A New Measure to Characterize the Self-Similarity of Binary Time Series and its Application
In this study, the branch-length similarity entropy profile is estimated by mapping the time-series signal to the circumference of the time circle, and the self-similarity is defined based on the profile. To explore the self-similarity property, the effect of the distance between two signals, &#...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9433597/ |
| _version_ | 1831552786006802432 |
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| author | Sang-Hee Lee Cheol-Min Park |
| author_facet | Sang-Hee Lee Cheol-Min Park |
| author_sort | Sang-Hee Lee |
| collection | DOAJ |
| description | In this study, the branch-length similarity entropy profile is estimated by mapping the time-series signal to the circumference of the time circle, and the self-similarity is defined based on the profile. To explore the self-similarity property, the effect of the distance between two signals, “0” and “1”, on the entropy value for signal “1” is investigated. Furthermore, two application problems are addressed: quantification of the mixing state of fragments and clusters, and characterization of the behavioral trajectory of an organism. The results indicate that use of the self-similarity property solves both the problems. Additionally, the problems that must be addressed to broaden the applicability of self-similarity are discussed. |
| first_indexed | 2024-12-17T03:33:36Z |
| format | Article |
| id | doaj.art-111a82cb0176480489f2832ef874c036 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T03:33:36Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-111a82cb0176480489f2832ef874c0362022-12-21T22:05:12ZengIEEEIEEE Access2169-35362021-01-019737997380710.1109/ACCESS.2021.30814009433597A New Measure to Characterize the Self-Similarity of Binary Time Series and its ApplicationSang-Hee Lee0https://orcid.org/0000-0003-2708-071XCheol-Min Park1https://orcid.org/0000-0001-5257-7723Division of Industrial Mathematics, National Institute for Mathematical Sciences, Daejeon, South KoreaDivision of Industrial Mathematics, National Institute for Mathematical Sciences, Daejeon, South KoreaIn this study, the branch-length similarity entropy profile is estimated by mapping the time-series signal to the circumference of the time circle, and the self-similarity is defined based on the profile. To explore the self-similarity property, the effect of the distance between two signals, “0” and “1”, on the entropy value for signal “1” is investigated. Furthermore, two application problems are addressed: quantification of the mixing state of fragments and clusters, and characterization of the behavioral trajectory of an organism. The results indicate that use of the self-similarity property solves both the problems. Additionally, the problems that must be addressed to broaden the applicability of self-similarity are discussed.https://ieeexplore.ieee.org/document/9433597/Animal behaviorclassificationdata structurediscrete transformsentropyenvironmental monitoring |
| spellingShingle | Sang-Hee Lee Cheol-Min Park A New Measure to Characterize the Self-Similarity of Binary Time Series and its Application IEEE Access Animal behavior classification data structure discrete transforms entropy environmental monitoring |
| title | A New Measure to Characterize the Self-Similarity of Binary Time Series and its Application |
| title_full | A New Measure to Characterize the Self-Similarity of Binary Time Series and its Application |
| title_fullStr | A New Measure to Characterize the Self-Similarity of Binary Time Series and its Application |
| title_full_unstemmed | A New Measure to Characterize the Self-Similarity of Binary Time Series and its Application |
| title_short | A New Measure to Characterize the Self-Similarity of Binary Time Series and its Application |
| title_sort | new measure to characterize the self similarity of binary time series and its application |
| topic | Animal behavior classification data structure discrete transforms entropy environmental monitoring |
| url | https://ieeexplore.ieee.org/document/9433597/ |
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