A Bayesian network for reliability assessment of man-machine phased-mission system considering the phase dependencies of human cognitive error

Wang, Zengkai; Zeng, Shengkui; Guo, Jianbin; Che, Haiyang

doi:10.1016/j.ress.2020.107385

Cited by 42 publications

(7 citation statements)

References 45 publications

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“…Pilot task load and external environmental factors, such as confidence in automation and control mode preference, also impact equipment task load (Mosier et al , 2013). Information readability and fatigue and sleepiness levels affect visual search speed, and pilots' reaction and judgement times take longer as fatigue and sleepiness increase (Wang et al , 2021). Decision time is also influenced by experience and mental load, with more experience resulting in shorter decision times and higher mental workloads resulting in longer decision times.…”

Section: Introductionmentioning

confidence: 99%

An information flow field model for human–machine interaction

Wu,

Sun,

2024

AEAT

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Purpose About 70% of all aircraft accidents are caused by human–machine interaction, thus identifying and quantifying performance shaping factors is a significant challenge in the study of human reliability. An information flow field model of human–machine interaction is put forward to help better pinpoint the factors influencing performance and to make up for the lack of a model of information flow and feedback processes in the aircraft cockpit. To enhance the efficacy of the human–machine interaction, this paper aims to examine the important coupling factors in the system using the findings of the simulation. Design/methodology/approach The performance-shaping factors were retrieved from the model, which was created to thoroughly describe the information flow. The coupling degree between the performance shaping factors was calculated, and simulation and sensitivity analysis are based on system dynamics. Findings The results show that the efficacy of human–computer interaction is significantly influenced by individual important factors and coupling factors. To decrease the frequency of accidents after seven hours, attention should be paid to these factors. Originality/value The novelty of this work lies in proposing a theoretical model of cockpit information flow and using system dynamics to analyse the effect of the factors in the human–machine loop on human–machine efficacy.

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Section: Introductionmentioning

confidence: 99%

An information flow field model for human–machine interaction

Wu,

Sun,

2024

AEAT

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“…UAV maritime reconnaissance refers to the use of wireless sensing and electromagnetic spectrum management methods by UAV equipment, integrating a series of information technology means such as intelligence, big data, and satellites, to complete tasks such as reconnaissance and detection of enemy targets at sea [5]. Tere are many application scenarios for UAVs in combat reconnaissance on the sea battlefeld, and the requirements for the efectiveness of UAV combat are higher [6]. Terefore, this article takes UAV sea battlefeld reconnaissance combat tasks as the research object, uses particle swarm optimization algorithm to efciently optimize the operation of nonlinear constraint problems, and completes diferent types or characteristics of UAV equipment for diferent sea combat reconnaissance tasks to achieve optimal task confguration efciency, and I hope to contribute to the improvement of drone combat reconnaissance capabilities and drone battlefeld combat efectiveness [7].…”

Section: Introductionmentioning

confidence: 99%

Research on Combat Mission Configuration of Unmanned Aerial Vehicle Maritime Reconnaissance Based on Particle Swarm Optimization Algorithm

Dong,

Chen,

Wang

et al. 2024

Complexity

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In recent years, in the classic battles and armed conflicts around the world, battlefield environment reconnaissance and the collection and processing of operational information play an increasingly critical role in the victory and defeat of the battlefield. Unmanned equipment, especially UAV equipment, is used by more and more countries in the field of combat reconnaissance. Meanwhile, the types of UAV are gradually diversified with the change of operational requirements. UAVs adapted to different combat environments shine brightly on the battlefield. In terms of naval battle field, due to the limitations and deficiencies of reconnaissance methods such as surface radar, UAVs play a more prominent role in combat reconnaissance. There are more scenarios for UAVs to be used in combat reconnaissance in naval battle field and higher requirements for UAVs’ combat effectiveness. Therefore, this paper takes UAVs’ naval battle reconnaissance missions as the research object. By using PSO as the research method, this paper studies the combat reconnaissance task configuration of UAVs, hoping to contribute to the improvement of UAVs’ combat reconnaissance capability and combat effectiveness.

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“…BBNs allow modeling strong factor effects and interactions, provided that these effects can be quantified: this potentially overcomes the assumption of some methods of independent factor effects [20]. Examples of BBN applications in HRA are abundant in the recent literature: to capture causal relationships among factors influencing human failures [21][22][23], as well as overall accidents [24,25]; to incorporate organizational aspects and safety culture in risk analysis [26,27], to capture dynamic aspects of human performance [28,29], and different types of dependencies (among influencing factors and among failures) [30,31]. In [32], Bayesian network models are proposed as the foundation of third generation HRA methods, supporting rigorous treatment of causality relationships across influencing factors and aggregation of multiple data types.…”

Section: Introductionmentioning

confidence: 99%