Published

(Note: * denotes students/postdocs advised/co-advised, # denotes visiting students advised/co-advised) 

  1. Agarwal. G.*, Tu, W., Sun, Y. and Kong, L. (2022), "Flexible quantile contours for multivariate functional data: beyond convexity," Computational Statistics and Data Analysis, 168, 107400. 
  2. Euan, C.*, Sun, Y. and Reich, B. (2022), "Statistical analysis of multi-day solar irradiance using a threshold time series model", Environmetrics, e2716.
  3. Horiguchi, A., Santner T. J., Sun, Y. and Pratola, M. T. (2022), "Using BART to perform Pareto optimization and quantify its uncertainties," Technometrics, to appear.
  4. Wang, W.*, Sun, Y. and Wang, H. (2021), "Latent group detection in functional partially linear regression models," Biometrics, 1-12.
  5. Lee, J.*, Sun, Y. and Wang, H. (2021), "Spatial cluster detection with threshold quantile regression," Environmetrics, e2696.
  6. Hong, Y.#, Abdulah, S.*, Genton, M., and Sun, Y. (2021), "Efficiency assessment of approximated spatial predictions for large datasets,” Spatial Statistics, 43: 100517.
  7. Li, Y.* and Sun, Y. (2021), “Multi-site high-frequency stochastic precipitation generator using censored skew-symmetric distributions,” Spatial Statistics, 41, 100474.
  8. Qadir, G.*, Sun, Y. and Kurtek, S. (2021), "Estimation of spatial deformation for non-stationary processes via variogram alignment," Technometrics, 1-12.
  9. Agarwal, G.*, Sun, Y. and Wang, H. (2021), "Copula-based multiple indicator kriging for non-Gaussian random fields," Spatial Statistics, 44, 100524.
  10. Abdulah, S., Cao, Q., Pei, Y., Bosilca, G., Dongarra, J., Genton, M. G., Keyes, D. E., Ltaief, H., and Sun, Y. (2021), "Accelerating geostatistical modeling and prediction with mixed-precision computations: A high-productivity approach with PaRSEC," IEEE Transactions on Parallel and Distributed Systems, to appear. 
  11. Huang, H., Abdulah, S., Sun, Y., Ltaief, H., Keyes, D. E., and Genton, M. G. (2021), "Competition on spatial statistics for large datasets (with discussion),"  Journal of Agricultural, Biological, and Environmental Statistics, to appear. 
  12. Salvana, M. L., Abdulah, S., Huang, H., Ltaief, H., Sun, Y., Genton, M. G., and Keyes, D. E. (2021), "High performance multivariate geospatial statistics on manycore systems," IEEE Transactions on Parallel and Distributed Systems, 32, 2719-2733. 
  13. Qadir, G.*, Euan, C.* and Sun, Y. (2020), "Flexible modeling of variable asymmetries in cross-covariance functions for multivariate random fields,"  Journal of Agricultural, Biological and Environmental Statistics, 26, 1–22.
  14. Chen, T.*, Sun, Y. and Li, T. (2021), "A semi-parametric estimation method for the quantile spectrum with an application to earthquake classification using convolutional neural network," Computational Statistics and Data Analysis, 154, 107069.
  15. Chen, T.*, Sun, Y., Euan, C. and Ombao, H. (2020), "Clustering brain signals: A robust approach using functional data ranking," Journal of Classification, to appear.
  16. Qadir G.* and Sun, Y. (2021), "Semiparametric estimation of cross-covariance functions for multivariate random fields," Biometrics, 77, 547-560.
  17. Chen, W., Genton, M. G. and Sun, Y. (2021), “Space-time covariance structures and models,” Annual Review of Statistics and Its Application, 8, 191-215.
  18. Agarwal, G.* and Sun, Y. (2021), "Multivariate functional quantile envelopes with application to radiosonde wind data," Technometrics, 63, 2, 199-211.
  19. Genton, M. G., and Sun, Y. (2020),  "Functional data visualization," Handbook of Computational Statistics and Data Science, Wiley StatsRef: Statistics Reference Online, 1-11.
