Company Filing History:
2 out of 10 
1 out of 1 
1 out of 2 Years Active: 2015-2021
Title: Innovations and Contributions of Inventor Bin Wang
Introduction
Bin Wang is a notable inventor based in Ithaca, NY, who has made significant contributions to the fields of predictive analytics and electric power systems. With a total of three patents to his name, Wang's work focuses on developing advanced methodologies that enhance the efficiency and stability of complex systems.
Latest Patents
Wang's latest patents include a "System and method for hierarchically building predictive analytic models on a dataset." This innovative approach involves preprocessing input and output data, followed by a hierarchical clustering process that builds predictive models at multiple levels. The resulting models are designed to improve the accuracy of predictions based on the training dataset.
Another significant patent is the "Method and apparatus for optimal power flow with voltage stability for large-scale electric power systems." This invention addresses the optimal power flow (OPF) problem by generating secure OPF solutions that optimize the operation of electric power systems. The method includes creating a list of contingencies, computing OPF solutions, and performing voltage stability analysis to ensure the reliability of the power system.
Career Highlights
Throughout his career, Bin Wang has worked with several companies, including Bigwood Technology Incorporated and Global Optimal Technology, Inc. His experience in these organizations has allowed him to apply his innovative ideas in practical settings, contributing to advancements in technology and engineering.
Collaborations
Wang has collaborated with notable professionals in his field, including Hsiao-Dong Chiang and Hartley Chiang. These collaborations have further enriched his work and expanded the impact of his inventions.
Conclusion
Bin Wang's contributions to predictive analytics and electric power systems demonstrate his commitment to innovation and excellence. His patents reflect a deep understanding of complex systems and a drive to enhance their functionality and reliability.