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.
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: 19391404 (ISSN).
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=53ee1c9695b3f2e78d6b565ed47f7585},
doi = {10.1109/JSTARS.2024.3438376},
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 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.},
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 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
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.