Efficient graph pattern matching framework for network-based in-vehicle fault detection

Baek, Sun Geol; Kang, Dong Hyun; Lee, Sungkil; Eom, Young Ik

doi:10.1016/j.jss.2018.02.050

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…Even though each block is represented by a multigraph G(V,E), we can easily reduce the multigraph to the simple graph.). There are many types of graph pattern queries such as subgraph query (with/without labels), supergraph query, similar query [17][18][19][22][23][24][25]. In this paper, we use the subgraph query without labels since there is no standard graph similarity measure and the subgraph query without labels includes at least the pattern structure which the user wants to find.…”

Section: Queries For Devs Model Retrievalmentioning

confidence: 99%

GO-DEVS: Storage and Retrieval System for DEVS Models Using Graph and Ontology Representation

Lee

Bae

Paik

2021

Sensors

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DEVS is a powerful formal language to describe discrete event systems in modeling and simulation areas and useful for component-based design. One of the advantages of component-based design is reusability. To reuse or share DEVS models developed by many other modelers, a system to systematically store and retrieve many DEVS models should be supported. However, to the best of our knowledge, there does not exist such a system. In this paper, we propose GO-DEVS (Graph/Ontology-represented DEVS storage and retrieval system) to store and retrieve DEVS models using graph and ontology representation. For effective model sharing, an ontology is introduced when a DEVS model is developed. To search for DEVS models in an effective and efficient way, we propose two types of queries, IO query and structure query, and provide a method to store and query DEVS models on an RDBMS. Finally, we experimentally show GO-DEVS can process the queries efficiently.

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Section: Queries For Devs Model Retrievalmentioning

confidence: 99%

GO-DEVS: Storage and Retrieval System for DEVS Models Using Graph and Ontology Representation

Lee

Bae

Paik

2021

Sensors

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“…Obviously, the in‐vehicular communication requires a more holistic diagnosis. A recent modelling approach has focused on characterising the message‐induced sequences instead of message content since the creation of message series is usually triggered by a particular event [99]. It is worth mentioning that the authors in [95–98] provide interesting comparison between stochastically estimated temporal distribution of error records and experimentally derived results.…”

Section: Can System‐level Diagnosismentioning

confidence: 99%

Controller area network reliability: overview of design challenges and safety related perspectives of future transportation systems

Lakhal

Nasri

Adouane

et al. 2020

IET intell. transp. syst

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The in‐vehicular networked control system is among the most critical embedded processes. The controller area network (CAN) has prevailed intra‐vehicle communication for decades. Meanwhile, requirements of future transportation systems are expected to emphasise the in‐vehicle communication complexity, which endangers the reliability/safety of the intelligent navigation. At first, this study reviews the recent solutions proposed to overcome the CAN expanding complexity. Challenges that tomorrow's intelligent vehicles may raise for CAN reliability are investigated. The comprehensive coverage of current research efforts to remove the impact of these challenges is presented. Further, the in‐vehicle system reliability of future automated vehicles is also related to the fault diagnosis performances. Hence, different classes of system‐level diagnosis strategies are compared relatively to the requirements of automotive embedded networks. Furthermore, to thoroughly cover CAN reliability engineering issues, focus is given to the automotive validation techniques. The hardware in the loop, real‐time analysis and computer‐aided‐design tools intervene in various phases along the in‐vehicular network life cycle. Parameters that stand behind the efficiency and accuracy of these techniques in validating the new generation of vehicles are analysed. The authors finally draw some deductive predictions about the future directions related to the reliability of the intelligent transportation system in‐vehicular communication.

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Fault Detection Based on Graph Model for Dead Zone of Steam Turbine Control Valve

Zhang

Yuan

2022

Int. J. Control Autom. Syst.

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Efficient graph pattern matching framework for network-based in-vehicle fault detection

Cited by 6 publications

References 14 publications

GO-DEVS: Storage and Retrieval System for DEVS Models Using Graph and Ontology Representation

GO-DEVS: Storage and Retrieval System for DEVS Models Using Graph and Ontology Representation

Controller area network reliability: overview of design challenges and safety related perspectives of future transportation systems

Fault Detection Based on Graph Model for Dead Zone of Steam Turbine Control Valve

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