OTN (Optical Transmission Networks) is one of the mainstream infrastructures over the ground-transmission networks, with the characteristics of large bandwidth, low delay, and high reliability. To ensure a stable working of OTN, it is necessary to preform high-level accurate functions of data traffic analysis, alarm prediction, and fault location. However, there is a serious problem for the implementation of these functions, caused by the shortage of available data but a rather-large amount of dirty data existed in OTN. In the view of current pretreatment, the extracted amount of effective data is very less, not enough to support machine learning. To solve this problem, this paper proposes a data augmentation algorithm based on deep learning. Specifically, Data Augmentation for Optical Transmission Networks under Multi-condition constraint (MVOTNDA) is designed based on GAN Mode with the demonstration of variable-length data augmentation method. Experimental results show that MVOTNDA has better performances than the traditional data augmentation algorithms.
Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm
