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Related Concept Videos

Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
¹H NMR of Conformationally Flexible Molecules: Temporal Resolution00:52

¹H NMR of Conformationally Flexible Molecules: Temporal Resolution

At room temperature, the chair conformer of cyclohexane undergoes rapid ring flipping between two equivalent chair conformers at a rate of approximately 105 times per second. These two chair conformers are in equilibrium. The rapid ring flipping results in the interconversion of the axial proton to an equatorial proton and an equatorial to the axial proton. Such interconversions are too rapid and cannot be detected on the NMR timescale. Hence, the NMR spectrometer cannot distinguish between the...
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.
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¹H NMR of Labile Protons: Temporal Resolution01:10

¹H NMR of Labile Protons: Temporal Resolution

Protons bonded to heteroatoms such as nitrogen and oxygen exhibit a range of chemical shift values. This is due to the varying degree of hydrogen bonding between the proton and the heteroatom in other molecules. The extent of hydrogen bonding affects the electron density around the proton, thereby giving different chemical shift values for the protons in the proton NMR spectrum.
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Time-Series Graph00:54

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
BIBO stability of continuous and discrete -time systems01:24

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System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Discovering metric temporal constraint networks on temporal databases.

Miguel R Álvarez1, Paulo Félix, Purificación Cariñena

  • 1Centro de Investigación en Tecnoloxías da Información (CITIUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain. miguel.rodriguez@usc.es

Artificial Intelligence in Medicine
|May 11, 2013
PubMed
Summary
This summary is machine-generated.

ASTPminer is a new algorithm for discovering frequent temporal patterns in time-stamped data. It uses user-provided knowledge to efficiently find patterns consistent with Simple Temporal Problem (STP) constraints.

Keywords:
Constraint satisfaction problemsSleep apnea–hypopnea syndromeTemporal data miningTemporal knowledge representation

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Area of Science:

  • Computer Science
  • Data Mining
  • Artificial Intelligence

Background:

  • Healthcare organizations require advanced computational tools for extracting knowledge from large datasets.
  • Temporal data mining is an emerging field focused on algorithms for discovering temporal knowledge.
  • Existing methods often present temporal knowledge in a qualitative format, limiting expressiveness.

Purpose of the Study:

  • To propose the ASTPminer algorithm for mining time-stamped sequences.
  • To discover frequent temporal patterns represented in the Simple Temporal Problem (STP) formalism.
  • To enable user-guided pattern discovery through seed patterns.

Main Methods:

  • ASTPminer employs an Apriori-like iterative strategy.
  • It utilizes a clustering procedure on temporal distance distributions to identify similar event occurrences.
  • Consistency checking ensures the soundness of discovered patterns, and seed patterns guide the search.

Main Results:

  • Experiments on polysomnography data for sleep apnea-hypopnea syndrome demonstrated ASTPminer's effectiveness.
  • The algorithm successfully extracted known temporal patterns associated with the syndrome.
  • Using seed patterns significantly reduced the search space and the number of frequent patterns found, enhancing efficiency.

Conclusions:

  • ASTPminer is a novel temporal data mining technique for identifying frequent temporal patterns in event sequences.
  • The discovered patterns reveal distinct temporal arrangements and similarities between event types.
  • User-guided mining via STP formalism improves performance by constraining the search space.