Publications
1. D. Tan, Y. Su, X. Peng, X. Zhang, C-H. Zheng, and W. Zhong*. Large-Scale Data-Driven Optimization in Deep Modeling with An Intelligent Decision-Making Mechanism,
IEEE Transactions on Cybernetics, Accepted, 2023.
2. D. Tan, Z. Huang, X. Peng, W. Zhong*, and V. Mahalec*. Deep adaptive fuzzy clustering for evolutionary
unsupervised representation learning, IEEE Transactions on Neural Networks and Learning Systems, 2023, DOI: 10.1109/TNNLS.2023.3243666.
3. D. Tan, L. Chen, C. Jiang, W. Zhong*, W. Du, F. Qian, and V. Mahalec. A circular targets feature detection framework based on DCNN for industrial applications,
IEEE Transactions on Industrial Informatics, 17(5): 3303-3313, 2021.
4. D. Tan, W. Zhong*, X. Peng, Q. Wang, and V. Mahalec. Accurate and fast deep evolutionary networks structured representation through activating and freezing dense networks,
IEEE Transactions on Cognitive and Developmental Systems, 14(1): 102-115, 2022.
5. Y. Su, R. Lin, J. Wang, D. Tan*, and C-H. Zheng. Denoising adaptive deep clustering with self-attention mechanism on single-cell sequencing data,
Briefings in Bioinformatics, 2023, DOI: 10.1093/bib/bbad021.
6. D. Tan, W. Zhong*, X. Peng, Q. Wang, and V. Mahalec. Automatic determining optimal parameters in multi-kernel collaborative fuzzy clustering based on dimension constraint,
Neurocomputing, 443: 58-74, 2021.
7. D. Tan, W. Zhong*, C. Jiang, X. Peng, and W. He. High-order fuzzy clustering algorithms based on multikernel mean shift, Neurocomputing, vol. 385, pp. 63-79, 2020.
8. W. Zhong*, D. Tan, X. Peng, Y. Tang, and W. He. Fuzzy high-order hybrid clustering algorithm based on swarm intelligence sets, Neurocomputing, 314, 347-359, 2018.
9. J. Wang, J. Xia, D. Tan, R. Lin, Y. Su, and C-H. Zheng*. scHFC: a hybrid fuzzy clustering method for single-cell RNA-seq data optimized by natural computation,
Briefings in Bioinformatics, 23(2): 1-13, 2022.
10. C. Jiang, W. Zhong*, Y. Lu, B. Huang*, W. Song, D. Tan, and F. Qian. Deep Bayesian Slow Feature Extraction with Application to Industrial Inferential Modeling,
IEEE Transactions on Industrial Informatics, 19(1): 40-51, 2023.
11. Z. Zhang, Y. Zhang, D. Yue*, C-X. Dou, L. Ding, and D. Tan. Voltage regulation with high penetration of low-carbon energy in distribution networks: a source-grid-load-collaboration-based perspective,
IEEE Transactions on Industrial Informatics, 18(6), 3987-3999, 2022.
12. P. Wei, Z. Pang, L. Jiang, D. Tan, Y. Su, and C-H. Zheng*. Promoter prediction in nannochloropsis based on densely connected convolutional neural networks,
Methods, 204: 38-46, 2022.
13. Q. Wang, W. He*, L. Zino, D. Tan, and W. Zhong*. Bipartite consensus for a class of nonlinear multi-agent systems under switching topologies: a disturbance observer-based approach,
Neurocomputing, 488: 3130-3143, 2022.
14. Q. Wang, W. Zhong*, J. Xu, W. He, and D. Tan. Bipartite tracking consensus control of nonlinear high-order Multi-agent systems subject to exogenous disturbances,
IEEE Access, 145910-145920, 2019.
15. R. Cao, Q. Gu, D. Tan, P. Wei, and C-H. Zheng*. Prediction of microsatellite instability of colorectal cancer using multi-scale pathological images based on deep learning,
Proc. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022, DOI:10.1109/BIBM55620.2022.9995576. (CCF B)
