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Related Concept Videos

Instrument Calibration01:12

Instrument Calibration

Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
Glassware Calibration01:11

Glassware Calibration

Accurate calibration of glassware, such as volumetric flasks, pipettes, and burettes, is essential to ensure accurate measurements in the analytical laboratory. Calibration helps maintain consistency across measurements and prevents errors arising from inaccurate volumes.
Volumetric flasks: Volumetric flasks are designed to prepare aqueous solutions of precise volumes accurately with a calibration line on the neck. To calibrate a volumetric flask, it is important to fill it with distilled...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...
Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
Vector Functions and Motion: Problem Solving01:30

Vector Functions and Motion: Problem Solving

Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...

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Related Experiment Video

Updated: Jul 7, 2026

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads
07:58

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads

Published on: July 25, 2025

Self-calibration of a space robot.

V R de Angulo1, C Torras

  • 1CSIC, Univ. Politecnica de Catalunya, Barcelona.

IEEE Transactions on Neural Networks
|January 1, 1997
PubMed
Summary

This study introduces a neural network for automatic robot recalibration, enhancing existing methods to quickly adapt to wear and damage. The system successfully learned kinematic deviations, proving effective in real-world space station mock-up tests.

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Commercial robots require recalibration to maintain accuracy after wear or damage.
  • Existing methods for robot recalibration can be time-consuming and complex.
  • Learning inverse kinematics is crucial for robot control and adaptation.

Purpose of the Study:

  • To develop an automated neural-network-based method for robot recalibration.
  • To adapt and improve upon existing self-organizing map techniques for kinematic learning.
  • To enhance the speed and stability of robot recalibration processes.

Main Methods:

  • Utilized a neural network approach building upon extended self-organizing maps.
  • Focused on learning deviations from nominal robot kinematics rather than the entire mapping.

Related Experiment Videos

Last Updated: Jul 7, 2026

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads
07:58

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads

Published on: July 25, 2025

  • Implemented modifications to improve neuron cooperation, accelerating learning and ensuring parameter stability.

    • Main Results:

    • Achieved a learning speed increase of two orders of magnitude compared to prior methods.
    • Demonstrated parameter stability as a beneficial side effect of the modifications.
    • Validated the recalibration system on a REIS robot in a space-station mock-up, with results aligning with simulations.

    Conclusions:

    • The proposed neural network method offers an efficient and stable approach to automatic robot recalibration.
    • The system effectively learns kinematic deviations, adapting robots to wear or damage.
    • Successful real-world testing confirms the practical applicability of the developed recalibration technique.