2,826 research outputs found

    Engage D3.10 Research and innovation insights

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    Engage is the SESAR 2020 Knowledge Transfer Network (KTN). It is managed by a consortium of academia and industry, with the support of the SESAR Joint Undertaking. This report highlights future research opportunities for ATM. The basic framework is structured around three research pillars. Each research pillar has a dedicated section in this report. SESAR’s Strategic Research and Innovation Agenda, Digital European Sky is a focal point of comparison. Much of the work is underpinned by the building and successful launch of the Engage wiki, which comprises an interactive research map, an ATM concepts roadmap and a research repository. Extensive lessons learned are presented. Detailed proposals for future research, plus research enablers and platforms are suggested for SESAR 3

    Using eye-tracking to investigate strategy and performance of expert and novice control room operators

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    There is lacking research within the Petrochemical Industry that uses eye-tracking to explore the differences between the strategies of expert and novice control room operators as they monitor and address process parameters that could be used to improve novice training programs and interface design. Scan paths and three eye-tracking metrics (Fixation Frequency, Gaze Duration Mean, and Gaze Percentage) were used to investigate the differences in eye behavior of three expert control room operators and six novice students as they monitored and corrected a Crude Refinement simulation. A 2x2x2 mixed factor design was used to explore the effects that expertise (expert and novice), interface type (black and grey), and alarm activity (active and inactive) had on participant eye behavior specifically, fixation frequency, gaze duration mean, and gaze percentage for certain areas of interest. The display was separated into 6 different areas and each area resulted in distinct eye statistics. Scan paths were plotted surrounding a subtle setpoint change within the simulation and were qualitatively analyzed to reveal differences due to expertise, interface type and alarm activity. The MANOVA revealed no significant differences due to expertise, interface type, and alarm activity. The single ANOVAs revealed that participants had higher fixation frequencies on the Main display during monitoring periods than during active periods revealing that both expert and novice participants’ attention was more divided when there were failures and alarms present than when the process was running at normal conditions. Also, experts spent a larger percentage of time monitoring the critical crude temperature and flow controller than novices. Pearson’s correlation between dependent variables revealed a positive correlation between fixation frequency and gaze percentage that indicated that participants typically had many, quick fixations rather than few, long fixations. Scan path analysis revealed that active monitoring and interface background color influenced how quickly operators discovered the setpoint change on screen. Overall, eye-tracking successfully detected differences between participants and interface types that can benefit novice training and display design

    Human-centred design for next generation of air traffic management systems.

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    Designing and deploying air traffic management systems requires an understanding of cognitive ergonomics, system integration, and human-computer interactions. The aim of this research is to develop an effective Human-centred design for Air Navigation Services Providers to permit more effective air traffic controller training and regulations. Therefore, this research consists of both evaluating human-computer interactions on COOPANS Air Traffic Management system and multiple remote tower operations. The COOPANS Alliance is an international cooperation among the air navigation service providers of Austria, Croatia, Denmark, Ireland, Portugal and Sweden with Thales as the industry supplier. The findings of this project indicate that the context-specified design of semantic alerts could improve ATCO’s situational awareness and significantly reduce response time when responding to aircraft conflict resolution alerts. Civil Aviation Authorities, Air Navigation Service Providers and Air Traffic Management System Providers could all benefit from the findings of this research with a view to ensuring that Air Traffic Controllers are provided with the optimal context-specified alerting schemes to increase their situational awareness during both training and operations. The EU Single European Sky initiative was introduced to restructure European airspace and propose innovative measures for air traffic management to achieve the objectives of enhanced cost-efficiency and improved airspace design and airport capacity whilst simultaneously improving safety performance. There is potential to save approximately €2.21 million Euro per annum per installation of remote tower versus traditional control towers. However, ATCO’s visual attention and monitoring performance can be affected by how information is presented, the complexity of the information presented, and the operating environment in the remote tower centre. To achieve resource-efficient and sustainable air navigation services, there is a need to improve the design of human-computer interactions in multiple remote tower technology deployment. These must align with high technology-readiness levels, operators’ practices, industrial developments, and the certification processes of regulators. From a regulatory perspective the results of this project may contribute to European Aviation Safety Agency rulemaking activity for future Air Traffic Management Systems. Overall, the results of this research are in line with the requirements of Single European Sky and facilitate the harmonisation of European ATM systems.PhD in Transport System

