• Engineering Design under Uncertainty
• Battery Health Diagnostics and Prognostics
• Machine and Structural Health Monitoring

• Design under Uncertainty and Health Prognostics (Spring 2026)
• Design of Machine Elements (Fall 2023, Fall 2024, Fall 2025)
• Probabilistic Machine Learning for Engineering Design and Health Prognostics (Spring 2024)
• Basic Concepts of Continuum Mechanics (Spring 2023)

Patents and Invention Disclosures

1. Sadoughi M., Lu H., Hu C., Shawn K., Sidon J., and Krishnaswamy S., “Fault Detection Technique for a Bearing,” US Patent No. 12/517/010, 2026.
2. Nemani V., Hu C., Novak C., and Zimmerman A., “Joint Training Method For Predicting Remaining Useful Life of Industrial Systems Using Time-Series Data,” Utility Patent Filed, ISURF #05122, 2024.
3. Nemani V., Hu C., Lu H., and Zimmerman A., “Two-Tier Prognostic Model for Explainable Remaining Useful Life Prediction,” Utility Patent Filed, ISURF #05404, 2023.
4. Louwagie J., Aamodt P., Brama V., Finstrom N., Hintz M., Yakovlev V., O’Connell K., Swenson R., Schmidt B., Viavattine J., Zhang P., Zhao H., and Hu C., “Battery Assembly for Medical Device,” Provisional Patent Filed, US Patent No. US11413466B2, 2022.

Books, Chapters, and Journal Editorials

1. Hu Z., Hu C., and Hu W., “A Tutorial on Digital Twin for Predictive Maintenance,” Chapter in Structural Health Monitoring (SHM) in Aerospace Structures, Edited by Yuan F.G., 2024.
2. Hu C., Hu Z., Zheng P., Kim T., González V.A., and San O., Special issue on advanced optimization enabling digital twin technology. Structural and Multidisciplinary Optimization, v66, n10, p1–5, 2023. [ DOI ]
3. Hu C., Goebel K., Howey D., Peng Z., Wang D., Wang P., and Youn B.D. “Editorial: Special issue on Physics-informed machine learning enabling fault feature extraction and robust failure prognosis,” Mechanical Systems and Signal Processing, v192, p.110219, 2023. [ DOI ]
4. Laflamme S., Hu C., and Dodson J., “Real-Time Machine Learning for High-Rate Structural Health Monitoring,” Chapter 6 in Structural Health Monitoring Based on Data Science Techniques, Edited by Cury A., Ribeiro D., Ubertini F., and Todd M., 2021.
5. Hu C., Youn B.D., and Wang P., “Engineering Design under Uncertainty and Health Prognostics,” Springer Series in Reliability Engineering, Springer, 2019. [ Link ]
6. Hu C., Wang P., and Youn B.D., “Advances in System Reliability Analysis under Uncertainty,” Chapter 9 in Numerical Methods for Reliability and Safety Assessment: Multiscale and Multiphysics Systems, Springer, 2015. [ Link ]

Journal Publications

1. Ramamurthy J., Thelen A., Ojha M., Alaei S., Nowacki B., Li T., Navidi S., Jiao W., Kingston T.A., Hu C., and Pint C., “Practical Regeneration of Li-ion Batteries for a Second Life: The Role of Degradation Modes,” Journal of Power Sources, v687, 240500 (9pp), 2026.
2. Razmarashooli A., Okumu M., Salazar D., Chua Y.K., Laflamme S., Hu C., Schrader P.T., and Blasch E., “Topology-Informed Deep Learning Estimation using Feedforward Attention Layer for High-Rate State Estimation,” Accepted, Neural Computing and Applications, 2026.
3. Li T., Y. Zhang, Nowacki B., Navidi S., Schmitt T., Hu S., and Hu C., “Benchmarking Half-Cell Model Fitting Approaches for Battery Degradation Diagnostics,” eTransportation, v29, 100593 (28pp), 2026.
