89. Wang, H., Y. Hou, Y. He, C. Wen, B.
Giron-Palomares, Y. Duan, B. Gao, V. P. Vavilov,
and Y. Wang. 2024. “A Physical-Constrained
Decomposition Method of Infrared Thermog-
raphy: Pseudo Restored Heat Flux Approach
Based on Ensemble Bayesian Variance Tensor
Fraction.” IEEE Transactions on Industrial Infor-
matics 20 (3): 3413–24. https://doi.org/10.1109/
TII.2023.3293863.
90. Zaini, M. A. H. P., M. M. Saari, N. A. Nadzri,
Z. Aziz, N. H. Ramlan, and K. Tsukada. 2021.
“Extraction of Flux Leakage and Eddy Current
Signals Induced by Submillimeter Backside Slits
on Carbon Steel Plate Using a Low-Field AMR
Differential Magnetic Probe.” IEEE Access: Prac-
tical Innovations, Open Solutions 9: 146755–70.
https://doi.org/10.1109/ACCESS.2021.3123421.
91. Khan, T., and P. Ramuhalli. 2008. “A
Recursive Bayesian Estimation Method for
Solving Electromagnetic Nondestructive Eval-
uation Inverse Problems.” IEEE Transactions
on Magnetics 44 (7): 1845–55. https://doi.
org/10.1109/TMAG.2008.921842.
92. Piao, G., J. Guo, T. Hu, and H. Leung. 2020.
“The Effect of Motion-Induced Eddy Current
on High-Speed Magnetic Flux Leakage (MFL)
Inspection for Thick-Wall Steel Pipe.” Research
in Nondestructive Evaluation 31 (1): 48–67.
https://doi.org/10.1080/09349847.2019.1595987.
93. Dai, W., X. Li, and K.-T. Cheng, 2023.
“Semi-Supervised Deep Regression with
Uncertainty Consistency and Variational Model
Ensembling via Bayesian Neural Networks.”
arXiv. https://doi.org/10.48550/arXiv.2302.07579.
94. Wan, Q., and X. Fu. 2020. “Fast-BCNN:
Massive Neuron Skipping in Bayesian Convo-
lutional Neural Networks.” In 2020 53rd
Annual IEEE/ACM International Symposium
on Microarchitecture (MICRO): 229–240.
Athens, Greece: IEEE. https://doi.org/10.1109/
MICRO50266.2020.00030.
95. Ahmed, S. T., K. Danouchi, M. Hefenbrock,
G. Prenat, L. Anghel, and M. B. Tahoori. 2024.
“Spatial-SpinDrop: Spatial Dropout-Based
Binary Bayesian Neural Network with Spin-
tronics Implementation.” IEEE Transactions
on Nanotechnology 23: 636–43. https://doi.
org/10.1109/TNANO.2024.3445455.
96. Xue, H., M. Zhang, P. Yu, H. Zhang, G.
Wu, Y. Li, and X. Zheng. 2021. “A Novel Multi-
Sensor Fusion Algorithm Based on Uncertainty
Analysis.” Sensors (Basel) 21 (8): 2713. https://
doi.org/10.3390/s21082713.
97. Ernst, D., S. Vogel, H. Alkhatib, and I.
Neumann. 2024. “Monte Carlo variance prop-
agation for the uncertainty modeling of a
kinematic LiDAR-based multi-sensor system.”
Journal of Applied Geodesy 18 (2): 237–52.
https://doi.org/10.1515/jag-2022-0033.
98. W. Chen, T. Huang, and A. Maalla,
“Research on Adaptive Monte Carlo Location
Method Based on Fusion Posture Estimation,” in
2019 IEEE 3rd Advanced Information Manage-
ment, Communicates, Electronic and Automa-
tion Control Conference (IMCEC), Chongqing,
China: IEEE, Oct. 2019, pp. 1209–1213. https://
doi.org/10.1109/IMCEC46724.2019.8983808.
