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  • 1.
    Alfredson, Jens
    et al.
    Linköping University, Department of Computer and Information Science. Linköping University, Faculty of Science & Engineering. Saab Aeronaut, Linkoping, Sweden.
    Trabasso, Luís Gonzaga
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering. ITA, Brazil.
    Blomstrand, Niklas
    Linköping University, Department of Computer and Information Science. Linköping University, Faculty of Science & Engineering.
    Eckerberg, Maria
    Linköping University, Department of Computer and Information Science. Linköping University, Faculty of Science & Engineering.
    Klamer, Linda
    Linköping University, Department of Computer and Information Science. Linköping University, Faculty of Science & Engineering.
    Ledin, Johanna
    Linköping University, Department of Computer and Information Science. Linköping University, Faculty of Science & Engineering.
    Tarander, Jasmine
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Bång, Magnus
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Engine Failure Induced Task Load Transient for Simulation Based Certification Aiding for Aircraft2018In: ADVANCES IN HUMAN ASPECTS OF TRANSPORTATION, SPRINGER INTERNATIONAL PUBLISHING AG , 2018, Vol. 597, p. 79-86Conference paper (Refereed)
    Abstract [en]

    This study is one of a series of studies, researching various aspects that all aim at enhanced simulation based certification aiding for aircraft. An experimental within-group design study was performed with 10 participants ( 5 male, and 5 female). The results showed a significant difference, F(2,16) = 5.11, p = 0.019, in mental workload between an engine failure condition and an normal condition for eye blink frequency. No effect of speed at the engine failure event on mental workload was found.

  • 2.
    Arjoni, D. H.
    et al.
    TA Instituto Tecnológico de Aeronáutica, São José dos Campo, Brazil.
    Rocha, G. C.
    Konatus, São José dos Campo, Brazil.
    Moreira, A. H.
    TA Instituto Tecnológico de Aeronáutica, São José dos Campo, Brazil.
    Nicola, R. M.
    TA Instituto Tecnológico de Aeronáutica, São José dos Campo, Brazil.
    Oliveira, W. R.
    TA Instituto Tecnológico de Aeronáutica, São José dos Campo, Brazil.
    Silva, A. V. S.
    TA Instituto Tecnológico de Aeronáutica, São José dos Campo, Brazil.
    Natal, G. S.
    TA Instituto Tecnológico de Aeronáutica, São José dos Campo, Brazil.
    Silveira, L.
    NAC, São José dos Campo, Brazil.
    Thomas, E.
    Embraer, São José dos Campo, Brazil.
    Villani, E.
    TA Instituto Tecnológico de Aeronáutica, São José dos Camp, Brazil.
    Trabasso, Luís Gonzaga
    TA Instituto Tecnológico de Aeronáutica, São José dos Camp, Brazil.
    Experimental Evaluation of the Human Performance on a RoboticFlight Simulator based on FOQA Parameters2016In: Proceedings of the Aerospace Technology Congress / [ed] Kaj Lundahl, Roland Karlsson, Björn Jonsson and Knut Övrebö, Stockholm, 2016, Vol. 1, p. 1-11Conference paper (Refereed)
    Abstract [en]

    The SIVOR project, currently being developed by ITA and Embraer, consists of designing andimplementing a high fidelity flight simulator based on the use of COTS industrial robots. The aim of theproject is to provide a cost-efficient and flexible platform that can be used along the design phases of theaircraft. One of the advantages of an industrial robot over the traditional Stewart platform is theavailability of a large workspace, which provides more flexibility for defining the washout filter. Thisfilter converts the aircraft dynamics into robot movements, which has a limited workspace. The mainpurpose of the flight simulator is to provide a motion feeling similar to the one imposed by the aircraftmovements in a real flight. The representativeness of the motion cue is usually evaluated in a qualitativeway by the pilots that fly the simulator. Quantitative methods to evaluate the entire range of actuation of asimulator are complex, inducing tests in fractions of the flight to increase performance. In this work, wediscuss the use of FOQA (Flight Operational Quality Assurance) as an additional quantitative tool for theevaluation of the motion cue in the SIVOR flight simulator. FOQA is a voluntary safety program fromFAA, detailed in AC-120-82. It proposes a set of parameters that can be used by airliners to analyse flightsafety and increase operational efficiency. The verification of FOQA parameters checks whether or notthe pilot complies with the standard operational procedures defined by the airliners and aircraftmanufacturers. The purpose of this work is to analyse whether or not, and to what extent, the FOQAparameters can be used to evaluate the quality of the motion cue of flight simulators. For this purpose, wedefine an experimental procedure that compares flights performed by pilots under different motionmodes. It then calculates a set of behavioural parameters that has been proposed in order to quantify howthe motion affects the inputs of the pilot. The results are submitted to ANOVA statistical analysis thatverifies the relevance of the motion factor. Finally, we discuss the capability of a FOQA basedexperiment to estimate the contribution of the motion to the realism of the flight simulation.