4. Chua Y.K., Li T., Navidi S., Razmarashooli A., Mundiwala M., Salazar Martinez D.A., Okumu M., Hu C., Laflamme S., and Schrader P.T., “Conformalized Multi-Step Forecasting for High-Rate Dynamic Systems,” Structural and Multidisciplinary Optimization, v69, n124, 1–26, 2026.
5. Günay E.E., Ramadani R., Mundiwala M., Kashef A., Ma J., Hu C., Kremer P., and Kremer G.O., 2026, “TRIZ-Based Design Improvement for Facilitating Transmission Control Unit Remanufacturing,” Journal of Mechanical Design, v148, n7, p.074501 (6pp).
6. Mundiwala M., Chung I., Davied M., Lee M., Smith C., Thompson T., Wang P., and Hu C., “Probabilistic System-Level Cost Modeling for Design for Remanufacturing,” Journal of Mechanical Design,v148, n9, 092001 (13pp), 2026.
7. Hu C., and Hu Z., “Incorporating Physics into Machine Learning for Structural Health Monitoring,” Section 11 in Roadmap on Integrating Artificial Intelligence in Structural Health Monitoring Systems, Measurement Science and Technology, v37, n10, 103001 (105pp), 2026.
8. Navidi S., Das S., Nowacki B., Shrotriya P., Xu J., Hu C., “Coulombic Efficiency-Driven Optimization of Health-Aware Charging Protocols: An Experimental Investigation on LCO/Graphite Lithium-Ion Cells,” Journal of Materials Chemistry A, v14, p8100–8119, 2026.
9. Gu H., Behtash M., Hu Z., Hu C., and Norato J.A., “A Geometry Projection Method for Reliability-Based Topology Optimization with Dimensional and Positional Variability,” Structural and Multidisciplinary Optimization, v69, n33, 1–20, 2026.
10. Nowacki B., Schmitt T., Aquino P., and Hu C., “Fine-tuning for rapid capacity estimation of lithium-ion batteries,” Energy Storage Materials, v81, 104425 (16pp),
11. Navidi S., Bajarunas K., Chao M.A., and Hu C., “Forecasting Battery Capacity for Second-Life Applications Using Physics-Informed Recurrent Neural Networks,” eTransportation, v25, 100432 (29pp), 2025.
12. Li T., Liu J., Thelen A., Mishra A., Yang X.G., Wang Z., and Hu C., “Coupling a Capacity Fade Model with Machine Learning for Early Prediction of the Battery Capacity Trajectory,” Applied Energy, v389, 125703 (27pp), 2025.
13. Thompson, McMullen R., Nemani V., Hu Z., and Hu C., “A Comparative Study of Acquisition Functions for Active Learning Kriging in Reliability-Based Design Optimization,” Structural and Multidisciplinary Optimization, v68, n51, pp.1–40, 2025.
14. Liu X., Chung I.B., Behtash M., Davied M., Thompson T., Lopez R., Lee M., Bishop W., Wang P., and Hu C., “A Design for Remanufacturing Framework Incorporating Identification, Evaluation, and Validation: a Case Study of Hydraulic Manifold,” Journal of Mechanical Design, v147, n8, p.084502 (12pp), 2025.
15. Chua Y.K., Cobel D., Razmarashooli A., Paul S., Salazar D., Hu C., Downey, A., and Laflamme S., “Probabilistic Machine Learning Pipeline Using Topological Descriptors for Real-Time State Estimation of High-Rate Dynamic Systems,” Mechanical Systems and Signal Processing, v227, 112319 (22pp),
16. Razmarashooli A., Chua Y.K., Barzegar V., Salazar D., Laflamme S., Hu C., Downey, A., Dodson J., and Schraderf P.T., “Real-Time State Estimation of Nonstationary Systems Using Topological Data Analysis Features,” Mechanical Systems and Signal Processing, v224, 112048 (16pp),
17. Nowacki B., Ramamurthy J., Thelen A., Tischer C., Pint C.L., and Hu C., “Rapid Estimation of Lithium-Ion Battery Capacity and Resistances from Short Duration Current Pulses,” Journal of Power Sources, v628, 235813 (10pp), 2025.
