AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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Here you can find the complete list of our publications.
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You can expand the Abstract, Links and BibTex record for each paper.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Wan, X.; Luo, Y.
A Study of Anti-war Memorial Hall of Leshan City based on Virtual Museum Technology Proceedings Article
In: pp. 493–497, Association for Computing Machinery, Inc, 2025, ISBN: 9798400712432 (ISBN).
Abstract | Links | BibTeX | Tags: 3d modeling technologies, 3D reconstruction, Anti-war, Artificial intelligence, Augmented Reality, Digital researches, Historic Preservation, Human engineering, Interactive computer graphics, Knowledge graph, Knowledge graphs, Language Model, Localization and mappings, Metaverses, Model knowledge, Museum technology, Museums, Restoration, Three dimensional computer graphics, Virtual museum, Virtual Reality
@inproceedings{wan_study_2025,
title = {A Study of Anti-war Memorial Hall of Leshan City based on Virtual Museum Technology},
author = {X. Wan and Y. Luo},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011594066&doi=10.1145%2F3732801.3732887&partnerID=40&md5=ac25032b46edf5a9d5949b8ceb5a41e1},
doi = {10.1145/3732801.3732887},
isbn = {9798400712432 (ISBN)},
year = {2025},
date = {2025-01-01},
pages = {493–497},
publisher = {Association for Computing Machinery, Inc},
abstract = {This study adopted augmented reality (AR), virtual reality (VR), artificial intelligence (AI), metaverse (META), large language models (LLM), knowledge graphs (KG), and synchronous localization and mapping (SLAM) technologies to create a virtual museum (VM) with the theme of the history of Leshan anti-Japanese war. Its aim is to enrich the digital research of this area, and to restore and vividly reflect the significance of Leshan’s contributions during the anti-Japanese war. This study combines 3D modeling technology with historical scene restoration to create a method of field investigation of local history and anti-Japanese war sites, which constructed six unique exhibition areas to describe historical events. The virtual museum integrates lots of historical sites, stories, achievements, and cultural aspects into a unique cultural interaction center. Through diverse technological approaches, this study aims to enable the public to contemplate history, cultivate national pride and patriotism, and deliver novel strategies for the digital protection of historical heritage. © 2025 Elsevier B.V., All rights reserved.},
keywords = {3d modeling technologies, 3D reconstruction, Anti-war, Artificial intelligence, Augmented Reality, Digital researches, Historic Preservation, Human engineering, Interactive computer graphics, Knowledge graph, Knowledge graphs, Language Model, Localization and mappings, Metaverses, Model knowledge, Museum technology, Museums, Restoration, Three dimensional computer graphics, Virtual museum, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
This study adopted augmented reality (AR), virtual reality (VR), artificial intelligence (AI), metaverse (META), large language models (LLM), knowledge graphs (KG), and synchronous localization and mapping (SLAM) technologies to create a virtual museum (VM) with the theme of the history of Leshan anti-Japanese war. Its aim is to enrich the digital research of this area, and to restore and vividly reflect the significance of Leshan’s contributions during the anti-Japanese war. This study combines 3D modeling technology with historical scene restoration to create a method of field investigation of local history and anti-Japanese war sites, which constructed six unique exhibition areas to describe historical events. The virtual museum integrates lots of historical sites, stories, achievements, and cultural aspects into a unique cultural interaction center. Through diverse technological approaches, this study aims to enable the public to contemplate history, cultivate national pride and patriotism, and deliver novel strategies for the digital protection of historical heritage. © 2025 Elsevier B.V., All rights reserved.
2024
Wu, J.; Gan, W.; Chao, H. -C.; Yu, P. S.
Geospatial Big Data: Survey and Challenges Journal Article
In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 17007–17020, 2024, ISSN: 21511535 (ISSN); 19391404 (ISSN), (Publisher: Institute of Electrical and Electronics Engineers Inc.).
