GB
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- Associate Professor
- Supervisor of Doctorate Candidates
- Supervisor of Master's Candidates
- Name :Ge Bin
- Name (Pinyin):GB
- Date of Birth:1979-10-25
- School/Department:数学科学学院
- Degree:Doctoral Degree in Science
- Professional Title:Associate Professor
- Status:Employed
- Teacher College:College of Mathematical Sciences
- Discipline:Mathematics
Contact Information
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- Paper Publications
- Ge Bin, Zhou Qing-Mei, Xue Xiao-Ping. Infinitely many solutions for a differential inclusion problem in R^N involving p(x)-Laplacian and oscillatory terms,Zeitschrift für angewandte Mathematik und Physik,2012,63(4):691-711
- Ge Bin, Zhou Qing-Mei, Xue Xiao-Ping. Multiplicity of solutions for differential inclusion problems in R^N involving the p(x)-Laplacian,Monatshefte fur Mathematik,2012,168(3-4):363-380
- Ge Bin, Zhou Qingmei. Existence and multiplicity of solutions for p(x)-Laplacian equations in R^N,Electronic Journal of Differential Equations,2014,2014()
- Hou Gang-Ling, Ge Bin, Lu Jian-Fang. Infinitely many solutions for sublinear fractional Schrodinger-type equations with general potentials,Electron. J. Differential Equations,2018,2018()
- Jia Li-Jiang, Ge Bin, Cui Ying-Xin, Sun Liang-Liang. Multiplicity solutions of a class fractional Schrödinger equations,Open Mathematics,2017,15(1):1010-1023
- Gui Xue-Lin，Ge Bin. Existence and multiplicity of solutions for generalized quasilinear Schrödinger equations,Complex Variables and Elliptic Equations,2022,67(10):2360-2381
- Zhong Fengyuan, Xu Zicheng, Ge Bin. Hopf Bifurcation Analysis of a Class of Abstract Delay Differential Equation,Journal of Nonlinear Modeling and Analysis,2022,4(2):277-290
- Wu Yuhu, Ge Bin, Olímpio H. Miyagaki. Existence results for the Klein-Gordon-Maxwell system in rotationally symmetric bounded domains,Zeitschrift für Analysis und ihre Anwendungen,2019,38(2):209-229
- Han Nin，Ge Bin，Li Lin. Flocking Effects of the Stochastic Cucker-Smale System with Noise,Journal of Systems Science and Complexity,2024,37(5):1978-1994
- Shi Jibo, Ge Bin，Jiang Hang e.t.. Feature-Imitation Federated Learning: An Efficient Approach for Specific Emitter Identification in Low-Resource Environments,IEEE Transactions on Cognitive Communications and Networking,2024,10(6):1-1