Jove
Visualize
联系我们

相关概念视频

Reducing Line Loss01:18

Reducing Line Loss

141
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
141
Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

28
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
28
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

5.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
5.4K

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

Pathways to Wellbeing: Reconceptualizing Resilience to Foreground Marginalized Teachers' Agentic Resistance.

Behavioral sciences (Basel, Switzerland)·2025
Same author

Temporal-Spatial Redundancy Reduction in Video Sequences: A Motion-Based Entropy-Driven Attention Approach.

Biomimetics (Basel, Switzerland)·2025
Same author

Effective removing of rotifer contamination in microalgal lab-scale raceway ponds by light-induced phototaxis coupled with high-voltage pulse electroshock.

Bioresource technology·2023
Same author

Incision-based Blepharoplasty with Preservation of Superficial and Deep Blood Vessels.

The Journal of craniofacial surgery·2022
Same author

FTACMT study protocol: a multicentre, double-blind, randomised, placebo-controlled trial of faecal microbiota transplantation for autism spectrum disorder.

BMJ open·2022
Same author

The Dominance of Blended Emotions: A Qualitative Study of Elementary Teachers' Emotions Related to Mathematics Teaching.

Frontiers in psychology·2020
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: May 24, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.3K

研究L-YOLO算法在道路物体检测中的轻量化策略.

Ji Hong1, Kuntao Ye2, Shubin Qiu1

  • 1School of Science, Jiangxi University of Science and Technology, 1958 Hakka Avenue, Ganzhou, 341000, Jiangxi, China.

Scientific reports
|March 4, 2025
PubMed
概括

这项研究介绍了L-YOLO,这是一种用于自动驾驶的轻量级物体检测算法. L-YOLO显著降低了模型大小和计算负载,同时提高了道路物体检测的准确性.

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 自主系统 自主系统

背景情况:

  • 城市交通的复杂性需要有效的对象检测,以实现自动驾驶和交通管理.
  • 传统的对象检测算法因参数大小和高计算成本而面临局限性,阻碍了在资源有限的环境中部署.

研究的目的:

  • 开发一个轻量级和高效的道路物体检测算法,L-YOLO,基于YOLOv8s.
  • 为了增强特征提取,小物体检测,模型强度和计算效率.

主要方法:

  • 用L-HGNetV2取代YOLOv8的骨干,以改善特征提取和融合.
  • 引入了一个小物体检测层与CStar模块来增强小型车辆功能捕获.
  • 实现了FPIoU2损失函数,以提高模型的稳定性.
  • 应用层适应性基于大小的模型修剪 (LAMP) 以减少参数和计算负载.

主要成果:

  • 在KITTI数据集上,L-YOLO实现了93.8%的mAP50,比YOLOv8s有2.5%的改进.
  • 将模型参数从11.12M缩小到3.58M.
  • 计算负载从28.4 GFLOPs减少到14.2 GFLOPs.

更多相关视频

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K
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

451

相关实验视频

Last Updated: May 24, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.3K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K
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

451

结论:

  • 与YOLOv8s相比,L-YOLO在道路物体检测方面的效率和准确性得到了显著提高.
  • 拟议的轻量级算法适用于自动驾驶和智能交通管理中的资源有限的环境.