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相关概念视频

Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Classification of Systems-I01:26

Classification of Systems-I

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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:
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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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Classification of Systems-II01:31

Classification of Systems-II

242
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,
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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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相关实验视频

Updated: Sep 15, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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用深度学习算法对饼干质量进行分类

Oya Kilci1, Yusuf Eryesil2, Murat Koklu2

  • 1Graduate School of Natural and Applied Sciences, Department of Mechatronics Engineering, Selcuk University, Konya, Türkiye.

Journal of food science
|July 18, 2025
PubMed
概括
此摘要是机器生成的。

深度学习模型通过检测缺陷来显著改善饼干质量控制. EfficientNet实现了高精度,展示了自动化工业食品生产检查的潜力.

关键词:
饼干的分类 饼干的分类深度学习是一种深度学习.的CAM-CAM等级.机器学习是机器学习.质量控制质量控制质量控制

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科学领域:

  • 食品科学与技术 食品科学与技术
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 食品生产中的传统质量控制是劳动密集型的,容易出现人为错误.
  • 需要自动化缺陷检测系统来提高饼干制造的效率和降低成本.

研究的目的:

  • 开发和评估用于自动检测有缺陷饼干的深度学习模型.
  • 为了比较不同深度学习架构对二进制和多类缺陷分类的性能.

主要方法:

  • 创建两个数据集:一个用于二进制分类 (缺陷/无缺陷) 和一个用于多类分类 (过度煮熟,质地缺陷,不完整).
  • 深度学习模型的培训和评估,包括EfficientNet,ResNet,XceptionNet和MobileNet.
  • 使用Grad-CAM用于模型可解释性,以可视化关注缺陷区域.

主要成果:

  • 在二进制分类中,EfficientNet表现出卓越的性能,准确度为93.89%,在多类分类中为95.03%.
  • ResNet取得了可比的结果,而XceptionNet和MobileNet显示了具有竞争力的F1分数,特别是在纹理缺陷方面.
  • 格拉德-CAM分析证实了EfficientNet有效地专注于关键缺陷区域.

结论:

  • 深度学习模型,特别是EfficientNet,为饼干生产中高效准确的自动化质量控制提供了可行的解决方案.
  • 该研究强调了人工智能驱动的检查系统在工业食品制造中减少错误,时间和成本的潜力.
  • 这些发现支持集成先进的机器学习技术,以加强食品产品质量保证.