  20. Chen, T.*, Sun, Y. and Maadooliat, M. (2020), "Collective spectral density estimation and clustering for spatially-correlated data,” Spatial Statistics, 38, 100451.
  21. Dai, W., Mrkvicka, T., Sun, Y., and Genton, M. (2020) "Functional outlier detection and taxonomy by sequential transformations," Computational Statistics and Data Analysis, 149,106960.
  22. Litvinenko, A., Kriemann, R., Genton, M. G., Sun, Y., and Keyes, D. (2020), "HLIBCov: Parallel hierarchical matrix approximation of large covariance matrices and likelihoods with applications in parameter identification,"  MethodsX, 7:100600.
  23. Euan, C* and Sun, Y. (2020), "Bernoulli vector autoregressive model," Journal of Multivariate Analysis, 177, 104599.
  24. Lee, J.*, Sun, Y. and Chang, H. (2020), "Spatial cluster detection of regression coefficient in a mixed effect model," Environmentrics, 31(2), e2578.
  25. Agarwal, G.*, Saade, S., Shahid, M., Test, M. and Sun, Y. (2019), "Quantile function modeling applied to salinity tolerance analysis of plant data," BMC Plant Biology, 19, 526 (2019).
  26. Abdulah, S.*, Ltaief, H., Sun, Y., Genton, M. G., and Keyes, D. E. (2019), “Geostatistical modeling and prediction using mixed precision tile Cholesky factorization,” IEEE International Conference on High-Performance Computing, Data, Analytics, and Data Science (HiPC), 152-162.
  27. Castruccio, S., Genton, M. G., and Sun, Y. (2019), "Visualising spatio-temporal models with virtual reality: From fully immersive environments to apps in stereoscopic view," Journal of the Royal Statistical Society - Series A, 182, 379-387. (read before the Royal Statistical Society, Discussion and Rejoinder)
  28. Euan, C.*, Sun, Y. and Ombao, H. (2019), "Coherence-based time series clustering for brain connectivity visualization," Annals of Applied Statistics, 13(2), 990-1015.
  29. Euan, C.* and Sun, Y. (2019), "Directional spectra-based clustering for visualizing patterns of ocean waves and winds," Journal of Computational and Graphical Statistics, 28(3), 659-670.
  30. Genton, M. G., and Sun, Y. (2019), discussion of "Data science, big data, and statistics," by P. Galeano and D. Pena, TEST, 28, 338-341.
  31. Li, Y.* and Sun, Y. (2019), "Local likelihood estimation for nonstationary covariance functions with applications to climate model emulation," Statistica Sinica, 29, 1209-1231. (supplementary material)
  32. Litvinenko, A., Sun, Y., Genton, M. G., and Keyes, D. (2019), "Likelihood approximation with hierarchical matrices for large spatial datasets," Computational Statistics and Data Analysis, 137, 115-132.
  33. Wang, W.* and Sun, Y. (2019), "Penalized local polynomial regression for spatial data," Biometrics, 75(4), 1179-1190.
  34. Tang, Y., Wang, H., Sun, Y. and Hering, A. S. (2019), "Copula-based semiparametric model for spatio-temporal data," Biometrics, 75(4), 1156-1167.
  35. Huang, H.* and Sun, Y. (2019), "A decomposition of total variation depth for understanding functional outliers ," Technometrics, 4, 445-458.
  36. Huang, H.* and Sun, Y. (2017), "Visualization and assessment of spatio-temporal covariance properties," Spatial Statistics, 34, 100272. (KAUST Discovery Highlight)
  37. Maadooliat, M., Sun, Y. and Chen, T.* (2018), "Collective nonparametric spectral density estimation and clustering," Statistics in Medicine, 37(30), 4789-4806.
