Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

57
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
57

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Machine Learning for Intelligent and Adaptive Communication Systems: From Optimization to Emerging Paradigms.

Sensors (Basel, Switzerland)·2026
Same author

Context-Aware Beam Selection for IRS-Assisted mmWave V2I Communications.

Sensors (Basel, Switzerland)·2025
Same author

Machine Learning in Communication Systems and Networks.

Sensors (Basel, Switzerland)·2024
Same author

Use of Parallel Explanatory Models to Enhance Transparency of Neural Network Configurations for Cell Degradation Detection.

IEEE transactions on neural networks and learning systems·2024
Same author

A Lightweight Trust Mechanism with Attack Detection for IoT.

Entropy (Basel, Switzerland)·2023
Same author

Full-Duplex Relay with Delayed CSI Elevates the SDoF of the MIMO X Channel.

Entropy (Basel, Switzerland)·2021
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
查看所有相关文章

相关实验视频

Updated: Jul 11, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.1K

机器学习的最新进展用于O-RAN中的网络自动化.

Mutasem Q Hamdan1, Haeyoung Lee2, Dionysia Triantafyllopoulou3

  • 1Samsung Electronics R&D Institute, Staines TW18 4QE, UK.

Sensors (Basel, Switzerland)
|November 14, 2023
PubMed
概括
此摘要是机器生成的。

机器学习 (ML) 是自动化开放无线电接入网络 (O-RAN) 的关键. 本调查探讨了智能化,自动化O-RAN管理的ML应用,挑战和机会.

关键词:
人工智能的人工智能是人工智能.机器学习是机器学习.开放的无线电接入网络.

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

590

相关实验视频

Last Updated: Jul 11, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.1K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

590

科学领域:

  • 电信工程 电信工程 电信工程
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 电信行业正在转向开放和智能网络架构.
  • 开放无线电接入网络 (O-RAN) 架构为多供应商互操作性提供分类和虚拟化.
  • 管理和自动化复杂的O-RAN生态系统带来了重大挑战.

研究的目的:

  • 提供O-RAN中使用机器学习 (ML) 对网络自动化当前研究的全面调查.
  • 为了突出O-RAN架构中自动化需求.
  • 探索O-RAN对ML技术的支持,并确定研究机会.

主要方法:

  • 对O-RAN架构及其组件的概述.
  • 对O-RAN对ML技术的固有支持进行分析.
  • 探索应用ML用于O-RAN自动化的挑战.
  • 对O-RAN自动化ML算法和框架的现有研究进行审查.

主要成果:

  • 确定了ML作为一个有希望的解决方案,用于自动化复杂的O-RAN环境.
  • 详细的当前研究工作,包括对O-RAN应用的ML算法和框架.
  • 突出了O-RAN中的ML驱动网络自动化的主要挑战和机遇.

结论:

  • 机器学习技术对于解决O-RAN网络自动化的复杂性至关重要.
  • 该调查为未来的研究提供了路线图,用于利用ML进行智能O-RAN管理.
  • 通过将ML应用于O-RAN的各个方面,进一步的研究可以释放显著的好处.