99. Thin, A., N. Kotelevskii, A. Doucet, A.
Durmus, E. Moulines, and M. Panov. 2021.
“Monte Carlo Variational Auto-Encoders.” arXiv.
https://doi.org/10.48550/ARXIV.2106.15921.
100. Şahin, T., D. Wolff, M. Von Danwitz, and
A. Popp. 2024. “Towards a Hybrid Digital Twin:
Fusing Sensor Information and Physics in Surro-
gate Modeling of a Reinforced Concrete Beam.”
In 2024 Sensor Data Fusion: Trends, Solutions,
Applications (SDF): 1–8. Bonn, Germany: IEEE.
https://doi.org/10.1109/SDF63218.2024.10773885.
101. Land, W. H., and J. D. Schaffer. 2020.
“Bayesian Probabilistic Neural Network
(BPNN).” In The Art and Science of Machine
Intelligence: 187–210. Springer Cham. https://doi.
org/10.1007/978-3-030-18496-4_7.
102. Bott, A., B. Liu, A. Puchta, and J. Fleischer.
2024. “Machine Learning-Driven RUL Prediction
and Uncertainty Quantification for Ball Screw
Drives in a Cloud-Ready Maintenance Frame-
work.” Journal of Machine Engineering 24 (3):
17–31. https://doi.org/10.36897/jme/192681.
103. Yardimci, Y., and M. Cavus. 2025.
“Rashomon perspective for measuring uncer-
tainty in the survival predictive maintenance
models.” arXiv. https://doi.org/10.48550./
arXiv.2502.15772.
104. Akbar, M. A., U. Qidwai, and M. R. Jahan-
shahi. 2019. “An evaluation of image-based
structural health monitoring using integrated
unmanned aerial vehicle platform.” Structural
Control and Health Monitoring 26 (1): e2276.
https://doi.org/10.1002/stc.2276.
105. Pairet, E., J. D. Hernandez, M. Lahijanian,
and M. Carreras. 2018. “Uncertainty-based
Online Mapping and Motion Planning for
Marine Robotics Guidance.” In 2018 IEEE/RSJ
International Conference on Intelligent Robots
and Systems (IROS): 2367–2374. Madrid: IEEE.
https://doi.org/10.1109/IROS.2018.8593394.
106. Ginting, M. F., D. D. Fan, S.-K. Kim, M. J.
Kochenderfer, and A.-A. Agha-Mohammadi.
2024. “Semantic Belief Behavior Graph: Enabling
Autonomous Robot Inspection in Unknown
Environments.” In 2024 IEEE/RSJ Interna-
tional Conference on Intelligent Robots and
Systems (IROS): 7604–7610. Abu Dhabi, United
Arab Emirates: IEEE. https://doi.org/10.1109/
IROS58592.2024.10802336.
107. Gupta, R., B. Tian, Y. Wang, and K.
Nahrstedt. 2024. “TWIN-ADAPT: Continuous
Learning for Digital Twin-Enabled Online
Anomaly Classification in IoT-Driven Smart
Labs.” Future Internet 16 (7): 239. https://doi.
org/10.3390/fi16070239.
108. Pérez, E., C. E. Ardic, O. Çakıroğlu, K.
Jacob, S. Kodera, L. Pompa, M. Rachid, H.
Wang, Y. Zhou, C. Zimmer, F. Römer, and A.
Osman. 2024. “Integrating AI in NDE: Tech-
niques, Trends, and Further Directions.” arXiv.
https://doi.org/10.48550/arXiv.2404.03449.
109. Jamil, M. N., O. Schelén, A. Afif Monrat,
and K. Andersson. 2024. “Enabling Industrial
Internet of Things by Leveraging Distributed
Edge-to-Cloud Computing: Challenges and
Opportunities.” IEEE Access: Practical Innova-
tions, Open Solutions 12: 127294–308. https://
doi.org/10.1109/ACCESS.2024.3454812.
