2025

  1. Liao, J. X., He, C., Li, J., Sun, J., Zhang, S., & Zhang, X. (2025). Classifier-guided neural blind deconvolution: A physics-informed denoising module for bearing fault diagnosis under noisy conditions. Mechanical Systems and Signal Processing, 222, 111750. [PDF]

2024

  1. Zhang, H., Huang, J., Zhang, X., & Wong, C. N. (2024). Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach. Additive Manufacturing, 86, 104208. [PDF]
  2. Deng, X., Jiang, W., & Zhang, X. (2024). Conditional plausibility entropy of belief functions based on Dempster conditioning. Information Sciences, 120959. [PDF]
  3. Deng, X., Xue, S., Jiang, W., & Zhang, X. (2024). Plausibility Extropy: The Complementary Dual of Plausibility Entropy. IEEE Transactions on Systems, Man, and Cybernetics: Systems. Published Online. [PDF]
  4. Zhang, X., & Bose, I. (2024). Reliability estimation for individual predictions in machine learning systems: A model reliability-based approach. Decision Support Systems, 114305. [PDF]
  5. Xie, W., Yu, Q., Fang, W., Zhang, X., Geng, J., Tang, J., Jing, W., Liu, M., Ma, Z., Yang, J. and Bi, J. (2024). Data-driven approaches linking wastewater and source estimation hazardous waste for environmental management. Nature Communications, 15(1), 5432. [PDF]
  6. Zhang, X., Long, X., Liu, Y., Zhou, K., & Li, J. (2024). A generic causality-informed neural network (CINN) methodology for quantitative risk analytics and decision support. Risk Analysis, Published Online. [PDF]
  7. Anwar, G. A., & Zhang, X. (2024). Deep reinforcement learning for intelligent risk optimization of buildings under hazard. Reliability Engineering & System Safety, 247, 110118. [PDF]
  8. Xue, L., Zhou, K., & Zhang, X. (2024). Continuous optimization for construction of neural network-based prediction intervals. Knowledge-Based Systems, 293, 111669. [PDF]
  9. Liao, J. X., Li, J., Dong, H. C., Sun, J., Zhang, M., Zhang, S., & Zhang, X. (2024). Logarithmic Cumulative Transformation: A Simple Yet Effective Approach for Bearing Remaining Useful Life Prediction. IEEE Transactions on Industrial Informatics. [PDF]
  10. Zhang, H., Huang, J., Zhang, X., & Wong, C. N. (2024). Autonomous optimization of process parameters and in-situ anomaly detection in aerosol jet printing by an integrated machine learning approach. Additive Manufacturing, 104208. [PDF]
  11. Li, J., Zhang, X., Yue, K., Chen, J., Chen, Z., & Li, W. (2024). An auto-regulated universal domain adaptation network for uncertain diagnostic scenarios of rotating machinery. Expert Systems with Applications, 249, 123836. [PDF]
  12. Li, J., Long, X., Deng, X., Jiang, W., Zhou, K., Jiang, C., & Zhang, X. (2024). A principled distance-aware uncertainty quantification approach for enhancing the reliability of physics-informed neural network. Reliability Engineering & System Safety, 109963. [PDF]
  13. Lai, Y., Zhu, Y., Fan, W., Zhang, X., & Zhou, K. (2024). Towards Adversarially Robust Recommendation from Adaptive Fraudster Detection. IEEE Transactions on Information Forensics and Security, 19, 907-909. [PDF]
  14. Long, X., Ding, X., Jiang, C., Zhang, X., Liao, W., & Liu, K. (2024). An intelligent crack damage assessment method by integrating information and physics. Engineering Fracture Mechanics, 295, 109737. [PDF]
  15. Chu, N., Ng, K. K., Liu, Y., Hon, K. K., Chan, P. W., Li, J., & Zhang, X. (2024). Assessment of approach separation with probabilistic aircraft wake vortex recognition via deep learning. Transportation Research Part E: Logistics and Transportation Review, 181, 103387. [PDF]

