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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Microcracking in Concrete01:20

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Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...
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Difference from Background: Limit of Detection01:05

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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Related Experiment Video

Updated: Aug 13, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Accurate Crack Detection Based on Distributed Deep Learning for IoT Environment.

Youngpil Kim1, Shinuk Yi2, Hyunho Ahn3

  • 1Department of Information and Telecommunication Engineering, Incheon National University, 119, Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea.

Sensors (Basel, Switzerland)
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces Rsef-Edge, an edge computing system for fast and accurate crack detection on low-power devices. It optimizes machine learning models for effective quality control without needing human experts.

Keywords:
Efficient-NetU-Netcrack detectionedge computing

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Materials Science

Background:

  • Surface defects like cracks impede quality control in infrastructure and products.
  • Machine learning offers automated defect detection but is computationally intensive for low-power devices.

Purpose of the Study:

  • To develop an efficient crack detection system for low-power edge computing devices.
  • To reduce inference time for defect detection without compromising accuracy.

Main Methods:

  • Developed Rsef, a real-time segmentation method optimizing deep learning for feature extraction.
  • Constructed Rsef-Edge, an edge-based system to accelerate Rsef's inference on IoT devices.

Main Results:

  • Achieved significantly decreased inference times for crack detection.
  • Demonstrated good accuracy in defect detection within a low-power computing environment.

Conclusions:

  • Rsef-Edge provides a viable solution for real-time crack detection on resource-constrained devices.
  • The system enhances quality control by enabling efficient defect identification in various applications.