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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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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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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.
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Two-Stream Network One-Class Classification Model for Defect Inspections.

Seunghun Lee1, Chenglong Luo1, Sungkwan Lee2

  • 1Division of Mechanical and Aerospace Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a novel one-class classification method for industrial defect inspection, effectively handling imbalanced data. The proposed two-stream network significantly improves accuracy in detecting welding flaws in automotive parts.

Keywords:
defect inspectionmachine visionone-class classificationtwo-stream network

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Area of Science:

  • Industrial Manufacturing
  • Artificial Intelligence
  • Machine Vision

Background:

  • Defect inspection is crucial for maintaining quality and efficiency in manufacturing.
  • AI-powered machine vision systems show promise but struggle with imbalanced datasets.
  • Data imbalance is a common challenge in industrial defect detection.

Purpose of the Study:

  • To propose a defect inspection method using a one-class classification (OCC) model for imbalanced datasets.
  • To develop a two-stream network architecture to address the representation collapse problem in OCC.
  • To enhance the decision boundary of OCC models to prevent collapse towards the training data.

Main Methods:

  • A novel two-stream network architecture integrating global and local feature extractors.
  • Combining object-oriented invariant features with training-data-oriented local features.
  • Application to automotive airbag bracket welding defect inspection with real-world and lab data.

Main Results:

  • The proposed two-stream OCC model demonstrated improved performance over previous methods.
  • Significant enhancements in accuracy (up to 8.19%), precision (up to 10.74%), and F1 score (up to 4.02%).
  • Analysis clarified the impact of the classification layer and network architecture on inspection accuracy.

Conclusions:

  • The proposed two-stream OCC model effectively handles imbalanced data in industrial defect inspection.
  • The method provides a robust solution for quality control in manufacturing, exemplified by automotive welding inspection.
  • The approach successfully mitigates representation collapse and establishes a more appropriate decision boundary.