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相关概念视频

Classification of Systems-II01:31

Classification of Systems-II

171
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
171
Classification of Systems-I01:26

Classification of Systems-I

211
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
211
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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Classification of Signals01:30

Classification of Signals

519
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...
519
Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Lumber Defects01:23

Lumber Defects

148
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: Jul 16, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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通过使用深度神经网络进行表面缺陷分类来控制碳外观组件的质量.

Andrea Silenzi1, Vincenzo Castorani2, Selene Tomassini1

  • 1Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
概括

这项研究表明,深度神经网络可以使用常规图像对碳纤维部件的表面缺陷进行分类,达到97%的准确性. 这使得智能工厂能够在没有专门设备的情况下进行质量控制.

关键词:
在CFRP的基础上.工业4.0 工业4.0 工业4.0 工业4.0 工业4.0 是什么?碳纤维增强聚合物的聚合物.深度学习是一种深度学习.质量控制质量控制质量控制转移学习转移学习

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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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相关实验视频

Last Updated: Jul 16, 2025

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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机科学 计算机科学
  • 制造业 工程 制造工程

背景情况:

  • 工业4.0依赖于数据驱动的方法,如机器学习 (ML) 和深度学习 (DL),用于智能工厂.
  • 自动质量控制对于制造业的精度和标准化至关重要.
  • 现有的方法经常将DL与非破坏性测试 (NDT) 结合起来,需要特定的设置.

研究的目的:

  • 研究深度神经网络 (DNN) 的应用,并转移用于表面缺陷分类的学习.
  • 评估使用平面图像用于汽车碳纤维部件缺陷检测的可行性.
  • 开发一种可复制的自动化质量控制方法.

主要方法:

  • 收集了1900张碳纤维增强聚合物 (CFRP) 组件图像的数据集.
  • 开发并测试了十个DNN分类器,使用十个预训练的卷积神经网络 (CNN) 作为特征提取器 (例如VGG16,ResNet,DenseNet121).
  • 评估的二进制 (缺陷/无缺陷) 和多类 (无缺陷,可恢复,不可恢复) 分类性能.

主要成果:

  • 基于DenseNet121的分类器在多类缺陷分类中实现了97%的准确性.
  • 该方法在不同的条件下成功地对智能手机图像中的缺陷进行了分类.
  • 不需要专门的设置或NDT设备.

结论:

  • 深度神经网络和转移学习对于使用标准图像对CFRP组件的表面缺陷进行分类是可行的.
  • 拟议的方法为汽车行业的自动化质量控制提供了具有成本效益和可重复性的解决方案.
  • 开放访问的数据和代码有助于进一步的研究和工业应用.