  38. Abdulah, S.*, Ltaief, H., Sun, Y., Genton, M. G., and Keyes, D. E. (2018), "ExaGeoStat: A high performance unified software for geostatistics on manycore systems," IEEE Transactions on Parallel and Distributed Systems, 29, 2778-2784. (ExaGeoStat, ExaGeoStat-R, Documentation)
  39. Abdulah, S.*, Ltaief, H., Sun, Y., Genton, M. G., and Keyes, D. E. (2018), "Parallel approximation of the maximum likelihood estimation for the prediction of large-scale geostatistics simulations," IEEE International Conference on Cluster Computing, 98-108.
  40. Sun, Y., Chang, X. and Guan, Y. (2018), "Flexible and efficient estimating equations for variogram estimation," Computational Statistics and Data Analysis, 122, 45-58.
  41. Castruccio, S., Genton, M. G. and Sun, Y. (2018), "Visualising spatio-temporal models with virtual reality: from fully immersive environments to apps in stereoscopic view," Journal of the Royal Statistical Society-Series A, 182, 379-387. (to be read before the Royal Statistical Society)
  42. Huang, H.* and Sun, Y. (2018), "Hierarchical low rank approximation of likelihoods for large spatial datasets," Journal of Computational and Graphical Statistics, 27:1, 110-118. (KAUST Discovery Highlight)
  43. Yin, G.*, McCabe, M. F., Mariethoz, G. and Sun, Y. (2017), "Comparison of gap-filling methods for Landsat 7 ETM+ SLC-off imagery,"  International Journal of Remote Sensing, 38(23), 6653-6679.
  44. Meng, R.*, Saade, S., Berger. B, Brien, C., Kurtek, S., Tester, M. and Sun, Y. (2017), "Growth curve registration for evaluating salinity tolerance in barley," Plant Methods, 13-18.
  45. Xie, W., Kurtek, S., Bharath, K. and Sun, Y. (2017), "A Geometric approach to visualization of variability in functional data," Journal of the American Statistical Association, 112:519, 979-993. (supplementary materials) (KAUST Discovery Highlight)
  46. Sun, Y., Hering, A. S. and Browning, J. M. (2017), "Robust bivariate error detection in skewed data with application to historical radiosonde winds," Environmetrics , 28, e2431 . (KAUST Discovery Highlight)
  47. Sun, Y. and Stein, M. L. (2016), "Statistically and computationally efficient estimating equations for large spatial datasets," Journal of Computational and Graphical Statistics,  25, 187-208.
  48. Sun, Y., Wang, H. and Fuentes, M. (2016), "Fused adaptive Lasso for spatial and temporal quantile function estimation,"  Technometrics, 58, 127-137. (supplementary materials)
  49. Sun, Y. and Stein, M. L. (2015), "A stochastic space-time model for intermittent precipitation occurrences," Annals of Applied Statistics, 9, 2110-2132. (KAUST Discovery Highlight)
  50. Ngo, D., Sun, Y., Genton, M. G., Wu, J., Srinivasan, R., Cramer, S. and Ombao, H. (2015), "An exploratory data analysis of electroencephalograms using the functional boxplots approach,"  Frontiers in Neuroscience, 9, Article 282, 1-18. (KAUST Discovery Highlight)
  51. Dupuis, D. J., Sun, Y. and Wang, H. (2015), "Detecting change-points in extremes, "Statistics And Its Interface, 8, 19-31.
  52. Genton, M. G., Castruccio, S., Crippa, P., Dutta, S., Huser, R., Sun, Y. and Vettori, S. (2015), "Visuanimation in statistics," Stat, 4, 81-96.
  53. Sun, Y., Bowman, K. P., Genton, M. G., and Tokay, A. (2015), "A Matern model of the spatial covariance structure of point rain rates," Stochastic Environmental Research and Risk Assessment, 29, 411-416.
  54. Lopez-Pintado, S., Sun, Y., Lin, J. K., and Genton, M. G. (2014), "Simplicial band depth for multivariate functional data, " Advances in Data Analysis and Classification, 8, 321-338.
  55. Genton, M. G., Johnson, C., Potter, K., Stenchikov, G., and Sun, Y. (2014), "Surface boxplots," Stat, 3, 1-11.  Surface boxplot software: Installation.  Movie: sbplot . 
