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Related Concept Videos

Dosage Compensation02:50

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In animals, gender is determined by the number and type of sex chromosome. For example, human females have two X chromosomes, and males have one X and one Y chromosome, whereas C.elegans with one X chromosome is a male, and the one with two X chromosomes is a hermaphrodite.
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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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The human body employs intricate mechanisms to counteract changes in blood pH, preventing conditions like acidosis (pH < 7.35) and alkalosis (pH > 7.45). These compensatory responses aim to restore normal arterial blood pH by engaging respiratory or renal systems, depending on the source of the imbalance.
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Instinctive drift refers to the tendency of animals to revert to their innate behaviors despite repeated reinforcement. Breland and Breland demonstrated this concept in an experiment with a raccoon. The raccoon was trained to pick up two coins and place them in a container in exchange for food. Initially, the raccoon learned to associate the coins with food, making them a conditioned stimulus or a substitute for food. However, over time, the raccoon became less willing to put the coins into the...
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Related Experiment Video

Updated: Feb 13, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine.

Zhiyuan Ma1, Guangchun Luo2, Ke Qin3

  • 1School of Computer Science and Engineering, University of Electronics and Technology of China, Chengdu 611731, China. yuliar3514@gmail.com.

Sensors (Basel, Switzerland)
|March 2, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces online drift compensation for electronic nose (E-Nose) systems, enhancing gas recognition accuracy efficiently. The new methods adapt to sensor changes in real-time, improving performance without sacrificing speed.

Keywords:
domain adaptationdrift compensationextreme learning machinegas sensoronline learning

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Area of Science:

  • Artificial Intelligence
  • Sensor Technology
  • Chemical Sensing

Background:

  • Sensor drift is a significant challenge in electronic nose (E-Nose) systems, impacting gas recognition accuracy.
  • Existing drift compensation methods often require offline processing, limiting their applicability in real-time scenarios.

Purpose of the Study:

  • To develop and evaluate online drift compensation models for E-Nose systems.
  • To adapt existing domain adaptation techniques for efficient online learning.
  • To maintain high gas recognition accuracy under drift conditions in a time-efficient manner.

Main Methods:

  • Transformed two domain adaptation methods into their online learning counterparts.
  • Implemented online learning versions to enable real-time adaptation to sensor response changes.
  • Evaluated performance using experimental data across three different settings.

Main Results:

  • The proposed online methods significantly reduced processing time compared to their offline versions.
  • Achieved superior recognition accuracy for diverse gases under drift conditions.
  • Demonstrated effective adaptation to sensor response variations in real-time.

Conclusions:

  • Online drift compensation models can be effectively developed by adapting domain adaptation techniques.
  • The proposed methods offer a time-efficient solution for E-Nose systems without compromising accuracy.
  • This approach enhances the practical applicability of E-Nose systems in dynamic environments.