    An Architectural Approach to Ensuring Consistency in Hierarchical Execution

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    Hierarchical task decomposition is a method used in many agent systems to organize agent knowledge. This work shows how the combination of a hierarchy and persistent assertions of knowledge can lead to difficulty in maintaining logical consistency in asserted knowledge. We explore the problematic consequences of persistent assumptions in the reasoning process and introduce novel potential solutions. Having implemented one of the possible solutions, Dynamic Hierarchical Justification, its effectiveness is demonstrated with an empirical analysis

    The integration of automatic speech recognition into the air traffic control system

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    Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1990.Includes bibliographical references (leaves 89-94).by Joakim Karlsson.M.S

    Success Factors Impacting Artificial Intelligence Adoption --- Perspective From the Telecom Industry in China

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    As the core driving force of the new round of informatization development and the industrial revolution, the disruptive achievements of artificial intelligence (AI) are rapidly and comprehensively infiltrating into various fields of human activities. Although technologies and applications of AI have been widely studied, and factors that affect AI adoption are identified in existing literature, the impact of success factors on AI adoption remains unknown. Accordingly, the main study of this paper proposes a framework to explore the effects of success factors on AI adoption by integrating the technology, organization, and environment (TOE) framework and diffusion of innovation (DOI) theory. Particularly, this framework consists of factors regarding the external environment, organizational capabilities, and innovation attributes of AI. The framework is empirically tested with data collected by surveying telecom companies in China. Structural equation modeling is applied to analyze the data. The results indicate that compatibility, relative advantage, complexity, managerial support, government involvement, and vendor partnership are significantly related to AI adoption. Managerial capability impacts other organizational capabilities and innovation attributes of AI, but it is indirectly related to AI adoption. Market uncertainty and competitive pressure are not significantly related to AI adoption, but all the external environment factors positively influence managerial capability. The study provides support for firms\u27 decision-making and resource allocation regarding AI adoption. In addition, based on the resource-based view (RBV), this article conducts study 2 which explores the factors that influence the firm sustainable growth. Multiple regression model is applied to empirically test the hypotheses with longitudinal time-series panel data from telecom companies in China. The results indicate that at the firm level, the customer value and operational expenses are significantly related to sustainable growth. Also, at the industry level, industry investment significant impacts sustainable growth. Study 2 provides insights for practitioners the way to keep sustainable growth

    Abductive Design of BDI Agent-based Digital Twins of Organizations

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    For a Digital Twin - a precise, virtual representation of a physical counterpart - of a human-like system to be faithful and complete, it must appeal to a notion of anthropomorphism (i.e., attributing human behaviour to non-human entities) to imitate (1) the externally visible behaviour and (2) the internal workings of that system. Although the Belief-Desire-Intention (BDI) paradigm was not developed for this purpose, it has been used successfully in human modeling applications. In this sense, we introduce in this thesis the notion of abductive design of BDI agent-based Digital Twins of organizations, which builds on two powerful reasoning disciplines: reverse engineering (to recreate the visible behaviour of the target system) and goal-driven eXplainable Artificial Intelligence (XAI) (for viewing the behaviour of the target system through the lens of BDI agents). Precisely speaking, the overall problem we are trying to address in this thesis is to “Find a BDI agent program that best explains (in the sense of formal abduction) the behaviour of a target system based on its past experiences . To do so, we propose three goal-driven XAI techniques: (1) abductive design of BDI agents, (2) leveraging imperfect explanations and (3) mining belief-based explanations. The resulting approach suggests that using goal-driven XAI to generate Digital Twins of organizations in the form of BDI agents can be effective, even in a setting with limited information about the target system’s behaviour
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