[1]

General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China and Standardization Administration of the People's Republic of China. 2017. Specifications for surface meteorological observation wire icing. GB/T 35235-2017.1-6

[2]

Yang J, Xie Z. 2011. Advances of study on physical processes and modeling of ice accretion on wires. Meteorological Monthly 37(9):1158−65 (in Chinese)

[3]

Liao Z, Zhai P, Chen Y, Lu H. 2020. Differing mechanisms for the 2008 and 2016 wintertime cold events in Southern China. International Journal of Climatology 40(11):4944−55

doi: 10.1002/joc.6498
[4]

Huo Z, Li C, Kong R, Mao H, Jiang M, et al. 2021. Review on disaster of wire icing in China. Journal of Applied Meteorological Science 32(5):513−29 (in Chinese)

doi: 10.11898/1001-7313.20210501
[5]

Zhou F, Huai X, Yan P, Jiang X, Pan H, et al. 2024. Research on wire icing simulation technology considering the weights of meteorological elements. Frontiers in Energy Research 12:1346480

doi: 10.3389/fenrg.2024.1346480
[6]

Zhou F, Zhang H, Pan H, Li H, Geng H, et al. 2024. Impact of meteorological factors on the wire icing thickness and growth rate in mountain areas under dry and wet growth patterns. Atmosphere 15(8):875

doi: 10.3390/atmos15080875
[7]

Shen H, Wan B, Zhou S, Kang J, Chen H, et al. 2023. The synoptic characteristics of icing events on transmission lines in Southern China. Atmosphere 14(12):1789

doi: 10.3390/atmos14121789
[8]

Niu S, Zhou Y, Jia R, Yang J, Lü J, et al. 2012. The microphysics of ice accretion on wires: Observations and simulations. Science China Earth Sciences 55(3):428−37

doi: 10.1007/s11430-011-4325-8
[9]

Wang T, Niu S, Lü J, Zhou Y. 2019. Observational study on the supercooled fog droplet spectrum distribution and icing accumulation mechanism in Lushan, southEast China. Advances in Atmospheric Sciences 36(1):29−40

doi: 10.1007/s00376-018-8017-6
[10]

Zhao M, Dong X, Yang Y, Li M, Wang H, et al. 2025. A dynamic prediction approach for wire icing thickness under extreme weather conditions based on WGAN-GP-RTabNet. Computer Modeling in Engineering & Sciences 142(2):2091−109

doi: 10.32604/cmes.2025.059169
[11]

Wang T, Niu S, Lü J, Zhou Y, Wang Y. 2019. Observation and simulation studies of three types of wire icing. Atmosphere 10(5):234

doi: 10.3390/atmos10050234
[12]

Zhou Y, Wan R, Sun J, Gao Z, Yang J. 2023. Influence of key parameters of ice accretion model under coexisting rain and fog weather. Frontiers in Earth Science 10:1036692

doi: 10.3389/feart.2022.1036692
[13]

Zheng L, Chen L, Lin Y, Wang B, Yin B, et al. 2010. Investigation on monitoring system of electric wire with ice accretion based on the meteorological technical standards. Meteorological Monthly 36(10):97−101 (in Chinese)

doi: 10.7519/j.issn.1000-0526.2010.10.016
[14]

Lenhard J. 1955. An indirect method for estimating the weight of glaze on wires. Bulletin of the American Meteorological Society 36(1):1−5

doi: 10.1175/1520-0477-36.1.1
[15]

Makkonen L. 1989. Estimation of wet snow accretion on structures. Cold Regions Science and Technology 17(1):83−8

doi: 10.1016/S0165-232X(89)80018-7
[16]

Makkonen L. 1998. Modeling power line icing in freezing precipitation. Atmospheric Research 46(1−2):131−42

doi: 10.1016/S0169-8095(97)00056-2
[17]

Zhang H. 2007. The research on model of ice-coating of transmission line in east and north-east of Yunnan. Electric Power Survey & Design (4):40−42 (in Chinese)

doi: 10.3969/j.issn.1671-9913.2007.04.011
[18]

Zhou S, Su Z, Qin J, Li Q, Wang Q. 2010. Study on covered ice area of transmission line in Guangxi based on covered ice model and GIS. Guangxi Electric Power 33(2):11−13 (in Chinese)

doi: 10.3969/j.issn.1671-8380.2010.02.004
[19]

Du S, Zhou N, Han Y, Li Z, Lu Z, et al. 2019. Simulation of wire ice thickness during a freezing rain process in Henan Province. Meteorological Monthly 45(5):641−50 (in Chinese)

