Ting Huang

Title: Innovations of Ting Huang in Deep Learning Fault Diagnosis

Introduction

Ting Huang is an accomplished inventor based in Anhui, China. She has made significant contributions to the field of deep learning, particularly in fault diagnosis methods. Her innovative approach integrates prior knowledge to enhance the accuracy and efficiency of diagnosing faults in various systems.

Latest Patents

Ting Huang holds a patent for a deep learning method that integrates prior knowledge for fault diagnosis. This method involves processing a fault diagnosis data set using sliding window techniques to create a picture-like sample data set. The process includes obtaining an attention matrix and constructing a 2D-CNN model to generate a feature map. The model utilizes channel-oriented average and maximum pooling to produce outputs based on an attention mechanism, resulting in a refined feature map.

Career Highlights

Ting Huang is affiliated with Hefei University of Technology, where she continues to advance her research in deep learning and fault diagnosis. Her work has garnered attention for its innovative approach and practical applications in various industries.

Collaborations

Ting Huang collaborates with notable colleagues, including Qiang Zhang and Shanlin Yang, who contribute to her research endeavors. Their combined expertise enhances the development of advanced methodologies in their field.

Conclusion

Ting Huang's contributions to deep learning and fault diagnosis exemplify the impact of innovative thinking in technology. Her patent and ongoing research continue to pave the way for advancements in this critical area.