Books
Refereed Articles in Journals and Transactions
Research Articles
J1. Ding, Shi, and Ceglarek, 2002, “Diagnosability analysis of multi-station manufacturing processes,” ASME Transactions, Journal of Dynamic Systems, Measurement, and Control, Vol. 124, pp. 1-13. [pdf]
J2. Ding, Ceglarek, and Shi, 2002, “Fault diagnosis of multi-station manufacturing processes by using state space approach,” ASME Transactions, Journal of Manufacturing Science and Engineering, Vol. 124, pp. 313-322. [pdf]
J3. Ding, Ceglarek, and Shi, 2002, “Design evaluation of multi-station manufacturing processes by using state space approach,” ASME Transactions, Journal of Mechanical Design, Vol. 124, pp. 408-418. [pdf]
J4. Ding, Kim, Ceglarek, and Jin, 2003, “Optimal sensor distribution for variation diagnosis for multi-station assembly processes,” IEEE Transactions on Robotics and Automation, Vol. 19(4), pp. 543-556. [pdf]
J5. Zhou, Ding, Chen, and Shi, 2003, “Diagnosability study of multi-station manufacturing processes based on linear mixed model,” Technometrics, Vol. 45(4), pp. 312-325. [pdf]
J6. Ding, Gupta, and Apley, 2004, “Singularity of fixture fault diagnosis in multi-station assembly systems,” ASME Transactions, Journal of Manufacturing Science and Engineering, Vol. 126, pp. 200-210. [pdf]
J7. Jin and Ding, 2004, “Online automatic process control using observable noise factors for discrete-part manufacturing,” IIE Transactions, Vol. 36(9), pp. 899-911. [pdf]
J8. Kim and Ding, 2004, “Optimal design of fixture layout in multi-station assembly processes,” IEEE Transactions on Automation Science and Engineering, Vol. 1(2), pp. 133-145. [pdf]
J9. Apley and Ding, 2005, “A characterization of diagnosability conditions for variance components analysis in assembly operations,” IEEE Transactions on Automation Science and Engineering, Vol. 2(2), pp. 101-110. [pdf]
J10. Ding, Jin, Ceglarek, and Shi, 2005, “Process-oriented tolerancing for multi-station assembly systems,” IIE Transactions, Vol. 37(6), pp. 493-508. [pdf]
J11. Ding, Zhou, and Chen, 2005, “A comparison of process variance estimation methods for in-process dimensional measurement and control,” ASME Transactions, Journal of Dynamic Systems, Measurement, and Control, Vol. 127(1), pp. 69-79. [pdf]
J12. Kim and Ding, 2005, “Optimal engineering design guided by data-mining methods,” Technometrics, Vol. 47(3), pp. 336-348. [pdf]
J13. Liu, Ding, and Chen, 2005, “Optimal coordinate sensor placements for estimating mean and variance components of variation sources,” IIE Transactions, Vol. 37(9), pp. 877-889. [pdf]
J14. Ding, Zeng, and Zhou, 2006, “Phase-I analysis for monitoring nonlinear profile signals in manufacturing processes.” Journal of Quality Technology, Vol. 38(3), pp. 199-216. [pdf] [data] (note: This data file has 528 data records, two fewer than the 530 number mentioned in the paper.)
