Abstract
A large amount of equipment defect texts are left unused in power management system. According to the features of power equipment defect texts, a classification model of defect texts based on convolutional neural network is established. Firstly, the features of power equipment defect texts are extracted by analyzing a large number of defect records. Then, referencing general process of Chinese text classification and considering the features of defect texts, we establishes a classification model of defect texts based on convolutional neural network. Finally, we develop classification effect evaluation indicators to evaluate the effect of the model based on one case. Compared with multiple traditional machine learning classification models and according to the classification effect evaluation indicators, the proposed defect text classification model can significantly reduce error rate with considerable efficiency.
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Zhou, J., Luo, G., Hu, C., Chen, Y. (2019). A Classification Model of Power Equipment Defect Texts Based on Convolutional Neural Network. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11632. Springer, Cham. https://doi.org/10.1007/978-3-030-24274-9_43
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DOI: https://doi.org/10.1007/978-3-030-24274-9_43
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