  56. Sun, Y., Hart, J. D. and Genton, M. G. (2013), "Improved nonparametric inference for multiple correlated periodic sequences," Stat, 2, 197-210.
  57. Apanasovich, T. V., Genton, M. G. and Sun, Y. (2012), "A valid Matern class of cross-covariance functions for multivariate random fields with any number of components, "Journal of the American Statistical Association, 107, 180-193.
  58. Cooley, D., Cisewski, J., Erhardt, R. J., Jeon, S., Mannshardt, E., Omolo B. O. and Sun, Y. (2012), "A survey of spatial extremes: measuring spatial dependence and modeling spatial effects," REVSTAT, 10, 135-165.
  59. Sun, Y. and Genton, M. G. (2012), "Functional median polish," Journal of Agricultural, Biological, and Environmental Statistics, 17, 354-376.
  60. Sun, Y. and Genton, M. G. (2012), "Adjusted functional boxplots for spatio-temporal data visualization and outlier detection," Environmetrics, 23, 54-64
  61. Sun, Y., Genton, M. G. and Nychka, D. (2012), "Exact fast computation of band depth for large functional datasets: How quickly can one million curves be ranked?" Stat, 1, 68-74.
  62. Sun, Y., Hart, J. D. and Genton, M. G. (2012), "Nonparametric inference for periodic sequences," Technometrics, 54, 83-96. (ENVR Workshop Student Poster Competition Winner) R code: (CVmethod, applications).
  63. Sun, Y., Li, B. and Genton, M. G. (2012), "Geostatistics for large datasets, " in Advances And Challenges In Space-time Modelling Of Natural Events, J. M. Montero, E. Porcu, M. Schlather (eds), Springer, Vol. 207, Chapter 3, 55-77.
  64. Sun, Y. and Genton, M. G. (2011), "Functional boxplots," Journal of Computational and Graphical Statistics, 20, 316-334.(ASA Student Paper Competition Winner) R code: fbplot (fast computation), help file. Matlab code: fbplot (fast computation) .
  65. Xie, Y., Zhao, K., Sun, Y. and Chen, D. (2010), "Gaussian processes for short-Term traffic volume forecasting," Journal of the Transportation Research Board, 2165, 69-78.(TRB Best Paper Award)
  66. Sun, Y. and Lu, X. (2006), "A new method in the construction of two-level fractional factorial designs," Proceedings of the Fifth International Conference on Information and Management Sciences, Chengdu, China, 512-520.
Process Monitoring and Anomaly Detection
  • Scientific books
  1. Harrou, F., Sun, Y.  Amanda S. Hering, Madakyaru, M., and Dairi, A . (2020) Statistical Process Monitoring using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications, ISBN: 9780128193662, Publisher: Elsevier Science
  2. Harrou, F., Zeroual, A., Mohamad, H., Sun, Y. (2021), Advanced Road Traffic Modelling and Management Using Statistical and Deep Learning Methods, ISBN:9780128234327, Publisher: Elsevier Science
  • Edited books
  1. Harrou, F., Sun, Y.  Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems, IntechOpen, ISBN: 978-1-83880-092-5, April 2020.
  • Published peer-reviewed papers
  1. Wang, W., Harrou, F., Bouyeddou, B., Senouci, S. M., and  Sun, Y. (2022). Cyber-Attacks detection in industrial systems using artificial intelligence-driven methods. International Journal of Critical Infrastructure Protection (Elsevier), Accepted.
  2. Amin, W.,  Harrou, F., Dairi, A., and Sun, Y. 2022. Machine Learning and Deep Learning-Driven Methods for Predicting Ambient Particulate Matters Levels: A Case Study" to the Concurrency and Computation: Practice and Experience, e7035.
  3. Alkesaiberi, A., Harrou, F., Sun, Y., 2022. Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study. Energies 2022, 15, 2327.
  4. Alali, Y., Harrou, F.,  and Sun, Y., 2022. "A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models." Scientific Reports 12.1 (2022): 1-20.
