Using neural networks to assess flight deck human–automation interaction

Sullivan, Katlyn; Feigh, Karen M.; Mappus, Rudolph L.; Durso, Francis T.; Fischer, Ute; Pop, Vlad L.; Mosier, Kathleen L.; Morrow, Daniel G.

doi:10.1016/j.ress.2012.12.005

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Recently, because of more access to the information, increased accuracy, and less error, managers are more interested in using statistical models (Baptista et al., 2019; Cárdenas‐Gallo et al., 2017; Reder et al., 2018; Sullivan et al., 2013). The researchers used models like linear regression, generalized linear models (Baptista et al., 2019), Weibull Regression (Regattieri et al., 2010), and CART (Bevilacqua et al., 2003) that can be easily applied.…”

Section: Discussionmentioning

confidence: 99%

Environmental decision support system for Plane trees failure prediction: A comparison of multi‐layer perceptron and random forest modeling approaches

Jahani

Saffariha

2022

Agrosystems Geosci & Env

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Tree risk is the likelihood of property damage or personal injury from a potentially hazardous tree in cities. We aimed to compare multilayer perceptron (MLP) and random forest (RF) model techniques to predict Plane trees failure hazard in urban areas.In this research, 500 Plane trees in different urban structures were selected, and 12 variables were measured for target trees prediction as model independent variables. Tree failure hazard (tree fails along next 3 yr) was modeled by two machine learning techniques: MLP and RF. The RF model represents the higher accuracy in training (100%), test (94.4%), and all data sets (98.4%). The results of variable importance calculation of designed RF model revealed that wind-protected aspects, soil depth, length of cracks and cavities, and internal decay are the most important variables respectively that influence the classes of Plane tree failure. The urban foresters or designers can easily determine the possibility of Plane trees risk by running the designed graphical user interface and entering 12 variables of Plane tree. As a result, it will help foresters and urban area managers to make better decisions and predict the proper time to decrease harm from Plane tree failure.

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Section: Discussionmentioning

confidence: 99%

Environmental decision support system for Plane trees failure prediction: A comparison of multi‐layer perceptron and random forest modeling approaches

Jahani

Saffariha

2022

Agrosystems Geosci & Env

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“…Regarding the ergonomic design of the human-machine interface of weapons and equipment, many research institutions have carried out a lot of research work. Mosier [6] and Sullivan [7] studied the display interface of the aircraft cockpit, analyzed the influence of character, symbol, color, and other information coding methods on the identification performance, and studied the usability level of the interface design and its evaluation method. Yuan Xiuqian, Zhou Qianxiang, Zhuang Damin and others from Beijing university of Aeronautics and Astronautics have focused on the human-machine interface of the aircraft cockpit [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Form design in interface of display and control system based on visual search

Wang

et al. 2021

MATEC Web Conf.

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To explore the design pattern and guidelines for the forms displayed on the interfaces, we studied the factors of form design from the perspective of ergonomics. These factors include orientation (horizontal, vertical), color grouping and color combination. In combination with the analysis and conclusion of the experimental data, we proposed some design guidelines for human-computer interface that has many forms, such as weapon command and control system. A visual searching program was designed to simulate the display interface. Reaction time and accuracy are recorded by orthogonal experiment. Analyses such as double factor variance analysis, simple main effect are conducted on the experimental data. The following conclusions are obtained: two-color form has a faster reaction time than single color form; reaction time of the horizontal form is shorter than the vertical form. Color combination has significant influence on accuracy, and the blue-green is suiTable for form design; color grouping has significant influence on reaction time and accuracy; the interaction of color grouping and color combination shows significant differences. When designing forms in display interface, we should choose the horizontal layout with sparse coloring density as much as possible, and the effect of color combination on the recognition efficiency should be considered.

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