J15. Chen, Ding, Jin, and Ceglarek, 2006, “Integration of tolerance and maintenance design for multi-station manufacturing processes,” IEEE Transactions on Automation Science and Engineering, Vol. 3(4), pp. 440-453. [pdf]
J16. Gupta, Ding, Xu, and Reinikainen, 2006 “Optimal parameter selection for electronic packaging using sequential computer simulations,” ASME Transactions, Journal of Manufacturing Science and Engineering, Vol. 128(3), pp. 705-715. [pdf]
J17. Ren, Ding, and Zhou, 2006 “A data-mining approach to study the significance of nonlinearity in multi-station assembly processes.” IIE Transactions, Vol. 38 (12), pp. 1069 – 1083. [pdf]
J18. Xue, Tang, Sammes, and Ding, 2006 “Condition monitoring of PEM fuel cell using Hotelling T 2 control limits,” Journal of Power Sources, Vol. 162, pp. 388-399. [pdf]
J19. Li, Zhou, and Ding, 2007, “Pattern matching for root cause identification of manufacturing processes with the presence of unstructured noise,” IIE Transactions, Vol. 39(3), pp. 251-263. [pdf]
J20. Ding and Apley, 2007, “Guidelines for placing additional sensors to improve variation diagnosis in assembly processes,” International Journal of Production Research, Vol. 45 (23), pp. 5485-5507. [pdf]
J21. Cho, Chen, and Ding, 2007, “On the (co)girth of connected matroids,” Discrete Applied Mathematics, Vol. 155, pp. 2456-2470. [pdf] [code]
J22. Hao, Zhou, and Ding, 2008, “Multivariate process variability monitoring through projection based on a process model,” Journal of Quality Technology, Vol. 40(2), pp. 214-226. [pdf]
J23. Xia, Ding, and Wang, 2008, “Gaussian process method for form error assessment using coordinate measurements,” IIE Transactions, Vol. 40, pp. 931-946. [pdf]
J24. Lu, Wang, Tang, and Ding, 2008, “Damage detection using piezoelectric transducers and Lamb wave approach: II robust and quantitative decision making,” Smart Materials and Structures, Vol. 17(2), paper no. 025034. [pdf]
J25. Ren, Ding, and Liang, 2008, “Adaptive evolutionary Monte Carlo method for optimizations with applications to sensor placement problems” Statistics and Computing, Vol. 18(4), pp. 375-390. [pdf]
J26. Cho, Chen, and Ding, 2009, “Calculating the breakdown point condition of sparse linear models,” Technometrics, Vol. 51(1), pp. 34-46. [pdf] [code]
J27. Ren and Ding, 2009, “Optimal sensor distribution in multi-station assembly processes for maximal variance detection capability” IIE Transactions, special issue on Quality Control and Improvements in Multistage Systems, Vol. 41, pp. 804–818. [pdf]
J28. Cho, Ding, Chen, and Tang, 2010, “Robust calibration for localization in clustered wireless sensor networks,” IEEE Transactions on Automation Science & Engineering, Vol. 7(1), pp. 81-95. [pdf]
J29. Park, Tang, and Ding, 2010, “Aggressive data reduction for damage detection in structural health monitoring,” Structural Health Monitoring, Vol. 9(1), pp. 59-74. [pdf]
J30. Shrivastava and Ding, 2010, “Graph based isomorph-free generation of two-level regular fractional factorial designs.” Journal of Statistical Planning & Inference, Vol. 140, pp. 169 – 179. [pdf] [downloads design tables and C++ codes]
J31. Byon, Shrivastava, and Ding, 2010, “Ensemble classifier for highly imbalanced class sizes,” IIE Transactions, Vol. 42(4), pp. 288-303. [pdf]
J32. Byon, Ntaimo, and Ding, 2010, “Optimal maintenance strategies for wind turbine systems under stochastic weather conditions,” IEEE Transactions on Reliability, Vol. 59, pp. 393-404. [pdf] [online supplement] [correction]
J33. Park, Huang, and Ding, 2010, “A computable plug-in estimator of minimum volume sets for novelty detection,” Operations Research, Vol 58, pp. 1469-1480. [pdf] [E-Companion] [code]
J34. Byon and Ding, 2010, “Season-dependent condition-based maintenance for a wind turbine using a partially observed Markov decision process,” IEEE Transactions on Power Systems, Vol. 25, pp. 1823 -1834. [pdf]
J35. Gaukler, Li, Cannaday, Chirayath, and Ding, 2011, “Detecting nuclear materials smuggling: using radiography to improve container inspection policies,” Annals of Operations Research, Vol. 187, pp. 65 – 87. [pdf]
J36. Gokce, Shrivastava, Cho, and Ding, 2011, “Decision fusion from heterogeneous sensors in surveillance sensor systems,” IEEE Transactions on Automation Science & Engineering, Vol. 8, pp. 228 – 233 [pdf] [online supplement].
