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  4. Configurable abstraction of signals using signal temporal logic
 
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Configurable abstraction of signals using signal temporal logic

Publikationstyp
Conference Paper
Date Issued
2025-09-08
Sprache
English
Author(s)
Engeln, Ulrike 
Softwaresysteme E-16  
Schupp, Sibylle  
Softwaresysteme E-16  
TORE-URI
https://hdl.handle.net/11420/57495
Volume
16081
Start Page
309
End Page
326
Citation
51st Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2025
Contribution to Conference
51st Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2025  
Publisher DOI
10.1007/978-3-032-04190-6_19
Scopus ID
2-s2.0-105016201721
Publisher
Springer Nature Switzerland AG
ISBN of container
978-3-032-04189-0
Abstractions of signals help to avoid cognitive and storage overload, especially in systems with many signals such as cyber-physical systems (CPS). For assessing trends and reconstructing behavior, it often suffices to have a rough understanding how signals evolve, e.g., whether their behavior is monotonic or periodic. This work provides a configurable abstraction of signal behavior, where signals are described by a sequence of oscillation and linear patterns. We formalize templates of both behaviors in parameterized signal temporal logic (PSTL) and provide an algorithm that abstracts signals in terms of those patterns. The templates are configurable such that they allow for choosing the level of abstraction, e.g., by limiting the approximation error or the minimal oscillation frequency. For segmentation of the signal, we solve an optimization problem using a modified version of TeLEx. The evaluation demonstrates that configuration of the patterns is suitable to define the level of abstraction. Further, on control output from the ARCH wind turbine benchmark and flow data from medical ventilation, it demonstrates that the abstraction method can be applied to real-world signals.
Subjects
time series
signal temporal logic (STL)
temporal formula learning
pattern-based abstractions
DDC Class
005: Computer Programming, Programs, Data and Security
Funding(s)
GRK 2972: Konzepte und Algorithmen für - und Nutzung von - selbsterklärenden digital kontrollierten Systemen  
Publisher‘s Creditline
This work has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) project no. 513623283 as part of the Research Training Group CAUSE.
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