A4 Article in conference proceedings
Generative adversarial networks with bio-inspired primary visual cortex for Industry 4.0 (2022)


Branytskyi, V., Golovianko, M., Malyk, D., & Terziyan, V. (2022). Generative adversarial networks with bio-inspired primary visual cortex for Industry 4.0. In F. Longo, M. Affenzeller, & A. Padovano (Eds.), 3rd International Conference on Industry 4.0 and Smart Manufacturing (200, pp. 418-427). Elsevier. Procedia Computer Science. https://doi.org/10.1016/j.procs.2022.01.240


JYU authors or editors


Publication details

All authors or editors: Branytskyi, Vladyslav; Golovianko, Mariia; Malyk, Diana; Terziyan, Vagan

Parent publication: 3rd International Conference on Industry 4.0 and Smart Manufacturing

Parent publication editors: Longo, Francesco; Affenzeller, Michael; Padovano, Antonio

Conference:

  • International Conference on Industry 4.0 and Smart Manufacturing

Place and date of conference: Linz, Austria, 17.-19.11.2021

Journal or series: Procedia Computer Science

ISSN: 1877-0509

eISSN: 1877-0509

Publication year: 2022

Volume: 200

Pages range: 418-427

Number of pages in the book: 1918

Publisher: Elsevier

Publication country: Netherlands

Publication language: English

DOI: https://doi.org/10.1016/j.procs.2022.01.240

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/80182


Abstract

Biologicalization (biological transformation) is an emerging trend in Industry 4.0 affecting digitization of manufacturing and related processes. It brings up the next generation of manufacturing technology and systems that extensively use biological and bio-inspired principles, materials, functions, structures and resources. This research is a contribution to the further convergence of computer and human vision for more robust and accurate automated object recognition and image generation. We present VOneGANs, a novel class of generative adversarial networks (GANs) with the qualitatively updated discriminative component. The new model incorporates a biologically constrained digital primary visual cortex V1. This earliest cortical visual area performs the first stage of human‘s visual processing and is believed to be a reason of its robustness and accuracy. Experiments with the updated architectures confirm the improved stability of GANs training and the higher quality of the automatically generated visual content. The promising results allow considering VOneGANs as providers of high-quality training content and as enablers of future simulation-based decision-making and decision-support tools for condition-monitoring, supervisory control, diagnostics, predictive maintenance, and cybersecurity in Industry 4.0.
See presentation slides: https://ai.it.jyu.fi/ISM-2021-V1-GAN.pptx


Keywords: industry; production technology; artificial intelligence; computer vision; machine learning; neural networks (information technology)

Free keywords: Biologicalization; Industry 4.0; GAN; VOneGAN; primary visual cortex V1; hybrid CNN


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2022

JUFO rating: 1


Last updated on 2024-22-04 at 21:34