Abstract | Links | BibTeX | Tags: Artificial intelligence, artificial intelligence (AI), Behavioral Research, Big Data, Data challenges, Data Mining, Data surveys, Data visualization, Earth observation data, Environmental management, environmental protection, Geo-spatial, Geo-spatial analysis, Geo-spatial data, Geospatial big data, geospatial big data (GBD), geospatial data, GIS, Green products, Human behaviors, Knowledge graph, Knowledge graphs, satellite, sensor, spatial data, Sustainable development, urban planning
@article{wu_geospatial_2024,
title = {Geospatial Big Data: Survey and Challenges},
author = {J. Wu and W. Gan and H. -C. Chao and P. S. Yu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200804056&doi=10.1109%2FJSTARS.2024.3438376&partnerID=40&md5=63c39a7c302e6d9ff055633efab0349a},
doi = {10.1109/JSTARS.2024.3438376},
issn = {21511535 (ISSN); 19391404 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
volume = {17},
pages = {17007–17020},
abstract = {In recent years, geospatial big data (GBD) has obtained attention across various disciplines, categorized into big Earth observation data and big human behavior data. Identifying geospatial patterns from GBD has been a vital research focus in the fields of urban management and environmental sustainability. This article reviews the evolution of GBD mining and its integration with advanced artificial intelligence techniques. GBD consists of data generated by satellites, sensors, mobile devices, and geographical information systems, and we categorize geospatial data based on different perspectives. We outline the process of GBD mining and demonstrate how it can be incorporated into a unified framework. In addition, we explore new technologies, such as large language models, the metaverse, and knowledge graphs, and how they could make GBD even more useful. We also share examples of GBD helping with city management and protecting the environment. Finally, we discuss the real challenges that come up when working with GBD, such as issues with data retrieval and security. Our goal is to give readers a clear view of where GBD mining stands today and where it might go next. © 2024 Elsevier B.V., All rights reserved.},
note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
keywords = {Artificial intelligence, artificial intelligence (AI), Behavioral Research, Big Data, Data challenges, Data Mining, Data surveys, Data visualization, Earth observation data, Environmental management, environmental protection, Geo-spatial, Geo-spatial analysis, Geo-spatial data, Geospatial big data, geospatial big data (GBD), geospatial data, GIS, Green products, Human behaviors, Knowledge graph, Knowledge graphs, satellite, sensor, spatial data, Sustainable development, urban planning},
pubstate = {published},
tppubtype = {article}
}
In recent years, geospatial big data (GBD) has obtained attention across various disciplines, categorized into big Earth observation data and big human behavior data. Identifying geospatial patterns from GBD has been a vital research focus in the fields of urban management and environmental sustainability. This article reviews the evolution of GBD mining and its integration with advanced artificial intelligence techniques. GBD consists of data generated by satellites, sensors, mobile devices, and geographical information systems, and we categorize geospatial data based on different perspectives. We outline the process of GBD mining and demonstrate how it can be incorporated into a unified framework. In addition, we explore new technologies, such as large language models, the metaverse, and knowledge graphs, and how they could make GBD even more useful. We also share examples of GBD helping with city management and protecting the environment. Finally, we discuss the real challenges that come up when working with GBD, such as issues with data retrieval and security. Our goal is to give readers a clear view of where GBD mining stands today and where it might go next. © 2024 Elsevier B.V., All rights reserved.
Diaz, T. G.; Lee, X. Y.; Zhuge, H.; Vidyaratne, L.; Sin, G.; Watanabe, T.; Farahat, A.; Gupta, C.
AI+AR based Framework for Guided Visual Equipment Diagnosis Proceedings Article
In: C.S., Kulkarni; M.E., Orchard (Ed.): Proc. Annu. Conf. Progn. Health Manag. Soc., PHM, Prognostics and Health Management Society, 2024, ISBN: 23250178 (ISSN); 978-193626305-9 (ISBN).
