Recursive full convolutional network hybrid model based on industrial load data augmentation

Yanfeng Tian; Liyan Shao; Anni Wang; Shimeng Ma; Hongwei Jin; Yuan Yao; Hongtao Zhang

doi:10.1117/12.2686751

6 August 2023 Recursive full convolutional network hybrid model based on industrial load data augmentation

Yanfeng Tian, Liyan Shao, Anni Wang, Shimeng Ma, Hongwei Jin, Yuan Yao, Hongtao Zhang

Proceedings Volume 12781, International Conference on Optoelectronic Information and Functional Materials (OIFM 2023); 1278122 (2023) https://doi.org/10.1117/12.2686751
Event: 2023 International Conference on Optoelectronic Information and Functional Materials (OIFM 2023), 2023, Guangzhou, JS, China

Abstract

Hundreds of classification algorithms have been proposed for the task of classifying time series. These methods are broadly classified into three types: distance-based, feature-based, and deep learning-based. Problems such as small and diverse time series data have led to the low accuracy of deep learning models in time series classification tasks. To address the above problems, a recursive full convolutional network hybrid model combined with data augmentation is proposed in this paper. Firstly, data augmentation is used to address the problem of small and diverse raw data. Secondly, compared with the traditional depth model, this paper uses a recursive full convolutional network hybrid model, which makes the model training faster and more accurate. Finally, the effectiveness of the proposed method is verified by using an industrial park load data and the largest publicly available TSC benchmark dataset UCRArchive2018 for assisted training, and the results show that the time series classification method based on recursive full convolutional network hybrid model proposed in this paper has obvious superiority compared with existing methods.

Citation Download Citation

Yanfeng Tian, Liyan Shao, Anni Wang, Shimeng Ma, Hongwei Jin, Yuan Yao, and Hongtao Zhang "Recursive full convolutional network hybrid model based on industrial load data augmentation", Proc. SPIE 12781, International Conference on Optoelectronic Information and Functional Materials (OIFM 2023), 1278122 (6 August 2023); https://doi.org/10.1117/12.2686751

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KEYWORDS

Data modeling

Education and training

Performance modeling

Detection and tracking algorithms

Data storage

Deep learning

Image enhancement

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