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

Reducing Line Loss01:18

Reducing Line Loss

155
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...
155
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

342
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
342
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

406
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
406
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.3K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.3K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K

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

Discovery of (2<i>R</i>,4<i>R</i>)-4-((<i>S</i>)-2-Amino-3-methylbutanamido)-2-(4-boronobutyl)pyrrolidine-2-carboxylic Acid (AZD0011), an Actively Transported Prodrug of a Potent Arginase Inhibitor to Treat Cancer.

Journal of medicinal chemistry·2024
Same author

Design and Synthesis of Acyclic Boronic Acid Arginase Inhibitors.

Journal of medicinal chemistry·2024
Same author

CRISPR-Cas12a-regulated DNA adsorption on MoS<sub>2</sub> quantum dots: Enhanced enzyme mimics for sensitive colorimetric detection of human monkeypox virus and human papillomavirus DNA.

Talanta·2024
Same author

3D printing incorporating gold nanozymes with mesenchymal stem cell-derived hepatic spheroids for acute liver failure treatment.

Biomaterials·2024
Same author

Subglottic airway injury during fiberscope-monitored intubation with a supraglottic airway device: A randomized controlled comparison of three tracheal tubes.

Chinese medical journal·2024
Same author

Effects of filling substrates on remediation performance and sulfur transformation of sulfate reducing packed-bed bioreactors treating acid mine drainage.

Journal of environmental management·2024

相关实验视频

Updated: Jul 10, 2025

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

559

基于关键点回归策略和角度损失的YOLO用于对象检测.

Xiuling Wang1, Lingkun Kong1, Zhiguo Zhang1

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

Scientific reports
|November 18, 2023
PubMed
概括

这项研究介绍了KR-AL-YOLO,这是一个增强的YOLOv4模型,可以提高小物体检测的准确性. 通过结合关键点回归和角度损失,它实现了比标准YOLOv4.4更高的性能.

更多相关视频

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

9.4K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K

相关实验视频

Last Updated: Jul 10, 2025

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

559
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

9.4K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K

科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 对于实时物体检测,YOLOv4很受欢迎,但由于刚性界限框回归,它与小物体作斗争.
  • 现有的方法无法充分利用对象不对称性,限制了检测准确性.

研究的目的:

  • 为了增强YOLOv4的物体检测能力,特别是对于小物体.
  • 介绍一种新的方法,KR-AL-YOLO,解决YOLOv4的局限性.

主要方法:

  • 开发了KR-AL-YOLO (基于关键点回归策略和角度损失的YOLOv4).
  • 集成了一个关键点回归策略和一个角度损失函数.
  • 采用了改进的功能融合技术,以改善信息流动.

主要成果:

  • 在COCO2017数据集上,KR-AL-YOLO实现了45.6%的平均精度.
  • 与YOLOv4和其他单阶段探测器相比,其表现优越.
  • 验证了关键点回归和特征融合的有效性.

结论:

  • KR-AL-YOLO显著提高了对象检测的准确性,特别是对于小物体.
  • 拟议的关键点回归和角度损失模块提高了定位精度.
  • 增强的功能融合进一步提高了该车型的整体性能.