp. 2672-2680.
K. O’Shea and R. Nash, An introduction to convolutional neural networks, arXiv preprint arXiv: 1511.08458, 2015.
A. Veit, M. J. Wilber, and S. J. Belongie, Residual networks behave like ensembles of relatively shallow networks, in Proc. Advances in Neural Information Processing Systems, Barcelona, Spain, 2016, pp. 550-558.
K. M. He, X. Y. Zhang, S. Q. Ren, and J. Sun, Identity mappings in deep residual networks, in Proc. 14th European Conf. Computer Vision, Amsterdam, The Netherlands, 2016, pp. 630-645.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, Attention is all you need, in Proc. 31st Int. Conf. Neural Information Processing Systems, Long Beach, CA, USA, 2017, pp. 6000-6010.
J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, BERT: Pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv: 1810.04805, 2018.
S. K. Patnaik, C. N. Babu, and M. Bhave, Intelligent and adaptive web data extraction system using convolutional and long short-term memory deep learning networks, Big Data Mining and Analytics, vol. 4, no. 4, pp. 279-297, 2021.
C. A. F. Jorge, A. C. A. Mól, C. M. N. A. Pereira, M. A. C. Aghina, and D. V. Nomiya, Human-system interface based on speech recognition: Application to a virtual nuclear power plant control desk, Prog. Nucl. Energy, vol. 52, no. 4, pp. 379-386, 2010.
G. Chauhan and P. Chaudhari, Robotic control using speech recognition and android, Int. J. Eng. Res. Gen. Sci., vol. 3, no. 1, pp. 1210-1216, 2015.
H. Choi, Deep learning in nuclear medicine and molecular imaging: Current perspectives and future directions, Nucl. Med. Mol. Imaging, vol. 52, no. 2, pp. 109-118, 2018.
L. Sun, C. Zhao, Z. Yan, P. C. Liu, T. Duckett, and R. Stolkin, A novel weakly-supervised approach for RGB-D-based nuclear waste object detection, IEEE Sens. J., vol. 19, no. 9, pp. 3487-3500, 2019.
C. J. Wang, Z. H. Li, and B. Sarpong, Multimodal adaptive identity-recognition algorithm fused with gait perception, Big Data Mining and Analytics, vol. 4, no. 4, pp. 223-232, 2021.
C. W. Tang, X. Yang, J. C. Lv, and Z. A. He, Zero-shot learning by mutual information estimation and maximization, Knowl.-Based Syst., vol. 194, p. 105490, 2020.
J. Gao, P. Li, Z. K. Chen, and J. N. Zhang, A survey on deep learning for multimodal data fusion, Neural Comput., vol. 32, no. 5, pp. 829-864, 2020.
H. R. Roth, L. Lu, A. Farag, H. C. Shin, J. M. Liu, E. B. Turkbey, and R. M. Summers, DeepOrgan: Multi-level deep convolutional networks for automated pancreas segmentation, in Proc. 18th Int. Conf. Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015, pp. 556-564.
C. W. Tang, Z. A. He, Y. X. Li, and J. C. Lv, Zero-shot learning via structure-aligned generative adversarial network, IEEE Trans. Neural Netw. Learn. Syst., .
X. Hong, Y. L. Zan, F. H. Weng, W. J. Tao, Q. Y. Peng, and Q. Huang, Enhancing the image quality via transferred deep residual learning of coarse PET sinograms, IEEE Trans. Med. Imaging, vol. 37, no. 10, pp. 2322-2332, 2018.
K. Gong, J. H. Guan, K. Kim, X. Z. Zhang, J. Yang, Y. Seo, G. El Fakhri, J. Y. Qi, and Q. Z. Li, Iterative PET image reconstruction using convolutional neural network representation, IEEE Trans. Med. Imaging, vol. 38, no. 3, pp. 675-685, 2019.
L. Huang, Y. Zhou, Y. T. Han, J. K. Hammitt, J. Bi, and Y. Liu, Effect of the fukushima nuclear accident on the risk perception of residents near a nuclear power plant in China, Proc. Natl. Acad. Sci. USA, vol. 110, no. 49, pp. 19742-19747, 2013.
X. S. Si, W. B. Wang, C. H. Hu, and D. H. Zhou, Remaining useful life estimation-a review on the statistical data driven approaches, Eur. J. Oper. Res., vol. 213, no. 1, pp. 1-14, 2011.
Z. P. Guo, Z. W. Wu, S. Liu, X. Ma, C. Y. Wang, D. J. Yan, and F. L. Niu, Defect detection of nuclear fuel assembly based on deep neural network, Ann. Nucl. Energy, vol. 137, p. 107078, 2020.