  5. Kadri, F., Dairi, A., Harrou, F.,  Sun, Y. "Towards accurate prediction of patient length of stay at emergency department: a GAN-driven deep learning framework." Journal of Ambient Intelligence and Humanized Computing (2022): 1-15.
  6. Dairi, A., Harrou, F., Sun, Y., 2022. Deep Generative Learning-based 1-SVM Detectors for Unsupervised COVID-19 Infection Detection Using Blood Tests, IEEE Transactions on Instrumentation & Measurement, vol. 71, pp. 1-11, 2022.
  7. Harrou, F., Dairi, A., Kadri, F. and Sun, Y., 2022. Effective forecasting of key features in hospital emergency department: Hybrid deep learning-driven methods. Machine Learning with Applications, p.100200.
  8. Wang, W., Harrou, F., Bouyeddou, B., Senouci, S.-M., Sun, Y. 2021. A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas pipeline systems. Cluster Computing. doi:10.1007/s10586-021-03426-w (KAUST Discovery Highlight)
  9. Zerrouki, N., Dairi, A., Harrou, F., Zerrouki, Y. and Sun, Y., 2021. Efficient land desertification detection using a deep learning‐driven generative adversarial network approach: A case study. Concurrency and Computation: Practice and Experience, p.e6604.
  10. Dairi, A., Harrou, F., Khadraoui, S. and Sun, Y., 2021. Integrated multiple directed attention-based deep learning for improved air pollution forecasting. IEEE Transactions on Instrumentation and Measurement, 70, pp.1-15.
  11. Khaldi, B., Harrou, F., Benslimane, SM., and Sun, Y., 2021. A Data-Driven Soft Sensor for Swarm Motion Speed Prediction using Ensemble Learning Methods, IEEE Sensors Journal, Accepted.
  12. Harrou, F., Saidi, A., Sun, Y. and Khadraoui, S., 2021. Monitoring of photovoltaic systems using improved kernel-based learning schemes. IEEE Journal of Photovoltaics, 11(3), pp.806-818.
  13. Dairi, A., Harrou, F., Zeroual, A., Hittawe, M.M. and Sun, Y., 2021. Comparative study of machine learning methods for COVID-19 transmission forecasting. Journal of Biomedical Informatics, p.103791.
  14. Harrou, F., Kadri, F., Sun, Y. and Khadraoui, S., 2021. Monitoring patient flow in a hospital emergency department: ARMA-based nonparametric GLRT scheme. Health Informatics Journal, 27(2), p.14604582211021649.
  15. Khaldi, B., Harrou, F., Cherif, F. and Sun, Y., 2021. Towards Emerging Cubic Spline Patterns with a Mobile Robotics Swarm System", IEEE Transactions on Cognitive and Developmental Systems, Accepted.
  16. Taghezouit, B., Harrou, F., Sun, Y., Arab, A.H. and Larbes, C., 2021. A simple and effective detection strategy using double exponential scheme for photovoltaic systems monitoring. Solar Energy, 214, pp.337-354.
  17. Zerrouki, Y., Harrou, F.,  Zerrouki, N., Dairi, A., and Sun, Y., 2020, Desertification Detection using an Improved Variational AutoEncoder-Based Approach through ETM-Landsat Satellite Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2020): 202-213.
  18. Harrou, F., Dairi, A., Kadri, F. and Sun, Y., 2020. Forecasting emergency department overcrowding: A deep learning framework. Chaos, Solitons & Fractals, 139, p.110247. (KAUST Discovery Highlight)
  19. Dairi, A., Harrou, F., Sun, Y. and Khadraoui, S., 2020. Short-Term Forecasting of Photovoltaic Solar Power Production Using Variational Auto-Encoder Driven Deep Learning Approach. Applied Sciences, 10(23), p.8400.
  20. Harrou, F., Cheng, T., Sun, Y., Leiknes, T.O. and Ghaffour, N., 2020. A Data-Driven Soft Sensor to Forecast Energy Consumption in Wastewater Treatment Plants: A Case Study. IEEE Sensors Journal, vol. 21, no. 4, pp. 4908-4917.
