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Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Boundary Conditions: Lossless Lines01:21

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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.
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Design Example: Strain Gauge Bridge or Wheatstone Bridge01:15

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The utilization of strain gauges as transducers for converting mechanical strain into electrical signals is a common practice in various engineering applications. These strain gauges are frequently integrated into Wheatstone bridge circuits to accurately measure parameters such as force or pressure. Within this context, each element within the circuit exhibits a resistance that undergoes subtle variations when subjected to mechanical strain. The primary objective is to convert minuscule...
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Wheatstone Bridge01:29

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An ohmmeter is a resistance-measuring device. It works by applying a voltage to a resistor of unknown resistance and measuring the current across the resistor. The resistance value is deduced using Ohm's law. Usually, the standard configuration of an ohmmeter comprises a voltmeter or an ammeter. However, such configurations are limited in accuracy because the meters alter the voltage applied to the resistor and the current that flows through it.
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Reconstruction of Signal using Interpolation01:10

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Related Experiment Video

Updated: Oct 3, 2025

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

241

Data Loss Reconstruction Method for a Bridge Weigh-in-Motion System Using Generative Adversarial Networks.

Yizhou Zhuang1, Jiacheng Qin1, Bin Chen2,3

  • 1College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310014, China.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary

A deep learning generative adversarial network (GAN) effectively reconstructs missing data in bridge weigh-in-motion (WIM) systems. This method accurately restores vehicle and axle weights, ensuring reliable bridge condition assessments.

Keywords:
bridge weigh-in-motion systemconvolutional neural networkdata lossdata reconstructiondeep learninggenerative adversarial network

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Area of Science:

  • Civil Engineering
  • Data Science
  • Artificial Intelligence

Background:

  • Bridge weigh-in-motion (WIM) systems are crucial for structural health monitoring.
  • Data loss due to sensor or transmission failures compromises WIM system reliability and data integrity.
  • Accurate data is essential for assessing bridge condition and ensuring public safety.

Purpose of the Study:

  • To propose a deep learning-based generative adversarial network (GAN) model for reconstructing missing data in bridge WIM systems.
  • To enhance the reliability of bridge monitoring by addressing data loss issues.
  • To validate the effectiveness of the proposed GAN model using real-world engineering data.

Main Methods:

  • A generative adversarial network (GAN) was developed to model and predict missing WIM data.
  • The generator network was trained on retained features from functional sensors to reconstruct lost data.
  • A discriminator network provided feedback to the generator, improving reconstruction accuracy using generation and confrontation loss functions.

Main Results:

  • The GAN model successfully reconstructed missing data for the Hangzhou Jiangdong Bridge WIM system.
  • Reconstructed datasets showed strong agreement with actual data regarding total vehicle weight and axle weight.
  • The model reproduced the approximate contour and potential distribution characteristics of the original dataset.

Conclusions:

  • The proposed GAN-based method is effective for reconstructing missing data in bridge WIM systems.
  • This approach offers a promising solution for data recovery in real-world bridge monitoring applications.
  • The study highlights the potential of deep learning for improving the robustness of structural health monitoring systems.