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Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

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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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Lumber Defects01:23

Lumber Defects

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Lumber defects, which can affect both the appearance and structural integrity of wood, include a variety of growth and manufacturing flaws. Growth defects such as knots and knotholes occur where branches were once attached to the tree trunk, with knotholes forming when these knots fall out. Other natural defects include decay and insect damage, which compromise the wood's strength and durability.
Shakes are minor fractures that run along or across the wood's annual rings, while wane is...
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相关实验视频

Updated: May 2, 2026

Laser-induced Forward Transfer for Flip-chip Packaging of Single Dies
08:21

Laser-induced Forward Transfer for Flip-chip Packaging of Single Dies

Published on: March 20, 2015

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基于CDI-YOLO的PCB缺陷检测算法

Gaoshang Xiao1, Shuling Hou1, Huiying Zhou2

  • 1School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China.

Scientific reports
|March 29, 2024
PubMed
概括

本研究介绍了CDI-YOLO,这是一种有效的算法,用于检测印刷电路板 (PCB) 的缺陷. 这种新的方法提高了准确性和速度,同时减少了模型参数,以改善PCB制造质量控制.

科学领域:

  • 电气工程 电气工程
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 印刷电路板 (PCB) 制造容易出现影响性能和可靠性的缺陷.
  • 目前用于PCB缺陷检测的深度学习方法难以平衡准确性,速度和模型大小.

研究的目的:

  • 开发一种先进的PCB缺陷检测算法,克服现有方法的局限性.
  • 为了提高PCB缺陷识别的准确性,检测速度和效率.

主要方法:

  • 提出了一个新的算法,CDI-YOLO,基于YOLOv7-tiny.
  • 整合了协调注意力机制 (CA) 来促进特征提取.
  • 使用DSConv来降低计算成本和更快的检测.
  • 采用Inner-CIoU作为加速定位的边界框回归损失.

主要成果:

  • 在PCB缺陷数据集上实现了98.3%的平均精度 (mAP).
  • 演示了每秒128 (FPS) 的检测速度.
  • 保持了一个紧的模型大小,具有580万个参数和每秒12.6Giga浮点运算 (GFLOPs).

结论:

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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

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Detection of a CDH1 Rare Transcript Variant in Fresh-frozen Gastric Cancer Tissues by Chip-based Digital PCR
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Detection of a CDH1 Rare Transcript Variant in Fresh-frozen Gastric Cancer Tissues by Chip-based Digital PCR

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相关实验视频

Last Updated: May 2, 2026

Laser-induced Forward Transfer for Flip-chip Packaging of Single Dies
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Laser-induced Forward Transfer for Flip-chip Packaging of Single Dies

Published on: March 20, 2015

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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

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Detection of a CDH1 Rare Transcript Variant in Fresh-frozen Gastric Cancer Tissues by Chip-based Digital PCR
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  • 拟议的CDI-YOLO算法与现有方法相比,提供了优越的综合性能.
  • 这种方法为高精度,高速PCB缺陷检测提供了有效的解决方案.
  • 该方法有助于提高制造PCB的质量和可靠性.