Abstract | Links | BibTeX | Tags: Augmented Reality, Automated solutions, Customer loyalty, Customer satisfaction, Customers' satisfaction, Diagnosis, Equipment diagnosis, Failure Diagnosis, Failure repairs, High quality, Knowledge graphs, Language Model, Quality of Service, Query languages, Sales, Support services
@inproceedings{diaz_aiar_2024,
title = {AI+AR based Framework for Guided Visual Equipment Diagnosis},
author = {T. G. Diaz and X. Y. Lee and H. Zhuge and L. Vidyaratne and G. Sin and T. Watanabe and A. Farahat and C. Gupta},
editor = {Kulkarni C.S. and Orchard M.E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210227167&doi=10.36001%2fphmconf.2024.v16i1.3909&partnerID=40&md5=897ac8045a48e2e80aa7522870c2004f},
doi = {10.36001/phmconf.2024.v16i1.3909},
isbn = {23250178 (ISSN); 978-193626305-9 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. Annu. Conf. Progn. Health Manag. Soc., PHM},
volume = {16},
publisher = {Prognostics and Health Management Society},
abstract = {Automated solutions for effective support services, such as failure diagnosis and repair, are crucial to keep customer satisfaction and loyalty. However, providing consistent, high quality, and timely support is a difficult task. In practice, customer support usually requires technicians to perform onsite diagnosis, but service quality is often adversely affected by limited expert technicians, high turnover, and minimal automated tools. To address these challenges, we present a novel solution framework for aiding technicians in performing visual equipment diagnosis. We envision a workflow where the technician reports a failure and prompts the system to automatically generate a diagnostic plan that includes parts, areas of interest, and necessary tasks. The plan is used to guide the technician with augmented reality (AR), while a perception module analyzes and tracks the technician’s actions to recommend next steps. Our framework consists of three components: planning, tracking, and guiding. The planning component automates the creation of a diagnostic plan by querying a knowledge graph (KG). We propose to leverage Large Language Models (LLMs) for the construction of the KG to accelerate the extraction process of parts, tasks, and relations from manuals. The tracking component enhances 3D detections by using perception sensors with a 2D nested object detection model. Finally, the guiding component reduces process complexity for technicians by combining 2D models and AR interactions. To validate the framework, we performed multiple studies to:1) determine an effective prompt method for the LLM to construct the KG; 2) demonstrate benefits of our 2D nested object model combined with AR model. © 2024 Prognostics and Health Management Society. All rights reserved.},
keywords = {Augmented Reality, Automated solutions, Customer loyalty, Customer satisfaction, Customers' satisfaction, Diagnosis, Equipment diagnosis, Failure Diagnosis, Failure repairs, High quality, Knowledge graphs, Language Model, Quality of Service, Query languages, Sales, Support services},
pubstate = {published},
tppubtype = {inproceedings}
}
Automated solutions for effective support services, such as failure diagnosis and repair, are crucial to keep customer satisfaction and loyalty. However, providing consistent, high quality, and timely support is a difficult task. In practice, customer support usually requires technicians to perform onsite diagnosis, but service quality is often adversely affected by limited expert technicians, high turnover, and minimal automated tools. To address these challenges, we present a novel solution framework for aiding technicians in performing visual equipment diagnosis. We envision a workflow where the technician reports a failure and prompts the system to automatically generate a diagnostic plan that includes parts, areas of interest, and necessary tasks. The plan is used to guide the technician with augmented reality (AR), while a perception module analyzes and tracks the technician’s actions to recommend next steps. Our framework consists of three components: planning, tracking, and guiding. The planning component automates the creation of a diagnostic plan by querying a knowledge graph (KG). We propose to leverage Large Language Models (LLMs) for the construction of the KG to accelerate the extraction process of parts, tasks, and relations from manuals. The tracking component enhances 3D detections by using perception sensors with a 2D nested object detection model. Finally, the guiding component reduces process complexity for technicians by combining 2D models and AR interactions. To validate the framework, we performed multiple studies to:1) determine an effective prompt method for the LLM to construct the KG; 2) demonstrate benefits of our 2D nested object model combined with AR model. © 2024 Prognostics and Health Management Society. All rights reserved.