This inventor holds 1 USPTO granted patent and 1 published patent application. Top assignees: Gwangju Institute of Science and Technology, Hyundai Motor Company, Kia Corporation. Active years: 2026.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile
Company Filing History:

Years Active: 2026
Title: Innovations of Donghwi Yoo
Introduction
Donghwi Yoo is a notable inventor based in Gwangju, South Korea. He has made significant contributions to the field of noise source degradation prediction through his innovative patent. His work focuses on utilizing deep learning techniques to enhance the understanding and prediction of degradation levels in noise sources.
Latest Patents
Donghwi Yoo holds a patent for a "Degradation level prediction method and degradation level prediction system." This invention predicts the degradation level of a noise source by performing deep learning using frequency data that indicates the vibration of the noise source over a specific time period. The system also incorporates degradation data that reflects the noise source's condition during a predetermined period. By measuring first vibration signals at the noise point during a monitoring period and converting these signals into the frequency domain, the state estimation model can estimate time series degradation data. This model allows for the prediction of the remaining useful life of the noise source.
Career Highlights
Throughout his career, Donghwi Yoo has worked with prominent companies such as Hyundai Motor Company and Kia Corporation. His experience in these organizations has contributed to his expertise in the field of noise and vibration analysis.
Collaborations
Donghwi Yoo has collaborated with his coworker, Yong Hyun Ryu, to further enhance his research and development efforts in the area of degradation prediction.
Conclusion
Donghwi Yoo's innovative approach to predicting noise source degradation through deep learning exemplifies the potential of technology in improving industrial applications. His contributions are significant in advancing the understanding of noise management and degradation prediction systems.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile