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Related Concept Videos

Minor Losses in Pipes01:25

Minor Losses in Pipes

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In pipe systems, minor losses refer to energy losses arising from components such as valves, bends, fittings, expansions, and other features that disrupt the steady flow of fluid. These disturbances cause energy dissipation through turbulence and resistance, which engineers quantify to manage system efficiency effectively.
Valves play a significant role in generating minor losses by obstructing or redirecting the fluid flow. When a valve is closed or partially closed, it restricts the flow...
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Pipe Flowrate Measurement: Problem Solving01:28

Pipe Flowrate Measurement: Problem Solving

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A spray tank system is engineered to uniformly distribute a pest-control liquid across plants by using a pressurized mechanism. The tank, pressurized to 150 kPa, holds the pesticide at a height of 0.80 meters. Liquid flows from the tank through a 1.9 meter pipe with a diameter of 0.015 meters, angled at 0.698 radians, ultimately reaching a 0.007 meter nozzle that sprays the pesticide. Accurate calculation of the system's flow rate is crucial to ensure uniform application, and this is...
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Multiple Pipe Systems01:21

Multiple Pipe Systems

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Multipipe systems consist of complex configurations of interconnected pipes designed to transport fluids efficiently across intricate networks. They are essential in engineering applications requiring precise control over flow distribution, pressure, and head loss. They are categorized into series, parallel, loop, and network configurations, each distinguished by unique flow characteristics and applications.
Series Configuration
In a series configuration, fluid flows sequentially from one pipe...
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Pipe Flowrate Measurement01:28

Pipe Flowrate Measurement

285
In pipe flow measurement, orifice, nozzle, and Venturi meters are commonly used to determine fluid flowrates by constricting the flow area, which increases fluid velocity and reduces pressure. This pressure difference, governed by Bernoulli's principle and adjusted for real-world conditions, is essential for calculating flowrate. Each meter type is suited to specific applications based on accuracy, efficiency, and compatibility with various flow conditions.
The orifice meter is a simple,...
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Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

81
Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
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Single Pipe Systems01:24

Single Pipe Systems

102
In pipe flow analysis, problems are typically categorized into three types — Type I, Type II, and Type III — based on the known parameters and the desired outcome. Each type of problem addresses specific engineering requirements using fluid properties, pipe characteristics, and operational conditions.
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Updated: Jun 3, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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FP-YOLOv8: Surface Defect Detection Algorithm for Brake Pipe Ends Based on Improved YOLOv8n.

Ke Rao1, Fengxia Zhao1, Tianyu Shi1

  • 1School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450000, China.

Sensors (Basel, Switzerland)
|January 8, 2025
PubMed
Summary
This summary is machine-generated.

A new lightweight algorithm, FP-YOLOv8, enhances surface defect detection on brake pipe ends. It improves accuracy and reduces model size, making it ideal for online inspection systems.

Keywords:
YOLOv8nbrake pipe endslabel assignmentsurface defect detection

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Materials Science

Background:

  • Existing deep learning algorithms struggle with detecting surface defects on brake pipe ends.
  • There is a need for lightweight and accurate models for real-time defect detection.

Purpose of the Study:

  • To propose a novel lightweight deep learning algorithm, FP-YOLOv8, for improved surface defect detection on brake pipe ends.
  • To enhance detection accuracy and reduce model complexity for online applications.

Main Methods:

  • Developed FP-YOLOv8 based on the YOLOv8n framework, incorporating a C2f_GhostV2 module and a decoupled fully connected (DFC) attention mechanism.
  • Implemented a Dynamic ATSS label allocation strategy and an asymmetric small-target detection head (FADH) using depth-separable convolution.

Main Results:

  • FP-YOLOv8 achieved a mAP50 of 89.5% and an F1-score of 87%, outperforming YOLOv8n by 3.3% and 6.0% respectively.
  • The model reduced parameters by 14.3% and computational costs by 21.0%, with significant AP50 improvements for cracks (5.5%), scratches (5.6%), and flash defects (2.3%).

Conclusions:

  • FP-YOLOv8 effectively enhances defect detection accuracy and reduces missed detections on brake pipe end surfaces.
  • The algorithm's lightweight design and improved performance meet the demands of online defect detection systems.