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Entropy02:39

Entropy

36.6K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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Entropy01:18

Entropy

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The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
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Special Features of Adaptive Immunity01:20

Special Features of Adaptive Immunity

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The adaptive immune system, a crucial component of the overall immune response, offers a highly specialized defense against pathogens. It involves specific cell types and features, enabling it to combat infections effectively and efficiently.
The primary cell types involved in adaptive immunity are T cells and B cells. Each type has a unique role in defending the body against pathogens. T cells are responsible for cell-mediated immunity. They identify and eliminate infected cells directly,...
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Classification of Signals01:30

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

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Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
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Nuclear Localization Signals and Import01:46

Nuclear Localization Signals and Import

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Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of  2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...
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Related Experiment Video

Updated: Feb 15, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

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Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features.

Imene Mitiche1, Gordon Morison2, Alan Nesbitt3

  • 1Department of Engineering, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK. imene.mitiche@gcu.ac.uk.

Sensors (Basel, Switzerland)
|February 1, 2018
PubMed
Summary
This summary is machine-generated.

This study enhances partial discharge (PD) signal analysis in high-voltage (HV) equipment using electromagnetic interference (EMI) and dispersion entropy. Improved classification accuracy aids in real-world condition monitoring for power plants.

Keywords:
EMI events (discharge sources)EMI methodclassificationdispersion entropyexpert’s systempartial dischargepermutation entropy

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Condition Monitoring

Background:

  • Electromagnetic Interference (EMI) captures Partial Discharge (PD) signals in High-Voltage (HV) power plant apparatus.
  • Non-stationary EMI signals pose challenges for pattern recognition and fault analysis.
  • Accurate classification of PD events is crucial for effective condition monitoring.

Purpose of the Study:

  • To improve the classification accuracy of EMI events for PD signal analysis.
  • To develop a more robust feature extraction method for non-stationary signals.
  • To enhance a previously developed software condition-monitoring model.

Main Methods:

  • Utilized time-frequency signal decomposition and entropy features for EMI event classification.
  • Incorporated Dispersion Entropy (DE) alongside permutation entropy to create a robust feature vector.
  • Employed Multi-Class Support Vector Machine (MCSVM) for classifying different discharge sources.

Main Results:

  • Achieved improved classification accuracy for EMI events compared to previous methods.
  • Successfully mapped time-domain signals to a feature space for better fault information interpretation.
  • Demonstrated the effectiveness of the enhanced model using real-world field data.

Conclusions:

  • The developed method offers a robust approach for classifying PD signals captured via EMI.
  • The integration of Dispersion Entropy enhances the analysis of non-stationary EMI signals.
  • The system shows potential for real-world application in HV power plant condition monitoring.