Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Distance Corrections01:15

Distance Corrections

To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
Pipe Flowrate Measurement: Problem Solving01:28

Pipe Flowrate Measurement: Problem Solving

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 achieved...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Prevalence and genotypic distribution of virulence factor genes and antibiotic susceptibility profiles in clinical <i>Pseudomonas aeruginosa</i> isolates from a public hospital in Nanyang, China.

Microbiology spectrum·2026
Same author

From trial-and-error to intelligent prediction: Machine learning drives eco-efficient gold leaching in e-waste.

Journal of environmental management·2026
Same author

Unraveling MARCH6's role in cancer progression and metabolism from protein homeostasis to oncogenesis.

Pharmacological research·2026
Same author

Interpreting tissue stiffening with lung tumorigenesis by imaging architectural resembling of extracellular matrix components.

Communications biology·2026
Same author

Robust and Thermally Stable Silicone Aerogels with Hyperconnected Network via Kinetically Optimized Hyperbranched Silane Precursors.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Co-recycling wheat straw and eggshells for sustainable agriculture: A composite coated urea synergistically mitigating nitrogen loss and salt stress.

Journal of environmental management·2026

Related Experiment Video

Updated: Jul 8, 2026

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
09:48

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping

Published on: November 7, 2016

Intelligent compensation method for measurement errors in optical fiber current sensor caused by temperature

Lin Cheng1, Jianyong Luo2, Weibin Si1

  • 1State Grid Shaanxi Electric Power Co., Ltd. Electric Power Research Institute, Xi'an, China.

Plos One
|July 6, 2026
PubMed
Summary

An intelligent algorithm compensates for temperature errors in fiber optic current sensors (FOCS), improving accuracy in power systems. This method enhances measurement reliability without hardware changes.

More Related Videos

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings
08:23

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings

Published on: September 30, 2019

Related Experiment Videos

Last Updated: Jul 8, 2026

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
09:48

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping

Published on: November 7, 2016

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings
08:23

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings

Published on: September 30, 2019

Area of Science:

  • Electrical Engineering
  • Sensor Technology
  • Artificial Intelligence

Background:

  • Fiber optic current sensors (FOCS) are crucial for power systems but susceptible to temperature variations, impacting measurement accuracy.
  • Accurate current sensing is vital for high-voltage transmission and renewable energy integration, where environmental conditions fluctuate.
  • Existing compensation methods often require complex hardware modifications, limiting their applicability.

Purpose of the Study:

  • To develop an intelligent error compensation method for FOCS to mitigate temperature-induced inaccuracies.
  • To leverage easily measurable parameters for predicting and compensating temperature-dependent errors in FOCS.
  • To validate the proposed method's effectiveness and robustness in harsh environmental conditions.

Main Methods:

  • An improved Quantum-behaved Particle Swarm Optimization-Neural Network (Levy-Weighted-QPSO-NN) algorithm was developed.
  • The algorithm uses sensing ring temperature, optical power, half-wave voltage, and SLD parameters as inputs.
  • Experimental validation involved temperature cycling (-45 °C to 70 °C) on three sensing rings.

Main Results:

  • The Levy-Weighted-QPSO-NN model achieved 91.11% average prediction accuracy for current ratio difference (R²=0.9223).
  • The model outperformed standard QPSO-NN (85.69%) and Weighted-QPSO-NN (88.31%) algorithms.
  • Real-time compensation reduced measurement errors from 0.82% to 0.13%, meeting Class 0.2S accuracy standards.

Conclusions:

  • The Levy-Weighted-QPSO-NN algorithm provides a robust, algorithm-driven solution for temperature compensation in FOCS.
  • This method enhances FOCS accuracy and stability without requiring hardware modifications.
  • The approach offers a generic solution for improving FOCS performance in critical power applications.