Development of a Simulation Experiment to Investigate In-Flight Startle using Fuzzy Cognitive Maps and Pupillometry

Yusuf, Abiodun Brimmo; Kor, Ah-Lian; Tawfik, Hissam

doi:10.1109/ijcnn.2019.8851828

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…Therefore, this paper discusses research that focuses on benefitting GA operations but applying methods applicable across the board. A preliminary discussion of this research is available in [17].…”

Section: Rationalementioning

confidence: 99%

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

Yusuf

Kor

Tawfik

2022

Sensors

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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.

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Section: Rationalementioning

confidence: 99%

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

Yusuf

Kor

Tawfik

2022

Sensors

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“…The experimental set up of a doctoral research work (at Leeds Beckett University) on Gaze pattern recognition to interpret vision cognitive behavior of pilots during in-flight startle was used as the IoT Eye Tracking system [88]. Figure 3 illustrates the set-up which consists of a Flight Simulator, Flight controls and an Eye tracker device.…”

Section: Iot Eye Tracking Systemmentioning

confidence: 99%

Green Cloud Software Engineering for Big Data Processing

et al. 2020

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Internet of Things (IoT) coupled with big data analytics is emerging as the core of smart and sustainable systems which bolsters economic, environmental and social sustainability. Cloud-based data centers provide high performance computing power to analyze voluminous IoT data to provide invaluable insights to support decision making. However, multifarious servers in data centers appear to be the black hole of superfluous energy consumption that contributes to 23% of the global carbon dioxide (CO2) emissions in ICT (Information and Communication Technology) industry. IoT-related energy research focuses on low-power sensors and enhanced machine-to-machine communication performance. To date, cloud-based data centers still face energy–related challenges which are detrimental to the environment. Virtual machine (VM) consolidation is a well-known approach to affect energy-efficient cloud infrastructures. Although several research works demonstrate positive results for VM consolidation in simulated environments, there is a gap for investigations on real, physical cloud infrastructure for big data workloads. This research work addresses the gap of conducting real physical cloud infrastructure-based experiments. The primary goal of setting up a real physical cloud infrastructure is for the evaluation of dynamic VM consolidation approaches which include integrated algorithms from existing relevant research. An open source VM consolidation framework, Openstack NEAT is adopted and experiments are conducted on a Multi-node Openstack Cloud with Apache Spark as the big data platform. Open sourced Openstack has been deployed because it enables rapid innovation, and boosts scalability as well as resource utilization. Additionally, this research work investigates the performance based on service level agreement (SLA) metrics and energy usage of compute hosts. Relevant results concerning the best performing combination of algorithms are presented and discussed.

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“…In [ 21 , 22 ], the authors use the fuzzy methods to effectively monitor the state of cognitive workload of an Unmanned Aerial Vehicle (UAV) operator. In [ 23 ], the authors successfully apply fuzzy cognitive mapping to analyze the pilots’ decision during the flight.…”

Section: Introductionmentioning

confidence: 99%

On the Improvement of Eye Tracking-Based Cognitive Workload Estimation Using Aggregation Functions

Kaczorowska

Karczmarek

Plechawska–Wójcik

et al. 2021

Sensors

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Cognitive workload, being a quantitative measure of mental effort, draws significant interest of researchers, as it allows to monitor the state of mental fatigue. Estimation of cognitive workload becomes especially important for job positions requiring outstanding engagement and responsibility, e.g., air-traffic dispatchers, pilots, car or train drivers. Cognitive workload estimation finds its applications also in the field of education material preparation. It allows to monitor the difficulty degree for specific tasks enabling to adjust the level of education materials to typical abilities of students. In this study, we present the results of research conducted with the goal of examining the influence of various fuzzy or non-fuzzy aggregation functions upon the quality of cognitive workload estimation. Various classic machine learning models were successfully applied to the problem. The results of extensive in-depth experiments with over 2000 aggregation operators shows the applicability of the approach based on the aggregation functions. Moreover, the approach based on aggregation process allows for further improvement of classification results. A wide range of aggregation functions is considered and the results suggest that the combination of classical machine learning models and aggregation methods allows to achieve high quality of cognitive workload level recognition preserving low computational cost.

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Development of a Simulation Experiment to Investigate In-Flight Startle using Fuzzy Cognitive Maps and Pupillometry

Cited by 4 publications

References 28 publications

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

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

Green Cloud Software Engineering for Big Data Processing

On the Improvement of Eye Tracking-Based Cognitive Workload Estimation Using Aggregation Functions

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