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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
330
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
181
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

385
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
385
Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

12.8K
Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
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Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

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Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Related Experiment Video

Updated: Nov 27, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

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Mutual Information Gain and Linear/Nonlinear Redundancy for Agent Learning, Sequence Analysis, and Modeling.

Jerry D Gibson1

  • 1Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106-9560, USA.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

Intelligent agents can now quantify sequence structure and randomness using relative entropy. New mutual information gain measures capture learning in sequence modeling and analysis.

Keywords:
agent learninglinear redundancymutual information gainnonlinear redundancy

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Area of Science:

  • Information theory
  • Machine learning
  • Signal processing

Background:

  • Intelligent agents require understanding environmental structure and randomness.
  • Distinguishing between linear and nonlinear patterns is crucial for appropriate responses.

Purpose of the Study:

  • To introduce novel methods for quantifying linear and nonlinear redundancy in sequences.
  • To develop new metrics for analyzing sequence structure and apparent randomness.
  • To demonstrate the utility of these metrics in sequence modeling and analysis.

Main Methods:

  • Utilizing relative entropy to differentiate and measure linear and nonlinear redundancy.
  • Introducing total mutual information gain and incremental mutual information gain.
  • Applying these measures to autoregressive sequences and speech signals.

Main Results:

  • Successfully separated and quantified linear and nonlinear redundancy.
  • Demonstrated the effectiveness of mutual information gain for analyzing sequence structures.
  • Validated the approach on both synthetic and real-world (speech) data.

Conclusions:

  • Mutual information gain is a powerful new tool for quantifying learning in sequence modeling.
  • The developed methods enhance the ability of intelligent agents to understand complex environments.
  • This work provides a robust framework for analyzing sequence data with varying degrees of structure and randomness.