Measuring the impact of avionics faults with a set of safety metrics

Jacobs, Michael A.; Sudarsanan, Varun S.; Subramanian, Shreyas Vathul; DeLaurentis, Daniel A.; Zhang, Zixu; Landry, Steven J.

doi:10.1109/smc.2017.8123030

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Using the agent-based model, Ryan and Cummings (2016) studied the system-level influences of different UAV control modes on air carrier operations, considering the human–machine–environment factors. Jacobs et al (2017) built an air traffic system model based on DAF, which can realize the communication between multiple agents and simulate the events triggered by different failure modes. Stroeve and Everdij (2017) simulated the human performance in different technical systems during the approaching phase of flight using ABMS, but the model focuses on qualitative description, and the simulation results of different psychological states can only reflect the relative changes.…”

Section: Introductionmentioning

confidence: 99%

Ontology modelling of intelligent HCI in aircraft cockpit

Xia

Sun

Zhang

2021

AEAT

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Purpose Semantic modelling is an essential prerequisite for designing the intelligent human–computer interaction in future aircraft cockpit. The purpose of this paper is to outline an ontology-based solution to this issue. Design/methodology/approach The scenario elements are defined considering the cognitive behaviours, system functions, interaction behaviours and interaction situation. The knowledge model consists of a five-tuple array including concepts, relations, functions, axioms and instances. Using the theory of belief-desire-intention, the meta-model of cognitive behaviours is established. The meta-model of system functions is formed under the architecture of sub-functions. Supported by information flows, the meta-model of interaction behaviours is presented. Based on the socio-technical characteristics, the meta-model of interaction situation is proposed. The knowledge representation and reasoning process is visualized with the semantic web rule language (SWRL) on the Protégé platform. Finally, verification and evaluation are carried out to assess the rationality and quality of the ontology model. Application scenarios of the proposed modelling method are also illustrated. Findings Verification results show that the knowledge reasoning based on SWRL rules can further enrich the knowledge base in terms of instance attributes and thereby improve the adaptability and learning ability of the ontology model in different simulations. Evaluation results show that the ontology model has a good quality with high cohesion and low coupling. Practical implications The approach presented in this paper can be applied to model complex human–machine–environment systems, from a semantics-driven perspective, especially for designing future cockpits. Originality/value Different from the traditional approaches, the method proposed in this paper tries to deal with the socio-technical modelling issues concerning multidimensional information semantics. Meanwhile, the constructed model has the ability of autonomous reasoning to adapt to complex situations.

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Section: Introductionmentioning

confidence: 99%