J37. Byon, Pérez, Ding, and Ntaimo, 2011, “Simulation of wind farm operations and maintenance using DEVS,” Simulation, Vol. 87(12), pp. 1093-1117. [pdf]
J38. Xia, Ding, and Mallick, 2011, “Bayesian hierarchical models for combining misaligned two-resolution metrology data,” IIE Transactions, Vol. 43, pp. 242 – 258 [pdf] [online supplement] [data].
J39. Kianfar, Pourhabib, and Ding, 2011, “An integer programming approach for analyzing the measurement redundancy in structured linear systems,” IEEE Transactions on Automation Science & Engineering, Vol. 8, pp. 447 – 450. [pdf]
J40. Park, Huang, and Ding, 2011, “Domain decomposition approach for fast Gaussian process regression of large spatial datasets,” Journal of Machine Learning Research, Vol. 12, pp. 1697 – 1728. [pdf] [code]
There is a companion paper [pdf] discussing a toolbox called GPLP. The toolbox and the supporting documents are accessible at MLOSS (Machine Learning Open Source Software) project website http://mloss.org/revision/view/990/.
J41. Park, Huang, Huitink, Kundu, Mallick, Liang, and Ding, 2012, “A multi-stage, semi-automated procedure for analyzing the morphology of nanoparticles,” IIE Transactions; special issue on Nano Manufacturing, Vol. 40, pp. 507-522. [pdf]
J42. Gaukler, Li, Ding, and Chirayath, 2012, “Detecting nuclear materials smuggling: performance evaluation of container inspection policies,” Risk Analysis,Vol. 32, pp. 531-554. [pdf]
J43. Park, Huang, Ji, and Ding, 2013, “Segmentation, inference and classification of partially overlapping nanoparticles,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35 (3), pp. 669-681. [pdf][code][reproducibility report]
J44. Konomi, Dahvala, Huang, Kundu, Huitink, Liang, Ding, and Mallick, 2013, “Bayesian object classification of gold nanoparticles,” Annals of Applied Statistics, Vol. 7(2), pp. 640-668. [pdf]
J45. Bansal, Kianfar, Ding, and Moreno-Centeno, 2013, “Hybridization of bound-and-decompose and mixed integer feasibility checking to measure redundancy in structured linear systems,” IEEE Transactions on Automation Science & Engineering, Vol. 10, pp. 1151-1157. [pdf][code]
J46. Gokce, Shrivastava, and Ding, 2013 “Fault tolerance analysis of surveillance sensor systems,” IEEE Transactions on Reliability, Vol. 62(2), pp. 478-489. [pdf]
J47. Li, Gaukler, and Ding, 2013, “Using container inspection history to improve interdiction of illicit nuclear materials,” Naval Research Logistics, Vol. 60, pp. 433-448. [pdf]
J48. Lee, Byon, Ntaimo, and Ding, 2013, “Bayesian spline method for assessing extreme loads on wind turbines,” Annals of Applied Statistics, Vol. 7, pp. 2034-2061. [pdf][data & code]
J49. Pourhabib, Liang, and Ding, 2014, “Bayesian site selection for fast Gaussian process regression,” IIE Transactions, Vol. 46(5), pp. 543-555. [pdf]
J50. Pourhabib, Huang, Wang, Zhang, Wang, and Ding, 2015, “Modulus prediction of buckypaper based on multi-fidelity analysis involving latent variables,” IIE Transactions, Vol. 47(2), pp. 141-152. [pdf]
J51. Lee, Ding, Xie, and Genton, 2015, “Kernel Plus method for quantifying wind turbine upgrades,” Wind Energy, Vol. 18, pp. 1207-1219. [pdf][data]
J52. Pourhabib, Mallick, and Ding, 2015, “Absent data generating classifier for imbalanced class sizes,” Journal of Machine Learning Research, Vol. 16, pp. 2695-2724. [pdf]
J53. Lee, Ding, Genton, and Xie, 2015, “Power curve estimation with multivariate environmental factors for inland and offshore wind farms,” Journal of the American Statistical Association, Vol. 110, pp. 56-67. [pdf] [data] [R Package] [Note: This power curve function was previously implemented in an R function called “kernplus“. This function is now replaced by the AMK function in the DSWE package. The kernplus function will no longer be maintained and updated. Please use the AMK function in the DSWE package instead. The datasets shared here include the normalized power, a percentage relative to the rated power. Some of the tables in the paper may not be precisely reproducible using the normalized power data. But the relative performance of the methods should remain in the same order.]
