Abstract:

This thesis develops a data-driven dominant balance model to recognise and cluster the flow pattern blowing through a high-rise building in an urban area under neutral atmospheric conditions. To be consistent with the governing equation used in simulations, the Reynolds-Averaged Navier-Stokes (RANS) equation is selected as the governing equation. It is divided into six sub-parts... More

Abstract:

Few researchers provide a wider vision of artificial feet, hands, mouths, eyes, ears, and brains. This limits our vision of them and their significant impacts on the modern Industrial Revolution and Artificial Intelligence (AI) history.  This article presents a novel perspective on human-centered social development starting from artificial feet. After briefly reviewing AI, this article... More

Abstract:

Study and develop the machine learning algorithm to find the opportunity of profiting in high probability for the investor to buy a stock based on the analysis of big data. In the first place, a preparative algorithm to find the bull-bear points is established. And then, basing on the preparative algorithm, a weaker bull-bear point... More

Abstract:

Wind turbine blades are vital for energy generation, where defects can cause efficiency loss and costly maintenance. This paper proposes an improved object detection algorithm based on YOLOv8 for detecting defects in wind turbine blades. Enhancements include network architecture modifications and advanced attention mechanisms, which boost detection accuracy while maintaining real-time processing. Our approach is... More

Abstract:

In modern logistics and delivery systems, mobile robot delivery systems have garnered significant attention due to their efficiency and flexibility. However, existing robot delivery systems still face numerous challenges in complex and dynamically changing environments. For instance, traditional algorithms exhibit low efficiency when processing high-dimensional and unstructured data, making it difficult to adapt to real-time... More

Abstract:

U-Net is a widely recognized neural network model for medical image segmentation, renowned for its efficiency in extracting features from both current and past input data. However, traditional U-Net models exhibit limitations in extracting edge features, particularly in medical CT images characterized by complex gray distributions and close pixel intervals. This leads to suboptimal performance,... More

Abstract:

This paper summarizes and replicates multiple classical and cutting-edge knowledge transfer methods, including Factor Transfer (FT), Knowledge Distillation (KD), Deep Mutual Learning (DML), Contrastive Representation Distillation (CRD), and Born-Again Self-Distillation (BSS). Additionally, we studied three advanced knowledge transfer methods: Relational Knowledge Distillation (RKD), Similarity-Preserving (SP), and Attention-based Feature Distillation (AFD), successfully replicating an optimized version... More