SSIT: a sample selection-based incremental model training method for image recognition

Zhang, Yichuan; Liu, Yadi; Yang, Guang-Hong; Song, Jie

doi:10.1007/s00521-021-06515-4

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…We design an incremental training process 28 to increase the robustness of the model. Thus, on the basis of the original structure, the model can increase the feature expression of the new TGM data.…”

Section: Rpa Discriminationmentioning

confidence: 99%

Real-time railway plane alignment discrimination based on linear discriminant analysis of Lp norm with sparsity constraint

Qin

Zhang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part F:

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To improve the accuracy of detected parameters at curves by an in-use track geometry measurement (TGM) system, a real-time railway plane alignment (RPA) discrimination method based on linear discriminant analysis of an Lp norm with sparsity constraint (LDA-Lp-SC) is proposed. A standard database to identify the radius of a track curve is established, using the relevant ledger and track TGM data. Multi-dimensional feature extraction is performed on the TGM data, and the parameters of the method are optimized to improve the accuracy of the model classification. Finally, the method is embedded in a TGM system combined with the original RPA discrimination method, so that the key parameters of RPA discrimination can be automatically switched in real time according to the classification result of the track curve radius. Compared with the original method, the method in this study greatly improves the recognition accuracy of track curve with a radius in the range of 150–20,000 m, while ensuring real-time performance.

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Section: Rpa Discriminationmentioning

confidence: 99%