Hao WANG
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Published papers

  • Linfang Yu, Chenyu Guan, Hao Wang, Yuxin He, Wenming Cao, and Chi-Sing Leung. “ Robust Tensor Ring Decomposition for Urban Traffic Data Imputation.”
    IEEE Transactions on Intelligent Transportation Systems. Vol. 26, no. 6, pp. 8707-8719, June 2025, Indexed by SCI, JCR Ranking: Q1, IF: 7.9;

  • Enhao Chen, Hao Wang, Zhanglei Shi, Wei Zhang. “ Channel pruning for convolutional neural networks using l0-norm constraints.”
    Neurocomputing, 2025: 129925. Indexed by SCI, JCR Ranking: Q1, IF: 5.5;

  • Xiaobao Song, Liwei Deng, Hao Wang, Yaoan Zhang, Yuxin He, Wenming Cao. “ Deep learning-based time series forecasting.”
    Artificial Intelligence Review, 2024, 58 (1): 23. Indexed by SCI, JCR Ranking: Q1, IF: 11.7;

  • H. Wang, et al. “ Image Classification on Hypersphere Loss.”
    IEEE Transactions on Industrial Informatics (2024). Vol. 20, no. 4, pp. 6531-6541, Indexed by SCI, JCR Ranking: Q1, IF:12.3;

  • Jianqi Zhong, Conghui Ye, Wenming Cao, Hao Wang, “Parallel multi-stage rectification networks for 3D skeleton-based motion prediction.”
    Scientific Reports, 2024, 14(1): 26058. Indexed by SCI, JCR Ranking: Q1, IF:4.3;

  • Z.H. He, H. Wang, et al. “ Face Recognition Based on Center Bias Estimation and Adaptive Margin.”
    ACTA ELECTRONICA SINICA (2024), accepted.

  • H. Wang, et al. “ Two Analog Neural Models with the Controllability on Number of Assets for Sparse Portfolio Design.”
    Neurocomputing (2023), accepted.

  • H. Wang, et al. “l0-norm based Short-term Sparse Portfolio Optimization Algorithm Based on Alternating Direction Method of Multipliers,”
    Signal Processing (2023): 108957. accepted.

  • H. Wang, et al. “A Globally Stable LPNN Model for Sparse Approximation,”
    IEEE Transactions on Neural Networks and Learning Systems, (2023): vol. 34, no. 8, pp. 5218-5226, Indexed by SCI, JCR Ranking: Q1, IF: 10.451, DOI:10.1109/TNNLS.2021.3126730.

  • H. Wang, et al. “ A Lagrange Programming Neural Network Approach with an l0-Norm Sparsity Measurement for Sparse Recovery and Its Circuit Realization.”
    Mathematics (2022), 10(24): 4801. Indexed by SCI, JCR Ranking: Q1, IF: 2.4, DOI:https:doi.org10.3390math10244801.

  • Z.L. Shi, H. Wang, et al. “Constrained Center Loss for Convolutional Neural Networks,”
    IEEE Transactions on Neural Networks and Learning Systems (2021), accepted. Indexed by SCI, JCR Ranking: Q1, IF: 11.683, DOI: 10.1109/TNNLS.2021.3104392.

  • Z.L. Shi, H. Wang, et al. “ Robust Ellipse Fitting based on Lagrange Programming Neural Network and Locally Competitive Algorithm,”
    Neurocomputing (2020): Volume 399, pp. 399-413. Indexed by SCI, JCR Ranking: Q1, IF: 4.072, DOI: 10.1016j.neucom.2020.02.100. arXiv: https:arxiv.orgabs/1806.00004.

  • Z.L. Shi, H. Wang, et al. “ Robust MIMO Radar Target Localization based on Lagrange Programming Neural Network,”
    Signal Processing (2020):107574. Indexed by SCI, JCR Ranking: Q1, IF: 4.086, DOI: 10.1016j.sigpro.2020.107574.arXiv: https:arxiv.orgabs/1805.12300.

  • H. Wang, et al. “An L0-Norm-Based Centers Selection for Failure Tolerant RBF Networks,”
    IEEE Access (2019): Volume 7, pp. 151902-151914. Indexed by SCI, JCR Ranking: Q1, IF: 4.098, DOI: 10.1109/ACCESS.2019.2945807.

  • H. Wang, et al. “ ADMM-Based Algorithm for Training Fault Tolerant RBF Networks and Selecting Centers,”
    IEEE Transactions on Neural Networks and Learning Systems (2018): Volume 29, Issue 8, pp. 3870-3878. Indexed by SCI, JCR Ranking: Q1, IF: 11.683, DOI: 10.1109/TNNLS.2017.2731319.

  • H. Wang, et al. “Lagrange Programming Neural Network Approaches for Robust Time-of-Arrival Localization,”
    Cognitive Computation (2018): Volume 10, Issue 1, pp. 23-34. Indexed by SCI, JCR Ranking: Q1, IF: 3.479, DOI: 10.1007/s12559-017-9495-z.

  • H. Wang, et al. “ An Analog Neural Network Approach for the Least Absolute Shrinkage and Selection Operator Problem,”
    Neural Computing and Applications (2018): Volume 29, Issue 9, pp. 389-400. Indexed by SCI, JCR Ranking: Q1, IF: 4.213, DOI: 10.1007/s12559-017-9495-z.

  • H. Wang, et al. “32 Channels High-speed Data Acquisition System Based on Ethernet Transmission,”
    Nuclear Science and Engineering (2015): pp. 780-784.

Published Conference

  • L.F. Yu, H. Wang, et al. “ A Robust Tensor Decomposition Model for Traffic Data Imputation with Capped Frobenius Norm in Smart City,”
    International Conference on Neural Information Processing (ICONIP), 2024, accepted.

  • E.H. Chen, H. Wang, et al. “Explore Channel Pruning Based on l0-norm Sparse Optimization,”
    International Conference on Neural Information Processing (ICONIP), 2024, accepted.

  • L.F. Yu, H. Wang, et al. “ Traffic Data Recovery and Outlier Detection based on Non-Negative Matrix Factorization and Truncated-Quadratic Loss Function,”
    International Conference on Neural Information Processing (ICONIP), 2023, accepted.

  • H. Wang, et al. “Lagrange Programming Neural Networks for Sparse Portfolio Design,”
    International Conference on Neural Information Processing (ICONIP), 2022, accepted.

  • Z.L. Shi, H. Wang, et al. “Constrained Center Loss for Image Classification,”
    International Conference on Neural Information Processing (ICONIP), 2020, pp. 70-78. Indexed by EI, DOI: 10.1007/978-3-030-63823-8_9.

  • H. Wang, et al. “ MCP Based Noise Resistant Algorithm for Training RBF Networks and Selecting Centers,”
    International Conference on Neural Information Processing (ICONIP), 2018, pp. 668-679. Indexed by EI, DOI: 10.1007/978-3-030-04179-3_59.

  • H. Wang, et al. “A Lagrange Programming Neural Network Approach for Robust Ellipse Fitting,”
    International Conference on Neural Information Processing (ICONIP), 2017, pp. 686-696. Indexed by EI, DOI: 10.1007/978-3-319-70090-8_69.

  • H. Wang, et al. “A Robust TOA Source Localization Algorithm Based on LPNN,”
    International Conference on Neural Information Processing (ICONIP), 2016, pp. 367-375. Indexed by EI, DOI: 10.1007/978-3-319-46687-3_41.