Aircraft FDI and human factors analysis of a take-off maneuvre using SIVOR flight simulator

Kraemer, Aline Dahleni; Villani, Emília; Arjoni, Diego Hernandez

doi:10.1016/j.ifacol.2019.01.063

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…In the defense area, simulators to better respond bombs and learning-training tools for marine officers or firefighters can be found in [71][72][73][74][75]. In aviation, new types of simulators are arising to capture flight skills, which provides stronger sensations than traditional ones [76][77][78]. In terms of construction applications, both computer-aided-learning tool [79] and VR training systems for industrial training [80,81] have been found.…”

Section: The Use Of Management Flight Simulatorsmentioning

confidence: 99%

Safely learning Intensive Care Unit management by using a Management Flight Simulator

Garcia-Vicuña

Esparza²,

Giménez

2020

Operations Research for Health Care

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Section: The Use Of Management Flight Simulatorsmentioning

confidence: 99%

Safely learning Intensive Care Unit management by using a Management Flight Simulator

Garcia-Vicuña

Esparza²,

Giménez

2020

Operations Research for Health Care

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“…Igualmente como se indica en este artículo, existe la posibilidad de falla en los pilotos ante procedimientos anormales en la simulacion, señalan en su publicación (Kraemer, Villani & Arjoni, 2019) que el un piloto en condiciones normales de vuelo es menos propenso a tener errores o cometer fallas, en comparación con otros pilotos que fueron sometidos a otras condiciones de vuelo, estas pruebas las realizan en un simulador, se evidencia la importancia del factor humano en la toma decisión para aplicar los procedimientos correctos en este caso FDI (Fault Detection and Isolation).…”

Section: Fuente Elaboración Propiaunclassified

Análisis del training syllabus aplicado en el simulador de vuelo del AirBus A320

et al. 2020

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El objetivo de la investigación fue identificar los principales procedimientos o maniobras en los cuales los pilotos presentan mayor dificultad dentro del training syllabus del simulador de vuelo Airbus A320, a la vez que se comparan los resultados obtenidos entre los diversos segmentos de profesionales investigados y se proponen alternativas para mitigar este problema desde la parte educativa. El trabajo se desarrolló a través de una investigación no experimental – descriptiva con un enfoque cuantitativo transversal. Los resultados evidencian que tanto los pilotos como los copilotos, tienen dificultades en el simulador, las cuales se presentan durante las pruebas en simulador y su no aprobación, se puede superar con una adecuada capacitación basada en acompañamiento tutorial educativo permanente que permita dominar los procedimientos operacionales estándar de las aeronaves.

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“…To this end, a relatively complete theory and the methodic system have been formed, and many software and hardware tools have been designed and developed in long-term research and practice. More specifically, the Building Research Establishment (BRE) company's comprehensive test system for human–machine environment, Airbus A320 full-motion simulator, and Boeing 747 engineering simulator (Kraemer et al ., 2019) have been published so far. These documents are utilized to evaluate the cockpit from ergonomic points of view.…”

Section: Introductionmentioning

confidence: 99%

Evaluating aircraft cockpit emotion through a neural network approach

Chen

Chu

et al. 2020

AIEDAM

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Studies show that there are shortcomings in applying conventional methods for the emotional evaluation of the aircraft cockpit. In order to resolve this problem, a more efficient cockpit emotion evaluation system is established in the present study to simply and quickly obtain the cockpit emotion evaluation value. To this end, the neural network is applied to construct an emotional model to evaluate the emotional prediction of the interior design of the aircraft cockpit. Moreover, several technologies and the Kansei engineering method are applied to acquire the cockpit interior emotional evaluation data for typical aircraft models. In this regard, the radical basis function neural network (RBFNN), Elman neural network (ENN), and the general regression neural network (GRNN) are applied to construct the sentimental prediction evaluation model. Then, the three models are comprehensively compared through factors such as the model evaluation criteria, network structure, and network parameters. Obtained experimental results indicate that the GRNN not only has the highest classification accuracy but also has the highest stability in comparison to the other two neural networks, so that it is a more appropriate method for the emotional evaluation of the aircraft cockpit. Results of the present study provide decision supports for the emotional evaluation of the cockpit interior space.

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Aircraft FDI and human factors analysis of a take-off maneuvre using SIVOR flight simulator

Cited by 7 publications

References 11 publications

Safely learning Intensive Care Unit management by using a Management Flight Simulator

Safely learning Intensive Care Unit management by using a Management Flight Simulator

Análisis del training syllabus aplicado en el simulador de vuelo del AirBus A320

Evaluating aircraft cockpit emotion through a neural network approach

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