Publications

Full list please see Google Scholar.

Trainee name in Italics; Senior/corresponding Author in Bold; Impact Factor (IF).

Articles in peer-reviewed journals (10 ESI Highly Cited Papers):

X. Xing, T.Yan, and M. Xia, "Early Prediction of Battery Life using An Interpretable Health Indicator with Evolutionary Computing," in Reliability Engineering & System Safety, vol. 260, no.110980, 2025. (IF: 9.4)
D. Zhao, J. Chen, H. Yin, L. Cai and M. Xia, "A Novel Semi-Supervised Fault Diagnosis Method for Unbalanced Data," in IEEE Internet of Things Journal, vol. 12, no. 6, pp. 7599-7609, 2025. (IF: 8.2)
X. Liao, D. Wang, S. Qiu, M. Xia, and X. Ming, "SLDAE: An interpretable stacked Denoising Auto-Encoders for fan fault diagnosis on steelmaking workshops," Advanced Engineering Informatics, vol. 65, no. 103260, 2025. (IF: 8.0)
X. Chen, J. Li, A. Yu, B. Cai, Q. Wu and M. Xia, "Ultralow Latency ANN–SNN Conversion for Bearing Fault Diagnosis," in IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-10, 2025. (IF: 5.6)
S. Gong, S. Li, and M. Xia, "Compound fault feature separation with frequency segmentation and improved sparse filtering for rolling bearings," in Structural Health Monitoring, vol. 14, no. 3, pp. 123–145, 2025. (IF: 5.7)
T. Yan, X. Xing, D. Wang, K. Tsui, and M. Xia," Interpretable Degradation Tensor Modeling Through Multi-scale and Multi-level Time-Frequency Feature Fusion for Machine Health Monitoring," in Information Fusion, vol. 117, no. 102935, May 2025. (IF: 14.8)
F. Jiang, X. Hou, and M. Xia. "Spatio-temporal Attention-based Hidden Physics-informed Neural Network for Remaining Useful Life Prediction." in Advanced Engineering Informatics, vol. 63, no.102958, Jan. 2025, doi: 10.1016/j.aei.2024.102958. (IF: 8.0)
Y. Chen, Z. Li, Y. Jiang, C. Yang, M. Xia and K. Feng, "Enhanced Sparse LPV-ARMA Model With Ensemble Basis Functions for Mechatronic Transmission Fault Detection Under Variable Speed Conditions," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2025.3535580.(IF: 10.6)
T. Yan, D. Wang, T. Xia, L. Xi, and M. Xia," Two-dimensional Optimization Framework of Online Interpretable Time-frequency Feature Learning for Practical Machine Health Monitoring," in IEEE Transactions on Reliability, doi: 10.1109/TR.2024.3489589. (IF: 5.0)
D. Zhao, J. Chen, H. Yin, M. Xia and Y. Qin, “A Novel Semi-Supervised Fault Diagnosis Method for Unbalanced Data” in IEEE Internet of Things Journal (accepted) (IF: 10.6)
W. Chen, H. Zhou, T. Mao, L. Cheng and M. Xia, "A Novel State-of-Charge Estimation Method for Lithium-Ion Battery Using GDAformer and Online Correction," in IEEE Trans Industr Inform, vol. 20, no. 11, pp. 13473-13485, Nov. 2024, doi: 10.1109/TII.2024.3438236.(IF: 12.3)
X. Liao, C. Wang, S. Qiu, X. Zhang, Z. Jiang, X. Ming, and M.Xia, "A Neural-Symbolic Model for Fan Interpretable Fault Diagnosis On Steel Production Lines," IEEE Internet of Things Journal, 2024, vol. 11, no. 13, pp. 22926-22937, 2024, doi: 10.1109/JIOT.2024.3363654. (IF: 10.6)
S. Gong, S. Li, Z. Zhang and M. Xia, "Nonlinear Blind Deconvolution Based on Generalized Normalized lp/lq Norm for Early Fault Detection," IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-12, 2024. (IF: 5.6)