18. Tabassum M., Nemani V.P., Hu C., and Kremer G.E., “A Novel Design Optimization Framework to Sustain Remanufacturability,” Journal of Cleaner Production, v479, 14393510 (15pp),
19. Thelen A., Huan X., Paulson N., Onori S., Hu Z., and Hu C., “Probabilistic Machine Learning for Battery Prognostics: Review and Perspectives,” npj Materials Sustainability, v2, p14 (33pp), 2024. [Invited Review Article]
20. Navidi S., Thelen A., Li T., and Hu C., “Physics-Informed Machine Learning for Battery Degradation Diagnostics: A Comparison of State-of-the-Art Methods,” Energy Storage Materials, v68, p.103343 (27pp), 2024.
21. Li T., Zhou Z., Thelen A., Howey, D., and Hu C., “Predicting Battery Lifetime under Varying Usage Conditions from Early Aging Data,” Cell Reports Physical Science, v5, n4, 2024, p.101891 (23pp),
22. Behtash M., Liu X., Wang P., and Chao Hu, “Reman Co‑Design: A Combined Design and Remanufacturing Optimization Framework for the Sustainable Design of High‑Value Components,” Journal of Mechanical Design, v146, n2, p.020901 (13pp), 2024. [ DOI ]
23. Lu H., Thelen A., Fink O., Hu C., and Laflamme S., “Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis,” Mechanical Systems and Signal Processing, v210, 111068 (25pp), 2024. [ DOI ]
24. Nemani V., Biggio L., Huan X., Hu Z., Fink O., Tran A., Wang Y., Du X., Zhang X., and Hu C., “Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial,” Mechanical Systems and Signal Processing, v205, p.110976, 2023. [ DOI ]
25. Thelen A., Zohair M., Ramamurthy J., Jiao W., Ojha M., Ishtiaque M.U., Kingston T.A., Pint C.L., and Hu C., “Sequential Bayesian Optimization for Accelerating the Design of Sodium Metal Battery Nucleation Layers,” Journal of Power Sources, v581, 233508 (14pp), 2023. [ DOI ]
26. Mishra A., Liu X., Hu C., and Wang P., “Reliability-Informed End-of-Use Decision Making for a Product Family Using Two-stage Stochastic Optimization,” Applied Mathematical Modeling, v121, p364–385, 2023. [ DOI ]
27. Lu H., Nemani V.P., Barzegar V., Allen C., Hu C., Laflamme S., Sarkar S., and Zimmerman A.T., “A physics-informed feature weighting method for bearing fault diagnostics,” Mechanical Systems and Signal Processing, v191, p.110171, 2023. [ DOI ]
28. Nemani V.P., Thelen A., Hu C., and Daining S., “Degradation-Aware Ensemble of Diverse Learners for Remaining Useful Life Prediction,” Journal of Mechanical Design, v145, n3, p.031706, 2023. [ DOI ]
29. Thompson T., Liu J., and Hu C., “A Comparative Analysis of Step Loading and Staircase Testing for Fatigue Strength Estimation of an Engine Component,” Fatigue & Fracture of Engineering Materials & Structures, v46, n2, p667–681, 2023. [ DOI ]
30. Thelen A., Zhang X., Fink O., Lu Y., Ghosh S., Youn B.D., Todd M.D., Mahadevan S., Hu C., and Hu Z., “A Comprehensive Review of Digital Twin - Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives,” Structural and Multidisciplinary Optimization, v66, n1, p.1 2023. [ DOI ]
31. Nelson M., Laflamme S., Hu C., Moura A.G., Hong J., Downey A., Lander P., Wang Y., Blasch E., and Dodson J., “Generated datasets from dynamic reproduction of projectiles in ballistic environments for advanced research (DROPBEAR) testbed,” IOP SciNotes, v3, n4, p.044401, 2023. [ DOI ]
