Kai Zhou

Cao, P., Zhang, Y., Zhou, K., and Tang, J., “A reinforcement learning hyper-heuristic in multi-objective optimization with application to structural damage identification,” Structural and Multidisciplinary Optimization, accepted.

Zhou, K., Edward D., and Tang, J., 2023, “Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations”, Mechanical Systems and Signal Processing, 185, 109772. Link

Zhou, K., Zhang, Y., Shuai, Q., and Tang, J., 2022, “Probabilistic multi-objective inverse analysis for damage identification using piezoelectric impedance measurement under uncertainties,” Frontiers in Built Environment, 86. Link

Dykstra, G., Reynolds, B., Smith, R., Zhou, K., and Liu, Y., 2022, “Electropolymerized molecularly imprinted polymer synthesis guided by an integrated data-driven framework for cortisol detection,” ACS Applied Materials & Interfaces, 14(22), 25972–25983. Link (selected as supplementary journal cover)

Liang, M., and Zhou, K., 2022, “A hierarchical deep learning framework for combined rolling bearing fault localization and identification with data fusion,” Journal of Vibration and Control, in press. Link

Zhou, K., Enos, R., Xu, D., Zhang, D., and Tang, J., 2022, “Hierarchical multi-response Gaussian processes for uncertainty analysis with multi-scale composite manufacturing simulation,” Computational Materials Science, 207,111257. Link

Zhou, K., Enos, R., Zhang, D., and Tang, J., 2022, “Uncertainty analysis of spring-in angles associated with composites manufacturing utilizing physics-guided Gaussian process meta-modeling,” Composite Structures, 280,114816. Link

Liang, M., and Zhou, K., 2022, “Probabilistic bearing fault diagnosis using Gaussian process with tailored feature extraction,” The International Journal of Advanced Manufacturing Technology, 119, 2059-2076. Link

Zhou, K., and Liu, Y., 2021, "Early-stage gas identification using convolutional long short-term neural network with sensor array time series data," Sensors, 21(14), 4826. Link

Zhou K., and Tang, J., 2021, "Computational inference of vibratory system with incomplete modal information using parallel, interactive and adaptive Markov chains," Journal of Sound and Vibration, 511, 116331. Link

Zhou, K., and Tang, J., 2021, “Harnessing fuzzy neural network for gear fault diagnosis with limited data labels,” The International Journal of Advanced Manufacturing Technology, 115,1005-1019. Link

Zhou, K., Sun, H., Enos, R., Zhang, D., and Tang, J., 2021, “Harnessing deep learning for physics-informed prediction of composite strength with microstructural uncertainties,” Computational Materials Science, 107,110663. Link

Miller, L., Zhou, K., Tang, J., Frame, L., Sheeley, C., Hebert, R., Narayan, L.R., Alpay, P., and Kim, J., 2021, “Thermomechanical finite element simulation and correlation analysis for orthogonal cutting of normalized AISI 9310 steels,” The International Journal of Advanced Manufacturing Technology, 114(11-12), 3337-3356. Link

Zhou, K., and Tang, J., 2021,“Characterization of dynamic response variation using multi-fidelity data fusion through composite neural network,” Engineering Structures, 232, 111878. Link

Zhou, K., and Tang, J., 2021, “Structural model updating using adaptive multi-response Gaussian process meta-modeling,” Mechanical Systems and Signal Processing, 147, 107121. Link

Zhou, K., and Tang, J., 2021, “Uncertainty quantification of mode shape variation utilizing multi-level multi-response Gaussian process,” ASME Journal of Vibration and Acoustics, 143(1), 011003. Link

Wu, Z.Y., Zhou, K., Harry W. Shenton III and Michael J. Chajes, 2019, “Development of sensor placement optimization tool and application to large cable support bridge,” Journal of Civil Structural Health Monitoring, 9(1), 77-90. Link

Zhou, K., and Tang, J., 2018, “Uncertainty quantification in structural dynamic analysis using two-level Gaussian processes and Bayesian inference,” Journal of Sound and Vibration, 412(6), 95-115. Link

Zhou, K., and Wu, Z.Y., 2017, “Strain gauge placement optimization for structural performance assessment,” Engineering Structures, 141(15), 184-197. Link

Shuai, Q., Zhou, K., Zhou, S., and Tang, J., 2017, “Fault identification using piezoelectric impedance measurement and model-based intelligent inference with pre-screening,” Smart Materials and Structures, 26(4), 045007. Link

Zhou, K., Wu, Z.Y., Yi, X.H., Zhu, D.P., Narayan, R., and Zhao, J., 2017, “Generic framework of sensor placement optimization for structural health monitoring,” ASCE Journal of Computing in Civil Engineering, 31(4), 04017018. Link

Zhou, K., Hedge, A., Cao, P., and Tang, J., 2016, “Design optimization towards alleviating forced response variation in cyclically periodic structure using Gaussian Process,” ASME Journal of Vibration and Acoustics, 139(1), 011017. Link

Zhou, K., and Tang, J., Christenson, R., 2016, “Rapid identification of properties of column-supported bridge-type structure by using vibratory response,” Journal of Vibration and Control, 22(5), 1415-1430. Link