H. Zhou, W. Chen, P. Qiao, L. Cheng, and M. Xia, “Reliable fault diagnosis using evidential aggregated residual network under varying working conditions and noise interference,” Knowledge-Based Systems, 286, 111407, 2024. (IF: 8.8)
X. Li, J. Liu, S. Ding, Y. Xu, Y. Zhang, and M.Xia, “Dynamic modeling and vibration analysis of double row cylindrical roller bearings with irregular-shaped defects,” Nonlinear Dynamics, vol. 112, p. 2501–2521, 2024. (IF: 5.6)
W. Chen, H. Zhou, L. Cheng, and M. Xia, “Condition Monitoring and Anomaly Detection of Wind Turbines using Temporal Convolutional Informer and Robust Dynamic Mahalanobis Mahalanobis Distance,” IEEE Trans Instrum Meas, vol. 72, pp. 1-14, 2023, Art no. 3536914. (IF: 5.6)
H. Zhou, W. Chen, J. Liu, L. Cheng, and M. Xia, “Trustworthy and intelligent fault diagnosis with effective denoising and evidential stacked GRU neural network,” J Intell Manuf, pp. 1–20, 2023. (IF: 8.3)
X. Liao, X. Ming, and M. Xia, “KBRDBN: An Interpretable Deep Belief Network for the Fault Diagnosis of the Trolley Mechanism in Ship-to-Shore Cranes,” IEEE Trans Instrum Meas, vol. 73, pp. 1-12, 2024, Art no. 3511212. (IF: 5.6)
J. Zhu, Y. Wang, M. Xia, D. Williams, and C. W. De Silva, “A new multisensor partial domain adaptation method for machinery fault diagnosis under different working conditions,” IEEE Trans Instrum Meas, vol. 72, pp. 1-10, 2023, Art no. 3531410. (IF: 5.6)
J. Liu, L. Xue, L. Wang, Z. Shi, and M. Xia, “A new impact model for vibration features of a defective ball bearing,” ISA Trans, vol. 142, pp. 465–477, 2023. (IF: 7.3)
J. Liu, X. Li, and M. Xia, “A dynamic model for the planetary bearings in a double planetary gear set,” Mech Syst Signal Process, vol. 194, p. 110257, 2023. (IF: 8.4) (ESI Highly Cited Paper)
X. Song, J. Liu, and M. Xia, “Advanced Vibration-Based Fault Diagnosis and Vibration Control Methods,” Sensors, vol. 23, no. 18. MDPI, p. 7704, 2023. (IF: 3.9)
P. Gao, J. Wang, M. Xia, Z. Qin, and J. Zhang, “Dual-Metric Neural Network With Attention Guidance for Surface Defect Few-Shot Detection in Smart Manufacturing,” J Manuf Sci Eng, vol. 145, no. 12, 2023. (IF: 4.0)
M. Ma, W. Liang, X. Zhong, H. Deng, D. Shi, Y. Wang, and M. Xia, “Direct Noise-Resistant Edge Detection with Edge-Sensitive Single-Pixel Imaging Modulation,” Intelligent Computing, vol. 2, p. 0050, 2023.
M. Ma, L. Gu, Y. Shen, Q. Guan, C. Wang, H. Deng, X. Zhong, M. Xia, D. Shi, “Computational framework for turbid water single-pixel imaging by polynomial regression and feature enhancement,” IEEE Trans Instrum Meas, vol. 72, pp. 1-11, 2023, Art no. 5021111. (IF: 5.6)
W. Chen, H. Zhou, L. Cheng, J. Liu, and M. Xia, “Condition monitoring of wind turbine using novel deep learning method and dynamic kernel principal components Mahalanobis distance,” Eng Appl Artif Intell, vol. 125, p. 106757, 2023. (IF: 7.4)
J. Liu, X. Li, R. Pang, and M. Xia, “Dynamic modeling and vibration analysis of a flexible gear transmission system,” Mech Syst Signal Process, vol. 197, p. 110367, 2023. (IF: 8.4)
W. Chen, H. Zhou, L. Cheng, and M. Xia, “Prediction of regional wind power generation using a multi-objective optimized deep learning model with temporal pattern attention,” Energy, p. 127942, 2023. (IF: 9.0)