32. Thelen A., Zhang X., Fink O., Lu Y., Ghosh S., Youn B.D., Todd M.D., Mahadevan S., Hu C., and Hu Z., “A Comprehensive Review of Digital Twin - Part 1: Modeling and Twinning Enabling Technologies,” Structural and Multidisciplinary Optimization, v65, n12, p354, 2022. [ DOI ]
33. Lui Y.H., Shahriar M., Pan Y., Hu C., and Hu S., “Surrogate Modeling of Acoustic Field-Assisted Particle Patterning Process with Physics-Informed Encoder-Decoder Approach,” Structural and Multidisciplinary Optimization, 65(11), p.333, 2022. [ DOI ]
34. Nemani V.P., Bray, A., Thelen A., Hu C., and Daining S., “Health Index Construction with Feature Fusion Optimization for Predictive Maintenance of Physical Systems,” Structural and Multidisciplinary Optimization, 65(12), p.349, 2022. [ DOI ]
35. Thelen A., Li M., Hu C., Bekyarova E., Kalinin S., and Sanghadasa M.. “Augmented model-based framework for battery remaining useful life prediction,” Applied Energy, v324, 119624 (pp18), 2022. [ DOI ]
36. Nelson M., Barzegar V., Laflamme S., Hu C., Downey A., Bakos J., Thelen A., and Dodson J., “Multi-Step Ahead State Estimation with Hybrid Algorithm for High-Rate Dynamic Systems,” Mechanical Systems and Signal Processing, v182, 109536 (14pp), 2022. [ DOI ]
37. Lu H., Barzegar V., Nemani V.P., Thelen A., Hu C., Laflamme S., and Zimmerman A., “Joint training of a predictor network and a generative adversarial network for time series forecasting: A case study of bearing prognostics,”, Expert Systems with Applications, v203, 117415 (pp19), 2022. [ DOI ]
38. Thelen A., Lui Y.H., Shen S., Laflamme S., Hu S., and Hu C., “Integrating Physics-Based Modeling and Machine Learning for Degradation Diagnostics of Lithium-Ion Batteries,” Energy Storage Materials, v50, p668–695, 2022. [ DOI ]
39. Nemani V.P., Liu J., Ahmed N., Cartwright A., Kremer G.E., and Hu C., “Reliability-Informed Economic and Energy Evaluation for Bi-Level Design for Remanufacturing: A Case Study of Transmission and Hydraulic Manifold,” Journal of Mechanical Design, v144, n8, 08214 (17pp), 2022. [ DOI ]
40. Nemani V.P., Lu H., Thelen A., Hu C., and Zimmerman A., “Ensembles of Probabilistic LSTM Predictors and Correctors for Bearing Prognostics Using Industrial Standards,” Neurocomputing, v491, p575–596, 2022 [ DOI ]
41. Giahi R., MacKenzie C., and Hu C., “Optimizing the Flexible Design of Hybrid Renewable Energy Systems,” Engineering Economist, v67, n1, p25–51, 2022. [ DOI ]
42. Barzegar V., Laflamme S., Hu C., and Dodson J., “Ensemble of Recurrent Neural Networks with Long Short-Term Memory Cells for High-Rate Structural Health Monitoring,” Mechanical Systems and Signal Processing, v164, 108201 (15pp), 2022. [ DOI ]
43. Yang Y.H., Wei H.P., Han B., and Hu, C., “Implementation and Performance Evaluation of a Bivariate Cut-HDMR Metamodel for Semiconductor Packaging Design Problems with a Large Number of Input Variables,” Materials, v14, n16, 4619 (16pp), 2021. [ DOI ]