110. Aghaei, M., M. Kolahi, A. Nedaei, N. S.
Venkatesh, S. M. Esmailifar, A. M. Moradi
Sizkouhi, A. Aghamohammadi, A. K. V. Oliveira,
A. Eskandari, P. Parvin, J. Milimonfared, V.
Sugumaran, and R. Rüther. 2025. “Autonomous
Intelligent Monitoring of Photovoltaic Systems:
An In‐Depth Multidisciplinary Review.” Progress
in Photovoltaics: Research and Applications 33
(3): 381–409. https://doi.org/10.1002/pip.3859.
111. Knopp, J. S., J. C. Aldrin, and M. P. Blodgett.
2011. “Efficient Propagation of Uncertainty in
Simulations Via the Probabilistic Collocation
Method.” Electromagnetic Nondestructive Eval-
uation: 141–148. Szczecin, Poland: XIV. https://
doi.org/10.3233/978-1-60750-750-5-141.
112. Knopp, J. S., R. Grandhi, J. C. Aldrin, and I.
Park. 2013. “Statistical Analysis of Eddy Current
Data from Fastener Site Inspections.” Journal of
Nondestructive Evaluation 32 (1): 44–50. https://
doi.org/10.1007/s10921-012-0157-5.
113. Knopp, J. S. 2014. “Modern statistical
methods and uncertainty quantification for
evaluating reliability of nondestructive evalua-
tion systems.” Dissertation. Wright State Univer-
sity. https://corescholar.libraries.wright.edu/
etd_all/1178/.
114. Cheng, Y., Y. Deng, J. Cao, X. Xiong,
L. Bai, and Z. Li. 2013. “Multi-Wave and Hybrid
Imaging Techniques: A New Direction for
Nondestructive Testing and Structural Health
Monitoring.” Sensors, 13 (12): 16146–16190.
https://doi.org/10.3390/s131216146.
A U G U S T 2 0 2 5 • M AT E R I A L S E V A L U AT I O N 39
Giron-Palomares, Y. Duan, B. Gao, V. P. Vavilov,
and Y. Wang. 2024. “A Physical-Constrained
Decomposition Method of Infrared Thermog-
raphy: Pseudo Restored Heat Flux Approach
Based on Ensemble Bayesian Variance Tensor
Fraction.” IEEE Transactions on Industrial Infor-
matics 20 (3): 3413–24. https://doi.org/10.1109/
TII.2023.3293863.
90. Zaini, M. A. H. P., M. M. Saari, N. A. Nadzri,
Z. Aziz, N. H. Ramlan, and K. Tsukada. 2021.
“Extraction of Flux Leakage and Eddy Current
Signals Induced by Submillimeter Backside Slits
on Carbon Steel Plate Using a Low-Field AMR
Differential Magnetic Probe.” IEEE Access: Prac-
tical Innovations, Open Solutions 9: 146755–70.
https://doi.org/10.1109/ACCESS.2021.3123421.
91. Khan, T., and P. Ramuhalli. 2008. “A
Recursive Bayesian Estimation Method for
Solving Electromagnetic Nondestructive Eval-
uation Inverse Problems.” IEEE Transactions
on Magnetics 44 (7): 1845–55. https://doi.
org/10.1109/TMAG.2008.921842.
92. Piao, G., J. Guo, T. Hu, and H. Leung. 2020.
“The Effect of Motion-Induced Eddy Current
on High-Speed Magnetic Flux Leakage (MFL)
Inspection for Thick-Wall Steel Pipe.” Research
in Nondestructive Evaluation 31 (1): 48–67.
https://doi.org/10.1080/09349847.2019.1595987.
93. Dai, W., X. Li, and K.-T. Cheng, 2023.
“Semi-Supervised Deep Regression with
Uncertainty Consistency and Variational Model
Ensembling via Bayesian Neural Networks.”
arXiv. https://doi.org/10.48550/arXiv.2302.07579.
