21,186 research outputs found
Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions
This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices
Disruption Detection for a Cognitive Digital Supply Chain Twin Using Hybrid Deep Learning
Purpose: Recent disruptive events, such as COVID-19 and Russia-Ukraine
conflict, had a significant impact of global supply chains. Digital supply
chain twins have been proposed in order to provide decision makers with an
effective and efficient tool to mitigate disruption impact. Methods: This paper
introduces a hybrid deep learning approach for disruption detection within a
cognitive digital supply chain twin framework to enhance supply chain
resilience. The proposed disruption detection module utilises a deep
autoencoder neural network combined with a one-class support vector machine
algorithm. In addition, long-short term memory neural network models are
developed to identify the disrupted echelon and predict time-to-recovery from
the disruption effect. Results: The obtained information from the proposed
approach will help decision-makers and supply chain practitioners make
appropriate decisions aiming at minimizing negative impact of disruptive events
based on real-time disruption detection data. The results demonstrate the
trade-off between disruption detection model sensitivity, encountered delay in
disruption detection, and false alarms. This approach has seldom been used in
recent literature addressing this issue
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