AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2025
Angelopoulos, J.; Manettas, C.; Alexopoulos, K.
Industrial Maintenance Optimization Based on the Integration of Large Language Models (LLM) and Augmented Reality (AR) Proceedings Article
In: K., Alexopoulos; S., Makris; P., Stavropoulos (Ed.): Lect. Notes Mech. Eng., pp. 197–205, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 21954356 (ISSN); 978-303186488-9 (ISBN).
Abstract | Links | BibTeX | Tags: Augmented Reality, Competition, Cost reduction, Critical path analysis, Crushed stone plants, Generative AI, generative artificial intelligence, Human expertise, Industrial equipment, Industrial maintenance, Language Model, Large language model, Maintenance, Maintenance optimization, Maintenance procedures, Manufacturing data processing, Potential errors, Problem oriented languages, Scheduled maintenance, Shopfloors, Solar power plants
@inproceedings{angelopoulos_industrial_2025,
title = {Industrial Maintenance Optimization Based on the Integration of Large Language Models (LLM) and Augmented Reality (AR)},
author = {J. Angelopoulos and C. Manettas and K. Alexopoulos},
editor = {Alexopoulos K. and Makris S. and Stavropoulos P.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001421726&doi=10.1007%2f978-3-031-86489-6_20&partnerID=40&md5=63be31b9f4dda4aafd6a641630506c09},
doi = {10.1007/978-3-031-86489-6_20},
isbn = {21954356 (ISSN); 978-303186488-9 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Mech. Eng.},
pages = {197–205},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Traditional maintenance procedures often rely on manual data processing and human expertise, leading to inefficiencies and potential errors. In the context of Industry 4.0 several digital technologies, such as Artificial Intelligence (AI), Big Data Analytics (BDA), and eXtended Reality (XR) have been developed and are constantly being integrated in a plethora of manufacturing activities (including industrial maintenance), in an attempt to minimize human error, facilitate shop floor technicians, reduce costs as well as reduce equipment downtimes. The latest developments in the field of AI point towards Large Language Models (LLM) which can communicate with human operators in an intuitive manner. On the other hand, Augmented Reality, as part of XR technologies, offers useful functionalities for improving user perception and interaction with modern, complex industrial equipment. Therefore, the context of this research work lies in the development and training of an LLM in order to provide suggestions and actionable items for the mitigation of unforeseen events (e.g. equipment breakdowns), in order to facilitate shop-floor technicians during their everyday tasks. Paired with AR visualizations over the physical environment, the technicians will get instructions for performing tasks and checks on the industrial equipment in a manner similar to human-to-human communication. The functionality of the proposed framework extends to the integration of modules for exchanging information with the engineering department towards the scheduling of Maintenance and Repair Operations (MRO) as well as the creation of a repository of historical data in order to constantly retrain and optimize the LLM. © The Author(s) 2025.},
keywords = {Augmented Reality, Competition, Cost reduction, Critical path analysis, Crushed stone plants, Generative AI, generative artificial intelligence, Human expertise, Industrial equipment, Industrial maintenance, Language Model, Large language model, Maintenance, Maintenance optimization, Maintenance procedures, Manufacturing data processing, Potential errors, Problem oriented languages, Scheduled maintenance, Shopfloors, Solar power plants},
pubstate = {published},
tppubtype = {inproceedings}
}
Traditional maintenance procedures often rely on manual data processing and human expertise, leading to inefficiencies and potential errors. In the context of Industry 4.0 several digital technologies, such as Artificial Intelligence (AI), Big Data Analytics (BDA), and eXtended Reality (XR) have been developed and are constantly being integrated in a plethora of manufacturing activities (including industrial maintenance), in an attempt to minimize human error, facilitate shop floor technicians, reduce costs as well as reduce equipment downtimes. The latest developments in the field of AI point towards Large Language Models (LLM) which can communicate with human operators in an intuitive manner. On the other hand, Augmented Reality, as part of XR technologies, offers useful functionalities for improving user perception and interaction with modern, complex industrial equipment. Therefore, the context of this research work lies in the development and training of an LLM in order to provide suggestions and actionable items for the mitigation of unforeseen events (e.g. equipment breakdowns), in order to facilitate shop-floor technicians during their everyday tasks. Paired with AR visualizations over the physical environment, the technicians will get instructions for performing tasks and checks on the industrial equipment in a manner similar to human-to-human communication. The functionality of the proposed framework extends to the integration of modules for exchanging information with the engineering department towards the scheduling of Maintenance and Repair Operations (MRO) as well as the creation of a repository of historical data in order to constantly retrain and optimize the LLM. © The Author(s) 2025.