  21. Bouyeddou, B., Harrou, F. Kadri, B., and Sun, Y., 2020. Detecting network cyber-attacks using an integrated statistical approach, Cluster Computing,  pp.1-19.
  22. Cheng, T., Harrou, F., Kadri, F., Sun, Y. and Leiknes, T., 2020. Forecasting of Wastewater Treatment Plant Key Features using Deep Learning-Based Models: A Case Study. IEEE Access, 8, pp.184475-184485.
  23. Harrou, F., Hittawe, M.M., Sun, Y. and Beya, O., 2020. Malicious attacks detection in crowded areas using deep learning-based approach. IEEE Instrumentation & Measurement Magazine, 23(5), pp.57-62.
  24. Lee, J., Wang, W., Harrou, F. and Sun, Y., 2020. Wind Power Prediction Using Ensemble Learning-Based Models. IEEE Access, 8, pp.61517-61527.
  25. Wang, W., Lee, J., Harrou , F. and Sun, Y., 2020. Early Detection of Parkinson’s Disease Using Deep Learning and Machine Learning. IEEE Access, 8, pp.147635-147646.
  26. Lee, J., Wang, W., Harrou, F.,  Sun, Y. (2020). Reliable solar irradiance prediction using ensemble learning-based models: A comparative study. Energy Conversion and Management, 208, 112582.
  27. Zeroual, A., Harrou, F., Dairi, A. and Sun, Y., 2020. Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study. Chaos, Solitons & Fractals, 140, p.110121.
  28. Harrou, F., Khaldi, B., Sun, Y. and Cherif, F., 2020. An efficient statistical strategy to monitor a robot swarm. IEEE Sensors Journal. 20(4), pp. 1-10.
  29. Taghezouit, B., Harrou, F., Sun, Y., Arab, A.H. and Larbes, C., 2020. Multivariate statistical monitoring of photovoltaic plant operation. Energy Conversion and Management, 205, p.112317.
  30. Harrou, F., Zeroual, A. and Sun, Y., 2020. Traffic congestion monitoring using an improved kNN strategy. Measurement, Elsevier, p.107534.
  31. Bouyeddou, B., Kadri, B., Harrou, F. and Sun, Y., 2020. DDOS-attacks detection using an efficient measurement-based statistical mechanism. Engineering Science and Technology, an International Journal.
  32. Harrou, F., Kadri, F., & Sun, Y. (2020). Forecasting of Photovoltaic Solar Power Production Using LSTM Approach. In Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems. IntechOpen.
  33. Krupskii, P., Harrou, F., Hering, A.S. and Sun, Y., 2019. Copula-based monitoring schemes for non-Gaussian multivariate processes. Journal of Quality Technology, pp.1-16.
  34. Harrou, F., Taghezouit, B. and Sun, Y., 2019. Improved kNN-Based Monitoring Schemes for Detecting Faults in PV Systems. IEEE Journal of Photovoltaics, 9, n0. 3: 811 – 821.
  35. Cheng, T., Dairi, A., Harrou, F., Sun, Y. and Leiknes, T., 2019. Monitoring Influent Conditions of Wastewater Treatment Plants by Nonlinear Data-Based Techniques. IEEE Access, 7, pp.108827-108837.
  36. Harrou, F., Taghezouit, B. and Sun, Y., 2019. Robust and flexible strategy for fault detection in grid-connected photovoltaic systems. Energy Conversion and Management, 180, pp.1153-1166.
  37. Harrou, F., Dairi, A., Taghezouit, B. and Sun, Y., 2019. An unsupervised monitoring procedure for detecting anomalies in photovoltaic systems using a one-class Support Vector Machine. Solar Energy, 179, pp.48-58.
  38. Harrou, F., Saidi, A., & Sun, Y. (2019). Wind power prediction using bootstrap aggregating trees approach to enabling sustainable wind power integration in a smart grid. Energy Conversion and Management, 201, 112077.
