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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

544
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...
544

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

Cancer-Associated Fibroblast-Targeted Nanomedicine in Solid Tumor Therapy: From Mechanisms of Therapeutic Resistance to Precision Stromal Modulation.

International journal of nanomedicine·2026
Same author

Superior Selective and Fast NH<sub>3</sub> Adsorption of Soft Porous MOF/Ionic Liquid Composites with Ordering Phase Transitions.

Journal of the American Chemical Society·2026
Same author

<i>LsToll</i> Gene Mediates Antibacterial Immunity and Developmental Regulation in <i>Loxostege sticticalis</i>.

Insects·2026
Same author

Risk factors for recurrent abortion after induced abortion in Chinese women: a prospective study.

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians·2026
Same author

Rhizosphere Functional Plasticity and the Keystone Taxon <i>Sphingomonas</i> Facilitate Sweet Cherry Adaptation to Semi-Arid Stress.

Plants (Basel, Switzerland)·2026
Same author

Evolution Mechanism and High-Precision Quantitative Identification of MFL Signals from Defects Under Supersaturated Magnetization Conditions.

Sensors (Basel, Switzerland)·2026

相关实验视频

Updated: Apr 30, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

通过减去平均值的优化算法优化了多道CNN-SABO-SVM网络网络的HVCB故障诊断.

Qingjun Song1, Jiuxin Wang1, Qinghui Song1

  • 1College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China.

Scientific reports
|November 28, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的故障诊断模型,用于高压断路器 (HVCB),使用多通道CNN和SVM与SABO优化. 该模型在有限的样本场景中表现出色,提高了关键电力系统组件的诊断准确性.

关键词:
错误诊断是一个错误的诊断.高压断路器的电路断路器.多通道卷积神经网络多通道卷积神经网络多式联运数据多式联运数据参数优化 参数优化

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

6.7K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

982

相关实验视频

Last Updated: Apr 30, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
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

6.7K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

982

科学领域:

  • 电气工程 电气工程
  • 人工智能的人工智能
  • 机械工程 机械工程

背景情况:

  • 确保电力系统的稳定性取决于精确的高压断路器 (HVCB) 机械故障诊断.
  • 深度学习方法通常在有限的样本数据下表现不佳,这对HVCB故障检测构成了挑战.

研究的目的:

  • 开发一个先进的HVCB操作机制故障诊断模型,以小数据集克服深度学习的局限性.
  • 通过利用多模式数据融合和优化分类,提高HVCB故障诊断的准确性和概括性.

主要方法:

  • 采用多通道卷积神经网络 (CNN) 来从多模式HVCB数据 (振动和声音) 中提取和融合特征.
  • 支持矢量机 (SVM) 用于分类融合特征,在有限的数据场景中提供比Softmax更好的性能.
  • 引入了以减去平均值为基础的优化器 (SABO) 来对SVM分类器进行超参数优化,进一步提高了诊断准确性.

主要成果:

  • 拟议的多频道CNN-SABO-SVM (MCCSS) 模型与单模CNN和多频道CNN-SVM模型相比,表现出优越的性能.
  • 在MCCSS模型中,比较模型的准确度提高了2.66%和10.66%.
  • 在HVCB故障测试平台上的实验验证证证了该模型在用有限的样本数据诊断故障方面的有效性.

结论:

  • 开发的MCCSS模型有效地解决了在有限的样本条件下HVCB故障诊断的挑战.
  • 多模式数据融合与优化的SVM分类器相结合,为提高电力系统可靠性提供了强大的解决方案.
  • 该研究强调了先进的深度学习和优化技术在关键基础设施监控方面的潜力.