2023

  1. Liao, J.X., Wei, S.L., Xie, C.L., Zeng, T., Sun, J., Zhang, S., Zhang, X. and Fan, F.L. (2023). BearingPGA-Net: A lightweight and deployable bearing fault diagnosis network via decoupled knowledge distillation and FPGA acceleration. IEEE Transactions on Instrumentation and Measurement. [PDF]
  2. Yi, W., Chan, W., Lee, H., Boles, S., and Zhang, X. (2023) An Uncertainty-Aware Deep Learning Model for Reliable Detection of Steel Wire Rope Defects. IEEE Transactions on Reliability. [PDF]
  3. Xu, Y., Song, Y., Pi, D., Chen, Y., Qin, S., Zhang, X. and Yang, S. (2023). A reinforcement learning-based multi-objective optimization in an interval and dynamic environment. Knowledge-Based Systems, 111019. [PDF]
  4. Nemani, V., Biggio, L., Huan, X., Hu, Z., Fink, O., Tran, A., Wang, Y.,  Zhang, X. and Hu, C. (2023). Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial. Mechanical Systems and Signal Processing, 110796. [PDF]
  5. Zhang, Y., Zhang, D., Zhang, X., Qiu, L., Chan, F. T., Wang, Z., & Zhang, S. (2023). Guided probabilistic reinforcement learning for sampling-efficient maintenance scheduling of multi-component system. Applied Mathematical Modelling, 119, 677-697. [PDF]
  6. Lai, Y., Zhou, J., Zhang, X., & Zhou, K. (2023). Towards Certified Robustness of Graph Neural Networks in Adversarial AIoT Environments. IEEE Internet of Things Journal, 10(15), 13920 -13932. [PDF]
  7. Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B.D., Todd, M.D., Mahadevan, S., Hu, C. and Hu, Z. (2023). A Comprehensive Review of Digital Twin–Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives. Structural and Multidisciplinary Optimization, 66, 1. [PDF]
  8. Zhou, T., Zhang, X., Droguett, E. L., & Mosleh, A. (2023). A generic physics-informed neural network-based framework for reliability assessment of multi-state systems. Reliability Engineering & Systems Safety, 108835. [PDF]

2022

  1. Li, J., Zhang, X., Zhou, Q., Chan, F. T., & Hu, Z. (2022). A feature-level multi-sensor fusion approach for in-situ quality monitoring of selective laser melting. Journal of Manufacturing Processes, 84, 913-926. [PDF]
  2. Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B.D., Todd, M.D., Mahadevan, S., Hu, C. and Hu, Z. (2022). A Comprehensive Review of Digital Twin – Part 1: Modeling and Twinning Enabling Technologies. Structural and Multidisciplinary Optimization, 65, 354. [PDF]
  3. Zhang, X., Zhong, S., and Mahadevan, S. (2022). Airport surface movement prediction and safety assessment with spatial-temporal graph convolutional neural network. Transportation Researach Part C: Emerging Technologies, 144, 103873. [PDF]
  4. Yiu, C. Y., Ng, K. K., Li, X., Zhang, X., Li, Q., Lam, H. S., & Chong, M. H., (2022). Towards safe and collaborative aerodrome operations: Assessing shared situational awareness for adverse weather detection with EEG-enabled Bayesian neural networks. Advanced Engineering Informatics, 53, 101698. [PDF]
  5. Zhang, X., Chan, F.T.S., Yan, C., and Bose, I., (2022). Towards risk-aware artificial intelligence and machine learning systems: An overview. Decision Support Systems, 159, 113800. [PDF]
  6. Kong, Y., Zhang, X., and Mahadevan, S., (2022). Bayesian Deep Learning for Aircraft Hard Landing Safety Assessment. IEEE Transactions on Intelligent Transportation Sytstems, Published Online. [PDF]
  7. Zhang, X., Chan, F.T.S., and Mahadevan, S., (2022). Explainable Machine Learning in Image Classification Models: An Uncertainty Quantification Perspective. Knowledge-Based Systems, 243, 108418. [PDF]
  8. Zhang, X., Hu, Z., and Mahadevan, S., (2022). Bi-level optimization model for resilient configuration of logistics service centers. IEEE Transactions on Reliability, 71(1), pp.469-483. [PDF]
  9. Liu, Y., Jiang, C., Zhang, X., Mourelatos, Z.P., Barthlow, D., Gorsich, D., Singh, A. and Hu, Z., (2022). Reliability-Based Multivehicle Path Planning Under Uncertainty Using a Bio-Inspired Approach. Journal of Mechanical Design, 144(9), 091701. [PDF]