94. Wan, Q., and X. Fu. 2020. “Fast-BCNN:
Massive Neuron Skipping in Bayesian Convo-
lutional Neural Networks.” In 2020 53rd
Annual IEEE/ACM International Symposium
on Microarchitecture (MICRO): 229–240.
Athens, Greece: IEEE. https://doi.org/10.1109/
MICRO50266.2020.00030.
95. Ahmed, S. T., K. Danouchi, M. Hefenbrock,
G. Prenat, L. Anghel, and M. B. Tahoori. 2024.
“Spatial-SpinDrop: Spatial Dropout-Based
Binary Bayesian Neural Network with Spin-
tronics Implementation.” IEEE Transactions
on Nanotechnology 23: 636–43. https://doi.
org/10.1109/TNANO.2024.3445455.
96. Xue, H., M. Zhang, P. Yu, H. Zhang, G.
Wu, Y. Li, and X. Zheng. 2021. “A Novel Multi-
Sensor Fusion Algorithm Based on Uncertainty
Analysis.” Sensors (Basel) 21 (8): 2713. https://
doi.org/10.3390/s21082713.
97. Ernst, D., S. Vogel, H. Alkhatib, and I.
Neumann. 2024. “Monte Carlo variance prop-
agation for the uncertainty modeling of a
kinematic LiDAR-based multi-sensor system.”
Journal of Applied Geodesy 18 (2): 237–52.
https://doi.org/10.1515/jag-2022-0033.
98. W. Chen, T. Huang, and A. Maalla,
“Research on Adaptive Monte Carlo Location
Method Based on Fusion Posture Estimation,” in
2019 IEEE 3rd Advanced Information Manage-
ment, Communicates, Electronic and Automa-
tion Control Conference (IMCEC), Chongqing,
China: IEEE, Oct. 2019, pp. 1209–1213. https://
doi.org/10.1109/IMCEC46724.2019.8983808.
99. Thin, A., N. Kotelevskii, A. Doucet, A.
Durmus, E. Moulines, and M. Panov. 2021.
“Monte Carlo Variational Auto-Encoders.” arXiv.
https://doi.org/10.48550/ARXIV.2106.15921.
100. Şahin, T., D. Wolff, M. Von Danwitz, and
A. Popp. 2024. “Towards a Hybrid Digital Twin:
Fusing Sensor Information and Physics in Surro-
gate Modeling of a Reinforced Concrete Beam.”
In 2024 Sensor Data Fusion: Trends, Solutions,
Applications (SDF): 1–8. Bonn, Germany: IEEE.
https://doi.org/10.1109/SDF63218.2024.10773885.
101. Land, W. H., and J. D. Schaffer. 2020.
“Bayesian Probabilistic Neural Network
(BPNN).” In The Art and Science of Machine
Intelligence: 187–210. Springer Cham. https://doi.
org/10.1007/978-3-030-18496-4_7.
102. Bott, A., B. Liu, A. Puchta, and J. Fleischer.
2024. “Machine Learning-Driven RUL Prediction
and Uncertainty Quantification for Ball Screw
Drives in a Cloud-Ready Maintenance Frame-
work.” Journal of Machine Engineering 24 (3):
17–31. https://doi.org/10.36897/jme/192681.
103. Yardimci, Y., and M. Cavus. 2025.
“Rashomon perspective for measuring uncer-
tainty in the survival predictive maintenance
models.” arXiv. https://doi.org/10.48550./
arXiv.2502.15772.
104. Akbar, M. A., U. Qidwai, and M. R. Jahan-
shahi. 2019. “An evaluation of image-based
structural health monitoring using integrated
unmanned aerial vehicle platform.” Structural
Control and Health Monitoring 26 (1): e2276.
https://doi.org/10.1002/stc.2276.