  39. Dairi, A., Cheng, T., Harrou, F., Sun, Y. and Leiknes, T., 2019. Deep learning approach for sustainable WWTP operation: A case study on data-driven influent conditions monitoring. Sustainable Cities and Society, 50, p.101670.
  40. Zeroual, A., Harrou, F. and Sun, Y., 2019. Road traffic density estimation and congestion detection with a hybrid observer-based strategy. Sustainable Cities and Society, 46, p.101411.
  41. Khaldi, B., Harrou, F., Cherif, F. and Sun, Y., 2019. Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation. IEEE Access, 7, pp.96372-96383.
  42. Madakyaru, M., Harrou, F. and Sun, Y., 2019. Monitoring distillation column systems using improved nonlinear partial least squares-based strategies. 19 (13), IEEE Sensors Journal.
  43. Harrou, F., Zerrouki, N., Sun, Y. and Houacine, A., 2019. An integrated vision-based approach for efficient human fall detection in a home environment. IEEE Access, 7, pp.114966-114974.
  44. Zerrouki, N., Harrou, F., Sun, Y., & Hocini, L. (2019). A Machine Learning-Based Approach for Land Cover Change Detection Using Remote Sensing and Radiometric Measurements. IEEE Sensors Journal, 19(14), 5843-5850.
  45. Harrou, F., Khaldi, B., Sun, Y. and Cherif, F., 2019. Monitoring robotic swarm systems under noisy conditions using an effective fault detection strategy. IEEE Sensors Journal, 19(3), pp.1141-1152.
  46. Cheng, T., Harrou, F., Sun, Y. and Leiknes, T., 2018. Monitoring influent measurements at water resource recovery facility using data-driven soft sensor approach. IEEE Sensors Journal, 19(1), pp.342-352.
  47. Harrou, F., Khaldi, B., Sun, Y. and Cherif, F., 2018. Monitoring robotic swarm systems under noisy conditions using an effective fault detection strategy. IEEE Sensors Journal, 19(3), pp.1141-1152.
  48. Harrou, F., Dairi, A., Sun, Y., and Senouci, M. (2018). Statistical monitoring of a wastewater treatment plant: A case study. Journal of environmental management, 223, 807-814.
  49. Zerrouki, N., Harrou, F., Sun, Y., and Houacine, A. (2018). Vision-based Human Action Classification Using Adaptive Boosting Algorithm. IEEE Sensors Journal, 18(12), 5115-5121.
  50. Zerrouki, N., Harrou, F., and Sun, Y. (2018). Statistical Monitoring of Changes to Land Cover. IEEE Geoscience and Remote Sensing Letters, 15(6), 927-931.
  51. Harrou, F., Dairi, A., Sun, Y., and Kadri, F. (2018). Detecting Abnormal Ozone Measurements With a Deep Learning-Based Strategy. IEEE Sensors Journal, 18(17), 7222-7232.
  52. Dairi, A., Harrou, F., Senouci, M., and Sun, Y. (2018). Unsupervised obstacle detection in driving environments using deep-learning-based stereovision. Robotics and Autonomous Systems, 100, 287-301.
  53. Harrou, F., Sun, Y., Madakyaru, M., and Bouyedou, B. (2018). An improved multivariate chart using partial least squares with continuous ranked probability score. IEEE Sensors Journal, 18(16), 6715-6726.
  54. Dairi, A., Harrou, F., Sun, Y., and Senouci, M. (2018). Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and k-Nearest Neighbor Scheme. IEEE Sensors Journal, 18(12), 5122-5132.
  55. Zeroual, A., Harrou, F., Sun, Y., and Messai, N. (2018). Integrating Model-Based Observer and Kullback-Leibler Metric for Estimating and Detecting Road Traffic Congestion. IEEE Sensors Journal, 18(20), 8605-8616.
  56. Khaldi, B., Harrou, F., Sun, Y., and Cherif, F. (2018). Self-Organization in Aggregating Robot Swarms: A DW-KNN Topological Approach, BioSystems Journal, Elsevier, 165, 106-121.