Q. S. Jiang, D. P. Tan, Y. B. Li, S. M. Ji, C. P. Cai, and Q. M. Zheng, Object detection and classification of metal polishing shaft surface defects based on convolutional neural network deep learning, Appl. Sci., vol. 10, no. 1, p. 87, 2020.
X. Y. Suo, J. Liu, L. C. Dong, S. F. Chen, E. H. Lu, and N. Chen, A machine vision-based defect detection system for nuclear-fuel rod groove, J. Intell. Manuf., .
H. Wang, M. J. Peng, R. Y. Xu, A. Ayodeji, and H. Xia, Remaining useful life prediction based on improved temporal convolutional network for nuclear power plant valves, Front. Energy Res., vol. 8, p. 584463, 2020.
H. Li, W. Zhao, Y. X. Zhang, and E. Zio, Remaining useful life prediction using multi-scale deep convolutional neural network, Appl. Soft Comput., vol. 89, p. 106113, 2020.
F. Calivá, F. S. De Ribeiro, A. Mylonakis, C. Demazière, P. Vinai, G. Leontidis, and S. Kollias, A deep learning approach to anomaly detection in nuclear reactors, in Proc. 2018 Int. Joint Conf. Neural Networks, Rio de Janeiro, Brazil, 2018, pp. 1-8.
F. C. Chen, M. R. Jahanshahi, R. T. Wu, and C. Joffe, A texture-based video processing methodology using Bayesian data fusion for autonomous crack detection on metallic surfaces, Comput.-Aided Civ. Inf. Eng., vol. 32, no. 4, pp. 271-287, 2017.
Y. J. Jeon, J. P. Yun, D. C. Choi, and S. W. Kim, Defect detection algorithm for corner cracks in steel billet using discrete wavelet transform, in Proc. ICCAS-SICE, Fukuoka, Japan, 2009, p. 276973.
Y. J. Jeon, D. C. Choi, S. J. Lee, J. P. Yun, and S. W. Kim, Defect detection for corner cracks in steel billets using a wavelet reconstruction method, J. Opt. Soc. Am. A, vol. 31, no. 2, pp. 227-237, 2014.
J. P. Yun, Y. Park, B. Seo, S. W. Kim, S. H. Choi, C. H. Park, H. M. Bae, and H. W. Hwang, Development of real-time defect detection algorithm for high-speed steel bar in coil (BIC), in Proc. 2006 SICE-ICASE Int. Joint Conf., Busan, Republic of Korea, 2006, pp. 2495-2498.
F. C. Chen and M. R. Jahanshahi, NB-FCN: Real-time accurate crack detection in inspection videos using deep fully convolutional network and parametric data fusion, IEEE Trans. Instrum. Measure., vol. 69, no. 8, pp. 5325-5334, 2020.
F. C. Chen and M. R. Jahanshahi, ARF-Crack: Rotation invariant deep fully convolutional network for pixel-level crack detection, Mach. Vis. Appl., vol. 31, no. 6, p. 47, 2020.
Y. Gao, C. W. Tang, J. L. Lang, and J. C. Lv, End-to-end edge detection via improved transformer model, in Proc. 28th Int. Conf. Neural Information Processing, Sanur, Indonesia, 2021, pp. 514-525.
L. Qi, J. Kuen, J. X. Gu, Z. Lin, Y. Wang, Y. K. Chen, Y. W. Li, and J. Y. Jia, Multi-scale aligned distillation for low-resolution detection, in Proc. 2021 IEEE/CVF Conf. Computer Vision and Pattern Recognition, Nashville, TN, USA, 2021, pp. 14438-14448, 2021.
Z. Tian, C. H. Shen, H. Chen, and T. He, FCOS: Fully convolutional one-stage object detection, in Proc. IEEE/CVF Int. Conf. Computer Vision, Seoul, Republic of Korea, 2019, pp. 9626-9635.
G. Hinton, O. Vinyals, and J. Dean, Distilling the knowledge in a neural network, arXiv preprint arXiv: 1503.02531, 2015.
Z. Liu, Y. T. Lin, Y. Cao, H. Hu, Y. X. Wei, Z. Zhang, S. Lin, and B. N. Guo, Swin transformer: Hierarchical vision transformer using shifted windows, arXiv preprint arXiv: 2103.14030, 2021.
H. G. Kim, I. H. Kim, Y. I. Jung, D. J. Park, J. Y. Park, and Y. H. Koo, High-temperature oxidation behavior of CR-coated zirconium alloy, in Proc. of LWR Fuel Performance Meeting/TopFuel, Charlotte, NC, USA, 2013, pp. 842-846.