105. Pairet, E., J. D. Hernandez, M. Lahijanian,
and M. Carreras. 2018. “Uncertainty-based
Online Mapping and Motion Planning for
Marine Robotics Guidance.” In 2018 IEEE/RSJ
International Conference on Intelligent Robots
and Systems (IROS): 2367–2374. Madrid: IEEE.
https://doi.org/10.1109/IROS.2018.8593394.
106. Ginting, M. F., D. D. Fan, S.-K. Kim, M. J.
Kochenderfer, and A.-A. Agha-Mohammadi.
2024. “Semantic Belief Behavior Graph: Enabling
Autonomous Robot Inspection in Unknown
Environments.” In 2024 IEEE/RSJ Interna-
tional Conference on Intelligent Robots and
Systems (IROS): 7604–7610. Abu Dhabi, United
Arab Emirates: IEEE. https://doi.org/10.1109/
IROS58592.2024.10802336.
107. Gupta, R., B. Tian, Y. Wang, and K.
Nahrstedt. 2024. “TWIN-ADAPT: Continuous
Learning for Digital Twin-Enabled Online
Anomaly Classification in IoT-Driven Smart
Labs.” Future Internet 16 (7): 239. https://doi.
org/10.3390/fi16070239.
108. Pérez, E., C. E. Ardic, O. Çakıroğlu, K.
Jacob, S. Kodera, L. Pompa, M. Rachid, H.
Wang, Y. Zhou, C. Zimmer, F. Römer, and A.
Osman. 2024. “Integrating AI in NDE: Tech-
niques, Trends, and Further Directions.” arXiv.
https://doi.org/10.48550/arXiv.2404.03449.
109. Jamil, M. N., O. Schelén, A. Afif Monrat,
and K. Andersson. 2024. “Enabling Industrial
Internet of Things by Leveraging Distributed
Edge-to-Cloud Computing: Challenges and
Opportunities.” IEEE Access: Practical Innova-
tions, Open Solutions 12: 127294–308. https://
doi.org/10.1109/ACCESS.2024.3454812.
110. Aghaei, M., M. Kolahi, A. Nedaei, N. S.
Venkatesh, S. M. Esmailifar, A. M. Moradi
Sizkouhi, A. Aghamohammadi, A. K. V. Oliveira,
A. Eskandari, P. Parvin, J. Milimonfared, V.
Sugumaran, and R. Rüther. 2025. “Autonomous
Intelligent Monitoring of Photovoltaic Systems:
An In‐Depth Multidisciplinary Review.” Progress
in Photovoltaics: Research and Applications 33
(3): 381–409. https://doi.org/10.1002/pip.3859.
111. Knopp, J. S., J. C. Aldrin, and M. P. Blodgett.
2011. “Efficient Propagation of Uncertainty in
Simulations Via the Probabilistic Collocation
Method.” Electromagnetic Nondestructive Eval-
uation: 141–148. Szczecin, Poland: XIV. https://
doi.org/10.3233/978-1-60750-750-5-141.
112. Knopp, J. S., R. Grandhi, J. C. Aldrin, and I.
Park. 2013. “Statistical Analysis of Eddy Current
Data from Fastener Site Inspections.” Journal of
Nondestructive Evaluation 32 (1): 44–50. https://
doi.org/10.1007/s10921-012-0157-5.
113. Knopp, J. S. 2014. “Modern statistical
methods and uncertainty quantification for
evaluating reliability of nondestructive evalua-
tion systems.” Dissertation. Wright State Univer-
sity. https://corescholar.libraries.wright.edu/
etd_all/1178/.
114. Cheng, Y., Y. Deng, J. Cao, X. Xiong,
L. Bai, and Z. Li. 2013. “Multi-Wave and Hybrid
Imaging Techniques: A New Direction for
Nondestructive Testing and Structural Health
Monitoring.” Sensors, 13 (12): 16146–16190.
https://doi.org/10.3390/s131216146.
A U G U S T 2 0 2 5 • M AT E R I A L S E V A L U AT I O N 39
