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Related Concept Videos

Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The winding...
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the rated...
Source Transformation for AC Circuits01:11

Source Transformation for AC Circuits

The process of source transformation in the frequency domain entails the conversion of a voltage source, positioned in series with an impedance, into a current source that is parallel to an impedance, or the other way around. It is essential to maintain the following relationships while transitioning from one source type to another.
Sum and Difference OpAmps01:22

Sum and Difference OpAmps

Operational amplifiers (op-amps) are versatile devices that extend beyond amplification. In this context, two specific op-amp configurations are explored: the summing and difference amplifiers.
A summing amplifier, or an adder, utilizes an op-amp to merge multiple input signals into a single output signal. When audio signals are introduced into its input channels, the input resistors initiate currents that traverse feedback resistors, resulting in an output voltage. Applying Kirchhoff's current...
Transformers01:26

Transformers

A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
Energy Losses in Transformers01:21

Energy Losses in Transformers

In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the copper windings...

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Related Experiment Video

Updated: May 15, 2026

TD-DFT Guided Advanced E-Eye Sensing Technique for On-site Quantification of Fe, Cr, F, and As in the Environmental, Biological, and Food Samples
09:51

TD-DFT Guided Advanced E-Eye Sensing Technique for On-site Quantification of Fe, Cr, F, and As in the Environmental, Biological, and Food Samples

Published on: September 19, 2025

Feature-enhanced dual-input transformer for LIBS quantitative analysis of minor-content elements.

Qian Huang1, Haoyang Yu1, Zhaohui Jiang1

  • 1School of Automation, Central South University, Changsha, 410083, China.

Analytica Chimica Acta
|May 13, 2026
PubMed
Summary

A new dual-input transformer model enhances Laser-Induced Breakdown Spectroscopy (LIBS) analysis for minor elements. This approach improves accuracy by integrating physical knowledge with data-driven learning for better quantification of complex materials.

Keywords:
Deep learningLaser-induced breakdown spectroscopyNeural networkQuantitative analysis

More Related Videos

Quantification of Metal Leaching in Immobilized Metal Affinity Chromatography
05:35

Quantification of Metal Leaching in Immobilized Metal Affinity Chromatography

Published on: January 17, 2020

Related Experiment Videos

Last Updated: May 15, 2026

TD-DFT Guided Advanced E-Eye Sensing Technique for On-site Quantification of Fe, Cr, F, and As in the Environmental, Biological, and Food Samples
09:51

TD-DFT Guided Advanced E-Eye Sensing Technique for On-site Quantification of Fe, Cr, F, and As in the Environmental, Biological, and Food Samples

Published on: September 19, 2025

Quantification of Metal Leaching in Immobilized Metal Affinity Chromatography
05:35

Quantification of Metal Leaching in Immobilized Metal Affinity Chromatography

Published on: January 17, 2020

Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Materials Science

Background:

  • Laser-Induced Breakdown Spectroscopy (LIBS) offers rapid, in situ elemental analysis.
  • Accurate quantification of minor elements in LIBS is challenging due to weak signals, noise, and spectral interferences.
  • Current methods struggle with feature extraction, relying on incomplete physics-based features or data-driven models that may overfit or suppress trace signals.

Purpose of the Study:

  • To develop a novel method for robust quantitative analysis of minor-content elements using LIBS.
  • To overcome limitations of existing approaches in handling weak spectral signatures and matrix effects.
  • To improve the accuracy and reliability of elemental quantification in complex materials.

Main Methods:

  • A feature-enhanced dual-input transformer model was developed.
  • A cross-attention mechanism aligned physical atomic spectra with LIBS spectra to guide feature selection.
  • Self-attention processed LIBS spectra to learn informative wavelength contributions, and a gated attention fusion balanced physics-guided and data-driven insights.

Main Results:

  • The model significantly improved quantitative analysis of minor elements in LIBS.
  • Key performance indicators like R-squared, RMSE, and MAE showed substantial enhancements compared to existing methods.
  • The approach effectively integrated physical priors and data-driven learning to enhance target element features.

Conclusions:

  • The feature-enhanced dual-input transformer provides a robust strategy for accurate minor-element quantification in LIBS.
  • This method effectively combines physics-guided and data-driven approaches for improved spectral analysis.
  • The study enables more reliable elemental measurements of complex materials across diverse applications.