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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 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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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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Related Experiment Video

Updated: Jan 10, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

999

Explainable Deep Learning Framework for Binary Corrosion Image Classification Using Grad-CAM.

Muhammad Amir Imran Aminudin1, Mohd Na'im Abdullah1, Faizal Mustapha1

  • 1Department of Aerospace Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces deep learning models for automated corrosion detection in metallic materials, achieving high accuracy. Explainable AI (XAI) techniques like Grad-CAM were used to visualize and validate model predictions, enhancing reliability in corrosion analysis.

Keywords:
binary classificationconvolutional neural networks (CNN)corrosion detectiondeep learningnon-destructive testing

Related Experiment Videos

Last Updated: Jan 10, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

999

Area of Science:

  • Materials Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Metallic material corrosion poses significant maintenance and safety challenges.
  • Traditional visual inspection methods are inefficient and require expert interpretation.
  • Automated solutions are needed for accurate and reliable corrosion detection.

Purpose of the Study:

  • To investigate the efficacy of deep learning models for binary image classification of corrosion.
  • To integrate explainable artificial intelligence (XAI) techniques for model interpretability.
  • To compare the performance of four pre-trained convolutional neural network (CNN) architectures.

Main Methods:

  • Utilized four pre-trained CNNs: ResNet50, MobileNetV2, NASNetMobile, and EfficientNetV2B0.
  • Trained models on a dataset of 9636 augmented images (corroded vs. non-corroded).
  • Applied Gradient-weighted Class Activation Mapping (Grad-CAM) for XAI to visualize decision-making.

Main Results:

  • ResNet50 achieved the highest classification accuracy (96.58%).
  • MobileNetV2 offered the fastest training time.
  • EfficientNetV2B0 demonstrated stable training with minimal overfitting and high activation in corroded regions via Grad-CAM.

Conclusions:

  • Deep learning models, particularly CNNs, show strong potential for automated corrosion detection.
  • XAI techniques like Grad-CAM enhance model transparency and trust in corrosion analysis.
  • EfficientNetV2B0 and ResNet50 present promising performance characteristics for corrosion classification and interpretability.