Location History:
- Lugong, TW (2023)
- Changhua County, TW (2020 - 2024)
Company Filing History:
Years Active: 2020-2024
Title: Innovations of Yu-Fang Wang
Introduction
Yu-Fang Wang is a notable inventor based in Changhua County, Taiwan. He has made significant contributions to the field of positioning systems and methods, holding a total of 4 patents. His work focuses on enhancing the accuracy and efficiency of localization technologies.
Latest Patents
Yu-Fang Wang's latest patents include a feature point integration positioning system and method, as well as a non-transitory computer-readable memory. The feature point integration positioning system comprises a moving object equipped with an image input source that captures environmental data to create a sequential image dataset. The analyzing module within the system utilizes a machine vision detecting unit to generate first feature points and a deep learning detecting unit to produce second feature points. These points are then integrated to form a comprehensive set of integrated feature points, allowing the positioning module to determine the moving object's location relative to its environment at various time points. Additionally, his method of simultaneous localization and mapping (SLAM) classifies detected objects in the environment as either moving or static, enabling the accurate positioning of a target object without interference from moving objects.
Career Highlights
Yu-Fang Wang is currently employed at the Automotive Research & Testing Center, where he applies his expertise in developing advanced positioning technologies. His innovative approaches have contributed to the advancement of automotive research and testing methodologies.
Collaborations
He collaborates with talented coworkers, including Yi-Shueh Tsai and Yi-Jie Lin, who share his commitment to innovation in the field.
Conclusion
Yu-Fang Wang's contributions to positioning systems and methods demonstrate his dedication to advancing technology in the automotive sector. His innovative patents reflect a deep understanding of machine vision and deep learning, positioning him as a key figure in his field.