44. Shen S., Lu H., Sadoughi M., Hu C., Nemani V., Thelen A., Webster K., Darr M., Kenny S., and Sidon J., “A Physics-Informed Deep Learning Approach for Bearing Fault Detection,” Engineering Applications of Artificial Intelligence, v103, 104295 (15pp), 2021. [ DOI ]
45. Li M., Nemani V.P., Liu J., Lee M.A., Ahmed N., Kremer G.E., and Hu C., “Reliability-Informed Life Cycle Warranty Cost and Life Cycle Analysis of Newly Manufactured and Remanufactured Units,” Journal of Mechanical Design, v143, n11, 112001 (14pp), 2021. [ DOI ]
46. Barzegar V., Laflamme S., Hu C., and Dodson J., “Multi-time Resolution Ensemble LSTMs for Enhanced Feature Extraction in High-Rate Time Series,” Sensors, v21, n6, 1954 (18pp), 2021 [ DOI ]
47. Li M., Shen S., Barzegar V., Sadoughi M., Hu C., and Laflamme S., “Kriging-Based Reliability Analysis Considering Predictive Uncertainty Reduction,” Structural and Multidisciplinary Optimization, v63, p2721–2737, 2021. [ DOI ]
48. Gargh P., Sarkar A., Lui Y.H., Shen S., Hu C., Hu S., Nlebedim I.C., and Shrotriya P., “Correlating Capacity Fade with Film Resistance Loss in Fast Charging of Lithium-ion Battery,” Journal of Power Sources, v485, 229360 (7pp), 2021. [ DOI ]
49. Lui Y., Li M., Downey A., Shen S., Nemani V.P., Ye H., VanElzen C., Jain G., Hu S., Laflamme S., and Hu C., “Physics-Based Prognostics of Implantable-Grade Lithium-Ion Battery for Remaining Useful Life Prediction,” Journal of Power Sources, v485, 229327 (15pp), 2021. [ DOI ]
50. Liu J., Hu C., Kimber A., and Wang Z., “Uses, Cost-Benefit Analysis, and Markets of Energy Storage Systems for Electric Grid Applications,” Journal of Energy Storage, v32, 101731 (16pp), 2020. [ DOI ]
51. Barzegar V., Laflamme S., Downey A., Li M., and Hu C., “Numerical Evaluation of a Novel Passive Variable Friction Damper for Vibration Mitigation,” Engineering Structures, v220, 110920 (12pp), 2020. [ DOI ]
52. Sadoughi M., Hu C., Moghadassian B., Sharma A., Beck J., and Mathiesen D., “Sequential Online Dispatch in Design of Experiments for Single- and Multi-Response Surrogate Modeling,” IEEE Transactions on Automation Science and Engineering, v17, n4, p1674–1688, 2020. [ DOI ]
53. Li M., Sadoughi M., Hu Z., and Hu C., “A Hybrid Gaussian Process Model for System Reliability Analysis,” Reliability Engineering and System Safety, v197, 106816 (15pp), 2020. [ DOI ]
54. Shen S., Sadoughi M., Li M., Wang Z., and Hu C., “Deep Convolutional Neural Networks with Ensemble Learning and Transfer Learning for Capacity Estimation of Lithium-ion Batteries,” Applied Energy, v260, p114296(14), 2020. [ DOI ]
55. Giahi R., MacKenzie C., and Hu C., “Design Optimization for Resilience for Risk-Averse Firms,” Computers and Industrial Engineering, v139, 106122 (14pp), 2020. [ DOI ]
56. Sadoughi M. and Hu C., “Physics-Based Convolutional Neural Network for Fault Diagnosis of Rolling Element Bearings,” IEEE Sensors Journal, v19, n11, p4181–4192, 2019. [ DOI ]
57. Li M., Sadoughi M., Hu C., Hu Z., Eshghi A.T., and Lee S., “High-Dimensional Reliability-based Design Optimization Involving Highly Nonlinear Constraints and Computationally Expensive Simulations,” Journal of Mechanical Design, v141, n5, 051402 (14pp), 2019. [ DOI ]
