王建军
姓名：王建军 籍贯：宁夏惠农 民族：汉族 职称：教授（研究员） 所在部门（教研室）：星际电子在线|中国有限公司官网 个人主页：https://wjjmath.github.io/ 办公室（电话）：星际电子在线|中国有限公司官网305 电子邮件：wjj@swu.edu.cn; wjjmath@163.com
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2003.09 至 2006.12，西安交通大学，应用数学，博士，导师：徐宗本
2000.09 至 2003.07，宁夏大学，基础数学，硕士，导师：薛银川
1996.09 至 2000.07，宁夏大学，数学与应用数学，学士
2020.09至今, 星际电子在线 星际电子在线，教授，副经理
2019.04-2020.09，星际电子在线，人工智能学院，教授，副经理
2016.03-2019.04，星际电子在线，星际电子在线，教授，副经理
2012.07-2019.05，星际电子在线，星际电子在线，教授
2006.12-2012.06，星际电子在线，星际电子在线，副教授
2012.08-2013.08，美国Texas A&M大学，访问学者，合作导师：Ronald DeVore教授（美国科学院院士）
2008.01-2010.01，西安交通大学，力学流动站，博士后，合作导师：徐宗本教授 (中科院院士)
高维数据建模、机器学习（深度学习）、张量分析、数据挖掘、压缩感知、函数逼近论等。
逼近论，高等数学，神经网络，学习理论，数值分析，支持向量机，最优化方法，模糊数学，应用统计与数据分析，数据挖掘等
1. 2018至今任重庆市工业与应用数学学会副理事长
2. 2018至今任国际期刊《Frontiers in Applied mathematics and Statistics-Mathematics of Computation and Data Science》编委
3. 2021至今任国际期刊《Frontiers in Signal Processing》编委
4. 《EURASIP Journal on Advances in Signal Processing》特刊主编（2019）
5. 2013年至今任美国数学评论评论员
6. CSIAM全国大数据与人工智能专家委员会委员
7. 重庆市数学会常务理事，重庆市药品监督管理局药品、医疗器械、化妆品专家委员会委员
1. 耦合多重先验信息的低秩张量恢复模型、理论与算法研究. 国家自然科学基金面上项目. 执行时间：2021.01-2024.12（主持）
2. 基于样本的非线性压缩感知理论及其应用. 国家自然科学基金面上项目. 执行时间：2017.01-2020.12（主持）
3. 低秩矩阵复原的Schatten-q正则化理论与算法研究. 国家自然科学基金面上项目. 执行时间：2013.01-2016.12（主持）
4. 基于L1/2正则化的压缩传感可重构性理论研究. 国家自然科学基金青年项目. 执行时间：2011.01-2013.12（主持）
5. 关于前馈神经网络结构与本质逼近阶研究. 国家自然科学基金青年项目. 执行时间：2008.01-2011.12（主持）
6. 关于神经网络拓扑选择与逼近阶研究. 教育部科学技术重点项目. 执行时间：2008.01-2010.12（主持）
7. 关于神经网络逼近能力与算法研究. 部委级科研项目面上项目. 执行时间：2008.06-2010.06（主持）
8. 关于前向神经网络逼近复杂性与算法研究. 部委级科研项目一般项目. 执行时间：2009.06-2012.06（主持）
9. 基于Lq极小化的压缩传感理论及应用研究. 中央高校基本科研业务费重点项目，执行时间：2010.10-2013.10（主持）
10. 块稀疏信号重构的非凸极小化方法及算法应用研究. 中央高校基本科研业务费重大项目，执行时间：2015.01-2017.12（主持）
11. 网络上的流行病动力系统的研究. 国家自然科学基金青年项目. 执行时间：2008.01-2010.12（主持子课题一项）
12. 直觉模糊近似空间和形式背景中知识获取研究. 国家自然科学基金青年项目(资助金额：22万元). 执行时间：2012.01-2014.12（主研）
13. 非线性算子方程的变号解及其应用. 国家自然科学基金青年项目(资助金额：3万元). 执行时间：2009.01-2009.12（参与）
2023
§ One-bit compressed sensing via total variation minimization. Zhong Y.X., Xu C.,Zhang B., Hou J.Y., Wang J.J. Signal Processing, 2023 [pdf]
2022
§ Tensor robust principal component analysis from multi-level quantized observations. Wang J.J., Hou.J., Eldar Y.C. IEEE Transactions on Information Theory,2022 [pdf]
§ Robust low rank matrix recovery fusing local-smoothness. Liu X.L., Hou J.Y., Wang J.J. IEEE Signal Processing Letters, 2022 [pdf]
§ One-bit block sparse signal recovery via nonconvex l2/lp(0<p<1)-minimization. Chen J.Q., Gao Y., Li J.X., Wang J.J. Journal of Electronic Imaging,2022. [pdf]