J54. Pérez, Ntaimo, and Ding, 2015, “Multi-component wind turbine modeling and simulation for wind farm operations and maintenance,” Simulation, Vol. 91(4), pp. 360-382. [pdf]
J55. Pourhabib, Huang, and Ding, 2016, “Short-term wind speed forecast using measurements from multiple turbines in a wind farm,” Technometrics, Vol. 58(1), pp. 138-147. [pdf]
J56. Qian, Huang, Li, and Ding, 2016, “Robust nanoparticles detection from noisy background by fusing complementary image information,” IEEE Transactions on Image Processing, Vol. 25(12), pp. 5713-5726 [pdf][code for both images and videos][reproducibility report]
J57. Wang, Moreno-Centeno, and Ding, 2017, “Matching misaligned two-resolution metrology data,” IEEE Transactions on Automation Science and Engineering, Vol. 14, pp. 222-237. [pdf]
J58. Dong, Li, Qian, Yu, Zhang, Zhang, and Ding, 2017, “Quantifying nanoparticle mixing state to account for both particle location and size effects,” Technometrics, Vol. 59(3), pp. 391-403. [pdf][reproducibility report]
J59. Qian, Huang, and Ding, 2017, “Identifying multi-stage nanocrystal growth using in situ TEM video data,” IISE Transactions, Vol. 49(5), pp. 532-543. [pdf][reproducibility report]
J60. Hwangbo, Johnson, and Ding, 2017, “A production economics analysis for quantifying the efficiency of wind turbines,” Wind Energy, Vol. 20, pp. 1501-1513. [pdf] [reproducibility report]
J61. Hwangbo, Ding, Eisele, Weinzierl, Lang, and Pechlivanoglou, 2017, “Quantifying the effect of vortex generator installation on wind power production: An academia-industry case study,” Renewable Energy, Vol. 113, pp. 1589-1597. [pdf]
J62. Hwangbo, Johnson, and Ding, 2018, “Spline model for wake effect analysis: characteristics of single wake and its impacts on wind turbine power generation.” IISE Transactions, Vol. 50, pp. 112-125. [pdf] [reproducibility report]
J63. Niu, Hwango, Zeng, and Ding, 2018, “Evaluation of alternative efficiency metrics for offshore wind turbines and farms,” Renewable Energy, Vol. 128, pp. 81-90. [pdf] [reproducibility report]
J64. Shin, Ding, and Huang, 2018, “Covariate matching methods for testing and quantifying wind turbine upgrades,” Annals of Applied Statistics, Vol. 12(2), pp. 1271-1292. [accepted version]
J65. Ezzat, Jun, Ding, 2018, “Spatio-temporal asymmetry of local wind fields and its impact on short-term wind forecasting,” IEEE Transactions on Sustainable Energy, Vol. 9(3), pp. 1437-1447. [accepted version] [reproducibility report]
J66. Ezzat, Pourhabib, and Ding, 2018, “Sequential design for functional calibration of computer models,” Technometrics, Vol. 60(3), pp. 286-296. [accepted version] [code&data]
J67. Ahmed, Dagnino, and Ding, 2019, “Unsupervised anomaly detection based on minimum spanning tree approximated distance measures and its application to hydropower turbines,” IEEEE Transactions on Automation Science and Engineering, Vol. 16(2), pp. 654-667. [accepted version] [code&data] [reproducibility report]
J68. Ezzat, Jun, and Ding,2019, “Spatio-temporal short-term forecast: A calibrated regime-switching method,” The Annals of Applied Statistics, Vol. 13(3), pp. 1484-1510. [pdf] [reproducibility report]