Xu, F.; Zhou, T.; Nguyen, T.; Bao, H.; Lin, C.; Du, J.
Integrating augmented reality and LLM for enhanced cognitive support in critical audio communications Journal Article
In: International Journal of Human Computer Studies, vol. 194, 2025, ISSN: 10715819 (ISSN).
Abstract | Links | BibTeX | Tags: Audio communications, Augmented Reality, Cognitive loads, Cognitive support, Decisions makings, Language Model, Large language model, LLM, Logic reasoning, Maintenance, Operations and maintenance, Oral communication, Situational awareness
@article{xu_integrating_2025,
title = {Integrating augmented reality and LLM for enhanced cognitive support in critical audio communications},
author = {F. Xu and T. Zhou and T. Nguyen and H. Bao and C. Lin and J. Du},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208467299&doi=10.1016%2fj.ijhcs.2024.103402&partnerID=40&md5=153d095b837ee1666a7da0f7ed03362c},
doi = {10.1016/j.ijhcs.2024.103402},
issn = {10715819 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {International Journal of Human Computer Studies},
volume = {194},
abstract = {Operation and Maintenance (O&M) missions are often time-sensitive and accuracy-dependent, requiring rapid and precise information processing in noisy, chaotic environments where oral communication can lead to cognitive overload and impaired decision-making. Augmented Reality (AR) and Large Language Models (LLMs) offer potential for enhancing situational awareness and lowering cognitive load by integrating digital visualizations with the physical world and improving dialogue management. However, synthesizing these technologies into a real-time system that effectively aids operators remains a challenge. This study explores the integration of AR and GPT-4, an advanced LLM, in time-sensitive O&M tasks, aiming to enhance situational awareness and manage cognitive load during oral communications. A customized AR system, incorporating the Microsoft HoloLens2 for cognitive monitoring and GPT-4 for decision making assistance, was tested in a human subject experiment with 30 participants. The 2×2 factorial experiment evaluated the effects of AR and LLM assistance on task performance and cognitive load. Results demonstrated significant improvements in task accuracy and reductions in cognitive load, highlighting the effectiveness of AR and LLM integration in supporting O&M missions. These findings emphasize the need for further research to optimize operational strategies in mission critical environments. © 2024 Elsevier Ltd},
keywords = {Audio communications, Augmented Reality, Cognitive loads, Cognitive support, Decisions makings, Language Model, Large language model, LLM, Logic reasoning, Maintenance, Operations and maintenance, Oral communication, Situational awareness},
pubstate = {published},
tppubtype = {article}
}
Operation and Maintenance (O&M) missions are often time-sensitive and accuracy-dependent, requiring rapid and precise information processing in noisy, chaotic environments where oral communication can lead to cognitive overload and impaired decision-making. Augmented Reality (AR) and Large Language Models (LLMs) offer potential for enhancing situational awareness and lowering cognitive load by integrating digital visualizations with the physical world and improving dialogue management. However, synthesizing these technologies into a real-time system that effectively aids operators remains a challenge. This study explores the integration of AR and GPT-4, an advanced LLM, in time-sensitive O&M tasks, aiming to enhance situational awareness and manage cognitive load during oral communications. A customized AR system, incorporating the Microsoft HoloLens2 for cognitive monitoring and GPT-4 for decision making assistance, was tested in a human subject experiment with 30 participants. The 2×2 factorial experiment evaluated the effects of AR and LLM assistance on task performance and cognitive load. Results demonstrated significant improvements in task accuracy and reductions in cognitive load, highlighting the effectiveness of AR and LLM integration in supporting O&M missions. These findings emphasize the need for further research to optimize operational strategies in mission critical environments. © 2024 Elsevier Ltd
2024
Xu, F.; Nguyen, T.; Du, J.
