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相关概念视频

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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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...
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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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Reducing Line Loss01:18

Reducing Line Loss

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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...
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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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相关实验视频

Updated: Jun 6, 2025

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

Published on: December 15, 2023

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运用基于改进的YOLOv5的交通形目标检测算法.

Mingwu Wang1, Dan Qu2, Zedong Wu1

  • 1Department of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001, China.

Sensors (Basel, Switzerland)
|November 27, 2024
PubMed
概括

一个新的轻量级神经网络 (YOLOv5-Lite-s) 通过实现自动交通识别和定位来增强高速公路维护自动化. 该系统实现了高精度和速度,以实现高效的圆部署和收回操作.

关键词:
自动交通回收机自动交通回收机深度学习是一种深度学习.网络部署 网络部署道路维护是道路维护的重要组成部分.目标检测 目标检测 目标检测

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

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

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相关实验视频

Last Updated: Jun 6, 2025

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

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

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 高速公路维护操作需要高效和自动化的交通部署和收回.
  • 现有的系统可能缺乏实时操作所需的速度和准确性.
  • 嵌入式系统为现场处理提供了潜力,但需要优化模型.

研究的目的:

  • 开发和部署一个轻量级的神经网络,用于自动化交通识别和定位.
  • 使用嵌入式设备提高高速公路维护操作的自动化水平.
  • 为了满足交通放置和收缩的速度和准确性要求.

主要方法:

  • 使用轻量级YOLOv5-Lite的神经网络与ShuffleNet骨干进行特征提取.
  • 通过将卷积层替换为焦点模块并最大限度地减少C3层的使用,降低了计算复杂性.
  • 在嵌入式设备上部署了优化的网络,以便实时识别和定位交通.

主要成果:

  • YOLOv5-Lite网络在各种条件下 (距离,照明,遮蔽) 实现了约89%的识别准确度和每秒9 (fps).
  • 该系统满足了在20公里/小时的车速下每分钟部署/检索30个的技术要求.
  • 证明了自动交通放置和收回系统的准确和稳定的运行.

结论:

  • 轻量级的YOLOv5-Lite-s网络有效地使机器视觉应用在交通回收操作中成为可能.
  • 开发的系统增强了高速公路维护自动化,具有可接受的模型推断准确性和速度.
  • 优化的神经网络适合在嵌入式设备上部署,用于实时流量管理任务.