§ Exact decomposition of joint low rankness and local smoothness plus sparse matrices. Peng J., Wang Y., Zhang H., Wang J.J., Meng D. IEEE Transactions on Pattern Analysis and Machine Intelligence,2022 [pdf]
§ low-rank high-order tensor completion with applications in visual data. Qin W., Wang H., Zhang F., Wang J.J. , Luo X., Huang T. IEEE Transactions on Image Processing,2022 [pdf]
§ An efficient L1-Tv solver for 1-Bit compressed sensing. Hou J., Liu X., Wang J.J. SSRN Electronic Journal,2022 [pdf]
§ Robust high-order tensor recovery via nonconvex low-rank approximation. Qin W., Wang H., Ma W., Wang J.J. Proceedings of the IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP),2022 [pdf]
2021
§ Robust low-rank tensor reconstruction using high-order t-SVD. Qin W., Wang H., Zhang F., Dai M., Wang J.J. Journal of Electronic Imaging,2021 [pdf]
§ Robust Low-tubal-rank Tensor Recovery from Binary Measurements. Hou J. , Zhang F., Qiu H., Wang J.J., Wang Y., Meng D. IEEE Transactions on Pattern Analysis and Machine Intelligence,2021 [pdf]
§ A novel approach to large-scale dynamically weighted directed network representation. Luo X., Wu H., Wang Z., Wang J.J., Meng D. IEEE Transactions on Pattern Analysis and Machine Intelligence,2021 [pdf]
§ Low-tubal-rank plus Sparse Tensor Recovery with Prior Subspace Information. Zhang F., Wang J.J. , Wang W.D.,Xu C. IEEE Transactions on Pattern Analysis and Machine Intelligence,2021 [pdf]
§ Low-rank matrix recovery via regularized nuclear norm minimization. Wang W., Zhang F., Wang J.J. Applied and Computational Harmonic Analysis,2021 [pdf]
§ Large-scale affine matrix rank minimization with a novel nonconvex regularizer. Wang Z., Liu Y., Luo X., Wang J.J., Gao C., Peng D., Chen W.
IEEE Transactions on Neural Networks and Learning Systems,2021 [pdf]
§ Generalized non-convex approach for low-tubal-rank tensor recovery. Wang H., Zhang F., Wang J.J., Huang T., Huang J., Liu X. IEEE Transactions on Neural Networks and Learning Systems,2021 [pdf]
§ Group sparse recovery in impulsive noise via alternating direction method of multipliers. Wang J.J., Huang J.W., Zhang F, Wang W.D. Applied and Computational Harmonic Analysis,2021 [pdf]
§ One-bit tensor completion via transformed tensor singular value decomposition. Hou J., Zhang F., Wang J.J. Applied Mathematical Modelling,2021 [pdf]
§ Estimating structural missing values via low-tubal-rank tensor completion. Wang H., Zhang F., Wang J.J., Wang Y. Proceedings of the 45th International Conference on Acoustics,2021 [pdf]
§ Low-tubal-rank tensor recovery from one-bit measurements. Hou J., Zhang F., Wang Y., Wang J.J. Proceedings of the 45th International Conference on Acoustics,2021 [pdf]
