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Xuanzhi Liao, Shahnorbanun Sahran, Azizi Abdullah and Syaimak Abdul Shukor
Adaptive gradient descent methods such as Adam, RMSprop, and AdaGrad achieve great success in training deep learning models. These methods adaptively change the learning rates, resulting in a faster convergence speed. Recent studies have shown their prob...
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Mohammad Asadzadeh and Larisa Beilina
Stability and convergence analyses for the domain decomposition finite element/finite difference (FE/FD) method are presented. The analyses are developed for a semi-discrete finite element scheme for time-dependent Maxwell?s equations. The explicit finit...
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Siva Kumar Mahalingam, Lenin Nagarajan, Chandran Velu, Vignesh Kumar Dharmaraj, Sachin Salunkhe and Hussein Mohamed Abdelmoneam Hussein
This work addresses an evolutionary algorithmic approach to reduce the surplus pieces in selective assembly to increase success rates. A novel equal area amidst unequal bin numbers (EAUB) method is proposed for classifying the parts of the ball bearing a...
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Michail-Alexandros Kourtis, Andreas Oikonomakis, Dimitris Santorinaios, Themis Anagnostopoulos, Giorgios Xilouris, Anastasios Kourtis, Ioannis Chochliouros and Charilaos Zarakovitis
This paper aims to develop an open Asset Administration Shell (AAS) solution for 5G Non-Public Network (NPN) management, focusing on manufacturing digitization and complete Information and Operational Technology (IT/OT) convergence. The proposed 5G NPN f...
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Ming Chen, Xinhu Zhang, Kechun Shen and Guang Pan
The geometrical dimensions and mechanical properties of composite materials exhibit inherent variation and uncertainty in practical engineering. Uncertainties in geometrical dimensions and mechanical properties propagate to the structural performance of ...
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