J69. Qian, Huang, Park, and Ding, 2019, “Fast dynamic nonparametric distribution tracking in electron microscopic data,” The Annals of Applied Statistics, Vol. 13(3), pp. 1537-1563. [pdf] [appendices ] [data&code]
J70. Payne, Guha, Ding, and Mallick, 2020, “A conditional density estimation partition model using logistic Gaussian processes,” Biometrica, Vol. 107(1), pp. 173-190. [pdf]
J71. Prakash, Panchang, Ding, and Ntaimo, 2020, “Sign constrained Bayesian inference for nonstationary models of extreme events,” Journal of Waterway, Port, Coastal Engineering, accepted, Vol. 146(5), pp. 04020029.1–04020029.9. [pdf]
J72. Jin, Iquebal, Bukkapatnam, Gaynor, and Ding, 2020, “A Gaussian process model-guided surface polishing process in additive manufacturing.” ASME Transactions, Journal of Manufacturing Science and Engineering, Vol. 142(1), pp. 011003.1–011003.12. [paper open access][data&code][reproducibility report]
J73. Ezzat, Tang, and Ding, 2020, “A model-based calibration approach for structural fault detection using piezoelectric impedance measurements and a finite element model,” Structural Health Monitoring, Vol. 19(6), pp. 1839–1855 [online pdf]
J74. Qian, Xu, Drummy, and Ding, 2020, “Effective super-resolution method for paired electron microscopic images,” IEEE Transactions on Image Processing, Vol. 29, pp. 7317–7330. [preprint] [data] [code].
J75. Ahmed, Hu, Acharya, and Ding, 2021, “Unsupervised point anomaly detection using neighborhood structure assisted non-negative matrix factorization,” Journal of Machine Learning Research, Vol. 22(34), pp. 1−32. [preprint] [code&data] [reproducibility report].
J76. Ezzat, Liu, Hochbaum, and Ding, 2021, “A graph-theoretic approach for spatial filtering and its impact on mixed-type spatial pattern recognition in wafer bin maps,” IEEE Transactions on Semiconductor Manufacturing, Vol. 34, pp. 194-206. [preprint] [data]
J77. Ding, Kumar, Prakash, Kio, Liu, Liu, and Li, 2021, “A case study of space-time performance comparison of wind turbines on a wind farm,” Renewable Energy, Vol. 171, pp. 735-746 . [preprint][code&data][reproducibility report]
J78. Prakash, Tuo, and Ding, 2022, “Gaussian process aided function comparison using noisy scattered data,” Technometrics, Vol. 64, pp. 92-102. [preprint][code][supplementary materials]
J79. Ahmed, Galoppo, Hu, and Ding, 2022, “Graph regularized autoencoder and its application in unsupervised anomaly detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, pp. 4110-4124. [paper open access][code&data][reproducibility report]
J80. Tuo, He, Pourhabib, Ding, and Huang, 2022, “A reproducing kernel Hilbert space approach to functional calibration of computer models,” Journal of the American Statistical Association, accepted. [preprint] [data&code for reproducibility]
J81. Shin, Zhou, and Ding, 2022, “Joint estimation of monotone curves via functional principal component analysis,” Computational Statistics and Data Analysis, Vol. 166, pp. 107343. [preprint] [data&code for reproducibility]
J82. Latiffianti, Ding, Sheng, Williams, Morshedizadeh, and Rodgers, 2022, “Analysis of leading edge protection application on wind turbine performance through energy and power decomposition approaches,” Wind Energy, Vol. 25(7), pp. 1203-1221. [paper][code][reproducibility report][data is under an NDA and cannot be released].