§ Non-convex sparse deviation modeling via generative models. Yang Y., Wang H., Wang J.J. IEEE International Conference on Acoustics,2021 [pdf]
§ CMCS-net: image compressed sensing with convolutional measurement via DCNN. Xie Y., Wang H., Wang J.J. IET Image Processing,2021 [pdf]
§ A denoising convolutional neural network inspired via multi-layer convolutional sparse coding. Wen Z., Wang H., Wang J.J. Journal of Electronic Imaging,2021 [pdf]
2020
§ Uniqueness guarantee of solutions of tensor tubal-rank minimization problem. Zhang F., Hou J., Wang J.J., Wang W. IEEE Signal Processing Letters,2020 [pdf]
§ One-bit Compressed sensing via lp minimization method. Hou J.Y., Wang J.J., Zhang F., Huang J.W. Inverse Problems,2020 [pdf]
§ RIP-based performance guarantee for low-tubal-rank tensor recovery. Zhang F, Wang W.D., Huang J.W., Wang J.J.,Wang Y. Journal of Computational and Applied Mathematics,2020 [pdf]
§ Tensor restricted isometry property analysis for a large class of random measurement ensembles. Zhang F, Wang W.D.,Hou J.Y., Wang J.J., Huang J.W. Science China .Information Sciences,2021 [pdf]
2019
§ A nonconvex penalty function with integral convolution approximation for compressed sensing. Wang J.J., Zhang F., Huang J.W., Wang W.D., Yuan C. Signal Processing,2019 [pdf]
§ Block-sparse signal recovery based on truncated l1- minimisation in non-Gaussian noise. Feng Q, Wang J.J.,Zhang F. IET Communications, 2019 [pdf]
§ Image denoising in impulsive noise via weighted Schatten p-norm regularization. Chen G., Wang J.J., Zhang F. Journal of Electronic Imaging,2019 [pdf]
§ Sharp sufficient condition of block signal recovery via l2/l1-minimization. Huang J.W., Wang J.J., Wang W.D. IET Signal Processing,2019 [pdf]
§ Enhanced Block-Sparse Signal Recovery Performance via Truncated ℓ2/ℓ1−2 Minimization. Kong W., Wang J.J., Wang W.D., Zhang F. Journal of Computational Mathematics,2020 [pdf]
§ Fast and efficient algorithm for matrix completion via closed-form 2/3-thresholding operator. Wang Z., Wang W., Wang J.J. Neurocomputing, 2019 [pdf]
2018
§ On asymptotic of extremes from generalized Maxwell distribution. Huang J.W., Wang J.J. Bull. Korean Math. Soc,2018 [pdf]
§ Block-sparse signal recovery via l2/l1-2minimisation method. Wang, W,D., Wang J.J., Zhang, Z.L. IET Signal Processing,2018 [pdf]
§ Reconstruction Analysis of Block Sparse Signal via Truncated ℓ2/ℓ1-minimization with Redundant Dictionaries.Jia y.L., Wang J.J.,Feng Z.
IET Signal Processing,2018 [pdf]
§ New Sufficient Conditions of Signal Recovery with Tight Frames via l1-Analysis Approach. Huang J.W., Wang J.J., Zhang F., Wang, W.D.
