OMAR ALHARASEES
@vrht.bme.hu
Department of Aeronautics and Naval Architecture
Budapest University of Technology and Economics
RESEARCH, TEACHING, or OTHER INTERESTS
Engineering, Automotive Engineering, Aerospace Engineering, Civil and Structural Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Omar Alharasees, Utku Kale, Jozsef Rohacs, Daniel Rohacs, Muller Enetta Eva, and Anita Boros
Elsevier BV
Utku Kale, Omar Alharasees, Jozsef Rohacs, and Daniel Rohacs
Emerald
Purpose The purpose of this paper is to investigate and evaluate the subjective decision-making of pilots during final approach with varying degrees of experience for landing and go-around. Design/methodology/approach In this research, the “Lorenz Attractor” was modified and used to model the subjective decision-making of pilots during the final approach. For landing and go-around situations, “hesitation frequency” and “decision-making time” were calculated for the subjective decision-making of pilots. Findings In this research, the modified Chaotic Lorenz Model was used on MATLAB with varying degrees of experience, namely, student pilots, less-skilled pilots, experienced pilots and well-experienced pilots. Based on the outcomes, the less-skilled pilot needs nearly four times more decision-making time on landing or go-around compared to the well-experienced pilot during the final approach. Practical implications Operators (pilots, air traffic controllers) need to make critical and timely decisions in a highly complex work environment, which is influenced by several external elements such as experience level and human factors. According to NASA, 80% of aviation accidents occur due to human errors specifically over the course of the aviation decision-making process in dynamic circumstances. Due to the consequences of this research the operators' training should be redesigned by assisting flight instructors on the weaknesses of pilots. Originality/value This research explores the endogenous dynamics of the pilot decision-making process by applying a novel “Chaotic Lorenz Model” on MATLAB. In addition, the operator's total decision time formula was improved by including the decision reviewing time and external factors. Moreover, subjective decision-making model created by the current authors and Wicken's information model were modified to the highly automated systems.
Omar Alharasees, Abeer Jazzar, Utku Kale, and Daniel Rohacs
Emerald
Purpose Ineffective communication consequences can be life-threatening and drastic. Communication misunderstandings are frequently reported in incidents, accidents and occurrences. This research paper aims to evaluate operator communication load in highly automated systems; distinguish and highlight the communication error factors during flight operations from different perspectives; and provide suggestions to operators to decrease the rate of misunderstandings in aviation communication. Design/methodology/approach This study is based on a questionnaire that investigated the critical communication load, including aviation training, standard phraseology, operators’ native language and cultural background. In addition to the effect of using controller–pilot data link communications will be discussed widely. In this research, 110 responses were obtained from pilots and air traffic controller (ATCOs) that vary in 44 countries; approximately 20% were ATCOs, and 75% were pilots. Findings This study was designed to assess the level of aviation operators communication load in highly automated systems, identify and illustrate the factors that contribute to communication errors during flight operations from multiple viewpoints, and offer recommendations to operators to minimize the rate of misunderstandings in aviation communication. Originality/value This research deals with evaluating the operators’ communication load, which is crucial for the air traffic safety and efficiency.
Abeer Jazzar, Omar Alharasees, and Utku Kale
Emerald
Purpose Technological advances and the adaption of higher levels of automation serve as a potential cause of aviation incidents and accidents. This study aims to investigate the effect of automated systems on the operator’s performance total load (work, task, information, communication and mental) in highly advanced systems. Design/methodology/approach A questionnaire was designed for aviation operators (Pilots, ATCOs) to understand the intensity to which automation has affected their working environment and personal behavior. In total, 115 responses were received from 44 countries worldwide. Approximately, 66% of respondents were pilots, 27% Air traffic controllers and 7% were both pilots and ATCOs with various experience levels. Findings Based on the results of this questionnaire, this study suggests the following: creating a total load management model to understand the best load balance an operator could perform at providing rapidly updated aviation training methods and approaches investigating the influence and consequences of adding new tools to the operator’s working station and redesigning it to achieve top operator-machine equilibrium redesigning information and alerting systems. Practical implications Intrinsic limitations include an implicit expression of bias in the way questions are phrased, ambiguity in question phrasing that leads to incorrect conclusions and challenges regarding articulating complex concepts. Originality/value In this paper, the authors aimed to assess and investigate factors leading to current and future incidents and accidents resulting from human factors, specifically caused or developed because of highly automated systems.
Omar Alharasees, Osama H. Adali, and Utku Kale
IEEE
This research investigates cognitive and physiological dynamics in UAV operators using ECG devices for comprehensive monitoring. By integrating diverse automation levels, operator expertise, and physiological measurements, nuanced interactions between operators and UAV technology emerge. Findings highlight the link between automation complexity and physiological responses, evident in heart rate profiles. ECG devices enable real-time heart rate monitoring, revealing patterns aligned with varying automation levels. Manual control scenarios (Level 0) exhibit heightened heart rate variability, suggesting cognitive demand. Increasing automation leads to reduced heart rate variation, potentially mitigating cognitive load. Outcomes underscore automation’s transformative impact on unmanned aviation, enhancing performance and decision-making. ECG integration bolsters empirical foundations, guiding interface design, training, and operational protocols in the dynamic UAV landscape.