K. An, J. Lu, Q. Zhu, X. Wang, C.W. De Silva, M. Xia, S. Lu, “Edge Solution for Real-time Motor Fault Diagnosis Based on Efficient Convolutional Neural Network,” IEEE Trans Instrum Meas, vol. 72, pp. 1-12, 2023, Art no. 3516912. (IF: 5.6)
Q. Wang, C. Guo, H.-N. Dai, and M. Xia, “Variant-Depth Neural Networks for Deblurring Traffic Images in Intelligent Transportation Systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 6, pp. 5792-5802, June 2023. (IF: 8.5)
F. Jiang, M. Xia, and Y. Hu, “Physics-Informed Machine Learning for Accurate Prediction of Temperature and Melt Pool Dimension in Metal Additive Manufacturing,” 3D Print Addit Manuf, vol.11, no.4, pp.1679-1689, 2023. (IF: 3.1)
Y. He, X. Zhang, Z. Zhao, S. Xu, M. Xia, C. Zhang, and Y. Hu, “Wire-feed laser additive manufacturing of dissimilar metals via dual molten pool interface interlocking mechanism,” Sci China Technol Sci, vol. 66, no. 4, pp. 976–986, 2023. (IF: 4.6)
H. Zhou, W. Chen, L. Cheng, D. Williams, C. W. De Silva, and M. Xia, “Reliable and Intelligent Fault Diagnosis With Evidential VGG Neural Networks,” IEEE Trans Instrum Meas, vol. 72, pp. 1–12, 2023. (IF: 5.6)
M. Xie, X. Yu, W. Bao, C. Liu, and M. Xia, “Side-Milling-Force Model Considering Tool Runout and Workpiece Deformation,” Electronics, vol. 12, no. 4, p. 968, 2023. (IF: 2.9)
N. Zhao, Y. Su, S. Wang, M. Xia, and C. Liu, “Chatter detection in variable cutting depth side milling using VMD and vibration characteristics analysis,” Electronics, vol. 11, no. 22, p. 3779, 2022. (IF: 2.9)
Q. Li, L. Chen, L. Kong, D. Wang, M. Xia, and C. Shen, “Cross-domain augmentation diagnosis: An adversarial domain-augmented generalization method for fault diagnosis under unseen working conditions,” Reliab Eng Syst Saf, vol. 234, p. 109171, 2023. (IF: 8.1)(ESI Highly Cited Paper)
H. Zhou, W. Chen, L. Cheng, J. Liu, and M. Xia, “Trustworthy Fault Diagnosis with Uncertainty Estimation through Evidential Convolutional Neural Networks,” IEEE Trans Industr Inform, vol. 19, no. 11, pp. 10842-10852, Nov. 2023. (IF: 12.3)
X. Zhang, J. Liu, M. Xia, and Y. Hu, “Laser shock peening enables 3D gradient metal structures: A case study on manufacturing self-armored hydrophobic surfaces,” Int J Mach Tools Manuf, vol. 185, p. 103993, 2023. (IF: 14.0)
X. Zhang, M. Xia, C. Zhang, and Y. Hu, “Multistage laser shock improves surface structural properties of aluminum alloy,” Int J Mech Sci, vol. 245, p. 108101, 2023. (IF: 7.3)
L. Chen, K. An, D. Huang, X. Wang, M. Xia, and S. Lu, “Noise-Boosted Convolutional Neural Network for Edge-based Motor Fault Diagnosis with Limited Samples,” IEEE Trans Industr Inform, vol. 19, no. 9, pp. 9491-9502, Sept. 2023. (IF: 12.3)
Z. Zhao, S. Xu, J. Liu, X. Zhang, M. Xia, and Y. Hu, “Force enhanced wire laser additive manufacturing of aluminum and titanium alloys,” J Alloys Compd, vol. 938, p. 168617, 2023. (IF: 6.2)
Q. Zhu, J. Lu, X. Wang, H. Wang, S. Lu, C.W. de Silva, M. Xia, “Real-Time Quality Inspection of Motor Rotor Using Cost-Effective Intelligent Edge System,” IEEE Internet Things J, vol. 10, no. 8, pp. 7393–7404, 2022. (IF: 10.6)
W. Chen, H. Zhou, L. Cheng, and M. Xia, “Wind Turbine Blade Icing Diagnosis Using Convolutional LSTM-GRU With Improved African Vultures Optimization,” IEEE Open Journal of Instrumentation and Measurement, vol. 1, pp. 1–9, 2022.
