Xinyu Gu

Innovative Artificial Intelligence Engineer possessing strong mathematical skills and detailed knowledge of machine learning evaluation metrics and best practices. Offering five years of experience in machine learning research, specializing in time series forecasting and computer vision, along with the creation of programs and algorithms that enable machines to take actions without being directed. Expertise in predictive analysis, data mining and computational statistics. Logical and detailed professional with exceptional Python coding committed to advancing the field through innovative solutions and meticulous research.

1 X Gu, KW See, P Li, K Shan, Y Wang, L Zhao, KC Lim, N Zhang. A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model. Energy 262, 125501. (First Author, SCI Q1, Impact factor 9.2)

2 X Gu, KW See, Y Liu, B Arshad, L Zhao, Y Wang. A time-series Wasserstein GAN method for state-of-charge estimation of lithium-ion batteries. Journal of Power Sources 581, 233472. (First Author, SCI Q1, Impact factor 9.72)

3 X Gu, K See, X Zhou, Y Wang, C Zang. Recent Advances in Data Preprocessing and Machine Learning Approaches for Battery's State of Charge and State of Health Estimation: A Review. 2023 IEEE International Future Energy Electronics Conference (IFEEC), 421-426. (First Author, Conference Paper)

4 X Gu, KW See, Y Wang, L Zhao, W Pu. The sliding window and SHAP theory—an improved system with a long short-term memory network model for state of charge prediction in electric vehicle application. Energies 14 (12), 3692. (First Author, SCI Q2, Impact factor 3.2)

5 KW See, G Wang, Y Zhang, Y Wang, L Meng, X Gu*, N Zhang, KC Lim, L Zhao, B Xie. Critical review and functional safety of a battery management system for large-scale lithium-ion battery pack technologies. International Journal of Coal Science & Technology 9 (1), 36. (Corresponding Author, SCI Q1, Impact factor 5.79)

6 X Wu, X Gu*, KW See. ADNNet: Attention-based deep neural network for Air Quality Index prediction. Expert Systems with Applications, 125128. (Corresponding Author, SCI Q1,  Impact factor 7.82)

7 Z Xie, X Gu, Y Shen. A machine learning study of predicting mixing and segregation behaviors in a bidisperse solid–liquid fluidized bed. Industrial & Engineering Chemistry Research 61 (24), 8551-8565. (Second Author, SCI Q1, Impact factor 4.2)