J83. Prakash, Tuo, and Ding, 2022, “The temporal overfitting problem with applications in wind power curve modeling,” Technometrics, accepted [preprint][supplemental materials][code][data]
J84. Latiffianti, Sheng and Ding, 2022 “Wind turbine gearbox failure detection through cumulative sum of multivariate time series data,” Frontiers in Energy Research, section Wind Energy, Vol. 10, pp. 904622 [paper][code][reproducibility report][data is from EDP Open Data.]
J85. Jin, Tuo, Tiwari, Bukkapatnam, Aracne-Ruddle, Lighty, Hamza, and Ding, 2022, “Hypothesis tests with functional data for surface quality change detection in surface finishing processes,” IISE Transactions, accepted [preprint][supplemental materials][data&code][reproducibility report].
J86. Xie, Ding, and Ji, 2022, “Augmented equivariant attention networks for electron microscopy image super-resolution,” IEEE Transactions on Medical Imaging, Vol. 44(11), pp. 3194-3206 [preprint][code][TEM data used is the same as in J74].
J87. Ahmed, Jun, and Ding, 2022, “A spatio-temporal track association algorithm based on marine vessel automatic identification system data,” IEEE Transactions on Intelligent Transportation Systems, Vol. 23(11), pp. 20783-20797 [preprint][data&code][reproducibility report]
J88. Santos, Andrews, Lin, De Jesus, Pas, Gross, Carillo, Stein, Ding, Xu, and Banerjee, 2022, “Multivariate hyperspectral data analytics across length scales to probe compositional, phase, and strain heterogeneities in electrode materials,” Patterns, Vol. 3(12), pp. 100634. [pdf]
J89. Jin, Bukkapatnam, Hayes, and Ding, 2023, “Vibration signal-assisted endpoint detection for long-stretch, ultraprecision polishing processes,” ASME Transactions, Journal of Manufacturing Science and Engineering, accepted.
Review/Survey/Perspective Articles
R1. Mandroli, Shrivastava, and Ding, Y., 2006, “A survey of inspection strategy and sensor distribution studies in discrete-part manufacturing processes,” IIE Transactions, Vol. 38(4), pp. 309-328. [pdf]
R2. Ding, Elsayed, Kumara, Lu, Feng, and Shi, 2006 “Distributed sensing for quality and productivity improvements,” IEEE Transactions on Automation Science and Engineering, Vol. 3(4), 344-359. [pdf]
R3. Park and Ding, 2019, “Automating material image analysis for material discovery,” MRS Communications, Vol. 9(2), pp. 545 – 555. [pdf]
R4. Handy, Zaheer, Rothfuss, McGranahan, Agbeworvi, Andrews, Garcia-Pedraza, Ponis, Ayala, Ding, Watson, and Banerjee, 2022, “Lone but not alone: Precise positioning of lone pairs for the design of photocatalytic architectures,” Chemistry of Materials, Vol. 34(4), pp. 1439–1458. [pdf]
R5. Ding, Barber, and Hammer, 2022, “Data-driven wind turbine performance assessment and quantification using field measurements,” Frontiers in Energy Research, section Wind Energy, Vol. 10, pp. 1050342. [pdf]
In Refereed Conference or Symposium Proceedings
C1. Ding, Ceglarek, and Shi, 2000, “Modeling and diagnosis of multi-station manufacturing processes: part 1 – state space model,” Proceedings of the 2000 Japan/USA Symposium on Flexible Automation, July 23-26, Ann Arbor, MI.
