Data-Driven Exponential Framing for Pulsive Temporal Patterns Without Repetition or Singularity.
Saved in:
| Title: | Data-Driven Exponential Framing for Pulsive Temporal Patterns Without Repetition or Singularity. |
|---|---|
| Authors: | Kono, Yohei1 yohei.kono.un@hitachi.com, Tajima, Yoshiyuki1 yoshiyuki.tajima.hh@hitachi.com |
| Source: | Journal of Dynamic Systems, Measurement, & Control. May2026, Vol. 148 Issue 3, p1-11. 11p. |
| Subjects: | Linear dynamical systems, Hankel operators, Time series analysis, Industrial applications |
| Abstract: | Extracting pulsive temporal patterns from a small dataset without their repetition or singularity shows significant importance in manufacturing applications but does not sufficiently attract scientific attention. We propose to quantify how long temporal patterns appear without relying on their repetition or singularity, enabling us to extract such temporal patterns from a small dataset. Inspired by the celebrated time-delay embedding and data-driven Hankel matrix analysis, we introduce a linear dynamical system model on the time-delay coordinates behind the data to derive the discrete-time bases, each of which has a distinct exponential decay constant. The derived bases are fitted onto subsequences that are extracted with a sliding window in order to quantify how long patterns are dominant in the set of subsequences. We call the quantification method data-driven exponential framing (DEF). A toy model-based experiment shows that DEF can identify multiple patterns with distinct lengths. DEF is also applied to electric current measurement on a punching machine, showing its possibility to extract multiple patterns from real-world oscillatory data. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Dynamic Systems, Measurement, & Control is the property of American Society of Mechanical Engineers 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.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 193160872 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Data-Driven Exponential Framing for Pulsive Temporal Patterns Without Repetition or Singularity. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kono%2C+Yohei%22">Kono, Yohei</searchLink><relatesTo>1</relatesTo><i> yohei.kono.un@hitachi.com</i><br /><searchLink fieldCode="AR" term="%22Tajima%2C+Yoshiyuki%22">Tajima, Yoshiyuki</searchLink><relatesTo>1</relatesTo><i> yoshiyuki.tajima.hh@hitachi.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Dynamic+Systems%2C+Measurement%2C+%26+Control%22">Journal of Dynamic Systems, Measurement, & Control</searchLink>. May2026, Vol. 148 Issue 3, p1-11. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Linear+dynamical+systems%22">Linear dynamical systems</searchLink><br /><searchLink fieldCode="DE" term="%22Hankel+operators%22">Hankel operators</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+applications%22">Industrial applications</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Extracting pulsive temporal patterns from a small dataset without their repetition or singularity shows significant importance in manufacturing applications but does not sufficiently attract scientific attention. We propose to quantify how long temporal patterns appear without relying on their repetition or singularity, enabling us to extract such temporal patterns from a small dataset. Inspired by the celebrated time-delay embedding and data-driven Hankel matrix analysis, we introduce a linear dynamical system model on the time-delay coordinates behind the data to derive the discrete-time bases, each of which has a distinct exponential decay constant. The derived bases are fitted onto subsequences that are extracted with a sliding window in order to quantify how long patterns are dominant in the set of subsequences. We call the quantification method data-driven exponential framing (DEF). A toy model-based experiment shows that DEF can identify multiple patterns with distinct lengths. DEF is also applied to electric current measurement on a punching machine, showing its possibility to extract multiple patterns from real-world oscillatory data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Dynamic Systems, Measurement, & Control is the property of American Society of Mechanical Engineers 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193160872 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1115/1.4070274 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1 Subjects: – SubjectFull: Linear dynamical systems Type: general – SubjectFull: Hankel operators Type: general – SubjectFull: Time series analysis Type: general – SubjectFull: Industrial applications Type: general Titles: – TitleFull: Data-Driven Exponential Framing for Pulsive Temporal Patterns Without Repetition or Singularity. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kono, Yohei – PersonEntity: Name: NameFull: Tajima, Yoshiyuki IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00220434 Numbering: – Type: volume Value: 148 – Type: issue Value: 3 Titles: – TitleFull: Journal of Dynamic Systems, Measurement, & Control Type: main |
| ResultId | 1 |
