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Yankai Cao
Researcher at University of British Columbia
Publications - 57
Citations - 501
Yankai Cao is an academic researcher from University of British Columbia. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 9, co-authored 26 publications receiving 213 citations. Previous affiliations of Yankai Cao include University of Wisconsin-Madison & Purdue University.
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Journal ArticleDOI
Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation
Jiangong Zhu,Yixiu Wang,Yuan Huang,R. Bhushan Gopaluni,Yankai Cao,Michael Heere,Martin J. Mühlbauer,Liuda Mereacre,Hai Feng Dai,Xinhua Liu,Anatoliy Senyshyn,Xuezhe Wei,Michael Knapp,Helmut Ehrenberg +13 more
TL;DR: In this article , a transfer learning model was developed by adding a featured linear transformation to the base model, which achieved a root-mean-square error of less than 1.7% on the datasets used for the model validation, indicating the successful applicability of the capacity estimation approach utilizing cell voltage relaxation.
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An interior-point method for efficient solution of block-structured NLP problems using an implicit Schur-complement decomposition
TL;DR: This paper shows that this bottleneck can be overcome by solving the Schur-complement equations implicitly, using a quasi-Newton preconditioned conjugate gradient method and dramatically reduces the computational cost for problems with many coupling variables.
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Machine Learning Algorithms for Liquid Crystal-Based Sensors.
TL;DR: It is shown that ML techniques can uncover valuable feature information from surface-driven LC orientational transitions triggered by the presence of different gas-phase analytes and can exploit such feature information to train accurate and automatic classifiers and feature extraction methods.
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Graph-based modeling and simulation of complex systems
TL;DR: In this paper, an algebraic graph abstraction is proposed to capture physical connectivity in complex optimization models and a computing graph abstraction to capture communication connectivity in computing architectures, which are used as the backbone of a Julia-based software package that is called Plasmo. jl.
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Clustering-based preconditioning for stochastic programs
TL;DR: A clustering-based preconditioning strategy for KKT systems arising in stochastic programming within an interior-point framework that can avoid scalability issues of Schur decomposition in problems with large first-stage dimensionality.