X. Ding, X. Hou, M. Xia, Y. Ismail, and J. Ye, “Predictions of macroscopic mechanical properties and microscopic cracks of unidirectional fibre-reinforced polymer composites using deep neural network (DNN),” Compos Struct, vol. 302, p. 116248, 2022. (IF: 6.3)
P. Lyu, P. Zheng, W. Yu, C. Liu, and M. Xia, “A Novel Multiview Sampling-Based Meta Self-Paced Learning Approach for Class-Imbalanced Intelligent Fault Diagnosis,” IEEE Trans Instrum Meas, vol. 71, pp. 1–12, 2022. (IF: 5.6)
H. Zhou, W. Chen, C. Shen, L. Cheng, and M. Xia, “Intelligent machine fault diagnosis with effective denoising using EEMD-ICA-FuzzyEn and CNN,” Int J Prod Res, pp. 1–13, 2022. (IF: 9.2)
J. Liu, X. Zhang, Y. He, Z. Zhao, M. Xia, and Y. Hu, “Suspended water droplet confined laser shock processing at elevated temperatures,” Int J Mach Tools Manuf, vol. 179, p. 103917, 2022. (IF: 14.0)
J. Liu, Y. He, M. Xia, and Y. Hu, “Ultrahigh strain rate-activated superplastic forming of aluminum and gold nanometals,” Mater Des, vol. 221, p. 110910, 2022. (IF: 8.4)
H. Liu, M. Xia, D. Williams, J. Sun, and H. Yan, “Digital twin-driven machine condition monitoring: A literature review,” J Sens, vol. 2022, 2022. (IF: 1.9)
M. Xia, H. Shao, Z. Huang, Z. Zhao, F. Jiang, and Y. Hu, “Intelligent process monitoring of laser-induced graphene production with deep transfer learning,” IEEE Trans Instrum Meas, vol. 71, pp. 1–9, 2022. (IF: 5.6)
J. Li, C. Shen, L. Kong, D. Wang, M. Xia, and Z. Zhu, “A new adversarial domain generalization network based on class boundary feature detection for bearing fault diagnosis,” IEEE Trans Instrum Meas, vol. 71, pp. 1–9, 2022. (IF: 5.6)
J. Xia, S. Wang, X. Wang, M. Xia, K. Xie, and J. Cao, “Multi-view Bayesian spatio-temporal graph neural networks for reliable traffic flow prediction,” International Journal of Machine Learning and Cybernetics, pp. 1–14, 2022. (IF: 5.6)
Q. Zhu, X. Wang, H. Wang, M. Xia, S. Lu, B. Liu, G. Li, and W. Cao, “Real-time defect detection of die cast rotor in induction motor based on circular flux sensing coils,” IEEE Trans Industr Inform, vol. 18, no. 12, pp. 9271–9282, 2021. (IF: 12.3)
H. Sun, M. Xia, Y. Hu, S. Lu, Y. Liu, and Q. Wang, “A new sorting feature-based temporal convolutional network for remaining useful life prediction of rotating machinery,” Computers and Electrical Engineering, vol. 95, p. 107413, 2021. (IF: 4.3)
H. Shao, W. Li, M. Xia, Y. Zhang, C. Shen, D. Williams, A. Kennedy, and C.W. de Silva, “Fault diagnosis of a rotor-bearing system under variable rotating speeds using two-stage parameter transfer and infrared thermal images,” IEEE Trans Instrum Meas, vol. 70, pp. 1–11, 2021. (IF: 5.6)
M. Xia, H. Shao, D. Williams, S. Lu, L. Shu, and C. W. de Silva, “Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning,” Reliab Eng Syst Saf, vol. 215, p. 107938, 2021. (IF: 8.1) (ESI Highly Cited Paper)
X. Wang, S. Lu, W. Cao, M. Xia, K. Chen, J. Ding, and S. Zhang, “Stray flux-based rotation angle measurement for bearing fault diagnosis in variable-speed BLDC motors,” IEEE Transactions on Energy Conversion, vol. 36, no. 4, pp. 3156–3166, 2021. (IF: 4.9)
L. Chen, Q. Li, C. Shen, J. Zhu, D. Wang, and M. Xia, “Adversarial domain-invariant generalization: A generic domain-regressive framework for bearing fault diagnosis under unseen conditions,” IEEE Trans Industr Inform, vol. 18, no. 3, pp. 1790–1800, 2021. (IF: 12.3) (ESI Highly Cited Paper)