Augmented Reality for Maintenance Tasks with ChatGPT for Automated Text-To-Action Journal Article
In: Journal of Construction Engineering and Management, vol. 150, no. 4, 2024, ISSN: 07339364 (ISSN).
Abstract | Links | BibTeX | Tags: Artificial intelligence systems, Augmented Reality, Augmented Reality (AR), ChatGPT, Complex sequences, Computational Linguistics, Diverse fields, Human like, Language Model, Maintenance, Maintenance tasks, Operations and maintenance, Optical character recognition, Sensor technologies, Virtual Reality
@article{xu_augmented_2024,
title = {Augmented Reality for Maintenance Tasks with ChatGPT for Automated Text-To-Action},
author = {F. Xu and T. Nguyen and J. Du},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183669638&doi=10.1061%2fJCEMD4.COENG-14142&partnerID=40&md5=6b02d2f4f6e74a8152adf2eb30ee2d88},
doi = {10.1061/JCEMD4.COENG-14142},
issn = {07339364 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Journal of Construction Engineering and Management},
volume = {150},
number = {4},
abstract = {Advancements in sensor technology, artificial intelligence (AI), and augmented reality (AR) have unlocked opportunities across various domains. AR and large language models like GPT have witnessed substantial progress and increasingly are being employed in diverse fields. One such promising application is in operations and maintenance (OM). OM tasks often involve complex procedures and sequences that can be challenging to memorize and execute correctly, particularly for novices or in high-stress situations. By combining the advantages of superimposing virtual objects onto the physical world and generating human-like text using GPT, we can revolutionize OM operations. This study introduces a system that combines AR, optical character recognition (OCR), and the GPT language model to optimize user performance while offering trustworthy interactions and alleviating workload in OM tasks. This system provides an interactive virtual environment controlled by the Unity game engine, facilitating a seamless interaction between virtual and physical realities. A case study (N=30) was conducted to illustrate the findings and answer the research questions. The Multidimensional Measurement of Trust (MDMT) was applied to understand the complexity of trust engagement with such a human-like system. The results indicate that users can complete similarly challenging tasks in less time using our proposed AR and AI system. Moreover, the collected data also suggest a reduction in cognitive load when executing the same operations using the AR and AI system. A divergence of trust was observed concerning capability and ethical dimensions. © 2024 American Society of Civil Engineers.},
keywords = {Artificial intelligence systems, Augmented Reality, Augmented Reality (AR), ChatGPT, Complex sequences, Computational Linguistics, Diverse fields, Human like, Language Model, Maintenance, Maintenance tasks, Operations and maintenance, Optical character recognition, Sensor technologies, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
Advancements in sensor technology, artificial intelligence (AI), and augmented reality (AR) have unlocked opportunities across various domains. AR and large language models like GPT have witnessed substantial progress and increasingly are being employed in diverse fields. One such promising application is in operations and maintenance (OM). OM tasks often involve complex procedures and sequences that can be challenging to memorize and execute correctly, particularly for novices or in high-stress situations. By combining the advantages of superimposing virtual objects onto the physical world and generating human-like text using GPT, we can revolutionize OM operations. This study introduces a system that combines AR, optical character recognition (OCR), and the GPT language model to optimize user performance while offering trustworthy interactions and alleviating workload in OM tasks. This system provides an interactive virtual environment controlled by the Unity game engine, facilitating a seamless interaction between virtual and physical realities. A case study (N=30) was conducted to illustrate the findings and answer the research questions. The Multidimensional Measurement of Trust (MDMT) was applied to understand the complexity of trust engagement with such a human-like system. The results indicate that users can complete similarly challenging tasks in less time using our proposed AR and AI system. Moreover, the collected data also suggest a reduction in cognitive load when executing the same operations using the AR and AI system. A divergence of trust was observed concerning capability and ethical dimensions. © 2024 American Society of Civil Engineers.
2010
Scianna, Andrea; Ammoscato, Alessio
3D gis data model using open source software Proceedings Article
In: A, Peled (Ed.): International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, pp. 120–125, International Society for Photogrammetry and Remote Sensing, 2010.
