Jove
Visualize
联系我们

相关概念视频

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
Rapidly Varying Flow01:24

Rapidly Varying Flow

Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...

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

Filter and Wrapper Stacking Ensemble (FWSE): a robust approach for reliable biomarker discovery in high-dimensional omics data.

Briefings in bioinformatics·2023
Same author

Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review.

Sensors (Basel, Switzerland)·2023
Same author

Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning.

Sensors (Basel, Switzerland)·2023
Same author

Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network.

Sensors (Basel, Switzerland)·2021
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

相关实验视频

Updated: Jun 28, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K

交通流基础设施安全的SIFT-SNN:一个实时的上下文意识异常检测框架.

Munish Rathee1, Boris Bačić1, Maryam Doborjeh1,2

  • 1School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand.

Journal of imaging
|February 26, 2026
PubMed
概括
此摘要是机器生成的。

本研究引入了一种增强的神经形态视觉系统,用于在运输基础设施中自动检测异常,提高安全性并降低检查成本. 该系统实现了高精度和高效率,为传统方法提供了可部署的替代方案.

关键词:
在SIFT中,功能提取是SIFT的功能.在基础设施中检测异常.背景感知视觉系统的视觉系统边缘人工智能部署智能运输系统 智能运输系统多个类别的缺陷分类分类.神经形态计算是一种神经形态计算.实时的结构监测时间空间图像分析尖的神经网络的神经网络.

相关实验视频

Last Updated: Jun 28, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K

科学领域:

  • 计算机视觉 计算机视觉
  • 神经形态工程的神经形态工程
  • 人工智能的人工智能

背景情况:

  • 手动检查运输基础设施是昂贵的,容易出现错误.
  • 现有的自动化系统与运动和照明变化作斗争.
  • 神经形态系统为高效,低功耗的异常检测提供了潜力.

研究的目的:

  • 开发一种改进的神经形态视觉系统,用于在交通基础设施中检测异常.
  • 通过使用时间特征聚合来增强上下文意识和序列稳定检测.
  • 为传统的基于CNN的检查工具提供可部署,可解释和节能的替代方案.

主要方法:

  • 实现了一个改进的尺度不变特征转换尖端神经网络 (SIFT-SNN) 与时间特征聚合.
  • 将编码的 SIFT 关键点转化为基于延迟的尖端列车,用于使用漏洞的整合和发射 (LIF) 尖端神经网络进行分类.
  • 在GPU,CPU和模拟嵌入式硬件配置中评估系统性能.

主要成果:

  • 实现了92.3%的准确性和91.0%的宏观F1得分,并进行了五次交叉验证.
  • 在硬件平台上,推断延迟从每9.5ms到~48.3ms不等.
  • 展示了一个紧的模型尺寸 (2.9 MB) 和低功耗 (5-65 W).

结论:

  • 拟议的暂时光滑的神经形态系统提供了一个强大的解决方案,用于检测基础设施中的关键故障模式,如屏障钉.
  • 时间平滑增强检测回忆,特别是在模两可的情况下.
  • 该系统的效率和低资源需求使其适合于现实世界的部署.