Omar Alharasees, Muftah S.M. Abdalla, and Utku Kale
IEEE
In the realm of aviation Maintenance, Repair, and Operations (MRO) training, the integration of digital technologies offers a transformative response to industry dynamics. This research delves into this paradigm shift, anchored in the Analytical Hierarchy Process (AHP) methodology. Through meticulous examination of diverse aviation experts representing various job roles and expertise contexts, this study unveils insightful revelations. User satisfaction emerges as a focal point, aligning with the need for enhanced contentment. The analysis accentuates the technical feasibility of digital integration, emphasizing compatibility, reliability, and seamless assimilation within established systems. Expert-derived weights and rankings enhance findings, serving as a foundation for informed decision-making in MRO training digitalization. This synthesis emphasizes aviation’s imperative to leverage digitalization, fostering adept professionals capable of navigating contemporary aircraft systems while accommodating diverse job roles and experience levels.
Omar Alharasees and Utku Kale
Inderscience Publishers
Omar Alharasees, Muftah S. M. Abdalla, and Utku Kale
IEEE
Artificial intelligence (AI) integration in Unmanned Aerial Vehicle (UAV) operations has significantly advanced the field through increased autonomy. However, evaluating the critical aspects of these operations remains a challenge. In order to address this, the current study proposes the use of a combination of the “Observe-Orient-Decide-Act (OODA)” loop and the “Analytic Hierarchy Process (AHP)” method for evaluating AI-UAV systems. The integration of the OODA loop into AHP aims to assess and weigh the critical components of AI-UAV operations, including (i) perception, (ii) decision-making, and (iii) adaptation. The research compares the results of the AHP evaluation between different groups of UAV operators. The findings of this research identify areas for improvement in AI-UAV systems and guide the development of new technologies. In conclusion, this combined approach offers a comprehensive evaluation method for the current and future state of AI-UAV operations, focusing on operator group comparison.
Omar Alharasees, Osama H. Adali, and Utku Kale
IEEE
This research reviews the current literature on the impact of Artificial Intelligence (AI) in the operation of autonomous Unmanned Aerial Vehicles (UAVs). This paper examines three key aspects in developing the future of Unmanned Aircraft Systems (UAS) and UAV operations: (i) design, (ii) human factors, and (iii) operation process. The use of widely accepted frameworks such as the "Human Factors Analysis and Classification System (HFACS)" and "Observe– Orient–Decide–Act (OODA)" loops are discussed. The comprehensive review of this research found that as autonomy increases, operator cognitive workload decreases and situation awareness improves, but also found a corresponding decline in operator vigilance and an increase in trust in the AI system. These results provide valuable insights and opportunities for improving the safety and efficiency of autonomous UAVs in the future and suggest the need to include human factors in the development process.
Omar Alharasees, Muftah S.M. Abdalla, and Utku Kale
IEEE
Unmanned aerial vehicles (UAVs) have become common in civil and defense environments. Due to growing accident rates relative to human flight operations. The demand for qualified UAV operators is increasing as well as clear and standard legislation. Many factors have a severe impact on the availability and usability of UAVs. A well-known method for assessing the risk of accidents causation is the Human Factors Analysis and Classification System (HFACS), which breaks down the causes of accidents into four main categories: (i) organizational influences, (ii) supervision, (iii) preconditions, and (iv) acts. In this study, a unique approach that combines the HFACS model with an analytical hierarchal process (AHP) decision-making model is presented. A questionnaire has been created for this study’s investigation of the crucial aspects of accidents as seen by UAV operators.
Omar Alharasees, Osama H. Adali, and Utku Kale
IEEE
In the aviation environment and air transport operations, safety is the most crucial element of the system; maintaining safe aeronautical operations is highly dependent on the aviation operators’ actions and reactions. Cognitive loads reflect the human contribution to the system; mental, communication, information, task, and workload shape the aviation total loads. The number of aviation accidents and incidents caused by human factors increases rapidly due to intensive technology and automation in aviation. Such recent examples of human-contributed aviation accidents can be given like (i) Casa 212-200, N497CA Flight accident, (ii) Ethiopian Airlines, ET-343 flight accident. Many previous studies focused on measuring the total load of pilots and ATCOs through different processes and flight scenarios using multiple sensors & devices. This paper reviews a variety of experimental studies differentiating the methodologies, scope, and outcomes. Eventually, the paper introduces a compromised and novel approach for measuring aviation operator loads in multiple scenarios.