Machine Learning Driven Advanced Packaging and Miniaturization of IoT for Wireless Power Transfer Solutions
| Autor: | Hakki Mert Torun, Mohamed L. F Belleradj, Colin Pardue, Madhavan Swaminathan, Anto Kavungal Davis |
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| Rok vydání: | 2018 |
| Předmět: |
Battery (electricity)
Scope (project management) Computer science business.industry Energy conversion efficiency 020206 networking & telecommunications 02 engineering and technology Inductor Machine learning computer.software_genre 020202 computer hardware & architecture Power (physics) 0202 electrical engineering electronic engineering information engineering Miniaturization Wireless power transfer Radio frequency Artificial intelligence business computer |
| Zdroj: | 2018 IEEE 68th Electronic Components and Technology Conference (ECTC). |
| DOI: | 10.1109/ectc.2018.00358 |
| Popis: | Increasing application scope of Internet of Things (IoT) devices have resulted in strict design requirements such as compact systems and efficient power delivery architectures to reduce battery wastage. RF Wireless Power Transfer (WPT) have shown to be promising to address these issues, but with the cost of increased design complexity. In this work, we use machine learning based optimization to select an optimal set of control parameters of a WPT architecture and miniaturize the system while increasing the overall RF-DC conversion efficiency. |
| Databáze: | OpenAIRE |
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