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相关概念视频

Reducing Line Loss01:18

Reducing Line Loss

150
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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Detection of Gross Error: The Q Test01:00

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
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Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Differential Leveling01:12

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Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
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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.
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相关实验视频

Updated: Jun 24, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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基于改进的YOLOv7的轻质条形钢缺陷检测算法.

Jianbo Lu1, MiaoMiao Yu2, Junyu Liu3

  • 1Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China.

Scientific reports
|June 10, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了SS-YOLO,这是一款用于检测钢带表面缺陷的轻量级模型. 它显著提高了准确性,并减少了计算负载,使质量控制更有效.

关键词:
D-SimSPPFF 是一个模拟器.深度学习是一种深度学习.轻量级网络轻量级的网络.带表面缺陷检测检测 带表面缺陷检测这就是YOLOv7的意义.

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相关实验视频

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 精确的钢带表面缺陷检测对于产品质量至关重要.
  • 现有的方法面临着大型模型大小和计算复杂性的挑战.

研究的目的:

  • 开发一种轻量级,高效的钢带表面缺陷检测模型.
  • 为了提高检测准确度,同时减少计算资源.

主要方法:

  • 推出了SS-YOLO,一种轻量级的YOLOv7变体.
  • 用MobileNetv3替换了CBS模块,以减少模型大小.
  • 集成的D-SimSPPF模块具有深度可分离的卷积和SimAM注意力.
  • 在特征提取的部和预测层应用SimAM注意力.

主要成果:

  • 在NEU-DET数据集上,SS-YOLO实现了97%的mAP50准确度,比YOLOv7.5有4.5%的改进.
  • 减少了79.3%的FLOP和20.7%的参数.
  • 在检测准确度,速度和模型大小之间取得了平衡.

结论:

  • SS-YOLO为钢带表面缺陷检测提供了一个有效的解决方案.
  • 该模型的轻量化性质和提高的准确性增强了工业质量控制.
  • 这种方法解决了当前复杂缺陷检测算法的局限性.