  • 3.
    Moreira, A. H.
    et al.
    TA Instituto Tecnológico de Aeronáutica, São José dos Campos, Brazil / Centro Universitário do Instituto Mauá de Tecnologia, São Caetano do Sul, Brazi.
    Arjoni, D. H.
    TA Instituto Tecnológico de Aeronáutica, São José dos Campos, Brazil.
    Nicola, R. M.
    TA Instituto Tecnológico de Aeronáutica, São José dos Campos, Brazil.
    Silva, E. T.
    Embraer, São José dos Campos, Brazil.
    Villani, E.
    TA Instituto Tecnológico de Aeronáutica, São José dos Campos, Brazil.
    Trabasso, Luís Gonzaga
    TA Instituto Tecnológico de Aeronáutica, São José dos Campos, Brazil.
    Experimental evaluation of the contribution of adding a motion system to an EDS2016In: Proceedings of the Aerospace Technology Congress / [ed] Kaj Lundahl, Roland Karlsson, Björn Jonsson and Knut Övrebö, Stockholm, 2016, Vol. 1, p. 1-10Conference paper (Refereed)
    Abstract [en]

    The use of flight simulators in pilot training campaigns has become a cheaper and saferalternative to the use of a real aircraft, as simulators will not cause any kind of humaninjury or vehicular damages. However, the degree of fidelity of the simulation is of theutmost importance for this application, thus it has become the subject of discussion inseveral studies.It is understood as a flight simulator with a high degree of fidelity, all kind of simulatorsthat are capable of providing motion cues that are sufficiently similar to those obtainedduring an actual flight, so much so that a human would be incapable of noticing anydifference (Giordano et al., 2010). Many argue that the only way to obtain such a highquality of simulation is by using a motion platform, which makes the cost of thisequipment the same order of magnitude of a real aircraft.Several recent studies have contributed in this topic of discussion, the influence of themotion platform is still unclear (McCauley, 2006), (Proctor, Bauer and Lucario, 2007),(McDaniel, Scott and Browning, 1983). Bürki-cohen, Sparko and Bellman (2011) madea thorough review of the need of motion platforms in aircraft simulators while discussesthe need of motion platforms in military helicopter simulators, butThe objective of this work is to analyze the contribution of adding a motion system to anEDS (Engineering Development System), yielding a flexible and reconfigurablesimulator, available as soon as the official aerodynamic databank is made available. Theadvantage (if any) of creating an EDS with motion platform is that it brings to the aircraftdevelopment cycle, the opportunity of anticipating the knowledge acquired in thelearning-by-using approach, by means of a simulation environment that resembles thebehavior of the final product, especially in the early development phases.

  • 4.
    Trabasso, Luís Gonzaga
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering. Aeronautics Institute of Technology, Dept. of Mechanical Engineering .
    Alfredson, Jens
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering. SAAB, Aeronautics Human Machine Interaction, Sweden.
    Functional Mapping as Means for Establishing a Human Factors Research Environment for Future Air Systems2016In: Proceedings of the 12th Swecog Cognition Conference, October 6-7, Göteborg, Sweden: Abstracts / [ed] Alexander Almér, Robert Lowe and Erik Billing, Skövde: Univeristy of Skövde , 2016, Vol. 1, p. 10-11Conference paper (Other academic)
    Abstract [en]

