K
Kai Zhang
Researcher at China University of Petroleum
Publications - 414
Citations - 6148
Kai Zhang is an academic researcher from China University of Petroleum. The author has contributed to research in topics: Computer science & Geology. The author has an hindex of 31, co-authored 303 publications receiving 3787 citations. Previous affiliations of Kai Zhang include Wuhan University of Science and Technology & Shandong University.
Papers
More filters
Journal ArticleDOI
Regulatory T Cells and Diabetes Mellitus.
Weidong Qin,Weidong Qin,Lei Sun,Mei Dong,Mei Dong,Guipeng An,Guipeng An,Kai Zhang,Kai Zhang,Cheng Zhang,Cheng Zhang,Xiao Meng,Xiao Meng +12 more
TL;DR: The regulatory T cells (Tregs) are a specialized T cell subpopulation that maintain per... as discussed by the authors, which causes dysregulation of immune homeostasis, which in turn leads to autoimmune diseases.
Journal ArticleDOI
Mechanical Properties and Damage in Lignite under Combined Cyclic Compression and Shear Loading
TL;DR: In this paper, the effects of inclination angle (θ) and upper limit of cyclic stress (σmax) on mechanical properties of coal samples were analyzed, and the damage variables of coal sample were studied based on energy dissipation theory.
Journal ArticleDOI
CFD simulation of jet behaviors in a binary gas-solid fluidized bed: comparisons with experiments
TL;DR: In this paper, a simple mathematical model, by introducing two additional force terms in both gas and particle phase momentum equations of Gidaspow's inviscid two-fluid model, is used to explore the effects of jet gas velocity and mixture combination on the jet penetration depth in the fluidized bed with a binary system.
Journal ArticleDOI
A distributed surrogate system assisted differential evolutionary algorithm for computationally expensive history matching problems
TL;DR: In this article , a distributed surrogate system assisted differential evolution algorithm, termed DSS-DE, is proposed for history matching problems, which builds a large number of basic learners before optimization, to effectively approximate different regions in the search space.
Proceedings ArticleDOI
A new feature selection method for the detection of architectural distortion in mammographic images
TL;DR: A novel feature selection which is based on Multiple Twin Bound Support Vector Machines Recursive Feature Elimination (MTWSVM-RFE) is proposed and results showed that the proposed method detect the region of architecture distortion with high accuracy.