IEEE Access, 2018 [pdf]
§ Higher order expansion for moments of extreme for generalized Maxwell distribution. Huang J.W., Wang J.J.,Luo G.W.,Pu H. Communications in Statistics - Theory and Methods,2018 [pdf]
§ Higher order asymptotic behaviour of partial maxima of random sample from generalized Maxwell distribution under power normalization. Huang J.W., Wang J.J. Applied Mathematics-A Journal of Chinese Universities,2018 [pdf]
§ Sparse signal recovery with prior information by iterative reweighted least squares algorithm. Feng N.C., Wang J.J.,Wang W.D. Journal of Inverse and Ill-posed Problems, 2018 [pdf]
§ Perturbations of Compressed Data Separation With Redundant Tight Frames. Zhang F., Wang J.J, Wang,Y., Huang, J., &Wang W. IEEE Access,2018 [pdf]
§ An inertial projection neural network for sparse signal reconstruction via l1− 2 minimization. Zhu L., Wang J.J, He, X., & Zhao Y. Neurocomputing, 2018 [pdf]
§ Enhancing Matrix Completion Using a Modified Second-Order Total Variation. Wang W.D., Wang J.J. Discrete Dynamics in Nature and Society,2018 [pdf]
§ A Novel Thresholding Algorithm for Image Deblurring Beyond Nesterov’s Rule. Wang Z., Wang J.J., Wang W.D. IEEE Access,2018 [pdf]
2017
§ Robust Signal Recovery With Highly Coherent Measurement Matrices. Wang W.D., Wang J.J.,Zhang Z.L. IEEE Signal Processing Letters,2017 [pdf]
§ Tail properties and approximate distribution and expansion for extreme of lgmd. Huang J.W., Wang J.J, Luo G.W., He J. Journal of Inequalities & Applications,2017 [pdf]
§ On the rate of convergence of maxima for the generalized Maxwell distribution. Huang J.W., Wang J.J.,Luo G.W. Statistics: A Journal of Theoretical and Applied Statistics,2017 [pdf]
§ Non-convex block-sparse compressed sensing with redundant dictionaries. Liu C.Y., Wang J.J., Wang W.D., Wang, Z. Iet Signal Processing,2017 [pdf]
§ Improved RIP Conditions for Compressed Sensing with Coherent Tight Frames. Wang Y., Wang J.J. Discrete Dynamics in Nature and Society,2017 [pdf]
§ 基于非凸极小化的扰动压缩数据分离[J]. 刘春燕,王文东,王建军.电子学报, 2017 [pdf]
§ 基于混合l2/l1范数极小化方法的块稀疏信号重构条件[J]. 王建军,袁建军,王尧.数学学报,2017 [pdf]
§ Nonlinear Compressed Sensing Based on Kernel Sparse Representation. Nie F., Wang J.J., Wang Y., & Jing J. In 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER),2017 [pdf]
2016
§ Block-sparse compressed sensing with partially known signal support via non-convex minimization. He S.Y., Wang Y,Wang J.J Xu Z.B, Iet Signal Processing,2016 [pdf]
§ 基于相干性理论的非凸块稀疏压缩感知. 王文东, 王建军, 王尧, 张自力. 中国科学 信息科学, 2016 [pdf]
§ Kernel canonical correlation analysis via gradient descent. Cai J, Tang Y,Wang J.J. Neurocomputing,2016 [pdf]
§ A perturbation analysis of block-sparse compressed sensing via mixed l2/l1 minimization. Zhang J.,Wang J.J., Wang W.D. Neurocomputing, 2016 [pdf]
§ Perona–Malik Model with a New Diffusion Coefficient for Image Denoising[J]. Yuan J., Wang J.J. International Journal of Image & Graphics,2016 [pdf]
§ Low rank tensor completion via partial sum minimization of singular values. Zhang F,Wang J.J., Jing J. International Conference on Automatic Control and Information Engineering,2016 [pdf]
2015
§ Confirming robustness of fuzzy support vector machine via ξ–α bound. Yang C Y., Wang J.J., Chou J J., et al. Neurocomputing,2015 [pdf]
§ A perturbation analysis of nonconvex block-sparse compressed sensing. Wang J.J., Zhang J., Wang W.D., et al. Communications in Nonlinear Science & Numerical Simulation,2015 [pdf]
§ 基于迭代重赋权最小二乘算法的块稀疏压缩感知. 王文东, 王尧, 王建军. 电子学报, 2015 [pdf]
2014
§ Restricted p-isometry properties of nonconvex block-sparse compressed sensin. Wang Y., Wang J.J., Xu Z.B. Signal Processing,2014 [pdf]
§ Active contours driven by local intensity and local gradient fitting energies. Yuan J.J., Wang J.J. International Journal of Pattern Recognition and Artificial Intelligence,2014 [pdf]
§ Recovery of Sparse Signal and Nonconvex Minimization. Jing J., Wang J.J.