[20]

Ren Y, Zhou Y, Xiao Y, Gao Z, Sun S. 2011. The method of calculating the ice thickness on wire in the areas without meteorological observation. Journal of the Meteorological Sciences 31(3):313−17 (in Chinese)

doi: 10.3969/j.issn.1009-0827.2011.03.010
[21]

Jiang Z, Hang Y, Liu D, Wu X, Xiong H. 2013. Reconstruction of an extreme wire icing series in southern China. Climatic and Environmental Research 18(3):407−13 (in Chinese)

doi: 10.3878/j.issn.1006-9585.2012.12011
[22]

Geetha G, Kirthigadevi T, Ponsam G, Karthik T, Safa M. 2020. Image captioning using deep convolutional neural networks (CNNs). Journal of Physics: Conference Series 1712(1):012015

doi: 10.1088/1742-6596/1712/1/012015
[23]

Palvanov A, Cho YI. 2019. VisNet: Deep convolutional neural networks for forecasting atmospheric visibility. Sensors 19(6):1343

doi: 10.3390/s19061343
[24]

Han Y, Zhang G, Huang X, Wang Y. 2020. A moist physics parameterization based on deep learning. Journal of Advances in Modeling Earth Systems 12(9):e2020MS002076

doi: 10.1029/2020MS002076
[25]

Zhou B, Lu X, Zheng F, Huang K, Hong S, et al. 2022. Research on the similarity recognition and application evaluation of subtropical high based on modified VGG16 model. Meteorological Monthly 48(12):1608−16 (in Chinese)

doi: 10.7519/j.issn.1000-0526.2022.042901
[26]

Hu J, Shen L, Sun G. 2018. Squeeze-and-excitation networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018. USA: IEEE. pp. 7132−41. doi: 10.1109/CVPR.2018.00745

[27]

Newell A, Huang Z, Deng J. 2017. Associative embedding: End-to-end learning for joint detection and grouping. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 2017. USA. doi: 10.48550/arXiv.1611.05424

[28]

He K, Zhang X, Ren S, Sun J. 2016. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA. June 27-30, 2016. USA: IEEE. pp. 770−78. doi: 10.1109/CVPR.2016.90

[29]

Kingma D, Ba J. 2014. Adam: a method for stochastic optimization. 3rd International Conference on Learning Representations (ICLR 2015), San Diego, USA. 2015. San Diego, USA: International Conference on Learning Representations. doi: 10.48550/arXiv.1412.6980

[30]

Dunne R, Campbell N. 1997. On the pairing of the softmax activation and cross-entropy penalty functions and the derivation of the softmax activation function. Proceedings of the 8th Australian Conference on Neural Networks, Melbourne, Citeseer, 1997. Melbourne, Citeseer. pp. 181−85. http://hdl.handle.net/102.100.100/221561?index=1

[31]

Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. 2014. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15(1):1929−58

[32]

Ioffe S. Szegedy C. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. 32nd International Conference on Machine Learning (ICML 2015), Lille, France. vol. 37. Lille, France: Proceedings of Machine Learning Research. pp. 448−56. https://proceedings.mlr.press/v37/ioffe15.html

[33]

Wu H, Niu S, Zhou Y, Sun J, Lv J, et al. 2023. Characteristics of raindrop size distributions in the southwest mountain areas of China according to seasonal variation and rain types. Remote Sensing 15(5):1246

doi: 10.3390/rs15051246
[34]

China Meteorological Administration. 2016. Grades of meteorological risk of wire icing (in Chinese), QX/T 355-2016.1-5

[35]

Felzenszwalb P, Girshick R, McAllester D, Ramanan D. 2009. Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9):1627−45

doi: 10.1109/TPAMI.2009.167
[36]

Jones K. 1998. A simple model for freezing rain ice loads. Atmospheric Research 46(1-2):87−97

doi: 10.1016/S0169-8095(97)00053-7
[37]

Liu M, Wang X, Zhou A, Fu X, Ma Y, et al. 2020. UAV-YOLO: Small object detection on unmanned aerial vehicle perspective. Sensors 20(8):2238

doi: 10.3390/s20082238
[38]

Ullah H, Muhammad K, Irfan M, Anwar S, Sajjad M, et al. 2021. Light-DehazeNet: A novel lightweight CNN architecture for single image dehazing. IEEE Transactions on Image Processing 30:8968−82

doi: 10.1109/TIP.2021.3116790
[39]

Zhang C, Bengio S, Hardt M, Recht B, Vinyals O. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3):107−15

doi: 10.1145/3446776