Model Checking Bounded Continuous-time Extended Linear Duration Invariants

An, Jie; Zhan, Naijun; Li, Xiaoshan; Zhang, Miaomiao; Yi, Wang

doi:10.1145/3178126.3178147

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…In fact, model checking of DC formulas is a subject of ongoing research (cf. e.g., [ 31 ] and the references there). In paper [ 32 ], the authors proposed a method for solving reachability problems in cases of timed automata possessing multiple cost variables evolving according to specified rates.…”

Section: Related Workmentioning

confidence: 99%

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Temporal Analysis and Classification of Sensor Signals

Kosiuczenko

2023

Sensors

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Understanding the behaviour of sensors, and in particular, the specifications of multisensor systems, are complex problems. The variables that need to be taken into consideration include, inter alia, the application domain, the way sensors are used, and their architectures. Various models, algorithms, and technologies have been designed to achieve this goal. In this paper, a new interval logic, referred to as Duration Calculus for Functions (DC4F), is applied to precisely specify signals originating from sensors, in particular sensors and devices used in heart rhythm monitoring procedures, such as electrocardiograms. Precision is the key issue in case of safety critical system specification. DC4F is a natural extension of the well-known Duration Calculus, an interval temporal logic used for specifying the duration of a process. It is suitable for describing complex, interval-dependent behaviours. Said approach allows one to specify temporal series, describe complex interval-dependent behaviours, and evaluate the corresponding data within a unifying logical framework. The use of DC4F allows one, on the one hand, to precisely specify the behaviour of functions modelling signals generated by different sensors and devices. Such specifications can be used for classifying signals, functions, and diagrams; and for identifying normal and abnormal behaviours. On the other hand, it allows one to formulate and frame a hypothesis. This is a significant advantage over machine learning algorithms, since the latter are capable of learning different patterns but fail to allow the user to specify the behaviour of interest.

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Section: Related Workmentioning

confidence: 99%

“…Due to the limited accuracy of measurements, at some level, their models are discrete and finite. Model checking is possible for restricted forms of DC (cf., [ 31 ]). At present, there is no research on how to model-check DC4F models, but this issue is not relevant for the signal specification, as considered in this paper.…”

Section: Evaluation and Conclusionmentioning

confidence: 99%

Temporal Analysis and Classification of Sensor Signals

Kosiuczenko

2023

Sensors

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“…The problem of DC model checking is a topic of ongoing research (cf. e.g., [11] and the references there). In [12], the authors propose a method for solving minimal and maximal reachability problems for the multipriced timed automata.…”

Section: Related Workmentioning

confidence: 99%

An Interval Temporal Logic for Time Series Specification and Data Integration

Kosiuczenko

2021

Remote Sensing

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The analysis of temporal series—in particular, analysis of multisensor data—is a complex problem. It depends on the application domain, the way the data have to be used, and sensors available, among other factors. Various models, algorithms, and technologies have been designed for this goal. Temporal logics are used to describe temporal properties of systems. The properties may specify the occurrence and the order of events in time, recurring patterns, complex behaviors, and processes. In this paper, a new interval logic, called duration calculus for functions (DC4F), is proposed for the specification of temporal series corresponding to multisensor data. DC4F is a natural extension of the well-known duration calculus, an interval temporal logic for the specification of process duration. The adequacy of the proposed logic is analyzed in the case of multisensor data concerning volcanic eruption monitoring. It turns out that the relevant behavior concerns time intervals, not only accumulated history as it is described in other kinds of temporal logics. The examples analyzed demonstrate that a description language is required to specify time series of various kind relative to time intervals. The duration calculus cannot be successfully applied for this task. The proposed calculus allows one to specify temporal series and complex interval-dependent behaviors, and to evaluate the corresponding data within a unifying logical framework. It allows to formulate hypotheses concerning volcano eruption phenomena. However, the expressivity of DC4F comes at the cost of its decidability.

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