Evidence map›Paper›PMID 33816923›Full record

ArticlePeerJ. Computer science2020

Exact acceleration of complex real-time model checking based on overlapping cycle.

Guoqing Wang, Lei Zhuang, Yu Song, Mengyang He, Ding Ma, Ling Ma

Abstract read
In one paragraph

Article in PeerJ. Computer science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Guoqing WangSchool of Information Engineering, Zhengzhou University, Zhengzhou, Henan, China.
Lei ZhuangSchool of Information Engineering, Zhengzhou University, Zhengzhou, Henan, China.
Yu SongSchool of Information Engineering, Zhengzhou University, Zhengzhou, Henan, China.
Mengyang HeSchool of Information Engineering, Zhengzhou University, Zhengzhou, Henan, China.
Ding MaCollege of Information Science and Engineering, Henan University of Technology, Zhengzhou, Henan, China.
Ling MaSchool of Information Engineering, Zhengzhou University, Zhengzhou, Henan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

When real-time systems are modeled as timed automata, different time scales may lead to substantial fragmentation of the symbolic state space. Exact acceleration solves the fragmentation problem without changing system reachability. The relatively mature technology of exact acceleration has been used with an appended cycle or a parking cycle, which can be applied to the calculation of a single acceleratable cycle model. Using these two technologies to develop a complex real-time model requires additional states and consumes a large amount of time cost, thereby influencing acceleration efficiency. In this paper, a complex real-time exact acceleration method based on an overlapping cycle is proposed, which is an application scenario extension of the parking-cycle technique. By comprehensively analyzing the accelerating impacts of multiple acceleratable cycles, it is only necessary to add a single overlapping period with a fixed length without relying on the windows of acceleratable cycles. Experimental results show that the proposed timed automaton model is simple and effectively decreases the time costs of exact acceleration. For the complex real-time system model, the method based on an overlapping cycle can accelerate the large scale and concurrent states which cannot be solved by the original exact acceleration theory.

Indexed as

Complex real-time systemExact accelerationOverlapping cycleReal-time model checkingTimed automata

Identifiers

PMID33816923
PMCPMC7924618

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.