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Updated: Feb 14, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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マルチアクセスエッジコンピューティングシステムのマルチエージェント深層補強学習による分散コンピューティングオフロード戦略

Emmanuella Adu1, Yeongmuk Lee2, Jihwan Moon3

  • 1IDEACONCERT Co., Ltd., Seongnam 13449, Republic of Korea.

Sensors (Basel, Switzerland)
|February 13, 2026
PubMed
まとめ

この研究は,マルチアクセスエッジコンピューティング (MEC) の分散型マルチエージェント深層補強学習 (MADRL) 戦略を導入しています. エッジデバイスが最適のオフロードポリシーを独立して学習できるようにすることで,タスク完了の遅延を最小限に抑え,全体的な遅延を削減します.

キーワード:
深層補強学習 (DEP) による学習です.補助金なしでアクセスできます.マルチアクセスのエッジコンピューティングoffloading offloading offloading 卸荷する 卸荷する 卸荷するタスク完了の遅延,タスク完了の遅延

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