    A typical environment for human factors research has equipment and methods for performing a set of experiments such as mental workload assessment, situational awareness evaluation, human resilience measurement and so forth. The common aspect between equipment and methods is that they accomplish a function. The TLX method is part of such an environment because it evaluates the mental workload; an EEG helmet is part of the same research environment because it measures the electrical activity originated by the brain. If the functional structure of a method or equipment is yet to be known, a method for function deployment might be used to this purpose such as FAST. Although cognitive processes in many regards are very different from functions in technical systems, it is possible to describe them in terms of functions for the sake using it for design considerations. For instance, the information-processing paradigm has inspired descriptions that in some regards could be described in functional terms. The multiple resource theory that outlines different mental resources related to various modalities and stages of processing is another example of that. Then a functional mapping engine identifies the equipment and method that address the cognitive functions required for a given experiment. A very simple example of functional mapping is as follows: the cognitive module <vision> has a function X {to track objects}. The equipment *eye tracker* and the method # EPOG – Eye Point of Gaze# have the functions Y [To look at through computer vision] and Z [to track objects]. The mapping among functions X, Y and Z indicate the equipment and method are suitable for addressing the cognitive characteristic under investigation. On the one hand, if an equipment or method do exist, then the functional mapping assist the research environment designer to identify them and help choosing if several options are available. On the other hand, if an equipment or method do not exist, then the functional mapping assist the research environment designer to design and build them. Moving forward from the very simple example to a more practical and realistic situation, the functional mapping can tackle the issues of choosing the necessary functions – from both sides, cognitive and equipment and methods – to meet fidelity requirements of an experiment. This is suggested to be resolved by the cost-benefit trade-off approach detailed as follows. Based on the functional mapping, selective fidelity can be obtained for modeling and simulation considerations. Thereby advantages and disadvantages of the human factors research environment for future air systems could be balanced by the functional mapping, potentially optimizing the use of simulations. System border definition ought to be considered; the border definition practice borrowed from aircraft product/system configuration can be used to this end. Selective fidelity has been applied to transfer of training in military aviation and simulator based design has been shown to be useful for development of air systems. The proposed functional mapping approach could have the potential of adding to this tradition.

  • 5.
    Turetta, F. M. S.
    et al.
    EMBRAER, Department of Systems Modeling and Simulation, Brazil.
    Ayala, H. V. H.
    EMBRAER, Department of Systems Modeling and Simulation, Brazil.
    Trabasso, Luís Gonzaga
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering. SAAB, Aeronautics Human Machine Interaction.
    Alfredson, Jens
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering. SAAB, Aeronautics Human Machine Interaction.
    Data-driven Pilot Behavior Modeling Applied to a VMCG Determination Flight Test Task2016In: Proceedings of the Aerospace Technology Congress / [ed] Kaj Lundahl, Roland Karlsson, Björn Jonsson and Knut Övrebö, Stockholm, 2016, Vol. 1, p. 1-10, article id 1Conference paper (Refereed)
    Abstract [en]

    Human models have been studied and used in engineering analysis for over 70 years to allow predictions of the pilot-vehicle system behavior. The difficulties in pilot modeling are evident due to the complexity of the brain, lack of repeatability in behavior and the great number of variables that can affect the human performance. This complexity, associated with the fact that there are no explicit laws to allow modeling based in first principles, could indicate that data-driven modeling techniques would be the most efficient way to obtain pilot models, such as black-box system identification methods that construct dynamic models according to measured input and output data, and where the parameters have no physical meaning. With this approach, it is advantageous to seek knowledge from other fields to allow a better understanding of the pilot behavior, select adequate input/output variables and define the experimental conditions and data. Criteria for evaluating the modeling approaches include adaptability as well as feasibility. Adaptability concerns coping with dynamic and uncertain conditions and feasibility refers to the models contribution to an applied context. This paper presents the results of the application of data-driven theoretical linear dynamic models in the task of representing the behavior of the pilot trying to keep the centerline of the runway after an engine failure. Real data is used, where PID with anti-windup and Hammerstein-Wiener model structures are compared. Results show that the Hammerstein-Wiener structure seems more appropriate to represent this specific behavior.

  • 6.
    Turetta, Felipe M. S.
    et al.
    EMBRAER Syst Modeling and Simulat, Brazil.
    Hultmann Ayala, Helon Vicente
    PUCPR Ind and Syst Engn Grad Program, Brazil.
    Trabasso, Luís Gonzaga
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering. Inst Tecnol Aeronaut, Brazil.
    Coelho, Leandro S.
    PUCPR Ind and Syst Engn Grad Program, Brazil.
    Alfredson, Jens
    Linköping University, Department of Computer and Information Science. Linköping University, Faculty of Science & Engineering. Saab Aeronaut, Linkoping, Sweden.
    Data-Driven Pilot Behavior Modeling Applied to an Aircraft Offset Landing Task2018In: ADVANCES IN HUMAN ASPECTS OF TRANSPORTATION, SPRINGER INTERNATIONAL PUBLISHING AG , 2018, Vol. 597, p. 117-127Conference paper (Refereed)
    Abstract [en]

    This paper shows studies for the development of a mathematical model that adequately represents a pilot behavior in the specific task of offset landing, using data-driven modeling techniques. Flight test data was used for the identification procedure. Considerations on the pilots cognitive process and mathematical modeling possibilities were discussed to select the most appropriate inputs and outputs for the model. This data was used to identify the model using artificial neural network techniques. The models obtained were validated against the identification data and different data not used in the training process to evaluate the quality of the models. Conclusions include the difficulties of showing the generalization capabilities of those non-linear models and further studies.

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