ORCID Profile
0000-0003-0998-5435
Current Organisations
UNSW Sydney
,
Harbin Engineering University
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Publisher: Springer Nature Singapore
Date: 2023
Publisher: Springer Nature Singapore
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Nature Singapore
Date: 2023
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: MDPI AG
Date: 21-01-2020
DOI: 10.3390/IJGI9020063
Abstract: Over recent decades, more and more cities worldwide have created semantic 3D city models of their built environments based on standards across multiple domains. 3D city models, which are often employed for a large range of tasks, go far beyond pure visualization. Due to different spatial scale requirements for planning and managing various built environments, integration of Geographic Information Systems (GIS) and Building Information Modeling (BIM) has emerged in recent years. Focus is now shifting to Precinct Information Modeling (PIM) which is in a more general sense to built-environment modeling. As scales change so do options to perform information modeling for different applications. How to implement data interoperability across these digital representations, therefore, becomes an emerging challenge. Moreover, with the growth of multi-source heterogeneous data consisting of semantic and varying 2D/3D spatial representations, data management becomes feasible for facilitating the development and deployment of PIM applications. How to use heterogeneous data in an integrating manner to further express PIM is an open and comprehensive topic. In this paper, we develop a semantic PIM based on multi-source heterogeneous data. Then, we tackle spatial data management problems in a Spatial Database Management System (SDBMS) solution for our defined unified model. Case studies on the University of New South Wales (UNSW) c us demonstrate the efficiency of our solution.
Publisher: MDPI AG
Date: 02-07-2021
DOI: 10.3390/S21134551
Abstract: Geo-social community detection over location-based social networks combining both location and social factors to generate useful computational results has attracted increasing interest from both industrial and academic communities. In this paper, we formulate a novel community model, termed geo-social group (GSG), to enforce both spatial and social factors to generate significant computational patterns and to investigate the problem of community detection over location-based social networks. Specifically, GSG detection aims to extract all group-venue clusters, where users are similar to each other in the same group and they are located in a minimum covering circle (MCC) for which the radius is no greater than a distance threshold γ. Then, we present a GSGD algorithm following a three-step paradigm to enumerate all qualified GSGs in a large network. We propose effective optimization techniques to efficiently enumerate all communities in a network. Furthermore, we extend a significant GSG detection problem to top-k geo-social group (TkGSG) mining. Rather than extracting all qualified GSGs in a network, TkGSG aims to return k feasibility groups to guarantee the ersity. We prove the hardness of computing the TkGSGs. Nevertheless, we propose the effective greedy approach with a guaranteed approximation ratio of 1−1/e. Extensive empirical studies on real and synthetic networks show the superiority of our algorithm when compared with existing methods and demonstrate the effectiveness of our new community model and the efficiency of our optimization techniques.
Publisher: MDPI AG
Date: 22-09-2022
DOI: 10.3390/RS14194746
Abstract: We study the problem of indoor positioning, which is a fundamental service in managing and analyzing objects in indoor environments. Unpredictable signal interference sources increase the degeneration of the accuracy and robustness of existing solutions. Deep learning approaches have recently been widely studied to overcome these challenges and attain better performance. In this paper, we aim to develop efficient algorithms, such as the dual-encoder-condensed convolution (DECC) method, which can achieve high-precision positioning for indoor services. In particular, firstly, we develop a convolutional module to add the original channel state information to the location information. Secondly, to explore channel differences between different antennas, we adopt a dual-encoder stacking mechanism for parallel calculation. Thirdly, we develop two different convolution kernels to speed up convergence. Performance studies on the indoor scenario and the urban canyon scenario datasets demonstrate the efficiency and effectiveness of our new approach.
Publisher: MDPI AG
Date: 27-09-2022
DOI: 10.3390/APP12199696
Abstract: The major problem with 3D room layout reconstruction is estimating the 3D room layout from a single panoramic image. In practice, the boundaries between indoor objects are difficult to define, for ex le, the boundary position of a sofa and a table, and the boundary position of a picture frame and a wall. We propose MreNet, a novel neural network architecture for predicting 3D room layout, which outperforms previous state-of-the-art approaches. It can efficiently model the overall layout of indoor rooms through a global receptive field and sparse attention mechanism, while prior works tended to use CNNs to gradually increase the receptive field. Furthermore, the proposed feature connection mechanism can solve the problem of the gradient disappearing in the process of training, and feature maps of different granularity can be obtained in different layers. Experiments on both cuboid-shaped and general Manhattan layouts show that the proposed work outperforms recent algorithms in prediction accuracy.
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Elsevier BV
Date: 10-2023
Publisher: Springer Nature Singapore
Date: 2023
No related grants have been discovered for Wei Li.