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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.
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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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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

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The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
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Types of Aggregate Grading

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Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
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Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
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Updated: Jun 21, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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An Optimization Method for Lightweight Rock Classification Models: Transferred Rich Fine-Grained Knowledge.

Mingshuo Ma1, Zhiming Gui1, Zhenji Gao2,3

  • 1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Sensors (Basel, Switzerland)
|July 13, 2024
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Summary

This study introduces a compact Feature Positioning Comparison Network (FPCN) for rock image classification. The FPCN enhances mobile model accuracy by focusing on subtle features and object scale variations.

Keywords:
contrastive learningknowledge distillationrock images classification

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Area of Science:

  • Computer Science
  • Geology

Background:

  • Rock image classification is a fine-grained task with subtle category differences.
  • Existing contrastive learning methods are often too large for mobile deployment in field rock identification.

Purpose of the Study:

  • To develop an innovative and compact model generation framework for fine-grained rock image classification.
  • To improve the accuracy of lightweight mobile models for in situ rock identification.

Main Methods:

  • Introduced a Feature Positioning Comparison Network (FPCN) for localized feature vector interaction.
  • Incorporated accommodation for variable object scales to capture contextual details.
  • Utilized knowledge distillation to streamline the architecture and focus on nuanced information.

Main Results:

  • The FPCN-based method improved classification accuracy in mobile lightweight models by nearly 2%.
  • The model maintained equivalent time and space consumption compared to existing methods.
  • The FPCN effectively captures shared and distinctive features while considering object size variability.

Conclusions:

  • The proposed FPCN framework offers a novel and efficient approach to fine-grained rock image classification.
  • This method enhances the accuracy of compact models for practical mobile applications in geology.
  • The technique successfully addresses limitations of current contrastive learning methods for resource-constrained environments.