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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 Systems-II01:31

Classification of Systems-II

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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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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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Related Experiment Video

Updated: Sep 16, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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ResNet-SE-CBAM Siamese Networks for Few-Shot and Imbalanced PCB Defect Classification.

Chao-Hsiang Hsiao1, Huan-Che Su2, Yin-Tien Wang3,4

  • 1Department of Computer Science and Information Engineering, Tamkang University, New Taipei City 251301, Taiwan.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel few-shot learning approach for product defect detection using a ResNet-SE-CBAM Siamese network. The method enhances accuracy and reduces miss rates, even with limited data, making it ideal for industrial applications.

Keywords:
Automatic Optical InspectionSiamese networksdefect detectionfew-shot learningimbalanced datasets

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Mass production defect detection faces challenges with small, imbalanced datasets, limiting traditional deep learning.
  • Few-shot learning is crucial for adapting models to real-world industrial scenarios with minimal data.

Purpose of the Study:

  • To develop and evaluate a few-shot learning model for effective product defect detection using limited data.
  • To enhance model generalization, stability, and applicability in industrial settings.

Main Methods:

  • Proposed a ResNet-SE-CBAM Siamese network for feature extraction, incorporating attention mechanisms and metric learning.
  • Utilized triplet loss for embedding learning and a Structural Similarity Index Measure (SSIM) for sample selection.
  • Implemented a high defect rate training strategy and a K-Nearest Neighbor (KNN) classifier for improved stability and reduced false negatives.

Main Results:

  • Achieved 94% classification accuracy and 2% False Negative Rate (FNR) with a 20:40 good-to-defect ratio.
  • Reached zero false negatives (FNR = 0%) when the number of defective samples increased to 80.
  • Outperformed traditional deep learning models like YOLO in accuracy and miss rates.

Conclusions:

  • The proposed metric learning approach demonstrates superior performance in few-shot defect detection compared to traditional deep learning models.
  • The system offers high reliability and potential for industrial deployment, effectively addressing challenges of limited and imbalanced datasets.