  57. Harrou, F., Sun, Y., Taghezouit, B., Saidi, A., and Hamlati, M. E. (2018). Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches. Renewable Energy, 116, 22-37.
  58. Harrou F,  Zerrouki N,  Sun Y,  Houacine A, (2017) Vision-Based Fall Detection System for Improving Safety of Elderly People, IEEE Instrumentation & Measurement Magazine, Vol. 20, No. 6, pp. 49-56.
  59. Khaldi, B., Harrou, F., Cherif, F., Sun, Y. (2017). Monitoring a robot swarm using a data-driven fault detection approach. Robotics and Autonomous Systems, 97, 193-203. (KAUST Discovery Highlight)
  60. Harrou, F., Sun, Y. and Madakyaru, M., (2017). An Improved Wavelet‐Based Multivariable Fault Detection Scheme. In Uncertainty Quantification and Model Calibration. InTech
  61. Harrou, F., Madakyaru, M. and Sun, Y., (2017). Improved nonlinear fault detection strategy based on the Hellinger distance metric: Plug flow reactor monitoring. Energy and Buildings, 143, pp.149-161.
  62. Zeroual, A., Harrou, F., Sun, Y.,  Messai, N. (2017). Monitoring road traffic congestion using a macroscopic traffic model and a statistical monitoring scheme. Sustainable Cities and Society, 35, 494-510.
  63. Garoudja, E., Harrou, F., Sun, Y., Kara, K., Chouder, A. and Silvestre. S. (2017), "Statistical fault detection in photovoltaic systems," Solar Energy, 150, 485-499.
  64. Madakyaru, M., Harrou, F. and Sun, Y. (2017), "Improved data-based fault detection strategy and application to distillation columns," Process Safety and Environmental Protection, 107, 22-34.
  65. Harrou, F., Madakyaru, M., Sun, Y., (2016).  Incipient Anomaly Detection Using PCA with Multivariate Memory Monitoring Charts: Application to An Air Flow Heating System.   Applied Thermal Engineering (Elsevier),109: 65-74. (KAUST Discovery Highlight)
  66. Harrou, F., Sun, Y. and Madakyaru, M. (2016), "Kullback-Leibler distance-based enhanced detection of incipient anomalies," Journal of Loss Prevention in the Process Industries (Elsevier), 44, 73-87.    
  67. Harrou, F., and Sun, Y. (2016), "Statistical monitoring of linear antenna arrays," Engineering Science and Technology, an International Journal, 19, 1781-1787. (KAUST Discovery Highlight)
  68. Zerrouki, N., Harrou, F. Sun, Y. and Houacine, A. (2016), "Accelerometer and camera-based strategy for improved human fall detection," Journal of Medical Systems , 40: 284. (KAUST Discovery Highlight)
  69. Harrou, F., Madakyaru, M., Sun, Y. and Khadraoui, S. (2016), "Improved detection of incipient anomalies via multivariate memory monitoring charts: application to an air flow heating system," Applied Thermal Engineering, 109, 65-74. (KAUST Discovery Highlight)
  70. Harrou, F., Kadri, F., Khadraoui, S. and Sun, Y. (2016), "Ozone measurements monitoring using data-based approach," Process Safety and Environmental Protection, 100, 220-231. (KAUST Discovery Highlight)
  71. Harrou, F., Sun, Y. and Khadraoui, S. (2016), "Amalgamation of anomaly-detection indices for enhanced process monitoring," Journal of Loss Prevention in the Process Industries, 40, 365-377.
  72. Kadri, F., Harrou, F., Chaabane, S., Sun, Y. and Tahon, C. (2016), "Seasonal ARMA-based SPC charts for anomaly detection: application to emergency department systems," Neurocomputing, 173, 2102-2114.
  73. Harrou, F., Kadri, F., Chaabane, S., Tahon, C. and Sun, Y. (2015), "Improved principal component analysis for anomaly detection: application to an emergency department," Computers & Industrial Engineering, 88, 63-77.