My Articles

Rollins, D. K. and P. Ghasemi, “Deep Knowledge Versus Deep Learning,” in preparation to be submitted to Industrial and Engineering Chemistry Research.

Trinidad, Y. A., V. R. Schaefer, and D. K. Rollins, “Factor of Safety Prediction Tool: Unreinforced Slopes,” submitted to Computers and Geosciences, in review.

Rollins, D. K. and C. Norfolk, “Statistical Estimation and Inference in Model Component Uncertainties from Experimental Data,” in preparation to be submitted to Chemical Engineering Education.

Hald, E. S., J. M. Goh and D. K. Rollins, “A Comparison of the Predictability of Two Gene Identification Methods in a Juvenile Cancer Study,” in preparation for submission to Data Mining.

Sterling, A. D., J. M. Goh and D. K. Rollins, "Enhancing and Improving Formal and Informal Statistical Inference for Large Sample Binomial Survey Data," in preparation for submission to Journal of Comparative Economics.

Rollins, D. K., L. Beverlin, Y. Mei, K. Kotz, D. Andre, N. Vyas, G. Welk and W. D. Franke, "A Noninvasive Virtual ContinuousTime Glucose Prediction and Monitoring System for NonInsulin Dependent People," Journal of Bioinformatics and Diabetes, in preparation.

Rollins, D. K., T. Camarillo, and B. Andrews, "A New Approach to Obtain DeadTimes for Modeled Inputs Affecting Blood Glucose Concentration," in preparation to be submitted to Journal of Diabetes Science & Technology.

Rollins, D. K., Y. Mei and H. Wu, "A Comprehensive Study of Mathematical Modeling and Data Modeling to Evaluate the Properties, Conditions, and Limits of Future Prediction Accuracy Using Continuous Glucose Sensors," in preparation to be submitted to Industrial and Engineering Chemistry Research.

Mei, Y., T. Huynh, R. Khor and D. K. Rollins, "A New Feedback Predictive Control Approach for Processes with Time Delay in the Manipulated Variable," in preparation to be submitted to the Canadian Journal of Chemical Engineering.

Trinidad, Y. A., V. R. Schaefer, and D. K. Rollins, “Statistical Insights of Fully Softened Shear Strength,” submitted to the Landslides Journal, in review.

Trinidad, Y. A., V. R. Schaefer, and D. K. Rollins, “Statistical Assessment of Factor of Safety I: Unreinforced Slopes,” submitted to the ASCE Journal of Geotechnical and Geoenvironmental Engineering, in revision.

Trinidad, Y. A., V. R. Schaefer, and D. K. Rollins, “Statistical Assessment of Factor of Safety for PileUnreinforced Slopes,” submitted to the ASCE Journal of Geotechnical and Geoenvironmental Engineering, accepted.

Mei, Y, R. Khor and D. K. Rollins, "An InSilico Study Of Feedforward Predictive Control In Blood Glucose Concentration For People With Type 1 Diabetes," submitted to Advanced Biomedical Engineering, in revision.

Rollins, D. K., "Statistical Inference in Artificial Pancreas Research Studies," submitted to Diabetes Technology & Therapeutics, in revision.

Sun, L., D. K. Rollins, Y. Qi, T. J. Mansell, J. Fredericks, A. Jergens, G. J. Phillips, M. Wannemuehler and Q. Wang, "Studies of Miniguts Isolated from Mice with Both Defined and Conventional Microbiota," submitted to the Journal of Scientific Reports, Revised and in review.

Hurd, D. G., Member, N. P. Gaunkar, M. Mina and D. K. Rollins, “Finite Element Simulations for the Development of Electromagnetic Pumping Systems for use in Heart Assist Devices,” submitted to the IEEE Transactions on Biomedical Engineering (TBME), in revision.

Ghasemi, P., M. Aslani, D. K. Rollins, R. C. Williams, “Developing a Robust Modeling Approach for Pavement Performance Prediction and Optimization,” accepted by the Association of Asphalt Paving Technologists.

Ghasemi, P., M. Aslani, D. K. Rollins, R. C. Williams, "Principal Component Neural Networks for Modeling, Prediction, and Optimization of Hot Mix Asphalt Dynamics Modulus," Infrastructures 2019, 4, 5374.

Ghasemi, P., M. Aslani, D. K. Rollins, R. C. Williams, “Principal component analysisbased predictive modeling and optimization of permanent deformation in asphalt pavement: elimination of correlated inputs and extrapolation in modeling. Structural and Multidisciplinary Optimization, 2019 59(4):1335–1353.

Ghasemi, P., M. Aslani, D. K. Rollins, R. C. Williams, "Developing a Machine Learning Based Framework for Prediction and Optimization of Hot Mix Asphalt Dynamic Modulus," Proceedings of the Transportation Research Board 2019 Annual Meeting Conference, Washington, DC., 2019.

Yong, M., T. Huynh, R. Khor and D. K. Rollins, "Simulation Studies Comparing Feedback Predictive Control to Model Predictive Control For Unmeasured Disturbances in the Artificial Pancreas Application," Journal of Dynamic Systems, Measurement and Control, 2019, 141(9): 091009 (8 pages).

