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dc.contributor.authorMaru, Vatsal
dc.contributor.authorNannapaneni, Saideep
dc.contributor.authorKrishnan, Krishna K.
dc.contributor.authorArishi, Ali
dc.identifier.citationMaru, V.; Nannapaneni, S.; Krishnan, K.; Arishi, A. Deep-Learning-Based Cyber-Physical System Framework for Real-Time Industrial Operations. Machines 2022, 10, 1001.
dc.descriptionThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
dc.description.abstractAutomation 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.
dc.subjectCyber-physical system
dc.subjectDeep learning
dc.subjectReal time
dc.subjectIndustrial operations
dc.titleDeep-learning-based cyber-physical system framework for real-time industrial operations
dc.rights.holder© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

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  • ISME Research Publications
    Research works published by faculty and students of the Department of Industrial, Systems, and Manufacturing Engineering

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