The maintenance of buildings, underpinned by the digital twin technique, becomes integral to heritage conservation efforts. To achieve efficient modelling with minimal manual intervention, automated component recognition based on semantic segmentation of point clouds is imperative. Confronted by the challenges of the paucity of requisite datasets and the inherent geometric diversity of historical buildings, a two-step strategy including feature extraction and classification is proposed. First, an improved SHOT descriptor is proposed to extract discriminative features by defining a specific local reference system and concatenating support fields at different scales. The extracted features are then classified with a learning-based network, avoiding a feature learning process that relies on sufficient data. Experiments on real-world heritage point clouds yield 93.7% accuracy and an 80.0% mean-intersection-over-union (mIoU) when descriptors with radii of 0.3 m and 0.9 m are combined, surpassing computationally expensive deep learning networks and data-intensive unsupervised learning. A slight decrease in segmentation performance with random removal of points indicates the high robustness of the proposed method against data missing and sampling density changes. Additionally, a geometric modelling process with an error of less than 10% is introduced to achieve a direct transition from point cloud to model, contributing to the establishment of digital twins for heritage structures.

Automatic design tools are being developed to assist designers handle tedious work at scale. However, knowledge gaps still exist in harnessing deep learning models to learn from human experience for more efficient design generation while keeping the data understandable and interoperable. Moreover, human-in-the-loop approach is largely neglected, which are essential for more user-centered design. This research utilizes graph data to parametrically represent housing designs and graph-representative deep generative models for design generation, which provides an interactive design approach for the users at every step. This method would facilitate the human-centered design process by returning feasible and parametric housing design alternatives.

This paper focuses on the conflict detection and resolution (CDR) of unmanned aerial vehicles (UAVs). Firstly, the airspace conflict problem of UAVs is studied and a taxonomy of conflict situation is presented. The multi-UAV conflict is studied in virtue of the graph theory. The CDR problem is casted to a nonlinear optimization problem. Secondly, a two layered optimization algorithm, which combines stochastic parallel gradient descent (SPGD) method and Sequential quadratic programming (SQP) algorithm, is presented to solve the nonlinear optimization problem. Numerical simulations are performed to demonstrate the computational efficiency of this solver. Thirdly, the proposed algorithm is extended to 3-D space. Finally, the proposed algorithm is demonstrated on several scenarios. The results demonstrate that the proposed method outperform the existing algorithms. It can obtain conflict free solutions that would not lead to unnecessary detors.