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مدیریت فناوری اطلاعات، جلد ۱۵، شماره Special Issue: Digital Twin Enabled Neural Networks Architecture Management for Sustainable Computing، صفحات ۱۱۲-۱۲۳
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عنوان انگلیسی Integrated Model-Based Engineering using Deep Learning with IIoT for Industry 4.0
چکیده انگلیسی مقاله The Industrial Internet of Things (IIoT) is a potential platform for developing industry 4.0 and its related applications, especially in cyber-physical systems. Such a new trend in manufacturing sectors offers further potential to optimize operations, realize business models, and reduce costs. Such accomplish may also lead to complex and complicated tasks; hence, to deal with such issues, Reference Architecture Model Industry 4.0 (RAMI 4.0) is developed to structure Industry 4.0. In this paper, the standardized framework is considered RAMI 4.0 and its integration with an IIoT software named Software Platform Embedded Systems (SPES). Integrating Model-Based Engineering (MBE) with a framework requires using a deep learning model called Recurrent Neural Network (RNN). The RNN-MBE, which optimizes the entire process, is responsible for optimizing the process and reducing industry costs. The optimization problem has been fixed, and the MBE simulation has shown that using the proposed MBE is efficient.
کلیدواژه‌های انگلیسی مقاله Model-based engineering,Recurrent neural network,Industrial Internet of Things (IIoT),Deep learning
نویسندگان مقاله P. Senthilkumar |
Department of Instrumentation and Control Engineering, Kalasalingam Academy of Research and Education, Tamilnadu, India.

K. Rajesh |
Department of Electrical and Electronics Engineering, Kalasalingam Academy of Research and Education, Tamilnadu, India

نشانی اینترنتی https://jitm.ut.ac.ir/article_91571_29571562b0323cf544d3e19aec793585.pdf
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