标题/作者	年份
Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies HN Dai, H Wang, G Xu, J Wan, M Imran Enterprise Information Systems 14 (9-10), 1279-1303	2020
Electricity theft detection in smart grid systems: A CNN-LSTM based approach MN Hasan, RN Toma, AA Nahid, MMM Islam, JM Kim Energies 12 (17), 3310	2019
Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities T Ahmad, D Zhang, C Huang, H Zhang, N Dai, Y Song, H Chen Journal of Cleaner Production 289, 125834	2021
Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives Y Himeur, K Ghanem, A Alsalemi, F Bensaali, A Amira Applied Energy 287, 116601	2021
A novel combined data-driven approach for electricity theft detection K Zheng, Q Chen, Y Wang, C Kang, Q Xia IEEE Transactions on Industrial Informatics 15 (3), 1809-1819	2018
Distributed deep learning model for intelligent video surveillance systems with edge computing J Chen, K Li, Q Deng, K Li, SY Philip IEEE Transactions on Industrial Informatics	2019
IoT in smart cities: A survey of technologies, practices and challenges AS Syed, D Sierra-Sosa, A Kumar, A Elmaghraby Smart Cities 4 (2), 429-475	2021
Machine learning driven smart electric power systems: Current trends and new perspectives MS Ibrahim, W Dong, Q Yang Applied Energy 272, 115237	2020
Electricity theft detection in power grids with deep learning and random forests S Li, Y Han, X Yao, S Yingchen, J Wang, Q Zhao Journal of Electrical and Computer Engineering 2019, 1-12	2019
Energy theft detection with energy privacy preservation in the smart grid D Yao, M Wen, X Liang, Z Fu, K Zhang, B Yang IEEE Internet of Things Journal 6 (5), 7659-7669	2019
Hybrid deep neural networks for detection of non-technical losses in electricity smart meters MM Buzau, J Tejedor-Aguilera, P Cruz-Romero, A Gomez-Exposito IEEE Transactions on Power Systems 35 (2), 1254-1263	2019
Machine learning and deep learning in smart manufacturing: The smart grid paradigm T Kotsiopoulos, P Sarigiannidis, D Ioannidis, D Tzovaras Computer Science Review 40, 100341	2021
Adversarial attacks on deep neural networks for time series classification HI Fawaz, G Forestier, J Weber, L Idoumghar, PA Muller 2019 International Joint Conference on Neural Networks (IJCNN), 1-8	2019
Big data analytics for large-scale wireless networks: Challenges and opportunities HN Dai, RCW Wong, H Wang, Z Zheng, AV Vasilakos ACM Computing Surveys (CSUR) 52 (5), 1-36	2019
A semi-supervised convolutional neural network-based method for steel surface defect recognition Y Gao, L Gao, X Li, X Yan Robotics and Computer-Integrated Manufacturing 61, 101825	2020
SDN-based secure and privacy-preserving scheme for vehicular networks: A 5G perspective S Garg, K Kaur, G Kaddoum, SH Ahmed, DNK Jayakody IEEE Transactions on Vehicular Technology 68 (9), 8421-8434	2019
An incremental learning framework for human-like redundancy optimization of anthropomorphic manipulators H Su, W Qi, Y Hu, HR Karimi, G Ferrigno, E De Momi IEEE Transactions on Industrial Informatics 18 (3), 1864-1872	2020
Deep learning methods and applications for electrical power systems: A comprehensive review AK Ozcanli, F Yaprakdal, M Baysal International Journal of Energy Research 44 (9), 7136-7157	2020
Performance analysis of different types of machine learning classifiers for non-technical loss detection KM Ghori, RA Abbasi, M Awais, M Imran, A Ullah, L Szathmary IEEE Access 8, 16033-16048	2019
Detecting false data attacks using machine learning techniques in smart grid: A survey L Cui, Y Qu, L Gao, G Xie, S Yu Journal of Network and Computer Applications 170, 102808	2020

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