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Related Concept Videos

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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...
Classification of Signals01:30

Classification of Signals

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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Related Experiment Video

Updated: May 12, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Sequence classification with side effect machines evolved via ring optimization.

Andrew McEachern1, Daniel Ashlock, Justin Schonfeld

  • 1Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada N1G 2W1. amceache@uoguelph.ca

Bio Systems
|April 23, 2013
PubMed
Summary
This summary is machine-generated.

Side effect machines, a new machine learning tool, are trained using a novel ring evolution model. This method shows improved and reliable performance in analyzing biological sequence data compared to standard algorithms.

Related Experiment Videos

Last Updated: May 12, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Area of Science:

  • Bioinformatics
  • Machine Learning
  • Computational Biology

Background:

  • The rapid increase in biological sequence data requires advanced analytical tools.
  • Existing machine learning methods may not be optimal for complex sequence analysis.

Purpose of the Study:

  • Introduce and evaluate a novel sequence-learning technology: side effect machines.
  • Compare the efficacy of a ring evolution model versus standard evolutionary algorithms for training these machines.

Main Methods:

  • Developed side effect machines, a sequence-learning technology.
  • Applied a ring species evolution model for training.
  • Utilized a nearest neighbor classifier at the core of training.
  • Conducted a parameter study on data division for training and assessment.
  • Tested on synthetic data (GC-content, motif recognition) and biological data (immune genes, genomic sequences, mitochondrial DNA).

Main Results:

  • Ring optimization technique demonstrated improved and more reliable training performance.
  • Parameter setting significantly impacted baseline runs but had minimal effect on ring-optimization runs.
  • Side effect machines showed effectiveness on diverse synthetic and biological datasets.

Conclusions:

  • The ring optimization technique offers a superior approach for training side effect machines.
  • Side effect machines show promise for analyzing complex biological sequence data.