Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

428
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
428
Computed Tomography01:10

Computed Tomography

8.9K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
8.9K
Design Example: Traverse Angle Computations01:25

Design Example: Traverse Angle Computations

345
Traverse angle computations are a critical component of surveying, used to compute the internal angles within a closed traverse. A traverse consists of a series of connected lines forming a closed loop, often used for land boundary delineation or mapping. Calculating the internal angles ensures accuracy in the traverse geometry and is essential for checking survey data integrity.The process begins with known azimuths and bearings of the traverse sides. Internal angles at each vertex are...
345
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

670
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
670
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

410
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
410
Multi-Step Reactions02:31

Multi-Step Reactions

8.8K
Chemical reactions often occur in a stepwise fashion involving two or more distinct reactions taking place in a sequence. A balanced equation indicates the reacting species and the product species, but it reveals no details about how the reaction occurs at the molecular level. The reaction mechanism (or reaction path) provides details regarding the precise, step-by-step process by which a reaction occurs. Each of the steps in a reaction mechanism is called an elementary reaction. These...
8.8K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Covert Communications in a Hybrid DF/AF Relay System.

Sensors (Basel, Switzerland)·2024
Same author

Performance Comparison of Relay-Based Covert Communications: DF, CF and AF.

Sensors (Basel, Switzerland)·2023
Same author

Disguised Full-Duplex Covert Communications.

Sensors (Basel, Switzerland)·2023
Same author

Resource-Efficient Parallelized Random Access for Reliable Connection Establishment in Cellular IoT Networks.

Sensors (Basel, Switzerland)·2023
Same author

Physical-Layer Security with Irregular Reconfigurable Intelligent Surfaces for 6G Networks.

Sensors (Basel, Switzerland)·2023
Same author

Entropy-Aware Model Initialization for Effective Exploration in Deep Reinforcement Learning.

Sensors (Basel, Switzerland)·2022
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
関連記事をすべて見る

関連する実験動画

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

3.5K

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
この要約は機械生成です。

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

さらに関連する動画

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.9K
Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics
10:23

Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics

Published on: December 1, 2023

1.0K

関連する実験動画

Last 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

3.5K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.9K
Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics
10:23

Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics

Published on: December 1, 2023

1.0K

科学分野:

  • コンピュータサイエンス コンピュータサイエンス
  • 人工知能 (AI) とは,人工知能 (AI) のことです.
  • 電気通信 電気通信について

背景:

  • マルチ アクセス エッジ コンピューティング (MEC) は,エッジ デバイスから集中的なコンピューティングをオフロードするために重要です.
  • 資源密集型アプリケーションを効率的に管理するには,分散型意思決定が必要です.
  • MECにおける同時アクセスを試みると,最適な卸載に課題が生じます.

研究 の 目的:

  • マルチエージェント深層補強学習 (MADRL) を使用した分散型オフロードの意思決定戦略を提案する.
  • MEC環境におけるエッジデバイスの全体的なタスク完了の遅延を最小限に抑えるために.
  • エッジデバイスがローカル観測に基づいて,オフロードポリシーを学習できるようにするためです.

主な方法:

  • マルチエージェント深層補強学習 (MADRL) に基づく分散コンピューティングのオフロード戦略.
  • ディープQネットワーク (DQN) を利用して,ディスクレートアクションスペースのディープ強化学習 (DRL) アプローチを行う.
  • 分散型卸載初期化のための補助金なしのアクセスメカニズムの実施.
  • 衝突を軽減するために,ユーザーアソシエーションとオフロードの決定を共同で最適化します.

主要な成果:

  • 提案されたMADRL戦略は,全体的なタスク完了の遅延を効果的に削減します.
  • 従来のスキームと比較して,学習パフォーマンスのより速い収束が達成されます.
  • 分散型アプローチは,多ユーザー MEC 環境における効率性とスケーラビリティを証明しています.

結論:

  • 提案されているMADRLベースの分散型オフロード戦略は,MECシステムにとって効率的です.
  • このアプローチは,タスクの完了の遅延を最小限に抑え,学習の収束性を改善します.
  • この方法は,多ユーザーエッジ環境での計算負荷を管理するためのスケーラブルなソリューションを提供します.