2021 and earlier

  1. Zhang, X., Srinivasan, P., and Mahadevan, S., 2021. Sequential Deep Learning from NTSB Reports for Aviation Safety Prognosis. Safety Science, 142, 105390. [PDF]
  2. Zhang, X., and Mahadevan, S., 2021. Bayesian network modeling of accident investigation reports for aviation safety assessment. Reliability Engineering and Systems Safety, 209, 107371. [PDF]
  3. Zhang, X., and Mahadevan, S., 2020. Bayesian Neural Networks for Flight Trajectory Prediction and Safety Assessment. Decision Support Systems, 131, 113246. [PDF].
  4. Zhang, X., Mahadevan, S., Lau, N. and Weinger, M.B., 2020. Multi-source information fusion to assess control room operator performance. Reliability Engineering & System Safety, 194, 106287. [PDF]
  5. Gao, C., Zhang, X., Yue, Z. and Wei, D., 2020. An accelerated physarum solver for network optimization. IEEE Transactions on Cybernetics, 50(2), pp.2168-2267. [PDF]
  6. Zhang, X., Mahadevan, S. and Goebel, K., 2019. Network reconfiguration for increasing transportation system resilience under extreme events. Risk Analysis, 39(9), pp.2054-2075.[PDF]
  7. Zhang, X. and Mahadevan, S., 2019. Ensemble machine learning models for aviation incident risk prediction. Decision Support Systems, 116, pp.48-63. [PDF]
  8. Wei, D., Zhang, X. and Mahadevan, S., 2018. Measuring the vulnerability of community structure in complex networks. Reliability Engineering & System Safety, 174, pp.41-52. [PDF]
  9. Zhang, X. and Mahadevan, S., 2018. A bio-inspired approach to traffic network equilibrium assignment problem. IEEE Transactions on Cybernetics, 48(4), pp.1304-1315. [PDF]
  10. Zhang, X., Mahadevan, S., Sankararaman, S. and Goebel, K., 2018. Resilience-based network design under uncertainty. Reliability Engineering & System Safety, 169, pp.364-379. [PDF]
  11. Liu, Y., Hu, Y., Chan, F.T., Zhang, X. and Deng, Y., 2018. Physarum polycephalum assignment: a new attempt for fuzzy user equilibrium. Soft Computing, 22(11), pp.3711-3720. [PDF]
  12. Zhang, X. and Mahadevan, S., 2017. A game theoretic approach to network reliability assessment. IEEE Transactions on Reliability, 66(3), pp.875-892. [PDF]
  13. Zhang, X., Adamatzky, A., Chan, F.T., Mahadevan, S. and Deng, Y., 2017. Physarum solver: a bio-inspired method for sustainable supply chain network design problem. Annals of Operations Research, 254(1-2), pp.533-552. [PDF]
  14. Zhang, X., Chan, F.T., Yang, H. and Deng, Y., 2017. An adaptive amoeba algorithm for shortest path tree computation in dynamic graphs. Information Sciences, 405, pp.123-140. [PDF]
  15. Zhang, X. and Mahadevan, S., 2017. Aircraft re-routing optimization and performance assessment under uncertainty. Decision Support Systems, 96, pp.67-82. [PDF]
  16. Zhang, X., Mahadevan, S. and Deng, X., 2017. Reliability analysis with linguistic data: An evidential network approach. Reliability Engineering & System Safety, 162, pp.111-121. [PDF]
  17. Zhang, X., Chan, F.T., Adamatzky, A., Mahadevan, S., Yang, H., Zhang, Z. and Deng, Y., 2017. An intelligent physarum solver for supply chain network design under profit maximization and oligopolistic competition. International Journal of Production Research, 55(1), pp.244-263. [PDF]
  18. Zhang, X., Adamatzky, A., Yang, X.S., Yang, H., Mahadevan, S. and Deng, Y., 2016. A Physarum-inspired approach to supply chain network design. Science China Information Sciences, 59(5), p.052203. [PDF]
  19. Zhang, X., Deng, Y., Chan, F.T., Adamatzky, A. and Mahadevan, S., 2016. Supplier selection based on evidence theory and analytic network process. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 230(3), pp.562-573. [PDF]
  20. Zhang, X., 2015. An Efficient Physarum Algorithm for Solving the Bicriteria Traffic Assignment Problem. International Journal of Unconventional Computing, 11(5-6), pp. 473-490. [PDF]
  21. Zhang, X., Deng, Y., Chan, F.T. and Mahadevan, S., 2015. A fuzzy extended analytic network process-based approach for global supplier selection. Applied Intelligence, 43(4), pp.760-772. [PDF]