C2. Ding, Ceglarek, and Shi, 2000, “Modeling and diagnosis of multi-station manufacturing processes: part 2 – fault diagnosis,” Proceedings of the 2000 Japan/USA Symposium on Flexible Automation, July 23-26, Ann Arbor, MI.
C3. Ding, Jin, Ceglarek, and Shi, 2000, “Process-oriented tolerance synthesis for multi-station manufacturing systems,” Proceedings of the 2000 International Mechanical Engineering Congress and Expositions, Nov 5-10, Orland, FL, pp. 15-22.
C4. Ding, Ceglarek, and Jin, 2001, “A sensor distribution strategy for multi-station manufacturing systems,” Proceedings of the 11th International Conference on Flexible Automation and Intelligent Manufacturing, July 16-18, Dublin, Ireland, Vol-II, pp. 891-900.
C5. Tang and Ding, 2004, “A frequency response based damage detection approach using shunted piezoelectric transducer with variable inductance,” Proceedings of SPIE — Symposium on Smart Structures and Materials / NDE, San Diego, CA, March 14-18.
C6. Wang, Pendse, and Ding, 2005, “An effective dimensional inspection based on zone fitting,” Transactions of SME/NAMRI, Vol. XXIII, pp. 137-144. [pdf]
C7. Shrivastava, Ding, Niu, Coody, and Ceglarek, 2006, “Modeling, analysis, and design of complex quality testing systems using a hierarchical simulation framework” Proceedings of the IEEE International Conference On Networking, Sensing and Control, Ft. Lauderdale, FL, April 23-25. [pdf]
C8. Ding, Byon, Park, Tang, Lu, and Wang, 2007, “Dynamic data-driven fault diagnosis of wind turbine systems,” Lecture Note in Computer Science, Vol. 4487, 1197-1204. [pdf]
C9. Cho, Ding, Chen, and Tang, 2007, “Robust calibration for localization in clustered wireless sensor networks,” Proceedings of the IEEE Conference on Automation Science and Engineering (CASE 2007), Scottsdale, AZ, September 22-25. [pdf]
C10. Park, Ding, and Byon, 2008, “Collaborative data reduction for energy efficient sensor networks,” Proceedings of the IEEE Conference on Automation Science and Engineering (CASE 2008), Washington, D.C. August 23-27. [pdf]
C11. Byon, Ding, and Ntaimo, 2009, “Optimal Maintenance Strategies for Wind Turbine Systems,” The 15th ISSAT International Conference on Reliability and Quality in Design, San Francisco, CA, August 6-8.
C12. Pérez, Ntaimo, Byon, and Ding, 2010, “A stochastic DEVS wind turbine component model for wind farm simulation”, Proceedings of 2010 Spring Simulation Multi-Conference, Orlando, FL, April 12-15. [pdf]
C13. Byon and Ding, 2011, “Integrating simulation and optimization for wind farm operations under stochastic conditions,” Proceedings of the 2011 Industrial Engineering Research Conference (edited by T. Doolen and E. Van Aken), Reno, NV, May 21-25.
