FYPs/Thesis/Journal from Higher Education Institutions in Hong Kong


Below Information is provided by the Higher Insitutions signed MoU with CIC.



Date: From


Institution Title Type Date Author(s) Abstract Link
HKUST Trends and opportunities of BIM-GIS integration in the architecture, engineering and construction industry: A review from a spatio-temporal statistical perspective Journal 12/2017 Song, Y., Wang, X., Tan, Y., Wu, P., Sutrisna, M., Cheng, J.C.P., et al. The integration of building information modelling (BIM) and geographic information system (GIS) in construction management is a new and fast developing trend in recent years, from research to industrial practice. BIM has advantages on rich geometric and semantic information through the building life cycle, while GIS is a broad field covering geovisualization-based decision making and geospatial modelling. However, most current studies of BIM-GIS integration focus on the integration techniques but lack theories and methods for further data analysis and mathematic modelling. This paper reviews the applications and discusses future trends of BIM-GIS integration in the architecture, engineering and construction (AEC) industry based on the studies of 96 high-quality research articles from a spatio-temporal statistical perspective. The analysis of these applications helps reveal the evolution progress of BIM-GIS integration. Results show that the utilization of BIM-GIS integration in the AEC industry requires systematic theories beyond integration technologies and deep applications of mathematical modeling methods, including spatio-temporal statistical modeling in GIS and 4D/nD BIM simulation and management. Opportunities of BIM-GIS integration are outlined as three hypotheses in the AEC industry for future research on the in-depth integration of BIM and GIS. BIM-GIS integration hypotheses enable more comprehensive applications through the life cycle of AEC projects. Link
HKUST Transfer learning enhanced AR spatial registration for facility maintenance management Journal 02/2020 Chen, K., Yang, J., Cheng, J.C.P., Chen, W., and Li, C.T. Augmented reality (AR), which requires a spatial registration technique, has proved to greatly improve the efficiency of facility maintenance management (FMM) activities. Being one of the most promising techniques for indoor localization, Wi-Fi fingerprinting has been widely used for AR spatial registration. However, localization accuracy of Wi-Fi fingerprinting decreases over time due to dynamics of environmental factors. Readings from different mobile devices can also affect the accuracy negatively. In this paper, a transfer learning technique named transferable CNN-LSTM is proposed for improving the robustness of Wi-Fi fingerprinting while implementing AR in FMM activities. Convolutional neural network (CNN), embedded with long short term memory (LSTM) networks, is utilized to predict the location of unlabeled fingerprints. Multiple kernel variant of maximum mean discrepancy (MK-MMD) is adopted to reduce the distribution difference between the source domain and the target domain, so that the location of the newly collected unlabeled fingerprints can be predicted accurately. As shown in the experimental validation, the transferable CNN-LSTM can achieve an accuracy of 97.1% in short-term (without significant environmental changes) spatial registration, 87.8% in long-term (with significant environmental changes) spatial registration, and around 90% in multi-device spatial registration, indicating a higher accuracy and better robustness over other conventional approaches. Link
HKU The Investigation on the Usage of Building Information Modeling (BIM) in Hong Kong Thesis 04/2014 POON Ho Yu -- N.A.
HKU The Empirical Study of the Challenges and Barriers of Adoption of Building Information Model (BIM) in Architecture, Engineering and Construction (AEC) Industry in Hong Kong Thesis 04/2013 CHUNG Man Sheung -- N.A.
HKU The Determinants of Information Technology Applications in Retail under the Context of Shopping Malls in Hong Kong Thesis 04/2018 KAM Oi Man -- N.A.
HKU The Application of Historic Building Information Modeling (HBIM) in Hong Kong Thesis 04/2016 CHAN Tsz Ho -- N.A.