H. Wang, M. J. Peng, Y. K. Liu, S. W. Liu, R. Y. Xu, and H. Saeed, Remaining useful life prediction techniques of electric valves for nuclear power plants with convolution kernel and LSTM, Sci. Technol. Nucl. Install., vol. 2020, p. 8349349, 2020.
H. Wang, M. J. Peng, Z. Miao, Y. K. Liu, A. Ayodeji, and C. M. Hao, Remaining useful life prediction techniques for electric valves based on convolution auto encoder and long short term memory, ISA Trans., vol. 108, pp. 333-342, 2021.
E. Madar, R. Klein, and J. Bortman, Contribution of dynamic modeling to prognostics of rotating machinery, Mech. Syst. Signal Process., vol. 123, pp. 496-512, 2019.
C. D. Manning, M. Surdeanu, J. Bauer, J. R. Finkel, S. Bethard, and D. McClosky, The Stanford CoreNLP natural language processing toolkit, in Proc. 52nd Ann. Meeting of the Association for Computational Linguistics, Baltimore, MD, USA, 2014, pp. 55-60.
Y. F. Zhao, X. X. Diao, J. Huang, and C. Smidts, Automated identification of causal relationships in nuclear power plant event reports, Nucl. Technol., vol. 205, no. 8, pp. 1021-1034, 2019.
W. B. Cottrell and R. B. Gallaher, Licensee event report sequence coding and search procedure workshop, Nucl. Saf., vol. 22, no. 2, pp. 162-164, 1981.
A. V. Oppenheim, Discrete-Time Signal Processing. Upper Saddle River, NJ, USA: Pearson Education India, 1999.
J. B. Ramgire and S. M. Jagdale, Speech control pick and place robotic arm with flexiforce sensor, in Proc. 2016 Int. Conf. Inventive Computation Technologies (ICICT), Coimbatore, India, 2016, pp. 1-5.
Y. S. Chen, M. Lin, R. Yu, and T. S. Wang, Research on simulation and state prediction of nuclear power system based on LSTM neural network, Sci. Technol. Nucl. Install., vol. 2021, p. 8839867, 2021.
Y. S. Chen, Y. H. Yang, M. Lin, and R. Yu, Fault diagnosis technology of nuclear power plant based on deep learning neural network, (in Chinese), J. Shanghai Jiaotong Univ., vol. 52, no. S1, pp. 58-61, 2018.
D. Lee, P. H. Seong, and J. Kim, Autonomous operation algorithm for safety systems of nuclear power plants by using long-short term memory and function-based hierarchical framework, Ann. Nucl. Energy, vol. 119, pp. 287-299, 2018.
J. K. She, S. Y. Xue, P. W. Sun, and H. S. Cao, The application of LSTM model to the prediction of abnormal operation in nuclear power plants, (in Chinese), Instrumentation, vol. 26, no. 12, pp. 39-44, 2019.
M. I. Radaideh, C. Pigg, T. Kozlowski, Y. J. Deng, and A. N. Qu, Neural-based time series forecasting of loss of coolant accidents in nuclear power plants, Expert Syst. Appl., vol. 160, p. 113699, 2020.
J. Choi and S. J. Lee, Consistency index-based sensor fault detection system for nuclear power plant emergency situations using an LSTM network, Sensors, vol. 20, no. 6, p. 1651, 2020.
S. Y. Zhang, T. Y. Lu, H. Zeng, C. Xu, Z. Zhang, Q. Y. Huang, Y. Y. Zhang, and Y. M. Wang, Multi-feature fusion multi-step state prediction of nuclear power sensor based on LSTM, (in Chinese), Nucl. Power Eng., vol. 42, no. 3, pp. 208-213, 2021.
S. Mandal, B. Santhi, S. Sridhar, K. Vinolia, and P. Swaminathan, Nuclear power plant thermocouple sensor-fault detection and classification using deep learning and generalized likelihood ratio test, IEEE Trans. Nucl. Sci., vol. 64, no. 6, pp. 1526-1534, 2017.
G. J. Liu, S. Yang, G. X. Wang, F. L. Li, and D. D. You, A decision-making method for machinery abnormalities based on neural network prediction and Bayesian hypothesis testing, Electronics, vol. 10, no. 14, p. 1610, 2021.
D. Wang, Z. H. Wei, H. Li, J. M. Li, and W. Y. Huang, Real-time calculation of nuclear critical safety data based on bp neural network, (in Chinese), Nucl. Sci. Technol., vol. 8, no. 4, pp. 220-228, 2020.
