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Related Concept Videos

Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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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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Classification of Systems-I01:26

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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:
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Classification of Systems-II01:31

Classification of Systems-II

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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,
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Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
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Classification of Connective Tissues01:30

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The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense....
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A Frequency-Based Approach for the Detection and Classification of Structural Changes Using t-SNE †.

David Agis1, Francesc Pozo1

  • 1Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d'Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain.

Sensors (Basel, Switzerland)
|November 27, 2019
PubMed
Summary

This study introduces a structural health monitoring method using t-distributed stochastic neighbor embedding (t-SNE) for accurate detection and classification of structural changes in materials. The approach effectively identifies damage states with high accuracy.

Keywords:
classification detectionprincipal component analysis (PCA)structural changesstructural health monitoring (SHM)t-distributed stochastic neighbor embedding (t-SNE)

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Area of Science:

  • Engineering
  • Materials Science
  • Data Science

Background:

  • Structural health monitoring (SHM) is crucial for assessing infrastructure integrity.
  • Existing methods often struggle with high-dimensional data from sensors.
  • Nonlinear dimensionality reduction techniques offer potential for improved SHM.

Purpose of the Study:

  • To develop and evaluate a novel SHM approach for detecting and classifying structural changes.
  • To leverage t-distributed stochastic neighbor embedding (t-SNE) for enhanced data representation.
  • To improve the accuracy and robustness of structural state diagnosis.

Main Methods:

  • Data preprocessing using mean-centered group scaling (MCGS).
  • Dimensionality reduction via principal component analysis (PCA).
  • Nonlinear embedding using t-distributed stochastic neighbor embedding (t-SNE) to form clusters representing structural states.
  • Classification of current structural states using distance-based and voting strategies.

Main Results:

  • The combination of PCA and t-SNE effectively clusters data according to structural states.
  • Experimental validation on an aluminum plate with piezoelectric transducers demonstrated high classification accuracy.
  • The proposed method shows strong performance in identifying structural changes.

Conclusions:

  • The developed SHM approach using PCA and t-SNE is effective for structural change detection and classification.
  • The method offers a robust way to diagnose structural conditions based on sensor data.
  • This technique holds promise for advanced structural health monitoring applications.