  22. Wang, Q., Zhang, X., Mahadevan, S. and Deng, Y., 2015. Solving the Longest Path Problem in Directed Acyclic Graphs Based on Amoeba Algorithm. International Journal of Unconventional Computing, 11(2). [PDF]
  23. Zhang, X., Adamatzky, A., Chan, F.T., Deng, Y., Yang, H., Yang, X.S., Tsompanas, M.A.I., Sirakoulis, G.C. and Mahadevan, S., 2015. A biologically inspired network design model. Scientific Reports, 5, p.10794. [PDF]
  24. Wang, Q., Lu, X., Zhang, X., Deng, Y. and Xiao, C., 2015. An anticipation mechanism for the shortest path problem based on Physarum polycephalum. International Journal of General Systems, 44(3), pp.326-340. [PDF]
  25. Zhang, X., Adamatzky, A., Yang, H., Mahadaven, S., Yang, X.S., Wang, Q. and Deng, Y., 2014. A bio-inspired algorithm for identification of critical components in the transportation networks. Applied Mathematics and Computation, 248, pp.18-27. [PDF]
  26. Zhang, X., Wang, Q., Adamatzky, A., Chan, F.T., Mahadevan, S. and Deng, Y., 2014. A biologically inspired optimization algorithm for solving fuzzy shortest path problems with mixed fuzzy arc lengths. Journal of Optimization Theory and Applications, 163(3), pp.1049-1056. [PDF]
  27. Zhang, X., Zhang, Y. and Deng, Y., 2014. An improved bio-inspired algorithm for the directed shortest path problem. Bioinspiration & Biomimetics, 9(4), p.046016. [PDF]
  28. Zhang, X., Zhang, Y., Zhang, Z., Mahadevan, S., Adamatzky, A. and Deng, Y., 2014. Rapid physarum algorithm for shortest path problem. Applied Soft Computing, 23, pp.19-26. [PDF]
  29. Li, Y., Hu, Y., Zhang, X., Deng, Y. and Mahadevan, S., 2014. An evidential DEMATEL method to identify critical success factors in emergency management. Applied Soft Computing, 22, pp.504-510. [PDF]
  30. Wang, H., Lu, X., Zhang, X., Wang, Q. and Deng, Y., 2014. A bio-inspired method for the constrained shortest path problem. The Scientific World Journal, 2014, Article ID 271280, 11 pages. [PDF]
  31. Zhang, X., Wang, Q., Adamatzky, A., Chan, F.T., Mahadevan, S. and Deng, Y., 2014. An improved physarum polycephalum algorithm for the shortest path problem. The Scientific World Journal, 2014, Article ID 487069, 9 pages. [PDF]
  32. Zhang, X., Wang, Q., Chan, F.T., Mahadevan, S. and Deng, Y., 2014. A Physarum polycephalum optimization algorithm for the bi-objective shortest path problem. International Journal of Unconventional Computing, 10(1-2), pp.143-162. [PDF]
  33. Zhang, X., Zhang, Y., Hu, Y., Deng, Y. and Mahadevan, S., 2013. An adaptive amoeba algorithm for constrained shortest paths. Expert Systems with Applications, 40(18), pp.7607-7616. [PDF]
  34. Zhang, X., Deng, Y., Chan, F.T., Xu, P., Mahadevan, S. and Hu, Y., 2013. IFSJSP: a novel methodology for the job-shop scheduling problem based on intuitionistic fuzzy sets. International Journal of Production Research, 51(17), pp.5100-5119. [PDF]
  35. Zhang, H., Deng, Y., Chan, F.T. and Zhang, X., 2013. A modified multi-criterion optimization genetic algorithm for order distribution in collaborative supply chain. Applied Mathematical Modelling, 37(14-15), pp.7855-7864. [PDF]
  36. Gao, C., Lan, X., Zhang, X. and Deng, Y., 2013. A bio-inspired methodology of identifying influential nodes in complex networks. Plos One, 8(6), p.e66732. [PDF]
  37. Zhang, X., Huang, S., Hu, Y., Zhang, Y., Mahadevan, S. and Deng, Y., 2013. Solving 0-1 knapsack problems based on amoeboid organism algorithm. Applied Mathematics and Computation, 219(19), pp.9959-9970. [PDF]
  38. Wei, D., Deng, X., Zhang, X., Deng, Y. and Mahadevan, S., 2013. Identifying influential nodes in weighted networks based on evidence theory. Physica A: Statistical Mechanics and its Applications, 392(10), pp.2564-2575. [PDF]
  39. Zhang, X., Zhang, Z., Zhang, Y., Wei, D. and Deng, Y., 2013. Route selection for emergency logistics management: A bio-inspired algorithm. Safety Science, 54, pp.87-91. [PDF]