58. Sarkar A., Shrotriya P., Chandra A., and Hu C., “Chemo-Economic Analysis of Battery Aging and Capacity Fade in Lithium-Ion Battery,” Journal of Energy Storage, v25, p100911, 2019. [ DOI ]
59. Shen S., Sadoughi M., Chen X.Y., Hong M.Y., and Hu C., “A Deep Learning Method for Online Capacity Estimation of Lithium-Ion Batteries,” Journal of Energy Storage, v25, p100817, 2019. [ DOI ]
60. Downey A., Lui Y., Hu C., Laflamme S., and Hu S., “Physics-Based Prognostics of Lithium-Ion Battery Using Non-linear Least Squares with Dynamic Bounds,” Reliability Engineering and System Safety, v182, p1–12, 2019. [ DOI ]
61. Li Z., Wu D., Hu C., and Terpenny J., “An Ensemble Learning-based Prognostic Approach with Degradation-Dependent Weights for Remaining Useful Life Prediction,” Reliability Engineering and System Safety, v184, p110–122, 2019. [ DOI ]
62. Nahvi A., Sadoughi M., Arabzadeh A., Sassani S., Hu C., Ceylan H., and Kim S., “Multi-objective Bayesian Optimization of Super hydrophobic Coatings on Asphalt Concrete Surfaces,” Journal of Computational Design and Engineering, v19, n11, p4181–4192, 2019. [ DOI ]
63. MacKenzie C. and Hu C., “Decision Making under Uncertainty for Design of Resilient Engineered Systems,” Reliability Engineering and System Safety, v192, 1061719 (10pp), 2019. [ DOI ]
64. Hu Z., Hu C., Zissimos P.M., and Sankaran M., “Model Discrepancy Quantification in Simulation-based Design of Dynamical Systems,” Journal of Mechanical Design, v141, n1, 011401 (13pp), 2018. [ DOI ]
65. Downey A., Sadoughi M., Laflamme S., and Hu C., “Incipient Damage Detection for Large Area Structures Monitored with a Network of Soft Elastomeric Capacitors Using Relative Entropy,” IEEE Sensors Journal, v18, n21, p8827–8834, 2018. [ DOI ]
66. Sadoughi M., Li M., and Hu C., “Multivariate System Reliability Analysis Considering Highly Nonlinear and Dependent Safety Events,” Reliability Engineering and System Safety, v180, p189–200, 2018. [ DOI ]
67. Hu C., Hui Y., Jain G., and Schmidt C., “Remaining Useful Life Assessment of Lithium-Ion Batteries in Implantable Medical Devices,” Journal of Power Sources, v375, p118–130, 2018.  [ DOI ]
68. Downey A., Sadoughi M., Laflamme S., and Hu C., “Fusion of sensor geometry into additive strain fields measured with sensing skin,” Smart Materials and Structures, v27, n7, p.075033, 2018. [ DOI ]
69. Sadoughi M., Li M., Hu C., MacKenzie C., Eshghi A.T., and Lee S., “A High-Dimensional Reliability Analysis Method for Simulation-Based Design Under Uncertainty,” Journal of Mechanical Design, v140, n7, 071401(12), 2018. [ DOI ]
70. Sadoughi M., Downey A., Yan J., Hu C., and Laflamme S., “Reconstruction of Unidirectional Strain Maps via Iterative Signal Fusion for Mesoscale Structures Monitored by a Sensing Skin,” Mechanical Systems and Signal Processing, v112, p401–416, 2018. [ DOI ]
71. Li Z., Jiang Y., Guo Q., Hu C., and Peng Z., “Multi-Dimensional Variational Decomposition for Bearing-Crack Detection in Wind Turbines with Large Driving-Speed Variations,” Renewable Energy, v116 (Part B), p55–73, 2018. [ DOI ]
