Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen


    Exportieren, teilen und zitieren



    Titel :

    1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks


    Beteiligte:

    Kongress:

    AIAA SciTech Forum ; 2023 ; National Harbor, MD, US


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Keine Angabe


    Sprache :

    Englisch




    1d-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

    Koushik Chennakesavan / Magnus A Haw / Alexandre Quintart | NTRS


    1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

    Koushik Chennakesavan / Magnus A. Haw / Alexandre Quintart | NTRS


    1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

    Chennakesavan, Koushik / Haw, Magnus A. / Quintart, Alexandre | AIAA | 2023


    Liver Tumor Segmentation Using Triplanar Convolutional Neural Network: A Pilot Study

    Chung, Sheng Hung / Gan, Keng Hoon / Achuthan, Anusha et al. | Springer Verlag | 2019


    Liver Tumor Segmentation Using Triplanar Convolutional Neural Network: A Pilot Study

    Chung, Sheng Hung / Gan, Keng Hoon / Achuthan, Anusha et al. | TIBKAT | 2019