C14. Pérez, Ntaimo, and Ding, 2013, “Simulation of wind farm operations and maintenance,” Proceedings of the ASME Turbo Expo 2013: Power for Land, Sea and Air (GT2013), San Antonio, TX, June 3-7. [pdf]
C15. Zhang, Tang, and Ding, 2014, “Modeling and analysis of time-periodic gearbox vibration,” Proceedings of the ASME Turbo Expo 2014: Power for Land, Sea and Air (GT2014), Dusseldorf, Germany, June 16-20. [pdf]
C16. Ding, Tang, and Huang, 2015, “Data analytics methods for wind energy applications,” Proceedings of ASME Turbo Expo 2015: Turbine Technical Conference and Exposition (GT 2015), Montreal, Canada, June 15-19. [pdf]
C17. Ding and Bukkapatnam, 2015, “Challenges and needs for automating nano image processing for nanomanufacturing applications,” Proceedings of SPIE, Vol. 9556 (Nanoengineering: Fabrication, Properties, Optics, and Devices XII, edited by Eva M. Campo, Elizabeth A. Dobisz, Louay A. Eldada), San Diego, CA, August 9-13. [pdf]
C18. Sy, Jacobs, Dagnino, and Ding, 2016, “Graph-based clustering for detecting frequent patterns in event log data,” Proceedings of the 12th IEEE Conference on Automation Science and Engineering (CASE 2016), Fort Worth, TX, August 21-25. [pdf]
C19. Vijayaraghavan, Kianfar, Ding, and Parsaei, 2017, “An L1-minimization based algorithm to measure the redundancy of state estimators in large sensor systems,” Proceedings of the 13th IEEE International Conference on Automation Science and Engineering (CASE 2017), Xi’an, China, August 20-23. [pdf]
C20. Vijayaraghavan, Kianfar, Ding, and Parsaei, 2018, “A mixed integer programming based recursive variance reduction method for reliability evaluation of linear sensor systems,” Proceedings of the 14th IEEE International Conference on Automation Science and Engineering (CASE 2018), Munich, Germany, August 20-24. [pdf]
C21. Ahmed, Dagnino, Bongiovi, and Ding, 2018, “Outlier detection for hydropower generation plant,” Proceedings of the 14th IEEE International Conference on Automation Science and Engineering (CASE 2018), Munich, Germany, August 20-24. [pdf]
C22. Ahmed, Galoppo, and Ding, Y, 2019, “O-LoMST: An online anomaly detection approach and its application in a hydropower generation plant,” Proceedings of the 15th IEEE International Conference on Automation Science and Engineering (CASE 2019), Vancouver, BC, Canada, August 22-26. [pdf]
C23. Wang, Lee, Ding, and Li, 2020, “A scalable FPGA engine for parallel acceleration of singular value decomposition,” Proceedings of the 21st International Symposium on Quality Electronic Design (ISQED 2020), Santa Clara, CA, March 25-26. [pdf]
C24. Jin, Deneault, Maruyama, and Ding, 2022, “Autonomous experimentation systems and benefit of surprise-based Bayesian optimization,” Proceedings of the ISFA 2022 International Symposium on Flexible Automation (ISFA2022), Yokohama, Japan, July 3-7.
C25. Sung, Brown, Moreno-Centeno, and Ding, 2022, “GUM: A guided undersampling method to preprocess imbalanced datasets for classification,” Proceedings of the 18th IEEE International Conference on Automation Science and Engineering (CASE 2022), Mexico City, Mexico, August 20-24. [pdf]
C26. Wang, Ding, and Shahrampour, 2022, “Tracking dynamic Gaussian density with a theoretically optimal sliding window approach,” in Proceedings of the 2022 International Conference on InfoSymbiotics/Dynamic Data Driven Applications Systems (DDDAS2022), Boston, MA, Oct 6-10.
Book Chapters
BC1. Ceglarek, Huang, and Ding, 2003, “Time-based competition in automotive industry: Stream-of-variation analysis methodology,” pp. 67-97 in New Challenges and Old Problems in Enterprise Management, edited by T. Krupa, Scientific and Technical Publishers, Warsaw and Berlin.
BC2. Byon, Ntaimo, Singh, and Ding, 2013, “Wind energy facility reliability and maintenance,” pp. 639 – 672 in Handbook of Wind Power Systems: Optimization, Modeling, Simulation and Economic Aspects, edited by Pardalos, Rebennack, Pereira, Iliadis and Pappu, Springer. [pdf] [Correction: on Page 640 (namely the second page), line 3, 2082GW should be 282GW.]
BC3. Park and Ding, 2020, “Dynamic data-driven distribution tracking of nanoparticle morphology,” pp. 132-139 in Dynamic Data Driven Application Systems 2020, LNCS 12312, edited by Darema, Blasch, Ravela and Aved, Springer. [pdf]