Diju Gao

Title: Innovations by Diju Gao in Hybrid Electric Ship Demand Forecasting

Introduction

Diju Gao is an innovative inventor based in Shanghai, China. He has made significant contributions to the field of hybrid electric ship technology. His work focuses on improving the efficiency and reliability of demand load forecasting during navigation.

Latest Patents

Diju Gao holds a patent titled "Method for forecasting demand load of hybrid electric ship by means of working condition." This patent describes a method for forecasting demand load based on the classification of working conditions during a ship's navigation. The method utilizes historical data values and employs a least squares support vector machine to create a classification plane. It distinguishes between fast-changing and stable working conditions, allowing for real-time determination of the ship's working condition type. For stable conditions, a Markov chain is used for forecasting, while a radial basis function neural network optimized by a genetic algorithm is employed for fast-changing conditions. This innovative approach achieves desirable forecasting effects by selecting suitable models for each working condition.

Career Highlights

Diju Gao's career is marked by his dedication to advancing maritime technology. His innovative methods have the potential to enhance the operational efficiency of hybrid electric ships. With a focus on real-time data analysis, his work contributes to the sustainability of maritime operations.

Collaborations

Diju Gao has collaborated with notable colleagues, including Wanneng Yu and Yao Jiang. Their combined expertise has furthered the development of advanced forecasting methods in the maritime industry.

Conclusion

Diju Gao's contributions to the field of hybrid electric ship technology exemplify the importance of innovation in maritime operations. His patented methods for demand load forecasting represent a significant advancement in ensuring efficient navigation.