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Related Concept Videos

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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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

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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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As the construction industry moves towards more eco-friendly practices, concrete's adaptability and its ability to incorporate sustainable features make it a key material in the drive towards greener building solutions.
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AI for Sustainable Recycling: Efficient Model Optimization for Waste Classification Systems.

Oriol Chacón-Albero1, Mario Campos-Mocholí1, Cédric Marco-Detchart2

  • 1Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 València, Spain.

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Summary
This summary is machine-generated.

This study enhances automated waste sorting using efficient computer vision models for real-time recycling assistance. Optimized models and diverse datasets improve accuracy, supporting circular economy goals and reducing landfill waste.

Keywords:
aggregation functionsimage classificationmodel quantizationrecycling

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Area of Science:

  • Environmental Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Global waste volume poses significant environmental challenges, necessitating advanced recycling solutions.
  • Computer Vision (CV) systems automate waste recognition for improved sorting accuracy.
  • Focus is shifting towards efficient, lightweight models for mobile and edge device deployment in waste management.

Purpose of the Study:

  • To enhance automated waste recognition systems by improving dataset diversity and model efficiency.
  • To develop robust, real-time recycling assistants for resource-constrained devices.
  • To contribute to circular economy objectives through improved waste sorting and material recovery.

Main Methods:

  • Expanded dataset with an organic waste class and heterogeneous images.
  • Evaluation of quantized Convolutional Neural Network (CNN) models for reduced inference time.
  • Exploration of ensemble strategies and validation on embedded hardware under varied lighting conditions.

Main Results:

  • Quantized CNN models demonstrated reduced resource usage and faster inference.
  • Ensemble strategies boosted classification performance for waste recognition.
  • Models validated on real hardware showed robustness under simulated lighting variations.

Conclusions:

  • Developed efficient, real-time waste sorting systems suitable for edge devices.
  • Proposed deployment scenarios for smart bins and cloud-assisted recycling solutions.
  • Enhanced waste sorting accuracy supports landfill reduction, citizen engagement, and circular economy goals.