Applied Mechanics & Materials, 2014 [pdf]
2013
§ On recovery of block-sparse signals via mixed l2/lq(0 <q<=1) minimization. Wang Y.,Wang J.J.,Xu Z.B. EURASIP Journal on Advances in Signal Processing,2013 [pdf]
§ A note on block-sparse signal recovery with coherent tight frames. Wang Y.,Wang J.J.,Xu Z.B. Discrete Dynamics in Nature and Society,2013 [pdf]
§ Lp Error estimate for minimal norm SBF interpolation. Wang J.J., Yang C. Y., Gu Z.G. Journal of Inequalities and Applications 2013 [pdf]
§ Estimation of Approximation with Jacobi Weights by Multivariate Baskakov Operator. Wang J.J., Guo H.F., Jing J. Journal of Function Spaces, 2013 [pdf]
2012
§ Derivatives of multivariate Bernstein operators and smoothness with Jacobi weights. Wang J.J., Peng Z.X.,Duan S.K., Jing J. Journal of Applied Mathematics,2012 [pdf]
§ Estimation of approximating rate for neural networks in L(w,p). Wang J.J.,Yang C.Y., Jing J. Journal of Applied Mathematics,2012 [pdf]
§ Constructive estimation of approximation for trigonometric neural networks. Wang J.J.,Xu W.H., Zou B. International Journal of Wavelets, Multiresolution and Information Processing,2012 [pdf]
§ Approximation of algebraic and trigonometric polynomials by feedforward neural networks. Wang J.J.,Chen B.L. , Yang C.Y. Neural Computing & Applications,2012 [pdf]
§ L2-Loss Twin Support Vector Machine for Classification. Gao B.B.,Wang J.J., Huang H. 5th International Conference on BioMedical Engineering and Informatics (BMEI),2012 [pdf]
§ Estimator for Fuzzy Support Vector Machine. Yang C.Y.,Wang J.J. Advanced Science Letters,2012 [pdf]
2011
§ Neural networks and the best Trigonometric approximation. Wang J.J., Xu Z.B. Journal of Systems Science and Complexity,2011 [pdf]
§ Sparse signal recovery based on lq(0 <q<=1)minimization. Wang J.J., Chen B.L., Yang C.Y. 2011 International Conference on Multimedia and Signal Processing,IEEE Computer Society,2011 [pdf]
§ Aproximation order for multivariate Durrmeyer operators with Jacobi weights. Wang J.J.,Yang C.Y., Duan S.K. Abstract & Applied Analysis,2011 [pdf]
§ Bernstein 型算子线性组合加Jacobi权逼近及高阶导数的等价定理. 彭联勇，王建军. 应用数学,2011 [pdf]
2010
§ New study of neural networks: the essential order of approximation. Wang J.J., Xu Z.B. Neural Networks,2011 [pdf]
§ Derivatives of Bernstein operators and smoothness with Jacobi weights. Wang J.J., Han G.D., et al. Taiwanese Journal of Mathematics,2011 [pdf]
§ 稳健Lq(0 <q<1)正则化理论：解的渐近分布与变量选择一致性. 常象宇,徐宗本, 张海, 王建军, 梁勇. 中国科学,2010 [pdf]
2009
§ Approximation with Jacobi weights by Baskakov operators. Taiwanese Journal of Mathematics. Wang J.J., Xu Z.B. Taiwanese Journal of Mathematics,2009. [pdf]
§ Margin calibration in SVM class-imbalanced learning. Yang C.Y., Yang J.S.,Wang J.J. Neurocomputing,2009 [pdf]
