Academic Journal

Advanced Deformation Models and Adaptive Mechanisms in Elastic Patterns.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Advanced Deformation Models and Adaptive Mechanisms in Elastic Patterns.
Συγγραφείς: Rodriguez-Cardos, Ruben, Olivas, Jose A.
Πηγή: Applied Sciences (2076-3417); Jun2026, Vol. 16 Issue 11, p5596, 23p
Θεματικοί όροι: Deformations (Mechanics), Classification, Pattern recognition systems, Parameterization
Περίληψη: The concept of Elastic Patterns was originally proposed as a prototype-based classification approach that unifies perspectives from cognitive psychology, fuzzy logic, and physics. At their core, Elastic Patterns operate across two levels of deformation: a parameter-level deformation, quantified in terms of axial strain, and a pattern-level deformation, understood as the accumulation of deformation energy to perfectly fit the sample to be classified. This dual representation supports an interpretable and adaptive recognition mechanism, where classification emerges from selecting the Elastic Pattern that requires the minimal deformation energy to align with a real case to classify. This paper extends the theoretical and practical foundations of the proposed Elastic Patterns approach for adaptive pattern classification by introducing several deformation models, Spring Hardening, Weighted Spring Deformation, or Group Parameter Deformation to improve the capacity of Elastic Patterns to adapt to different contexts. These deformation models allow the proposal to adapt to different semantic contexts by controlling how parameter contraction and elongation are penalised. Additionally, novel adaptive mechanisms are introduced, which enable Elastic Patterns to dynamically adjust parameter relevance, capture inter-parameter dependencies, and better reflect contextual knowledge. Furthermore, the framework offers inherently interpretable classification via explicit parameter deformations and energies, avoiding post hoc explanations. [ABSTRACT FROM AUTHOR]
Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  Data: Advanced Deformation Models and Adaptive Mechanisms in Elastic Patterns.
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  Data: Applied Sciences (2076-3417); Jun2026, Vol. 16 Issue 11, p5596, 23p
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  Data: <searchLink fieldCode="DE" term="%22Deformations+%28Mechanics%29%22">Deformations (Mechanics)</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink><br /><searchLink fieldCode="DE" term="%22Parameterization%22">Parameterization</searchLink>
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  Data: The concept of Elastic Patterns was originally proposed as a prototype-based classification approach that unifies perspectives from cognitive psychology, fuzzy logic, and physics. At their core, Elastic Patterns operate across two levels of deformation: a parameter-level deformation, quantified in terms of axial strain, and a pattern-level deformation, understood as the accumulation of deformation energy to perfectly fit the sample to be classified. This dual representation supports an interpretable and adaptive recognition mechanism, where classification emerges from selecting the Elastic Pattern that requires the minimal deformation energy to align with a real case to classify. This paper extends the theoretical and practical foundations of the proposed Elastic Patterns approach for adaptive pattern classification by introducing several deformation models, Spring Hardening, Weighted Spring Deformation, or Group Parameter Deformation to improve the capacity of Elastic Patterns to adapt to different contexts. These deformation models allow the proposal to adapt to different semantic contexts by controlling how parameter contraction and elongation are penalised. Additionally, novel adaptive mechanisms are introduced, which enable Elastic Patterns to dynamically adjust parameter relevance, capture inter-parameter dependencies, and better reflect contextual knowledge. Furthermore, the framework offers inherently interpretable classification via explicit parameter deformations and energies, avoiding post hoc explanations. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
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  Data: <i>Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/app16115596
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      – Code: eng
        Text: English
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        PageCount: 23
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      – SubjectFull: Deformations (Mechanics)
        Type: general
      – SubjectFull: Classification
        Type: general
      – SubjectFull: Pattern recognition systems
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      – SubjectFull: Parameterization
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      – TitleFull: Advanced Deformation Models and Adaptive Mechanisms in Elastic Patterns.
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              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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