Abstract | Links | BibTeX | Tags: 3-dimensional modeling, 3D Modelling, Blending, Computer software, Data visualization, Database systems, Environmental database, Environmental Technology, Free and open source softwares, Geographic information systems, Geographical Information Systems, High level languages, HTTP, Internet, Internet browsers, Internet protocols, Interoperability, Maintenance, Mapping, Maps, Open source software, Open systems, Query languages, Research management, Social networking (online), Software engineering, Spatial, Technology, Three dimensional computer graphics, Three-dimensional data, Topological information, Topology, World Wide Web
@inproceedings{scianna_3d_2010,
title = {3D gis data model using open source software},
author = {Andrea Scianna and Alessio Ammoscato},
editor = {Peled A},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880227655&partnerID=40&md5=502aa042af1693c18f34b5d74c4dd2bd},
year = {2010},
date = {2010-01-01},
booktitle = {International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives},
volume = {38},
pages = {120–125},
publisher = {International Society for Photogrammetry and Remote Sensing},
abstract = {Today many kinds of applications requires data containing actual three-dimensional data; fields like urban and town planning and pollution studies need 3D data, both for visualization purpose, as well as carry out many spatial analysis. This research-Management and use of distributed 3D data by open source Web-GIS software-is part of the Italian "PRIN 2007"∗ research project, aimed to build urban and suburban 3D models, and to interact with them using open source software only. Particularly free and open source software, used for the experimentation here shown, are Blender and PostGIS; the first one has been used to build and structure three-dimensional data, the second one for data allocation. These software interact using scripts, written in Python language. Buildings have been modeled upon the GIANT3D model (Geographical Interoperable Advanced Numerical Topological 3-Dimensional Model) developed in the research "PRIN 2004", regarding "Evolved structure of numerical cartography for Gis and Web-GIS". Python scripts, activated by Blender, allow to allocate data into a spatial database implemented through PostgreSQL and PostGis, that could be a remote database somewhere on the net; all geometrical and topological information, implemented in the 3D model, are so transferred in PostGIS. These information can be retrieved by Blender using other Python scripts, so Blender fully interacts with 3D data allocated in PostGIS. These data can be also accessed by many other clients, both directly using a database client, as using other protocols (like HTTP on the internet). Next step is to build an open source viewer, or a plugin for internet browsers, that allows client to visualize, explore and inquiry 3D model, retrieving data from database.},
keywords = {3-dimensional modeling, 3D Modelling, Blending, Computer software, Data visualization, Database systems, Environmental database, Environmental Technology, Free and open source softwares, Geographic information systems, Geographical Information Systems, High level languages, HTTP, Internet, Internet browsers, Internet protocols, Interoperability, Maintenance, Mapping, Maps, Open source software, Open systems, Query languages, Research management, Social networking (online), Software engineering, Spatial, Technology, Three dimensional computer graphics, Three-dimensional data, Topological information, Topology, World Wide Web},
pubstate = {published},
tppubtype = {inproceedings}
}
Today many kinds of applications requires data containing actual three-dimensional data; fields like urban and town planning and pollution studies need 3D data, both for visualization purpose, as well as carry out many spatial analysis. This research-Management and use of distributed 3D data by open source Web-GIS software-is part of the Italian "PRIN 2007"∗ research project, aimed to build urban and suburban 3D models, and to interact with them using open source software only. Particularly free and open source software, used for the experimentation here shown, are Blender and PostGIS; the first one has been used to build and structure three-dimensional data, the second one for data allocation. These software interact using scripts, written in Python language. Buildings have been modeled upon the GIANT3D model (Geographical Interoperable Advanced Numerical Topological 3-Dimensional Model) developed in the research "PRIN 2004", regarding "Evolved structure of numerical cartography for Gis and Web-GIS". Python scripts, activated by Blender, allow to allocate data into a spatial database implemented through PostgreSQL and PostGis, that could be a remote database somewhere on the net; all geometrical and topological information, implemented in the 3D model, are so transferred in PostGIS. These information can be retrieved by Blender using other Python scripts, so Blender fully interacts with 3D data allocated in PostGIS. These data can be also accessed by many other clients, both directly using a database client, as using other protocols (like HTTP on the internet). Next step is to build an open source viewer, or a plugin for internet browsers, that allows client to visualize, explore and inquiry 3D model, retrieving data from database.