H. Shao, M. Xia, J. Wan, and C. W. de Silva, “Modified stacked autoencoder using adaptive Morlet wavelet for intelligent fault diagnosis of rotating machinery,” IEEE/ASME Transactions on Mechatronics, vol. 27, no. 1, pp. 24–33, 2021. (IF: 6.4) (ESI Highly Cited Paper)
M. Xia, H. Shao, X. Ma, and C. W. de Silva, “A stacked GRU-RNN-based approach for predicting renewable energy and electricity load for smart grid operation,” IEEE Trans Industr Inform, vol. 17, no. 10, pp. 7050–7059, 2021. (IF: 12.3) (ESI Highly Cited Paper)
X. Wang, S. Lu, W. Huang, Q. Wang, S. Zhang, and M. Xia, “Efficient data reduction at the edge of industrial Internet of Things for PMSM bearing fault diagnosis,” IEEE Trans Instrum Meas, vol. 70, pp. 1–12, 2021. (IF: 5.6)
F. Xu, B. Yang, L. Feng, D. Huang, and M. Xia, “Improved interlaminar fracture toughness and electrical conductivity of CFRPs with non-woven carbon tissue interleaves composed of fibers with different lengths,” Polymers, vol. 12, no. 4, p. 803, 2020. (IF: 5.0)
W. Hong, Z. Xiong, J. You, X. Wu, and M. Xia, “CPIN: Comprehensive present-interest network for CTR prediction,” Expert Syst Appl, vol. 168, p. 114469, 2021. (IF: 8.5)
J. Wen, H. Yao, Z. Ji, B. Wu, and M. Xia, “On fault diagnosis for high-g accelerometers via data-driven models,” IEEE Sens J, vol. 21, no. 2, pp. 1359–1368, 2020. (IF: 4.3)
C. Wang, M. Xia, and M. Q.-H. Meng, “Stable autonomous robotic wheelchair navigation in the environment with slope way,” IEEE Trans Veh Technol, vol. 69, no. 10, pp. 10759–10771, 2020. (IF: 6.8)
H. Shao, M. Xia, G. Han, Y. Zhang, and J. Wan, “Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer convolutional neural network and thermal images,” IEEE Trans Industr Inform, vol. 17, no. 5, pp. 3488–3496, 2020. (IF: 12.3) (ESI Highly Cited Paper)
X. Wang, C. Shen, M. Xia, D. Wang, J. Zhu, and Z. Zhu, “Multi-scale deep intra-class transfer learning for bearing fault diagnosis,” Reliab Eng Syst Saf, vol. 202, p. 107050, 2020. (IF: 8.1) (ESI Highly Cited Paper)
M. Xia, X. Zheng, M. Imran, and M. Shoaib, “Data-driven prognosis method using hybrid deep recurrent neural network,” Appl Soft Comput, vol. 93, p. 106351, 2020. (IF: 8.7)
B. Chen, J. Wan, M. Xia, and Y. Zhang, “Exploring equipment electrocardiogram mechanism for performance degradation monitoring in smart manufacturing,” IEEE/ASME Transactions on Mechatronics, vol. 25, no. 5, pp. 2276–2286, 2020. (IF: 6.4)
Q. Shu, S. Lu, M. Xia, J. Ding, J. Niu, and Y. Liu, “Enhanced feature extraction method for motor fault diagnosis using low-quality vibration data from wireless sensor networks,” Meas Sci Technol, vol. 31, no. 4, p. 045016, 2020. (IF: 2.4)
J. Wan, J. Yang, S. Wang, D. Li, P. Li, and M. Xia, “Cross-network fusion and scheduling for heterogeneous networks in smart factory,” IEEE Trans Industr Inform, vol. 16, no. 9, pp. 6059–6068, 2019. (IF: 12.3)
T. Li, K. Tong, M. Xia, B. Li, and C. W. de Silva, “Information-based hierarchical planning for a mobile sensing network in environmental mapping,” IEEE Syst J, vol. 14, no. 2, pp. 1692–1703, 2019. (IF: 4.4)
X. Li, J. Wan, H.-N. Dai, M. Imran, M. Xia, and A. Celesti, “A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing,” IEEE Trans Industr Inform, vol. 15, no. 7, pp. 4225–4234, 2019. (IF: 12.3)
J. Wan and M. Xia, “Cloud-assisted cyber-physical systems for the implementation of Industry 4.0,” Mobile Networks and Applications, vol. 22, pp. 1157–1158, 2017. (IF: 3.8)
M. Xia, T. Li, T. Shu, J. Wan, C. W. De Silva, and Z. Wang, “A two-stage approach for the remaining useful life prediction of bearings using deep neural networks,” IEEE Trans Industr Inform, vol. 15, no. 6, pp. 3703–3711, 2018. (IF: 12.3) (ESI Highly Cited Paper)