§ How to measure the essential approximation capability of a FNN. Wang J.J., Zou B., Chen B.L. 2009 Fifth International Conference on Natural Computation, IEEE Computer Society,2009 [pdf]
§ Estimation of covering number in learning theory. Wang J.J., Huang H. Luo Z.T.,Bai l.C. Fifth International Conference on Semantics, Knowledge and Grid, IEEE Computer Society,2009 [pdf]
§ Generalization performance of ERM algorithm with geometrically ergodic markov chain samples. Xu J., Zou B.,Wang J.J. Fifth International Conference on Natural Computation; IEEE Computer Society,2009 [pdf]
§ 神经网络的加权本质逼近阶. 王建军, 徐宗本. 数学年刊:中文版,2009 [pdf]
§ 多元多项式函数的三层前向神经网络逼近方法. 王建军, 徐宗本. 计算机学报:中文版,2009 [pdf]
2008
§ Baskakov算子线性组合加Jacobi权逼近及高阶导数的正逆定理. 王建军, 徐宗本. 系统科学与数学,2008 [pdf]
§ Constructive approximation method of polynomial by neural networks. Wang J.J., Xu Z.B.,Jing J. International conference on congnitive neurodynamics(2007), Springer Science Business Media B.V,2008
[pdf]
§ Imbalanced SVM learning with margin compensation. Lecture Notes in Computer Science, Germany. Yang C.Y.,Wang J.J., Yang J.S.,Yu G.D. Springer-Verlag,2008 [pdf]
2007
§ Stechkin-marchaud type inequalities with Jacobi weights for Bernstein operators. Wang J.J., Xue Y.C., Li F.J. Journal of Applied mathematics and computing,2007 [pdf]
2006
§ 近似指数型神经网络的本质逼近阶. 王建军,徐宗本. 中国科学,2006 [pdf]
§ Multiple positive radial solutions of elliptic equations in an exterior domain. Monatshefte fur mathematik. Han G.D.,Wang J.J. Monatshefte fur mathematik,2006 [pdf]
§ and Meng D.Y., Approximation bound of mixture networks in L(w,p) spaces. Lecture Notes in Computer Science, Germany. Xu Z.B., Wang J.J., and Meng D.Y. Springer-Verlag 2006 [pdf]
§ Baskakov算子加Jacobi权逼近及导数的正逆定理. 王建军,薛银川. 数学年刊,2006 [pdf]
2004
§ Baskakov型算子加权逼近下的Stechkin-Marchand不等式. 王建军,薛银川. 数学研究与评论,2004 [pdf]
§ Approximation bounds by neural networks in L(w, p). Lecture Notes in Computer Science, Germany. Wang J.J., Xu Z.B.,and Xu W.J. Springer-Verlag,2004 [pdf]
2022.09.15：基于深度卷积神经网络与压缩感知的图像恢复方法
个人获奖
1. 重庆市首批英才计划·创新创业领军人才，2019年11月
2. 第三批重庆市学术技术带头人，2019年3月
3. 《复杂结构性高维数据稀疏建模的方法与算法应用》获重庆市自然科学三等奖，2018年度
4. 全国老员工数学建模竞赛中荣获重庆赛区优秀指导教师奖，2016年
5. 全国老员工数学建模竞赛中荣获重庆赛区优秀指导教师奖，2012年
6. 星际电子在线2010-2012年学年度优秀教师
员工指导获奖
1. 2022年，第一届全国高校计算机技能竞赛（初赛），一等奖
2. 2022年，第三届全国老员工算法设计与编程挑战赛（冬季赛），金奖
3. 2021年，第十八届“华为杯”中国研究生数学建模竞赛，二等奖
4. 2021年，全国高等院校英语能力大赛，重庆市三等奖
5. 2018年，美国数学建模大赛，一等奖2项
6. 2018年，高教社杯全国老员工数学建模竞赛，重庆市一等奖，二等奖各1项
7. 2017年，指导星际电子在线员工科技创新团队入选2017年度全国老员工“小平科技创新团队
8. 2017年，美国数学建模大赛，二等奖2项
9. 2017年，全国老员工统计建模大赛，一等奖1项，二等奖2项
10. 2017年，“国家级老员工创新创业训练计划”项目
11. 2016年，美国数学建模大赛，一等奖、二等奖各一项
12. 2016年，高教社杯全国老员工数学建模竞赛，二等奖
13. 2016年，高教社杯全国老员工数学建模竞赛，重庆市二等奖3项
14. 2015年，“国家级老员工创新创业训练计划”项目
15. 2012年，高教社杯全国老员工数学建模竞赛，一等奖
16. 2012年，高教社杯全国老员工数学建模竞赛，一等奖
17. 2010年，高教社杯全国老员工数学建模竞赛，二等奖
18. 2012年，高教社杯全国老员工数学建模竞赛，重庆市一等奖
19. 2011年，高教社杯全国老员工数学建模竞赛，重庆市一等奖