M. Mendoza, P. V. Tsvetkov, and M. Lewis, Multi-modal global surveillance methodology for predictive and on-demand characterization of localized processes using cube satellite platforms and deep learning techniques, Remote Sens. Appl.: Soc. Environ., vol. 22, p. 100518, 2021.
Y. Feldman, M. Arno, C. Carrano, B. Ng, and B. Chen, Toward a multimodal-deep learning retrieval system for monitoring nuclear proliferation activities, J. Nucl. Mater. Manage., vol. 46, no. 3, 2018.
K. Kim, D. F. Wu, K. Gong, J. Dutta, J. H. Kim, Y. D. Son, H. K. Kim, G. El Fakhri, and Q. Z. Li, Penalized PET reconstruction using deep learning prior and local linear fitting, IEEE Trans. Med. Imaging, vol. 37, no. 6, pp. 1478-1487, 2018.
D. Nie, X. H. Cao, Y. Z. Gao, L. Wang, and D. G. Shen, Estimating CT image from MRI data using 3D fully convolutional networks, in Proc. 1st Int. Workshop on Deep Learning in Medical Image Analysis, Athens, Greece, 2016, pp. 170-178.
A. Torrado-Carvajal, J. Vera-Olmos, D. Izquierdo-Garcia, O. A. Catalano, M. A. Morales, J. Margolin, A. Soricelli, M. Salvatore, N. Malpica, and C. Catana, Dixon-VIBE deep learning (DIVIDE) pseudo-CT synthesis for pelvis PET/MR attenuation correction, J. Nucl. Med., vol. 60, no. 3, pp. 429-435, 2019.
D. Hwang, S. K. Kang, K. Y. Kim, S. Seo, J. C. Paeng, D. S. Lee, and J. S. Lee, Generation of pet attenuation map for whole-body time-of-flight 18F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps, J. Nucl. Med., vol. 60, no. 8, pp. 1183-1189, 2019.
S. Kaplan and Y. M. Zhu, Full-dose PET image estimation from low-dose PET image using deep learning: A pilot study, J. Digit. Imaging, vol. 32, no. 5, pp. 773-778, 2019.
H. Choi and K. H. Jin, Fast and robust segmentation of the striatum using deep convolutional neural networks, J. Neurosci. Methods, vol. 274, pp. 146-153, 2016.
W. Shen, M. Zhou, F. Yang, C. Y. Yang, and J. Tian, Multi-scale convolutional neural networks for lung nodule classification, in Proc. 24th Int. Conf. Information Processing in Medical Imaging, Isle of Skye, UK, 2015, pp. 588-599.
A. A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. Van Riel, M. M. W. Wille, M. Naqibullah, C. I. Sánchez, and B. Van Ginneken, Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks, IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1160-1169, 2016.
H. K. Wang, Z. W. Zhou, Y. C. Li, Z. H. Chen, P. O. Lu, W. Z. Wang, W. Y. Liu, and L. J. Yu, Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images, EJNMMI Res., vol. 7, no. 1, p. 11, 2017.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, in Proc. 26th Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA, 2012, pp. 1106-1114.
P. Blanc-Durand, A. Van Der Gucht, N. Schaefer, E. Itti, and J. O. Prior, Automatic lesion detection and segmentation of 18F-FET PET in gliomas: A full 3D U-Net convolutional neural network study, PLoS One, vol. 13, no. 4, p. e0195798, 2018.
Z. Guo, X. Li, H. Huang, N. Guo, and Q. Z. Li, Deep learning-based image segmentation on multimodal medical imaging, IEEE Trans. Radiat. Plasma Med. Sci., vol. 3, no. 2, pp. 162-169, 2019.
J. Y. Choi, Radiomics and deep learning in clinical imaging: What should we do? Nucl. Med. Mol. Imaging, vol. 52, no. 2, pp. 89-90, 2018.
D. B. Liu, Z. N. He, D. D. Chen, and J. C. Lv, A network framework for small-sample learning, IEEE Trans. Neural Netw. Learn. Syst., vol. 31, no. 10, pp. 4049-4062, 2019.
D. B. Liu, Z. N. He, D. D. Chen, and J. C. Lv, An improved dual-channel network to eliminate catastrophic forgetting, IEEE Trans. Syst., Man, Cybern.: Syst., vol. 52, no. 1, pp. 415-425, 2022.
R. R. Ade and P. R. Deshmukh, Methods for incremental learning: A survey, Int. J. Data Min. Knowl. Manage. Process, vol. 3, no. 4, pp. 119-125, 2013.