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• W4308121163 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." @default.
• W4308121163 created "2022-11-08" @default.
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• W4308121163 date "2022-10-31" @default.
• W4308121163 modified "2023-09-30" @default.
• W4308121163 title "Deep-Learning-Based Cyber-Physical System Framework for Real-Time Industrial Operations" @default.
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• W4308121163 doi "https://doi.org/10.3390/machines10111001" @default.
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