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Updated: Jan 24, 2026

13:19
Deep Neural Networks for Image-Based Dietary Assessment
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磁性吸収体の誘電率エンジニアリングのためのニューラルネットワークベースのアプローチによるカスタマイズ可能なマイクロ波吸収
Chenxi Liu1, Jinzhe Li2, Sen Li3
1Key Laboratory of Advanced Technologies of Materials (Ministry of Education), School of Materials Science and Engineering, Southwest Jiaotong University, Chengdu, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|January 23, 2026
まとめ
本研究では、高度なマイクロ波吸収体を設計するためのAI主導の方法を紹介します。電磁ステルス用途向けに、優れた帯域幅と耐久性を備えた超薄型高性能材料を実現します。
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