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将 HFACS 框架与模糊认知图整合进行飞行中惊跳因果分析。

Integrating the HFACS Framework and Fuzzy Cognitive Mapping for In-Flight Startle Causality Analysis.

机构信息

School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds LS6 3QS, UK.

College of Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates.

出版信息

Sensors (Basel). 2022 Jan 29;22(3):1068. doi: 10.3390/s22031068.

Abstract

This paper discusses the challenge of modeling in-flight startle causality as a precursor to enabling the development of suitable mitigating flight training paradigms. The article presents an overview of aviation human factors and their depiction in fuzzy cognitive maps (FCMs), based on the Human Factors Analysis and Classification System (HFACS) framework. The approach exemplifies system modeling with agents (causal factors), which showcase the problem space's characteristics as fuzzy cognitive map elements (concepts). The FCM prototype enables four essential functions: explanatory, predictive, reflective, and strategic. This utility of fuzzy cognitive maps is due to their flexibility, objective representation, and effectiveness at capturing a broad understanding of a highly dynamic construct. Such dynamism is true of in-flight startle causality. On the other hand, FCMs can help to highlight potential distortions and limitations of use case representation to enhance future flight training paradigms.

摘要

本文讨论了作为开发合适的缓解飞行训练范式的前奏,对飞行中惊跳因果关系进行建模的挑战。本文基于人为因素分析和分类系统 (HFACS) 框架,概述了航空人为因素及其在模糊认知图 (FCM) 中的描述。该方法通过代理(因果因素)来展示系统建模,这些代理展示了问题空间的特征作为模糊认知图元素(概念)。FCM 原型实现了四个基本功能:解释性、预测性、反思性和战略性。模糊认知图之所以具有这种实用性,是因为它们具有灵活性、客观性和捕捉高度动态结构的广泛理解的有效性。飞行中惊跳因果关系就是这种动态性的体现。另一方面,FCM 可以帮助突出用例表示的潜在扭曲和限制,以增强未来的飞行训练范式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf7/8839057/66e7fc7b6fdf/sensors-22-01068-g001.jpg

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