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826 results on '"Sohrabi, Mohammad"'

45. Ergonomics Role in Sustainable Development: A Review Article for Updates the Recent Knowledge

Author

Sohrabi, Mohammad Sadegh, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Black, Nancy L., editor, Neumann, W. Patrick, editor, and Noy, Ian, editor

Published
2021
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46. Slice admission control in 5G cloud radio access network using deep reinforcement learning: A survey.

Author

Khani, Mohsen, Jamali, Shahram, Sohrabi, Mohammad Karim, Sadr, Mohammad Mohsen, and Ghaffari, Ali

Subjects
REINFORCEMENT learning, DEEP reinforcement learning, RADIO access networks, NETWORK performance, MACHINE learning
Abstract

Summary: The emergence of 5G networks has increased the demand for network resources, making efficient resource management crucial. Slice admission control (SAC) is a process that governs the creation and allocation of virtualized network environments, known as "network slices," which can be tailored to meet specific user requirements. However, traditional SAC methods face dynamic and heterogeneous challenges in wireless networks, especially in cloud radio access networks (C‐RANs). To address this issue, machine learning (ML) techniques, particularly deep reinforcement learning (DRL), have been proposed as powerful tools for optimizing SAC. DRL‐based approaches enable SAC systems to learn from previous interactions with the network environment and dynamically adapt to changing network conditions. This review article comprehensively explains the current state‐of‐the‐art DRL‐based SAC, focusing on C‐RANs. The article identifies key challenges and future research directions and highlights the potential benefits of using DRL for SAC, including improved network performance and efficiency. However, deploying these systems in real‐world scenarios presents several challenges and trade‐offs that need to be carefully considered. Further research and development are required to address these challenges and ensure the successful deployment of DRL‐based SAC systems in wireless networks. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Temperature-jump tensile tests to induce optimized TRIP/TWIP effect in a metastable austenitic stainless steel.

Author

Sohrabi, Mohammad Javad, Mirzadeh, Hamed, Sadeghpour, Saeed, Geranmayeh, Abdol Reza, and Mahmudi, Reza

Abstract

In the present work, plastic deformation mechanisms were initially tailored by adjusting the deformation temperature in the range of 0 to 200°C in AISI 304L austenitic stainless steel, aiming to optimize the strength-ductility synergy. It was shown that the combined twinning-induced plasticity (TWIP)/transformation-induced plasticity (TRIP) effects and a wider strain range for the TRIP effect up to higher strains by adjusting the deformation temperature are good strategies to improve the strength-ductility synergy of this metastable stainless steel. In this regard, by consideration of the observed temperature-dependency of plastic deformation, the controlled sequence of TWIP and TRIP effects for archiving superior strength-ductility trade-off was intended by the pre-designed temperature jump tensile tests. Accordingly, the optimum tensile toughness of 846 MJ/m3 and total elongation to 133% were obtained by this strategy via exploiting the advantages of the TWIP effect at 100°C and the TRIP effect at 25°C at the later stages of the straining. Consequently, a deformation-temperature-transformation (DTT) diagram was developed for this metastable alloy. Moreover, based on work-hardening analysis, it was found that the main phenomenon constraining further improvement in the ductility and strengthening was the yielding of the deformation-induced α′-martensite. [ABSTRACT FROM AUTHOR]

Published
2024
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