Zhou, K., Liang, G., and Tang, J., 2016, “Component mode synthesis order-reduction for dynamic analysis of structure modeled with NURBS finite element,” ASME Journal of Vibration and Acoustics, 138(2), 021016. Link

Zhou, K., and Tang, J., 2016, “Highly efficient probabilistic finite element model updating using intelligent inference with incomplete modal information,” ASME Journal of Vibration and Acoustics, 138(5), 051016. Link

Zhou, K., and Tang, J., 2015, “Reducing dynamic response variation using NURBS finite element-based geometry perturbation,” ASME Journal of Vibration and Acoustics, 137(6), 061008. Link

Liu, Y., Zhou, K., and Lei, Y., 2015, “Using Bayesian inference framework towards identifying gas species and concentration from high temperature resistive sensor array data,” Journal of Sensors, V2015, 351940. Link

Zhou, K., and Tang, J., 2022, “Probabilistic gear fault diagnosis using Bayesian convolutional neural network,” Proceedings of the MECC (Modeling, Estimation and Control Conference), accepted.

Zhou, K., Zhang, Y., and Tang, J., 2022, “Order-reduced modeling-based multi-level damage identification using piezoelectric impedance measurement,” Proceedings of the MoViC (International Conference on Motion and Vibration Control), accepted.

Zhang, Y., Zhou, K., and Tang, J., 2022, “Memetic optimizer for structural damage identification using electromechanical admittance,” Proceedings of the ASME IDETC/CIE, accepted.

Zhou, K., 2022, “A deep long short-term memory network for bearing fault diagnosis under time-varying condition,” Proceedings of the ASME IDETC/CIE, accepted.

Zhang, Y., Zhou, K., and Tang, J., 2022, “Structural damage identification using inverse analysis through optimization with sparsity,” Proceedings of SPIE, Smart Structures/ NDE, V12046, 1204606. Link

Zhou, K., and Tang, J., 2020, ”Physics based multi-fidelity fusion for efficient characterization of mode shape variation under uncertainties,” Proceedings of the ASME DSCC. Link

Zhou, K., and Tang, J., 2020, “Fuzzy classification of gear fault using principal component analysis-based fuzzy neural network,” Proceedings of the ASME ISFA. Link

Xu, D., Zhou, K., and Tang, J., 2020, “The application of MOSA algorithm in gleeble testing model updating,” Proceedings of the ASME ISFA. Link

Zhou, K., Li, R., Chen, W.J., Tang, J., and Zhang, D., 2019, “Uncertainty quantification of the dimensional variations of a curved composite flange,” American Society of Composites (ASC) 34^th Technical Conference. Link

Zhou, K., Liang, G., and Tang, J., 2016, “Vibration analysis of structure with uncertainty using two-level Gaussian processes and Bayesian inference”, Proceedings of MOVIC RASD. Link

Zhou, K., Wu, Z.Y., and Cashany, M., 2016, “Accelerated finite element model calibration by substructure analysis with parallel genetic algorithm optimization,” 8^th International Conference on Bridge Maintenance & Safety & Management. Link

Zhou, K., Cao, P., and Tang, J., 2015, “Efficient uncertainty quantification in structural dynamic analysis using two-level Gaussian processes,” Proceedings of ASME IDETC/CIE, V8, V008T13A102. Link

Shuai, Q., Zhou, K., and Tang, J., 2015, “Structural damage identification using piezoelectric impedance and Bayesian inference,” Proceedings of SPIE, Smart Structures/ NDE, V9435, 94351R. Link

Zhou, K., Shuai, Q., and Tang, J., 2014, “Adaptive damage detection using tunable piezoelectric admittance sensor and intelligent inference,” Proceedings of ASME SMASIS, V1, V001T05A007. Link

Zhou, K., and Tang, J., 2014, “Robust optimization towards reducing response variation by efficient NURBS finite element inverse analysis,” Proceedings of JSME MOVIC. Link

Zhou, K., Liang, G., and Tang, J., 2014, “Efficient model updating using Bayesian probabilistic framework based on measured vibratory response,” Proceedings of SPIE, Smart Structures/ NDE, V9063, 90631T. Link

Zhou, K., and Tang, J., 2013, “Perturbing structural design towards minimizing variation in vibratory response,” Proceedings of ASME DSCC, V2, V002T18A005. Link

Zhou, K., and Tang, J., 2013, “Design modification of cyclically periodic structure using Gaussian process,” Proceedings of SPIE, Smart Structures/ NDE, V8694, 869411. Link

Zhou, K., and Tang, J., 2012, “Towards alleviating vibration response variation based on reduced order modeling and analysis,” Proceedings of ASME DSCC, V3, pp.701-708. Link

Zhou, K., Tang, J., and Christenson, R., 2012, “Rapid identification of structural properties based on mass response method,” Proceedings of SPIE, Smart Structures/ NDE, V8347, 834703. Link

• MEEM 3750 – Dynamic Systems, 2021 Spring
• MEEM 3750 – Dynamic Systems, 2022 Spring