J. Wan, B. Chen, S. Wang, M. Xia, D. Li, and C. Liu, “Fog computing for energy-aware load balancing and scheduling in smart factory,” IEEE Trans Industr Inform, vol. 14, no. 10, pp. 4548–4556, 2018. (IF: 12.3)
T. Shu, M. Xia, J. Chen, and C. De Silva, “An energy efficient adaptive sampling algorithm in a sensor network for automated water quality monitoring,” Sensors, vol. 17, no. 11, p. 2551, 2017. (IF: 3.9)
M. Xia, T. Li, L. Xu, L. Liu, and C. W. De Silva, “Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks,” IEEE/ASME Transactions on Mechatronics, vol. 23, no. 1, pp. 101–110, 2017. (IF: 6.4) (ESI Highly Cited Paper, among most popular papers of TMECH)
T. Li, M. Xia, J. Chen, Y. Zhao, and C. De Silva, “Automated water quality survey and evaluation using an IoT platform with mobile sensor nodes,” Sensors, vol. 17, no. 8, p. 1735, 2017. (IF: 3.9)
M. Xia, T. Li, L. Liu, L. Xu, and C. W. de Silva, “Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder,” IET Science, Measurement & Technology, vol. 11, no. 6, pp. 687–695, 2017. (IF: 1.4)
M. Xia, T. Li, Y. Zhang, and C. W. De Silva, “Closed-loop design evolution of engineering system using condition monitoring through internet of things and cloud computing,” Computer Networks, vol. 101, pp. 5–18, 2016. (IF: 5.6)

Peer-reviewed conference proceedings (2 Best Paper Awards):

Y. F. Ugurluoglu, D. Williams, and M. Xia, “Enhancing Genetic Algorithm-Based Process Parameter Optimisation Through Grid Search-Optimised Artificial Neural Networks,” 2023 28th International Conference on Automation and Computing (ICAC), IEEE, 2023, pp. 1–6.
M. Roberts, M. Xia, and A. Kennedy. "Data-driven Process Parameter Optimisation for Laser Wire Metal Additive Manufacturing." 2022 27th International Conference on Automation and Computing (ICAC). IEEE, 2022.
H. Shao, W. Li, M. Xia, C. Wang, Q. Guan and T. Xu, "Rotating Machinery Fault Classification using IWGAN-GP and Small Gray Images," 2021 16th International Conference on Computer Science & Education (ICCSE), 2021, pp. 222-227, doi: 10.1109/ICCSE51940.2021.9569392. (Best Paper Award)
H. Cao, H. Shao, M. Xia, W. Luo, F. Zhu and D. Su, "Unsupervised Domain-shared Convolutional Neural Network for Bearing Fault Transfer Diagnosis," 2021 16th International Conference on Computer Science & Education (ICCSE), 2021, pp. 216-221, doi: 10.1109/ICCSE51940.2021.9569672.
M. Xia, T. Li, L. Liu, L. Xu, S. Gao and C. W. de Silva, "Remaining useful life prediction of rotating machinery using hierarchical deep neural network," 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, pp. 2778-2783, doi: 10.1109/SMC.2017.8123047.
T. Li, M. Xia, J. Chen, S. Gao and C.W.de Silva, "A hexagonal grid-based sampling planner for aquatic environmental monitoring using unmanned surface vehicles," 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, pp. 3683-3688, doi: 10.1109/SMC.2017.8123205.
M. Xia and C. W. de Silva, “A Framework of Design Weakness Detection through Machine Health Monitoring for the Evolutionary Design Optimization of Multi-Domain Systems”, 2014 9th International Conference on Computer Science & Education, 2014, pp. 205-210, doi: 10.1109/ICCSE.2014.6926455. (Best Paper Award)
M. Xia, F. Kong, and F. Hu, “An Approach for Bearing Fault Diagnosis based on PCA and Multiple Classifier Fusion”, 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, 2011, pp. 321-325, doi: 10.1109/ITAIC.2011.6030215.