• Login
    View Item 
    •   Shocker Open Access Repository Home
    • Engineering
    • Industrial, Systems, and Manufacturing Engineering
    • ISME Faculty Scholarship
    • ISME Research Publications
    • View Item
    •   Shocker Open Access Repository Home
    • Engineering
    • Industrial, Systems, and Manufacturing Engineering
    • ISME Faculty Scholarship
    • ISME Research Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Deep-learning-based cyber-physical system framework for real-time industrial operations

    View/Open
    Open Access PDF (4.343Mb)
    Date
    2022-10-31
    Author
    Maru, Vatsal
    Nannapaneni, Saideep
    Krishnan, Krishna
    Arishi, Ali
    Metadata
    Show full item record
    Citation
    Maru, V.; Nannapaneni, S.; Krishnan, K.; Arishi, A. Deep-Learning-Based Cyber-Physical System Framework for Real-Time Industrial Operations. Machines 2022, 10, 1001. https://doi.org/10.3390/machines10111001
    Abstract
    Automation in the industry can improve production efficiency and human safety when performing complex and hazardous tasks. This paper presented an intelligent cyber-physical system framework incorporating image processing and deep-learning techniques to facilitate real-time operations. A convolutional neural network (CNN) is one of the most widely used deep-learning techniques for image processing and object detection analysis. This paper used a variant of a CNN known as the faster R-CNN (R stands for the region proposals) for improved efficiency in object detection and real-time control analysis. The control action related to the detected object is exchanged with the actuation system within the cyber-physical system using a real-time data exchange (RTDE) protocol. We demonstrated the proposed intelligent CPS framework to perform object detection-based pick-and-place operations in real time as they are one of the most widely performed operations in quality control and industrial systems. The CPS consists of a camera system that is used for object detection, and the results are transmitted to a universal robot (UR5), which then picks the object and places it in the right location. Latency in communication is an important factor that can impact the quality of real-time operations. This paper discussed a Bayesian approach for uncertainty quantification of latency through the sampling–resampling approach, which can later be used to design a reliable communication framework for real-time operations.
    Description
    This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
    URI
    https://doi.org/10.3390/machines10111001
    https://soar.wichita.edu/handle/10057/24238
    Collections
    • ISME Research Publications

    Browse

    All of Shocker Open Access RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsBy TypeThis CollectionBy Issue DateAuthorsTitlesSubjectsBy Type

    My Account

    LoginRegister

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    DSpace software copyright © 2002-2023  DuraSpace
    DSpace Express is a service operated by 
    Atmire NV