72. Downey A., Hu C., and Laflamme S., “Optimal Sensor Placement within a Hybrid Dense Sensor Network using an Adaptive Genetic Algorithm with Learning Gene Pool,” Structural Health Monitoring, v17, n3, p450–460, 2018. [ DOI ]
73. Sadoughi M., Hu C., MacKenzie C., Eshghi A.T., and Lee S., “Sequential Exploration-Exploitation with Dynamic Trade-off for Efficient Reliability Analysis of Complex Engineered Systems,” Structural and Multidisciplinary Optimization, v57, n1, p235–250, 2018. [ DOI ]
74. Eshghi A.T., Lee S., Sadoughi M., Hu C., and Kim C., “Design Optimization under Uncertainty and Speed Variability for a Piezoelectric Energy Harvester Powering a Tire Pressure Monitoring Sensor,” Smart Materials and Structures, v26, n10, 105037, p1–18, 2017. [ DOI ]
75. Zhang C., Li Z., Hu C., Chen S., Wang J., and Zhang X., “An Optimized Ensemble Local Mean Decomposition Method for Fault Detection of Mechanical Components,” Measurement Science and Technology, v28, n3, 035102 (15pp), 2017. [ DOI ]
76. Seong S., Hu C., and Lee S., “Design under Uncertainty for Reliable Power Generation of Piezoelectric Energy Harvester,” Journal of Intelligent Material Systems and Structures,  v28, n17, p2437–2449, 2017. [ DOI ]
77. Li Z., Jiang Y., Hu C., and Peng Z., “Recent Progress on Decoupling Diagnosis of Hybrid Failures in Gear Transmission Systems Using Vibration Sensor Signal: A Review,” Measurement, v90, p4–19, 2016. [ DOI ]
78. Jiang Y., Li Z., Zhang C., Hu C., and Peng Z., “On the Bi-Dimensional Variational Decomposition Applied to Nonstationary Vibration Signals for Rolling Bearing Crack Detection in Coal Cutters,” Measurement Science and Technology, v27, n6, p065103, 2016. [ DOI ]
79. Youn B.D., Park K.M., Hu C., Yoon, J.T., and Bae Y.C., “Statistical Health Reasoning of Water-Cooled Power Generator Stator Bars Against Moisture Absorption,” IEEE Transactions on Energy Conversion, v30, n4, p1–10, 2015. [ DOI ]
80. Hu C., Jain G., Schmidt C., Strief C., and Sullivan M., “Online Estimation of Lithium-Ion Battery Capacity Using Sparse Bayesian Learning,” Journal of Power Sources, v289, p105–113, 2015. [ DOI ]
81. Hu C., Youn B.D., Kim T.J., and Wang P., “Semi-Supervised Learning with Co-Training for Data-Driven Prognostics,” Mechanical Systems and Signal Processing, v62–63, p75–90, 2015. [ DOI ]
82. Bai G., Wang P., and Hu C., “A Self-Cognizant Dynamic System Approach for Prognostics and Health Management,” Journal of Power Sources, v278, p163–174, 2015. [ DOI ]
83. Wang P., Youn B.D., Hu C., Jong M.H., and Jeon B., “A Probabilistic Detectability-Based Sensor Network Design Method for System Health Monitoring and Prognostics,” Journal of Intelligent Material Systems and Structures, v26, n9, p1079–1090, 2015. [ DOI ]
84. Bai G., Wang, P., Hu C., and Pecht M., “A Generic Model-Free Approach for Lithium-ion Battery Health Management,” Applied Energy, v135, p247–260, 2014. [ DOI ]
85. Fathi R., Burns J.C., Stevens D.A., Ye H., Hu C., Jain G., Scott E., Schmidt C., and Dahn J.R., “Ultra High-Precision Studies of Degradation Mechanisms in Aged LiCoO2/Graphite Li-Ion Cells,” Journal of The Electrochemical Society, v161, n10, A1572–A1579, 2014.  [ DOI ]
