This project aims to develops a completely manufacturing flexible decision framework by applying big data analytics and AI techniques that integrates data from different decision-making units such as APC, APS, capacity planning, and inventory management planning to increase the decision quality and resilient capability in uncertain risks based on the perspective of factory operation.

We have proposed in this project the first time series classification technique that considers accuracy, earliness, and varied lengths simultaneously, containing a novel deep reinforcement learning framework and a new multi-objective optimization neural network algorithm. The proposed technique is fit for the problem of early classification of cardiovascular diseases based on ECG signals and shown to deliver the best performance in this area, holding the leading position worldwide.

The application of voice assistants is becoming more and more popular, however, due to the inefficiency of artificial intelligence-based technology, current products are mostly built by using rules-based methods. Therefore, in this project, we would like to propose some corresponding solutions for different components of the dialogue system to improve the data efficiency and work efficiency of each component.

The NeRF-based technique describes a super-fast convergence approach to reconstructing the per-scene radiance field from a set of images that capture the scene with known poses.

A self-improving thermal error compensation model is established by federated learning to improve the machine tool performance under different ambient temperatures. Each machine can share and update the latest model parameter to improve the initial model from different ambient temperature variation trails, but still keep the private ambient information of each machine.