National Cheng-Kung University

Hao Tieng

Fan-Tien Cheng

Haw-Ching Yang

Tsung-Han Tsai

Min-Hsiung Hung

Chun-Fang Chen

Yu-Yong Li

Ting-Chia Ou

Embodiments of the present invention provide a hybrid system and method for distributed virtual power plants integrated intelligent net zero. In this method, a cyber physical agent (CPA) is utilized to collect a carbon emission information and an energy management information, and then an artificial intelligence (AI) optimization model of an intelligent central dispatch platform is utilized to obtain a power dispatch manner of the distributed virtual power plants based on the carbon emission information and the energy management information, such that the power dispatch manner of the distributed virtual power plants meets the requirements of enterprise economic benefits and net zero carbon emissions at the same time.

Hybrid system and method for distributed virtual power plants integrated intelligent net zero

A product quality prediction method for mass customization is provided. When a production system has a status change, data of sets of process parameters and actual measurement values of workpiece samples processed before the status change occurs, and data of sets of process parameters and actual measurement values of few workpiece samples processed after the status change occurs are used for build or retrain a prediction model, thereby predicting a metrology value of a next workpiece.

Product quality prediction method for mass customization

A virtual metrology based method for predicting machining quality of a machine tool is provided. In this method, each product accuracy item is correlated with operation paths of the machine tool. During a modeling stage, the machine tool is operated to process workpiece samples, and sample sensing data of the workpiece samples associated with the operation paths are collected during the operation of the machine tool. The sample sensing data of each workpiece sample is de-noised and converted into the sample feature data corresponding to each feature type. The workpiece samples are measured with respect to the product accuracy item and integrated into the feature data for building a predictive model, thereby obtaining quality predicted data for each product accuracy item. During a usage stage, accuracy item values of a workpiece are predicted using the feature data during processing the workpiece in accordance with the predictive models.

Method for predicting machining quality of machine tool

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Tainan, Taiwan

Hao Tieng