86. Hu C., Jain G., Zhang P., Schmidt C., Gomadam P., and Gorka T., “Data-Driven Approach Based on Particle Swarm Optimization and K-Nearest Neighbor Regression for Estimating Capacity of Lithium-Ion Battery,” Applied Energy, v129, p49–55, 2014. [ DOI ]
87. Hu C., Jain G., Tamirisa P., and Gorka T., “Method for Estimating Capacity and Predicting Remaining Useful Life of Lithium-Ion Battery,” Applied Energy, v126, p182–189, 2014. [ DOI ]
88. Xi Z., Wang P., Jing R., and Hu C., “A Copula-Based Sampling Method for Data-Driven Prognostics,” Reliability Engineering and System Safety, v132, p72–82, 2014. [ DOI ]
89. Tamilselvan P., Wang P., and Hu C., “Health Diagnostics Using Multi-Attribute Classification Fusion,” Engineering Applications of Artificial Intelligence, v32, p192–202, 2014. [ DOI ]
90. Hu C., and Youn B.D., “An Adaptive Dimension Decomposition and Reselection Method for Reliability Analysis,” Structural and Multidisciplinary Optimization, v47, n3, p423–440, 2013. [ DOI ]
91. Hu C., Youn B.D., and Wang P., “Ensemble of Data-Driven Prognostic Algorithms for Robust Prediction of Remaining Useful Life,” Reliability Engineering and System Safety, v103, p120–135, 2012. [ DOI ]
92. Hu C., Wang P., Youn B.D., and Lee W.R., “Copula-Based Statistical Health Grade System against Mechanical Faults of Power Transformers,” IEEE Transactions on Power Delivery, v27, n4, p1809–1819, 2012. [ DOI ]
93. Wang P., Youn B.D., and Hu C., “A Generic Probabilistic Framework for Structural Health Prognostic and Uncertainty Management,” Mechanical Systems and Signal Processing, v28, p622–637, 2012. [ DOI ]
94. Hu C., Youn B.D., and Chung J., “A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation,” Applied Energy, v92, p694–704, 2012. [ DOI ]
95. Xi Z., Hu C., and Youn B.D., “A Comparative Study of Probability Estimation Methods for Reliability Analysis,” Structural and Multidisciplinary Optimization, v45, n1, p33‒52, 2012. [ DOI ]
96. Youn B.D., Hu C., and Wang P., “Resilience-Driven System Design of Complex Engineered Systems,” Journal of Mechanical Design, v133, n10, 101011 (15pp), 2011. [ DOI ]
97. Wang P., Hu C., and Youn B.D., “A Generalized Complementary Intersection Method for System Reliability Analysis and Design,” Journal of Mechanical Design, v133, n7, 071003 (13pp), 2011. [ DOI ]
98. Youn B.D., Hu C., Wang P., and Yoon J.T., “Resilience Allocation for Resilient Engineered System Design,” Journal of Institute of Control, Robotics and Systems, v17, n11, p1082-1089, 2011. [ DOI ]
99. Hu C. and Youn B.D., “An Asymmetric Dimension-Adaptive Tensor-Product Method for Reliability Analysis,” Structural Safety, v33, n3, p218‒231, 2011. [ DOI ]
100. Hu C. and Youn B.D., “Adaptive-Sparse Polynomial Chaos Expansion for Reliability Analysis and Design of Complex Engineering Systems,” Structural and Multidisciplinary Optimization, v43, n3, p419‒442, 2011. [ DOI ]
101. Xi Z., Youn B.D., and Hu C., “Random Field Characterization Considering Statistical Dependence for Probability Analysis and Design,” Journal of Mechanical Design, v132, n10, 101008 (12pp), 2010. [ DOI ]

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