This inventor holds 1 USPTO granted patent and 3 published patent applications, plus 1 CIPO patent. Top assignee: Guangdong University of Technology. Active years: 2022.
Company Filing History:
Years Active: 2022
Title: Dewen Wang: Innovator in Decentralized Neural Network Models
Introduction
Dewen Wang is a prominent inventor based in Guangzhou, China. He has made significant contributions to the field of neural networks, particularly in the context of production processes. His innovative approach has led to the development of a unique method that enhances the efficiency of production systems.
Latest Patents
Dewen Wang holds a patent for a method titled "Method for constructing and training decentralized migration diagram neural network model for production process." This patent outlines a systematic approach to constructing and training a Decentralized Migration Diagram Neural Network (DMDNN) model. The method includes determining a production task input management node, distributed management nodes, and the granularity of each distributed management node. It also involves constructing a production system network, network calculation nodes on each distributed management node, and applying the trained DMDNN model in the management and control of the production process. He has 1 patent to his name.
Career Highlights
Dewen Wang is affiliated with the Guangdong University of Technology, where he contributes to research and development in advanced neural network applications. His work is instrumental in bridging the gap between theoretical research and practical applications in production management.
Collaborations
Dewen has collaborated with notable colleagues, including Guolei Ruan and Zisheng Lin. Their combined expertise enhances the research output and innovation potential within their projects.
Conclusion
Dewen Wang's contributions to the field of decentralized neural networks exemplify the intersection of technology and production efficiency. His innovative methods are paving the way for advancements in production processes, showcasing the importance of research and collaboration in driving technological progress.