Rollins, D. K. and Y. Mei, "A new feedback predictive control approach for processes with time delay in the manipulated variable," Chemical Engineering Research and Design, 2018, 136 806815.

Ghasemi, P., M. Aslani, D. K. Rollins, R. C. Williams and V. R. Schaefer, "Modeling Rutting Susceptibility of Asphalt Pavement Using Principal Component Pseudo Inputs in Regression and Neural Networks," International Journal of Pavement Research and Technology, https://doi.org/10.1016/j.ijprt.2018.01.003.

Ghasemi, P., M. Aslani, D. K. Rollins and R. C. Williams, "Principal component analysisbased predictive modeling and optimization of permanent deformation in asphalt pavement: elimination of correlated inputs and extrapolation in modeling," Journal Structural and Multidisciplinary Optimization, 2018, 119.

Rollins, D. K., “The Importance of Statistical Modeling in Data Analysis Inference,” Chemical Engineering Education, vol. 51, No. 3, Summer 2017.

Rollins, D. K., Y. Mei, S. D. Loveland, and N. Bhardari, “BlockOriented Feedforward Control with Demonstration to Nonlinear Parametrized Wiener Modeling,” Chemical Engineering Research and Design, 2016, 109, pp 3978404.

Rollins, D. K., A. K. Roggendorf, Y. Khor, Y. Mei, P. Lee and S. Loveland, “Dynamic Modeling With Correlated Inputs: Theory, Method and Experimental Demonstration,” Ind. Eng. Chem. Res. 2015, 54(7), pp 21362144.

Rollins, D. K., Y. Mei, K. Kotz, E. Littlejohn, L. Quinn, A. K. Roggendorf and A. Cinar, “An Extended Static and Dynamic Feedback/Feedforward Control Algorithm for Insulin Delivery in the Control of Blood Glucose Level,” Ind. Eng. Chem. Res., 2015, 54 (26), pp 6734–6748.

Rollins, D. K. and V. Pankayatselvan "A OneDimensional PCA Approach for Classifying Imbalanced Data," J Comput Sci Syst Biol 8: 245251 (2015).

Kotz, K., A. Cinar, Y. Mei, A. Roggendorf, E. Littlejohn, L. Quinn and D. K. Rollins, “MultipleInput SubjectSpecific Modeling Of Plasma Glucose Concentration For Feedforward Control,” Ind. Eng. Chem. Res., 2014, 53 (47), pp 18216–18225.

Hald, E. S., R. J. Stoner and D. K Rollins, “Determining Juvenile Cancer Types From Gene Expression Using Gene Contribution and Differential Analysis,” Journal of Medical Statistics and Informatics 2014 2: 2 (2 April 2014).

Rollins, D. K., L. Beverlin, Y. Mei, K. Kotz, D. Andre, N. Vyas, G. Welk and W. E. Franke, “Development of a ModelBased Noninvasive Glucose Monitoring Device for NonInsulin Dependent People,” Journal of Bioinformatics and Diabetes, May 10, 2014.

Teh, A., D. Layton, D. R. Hyduke, L. R. Jarboe and D. K. Rollins, “Data Mining Based on Principal Component Analysis: Engineering Chemistry Research, 52(35) (2013).

Turksoy, K., E. S. Bayrak, L. Quinn, E. Littlejohn, D. K. Rollins and A. Cinar,
“Hypoglycemia Early Alarm Systems Based On Multivariable Models” Industrial & Engineering Chemistry Research, 52(35) (2013). 
Bayrak, E. S., K. Turksoy, A. Cinar, L. Quinn, E. Littlejohn, and D. K. Rollins, “Hypoglycemia Early Alarm Systems Based On Recursive Autoregressive Partial Least Squares Models," Journal of Diabetes Science and Technology, 7(1) 206214 (2013).

ErenOruklu, A. Cinar, D. K. Rollins and L. Quinn."Adaptive System Identification for Estimating Glucose Concentrations and Hypoglycemia Alarms,”Automatica 48(8) 18921897 (2012).

Rollins, D. K., D. Zhai, N. Bhandari, A. Roggendorf, R. Dua and H. Wu, “Dynamic Predictive Modeling Under Measured and Unmeasured ContinuousTime Stochastic Input Behavior,” Industrial & Engineering Chemistry Research 51(15) 54695479 (2012).

Rollins, D. K., C. K. Stiehl, K. Kotz, L. C. Beverlin and L. Brasche, “A Performance Measure Based on Principal Component Analysis for Ceramic Armor Integrity,”Review of Progress in Quantitative Nondestructive Evaluation, Volume 31B, D. O. Thompson and D, Chimenti, Editors, published by American Institute of Physics, Melville, New York, 19841989 (2012).

Beverlin, Lucas P., D. K. Rollins, N. Vyas and David Andre, “An Algorithm for Optimally Fitting a Wiener Model,” Journal of Mathematical Problems in Engineering, Volume 2011 (2011), Article ID 570509, 15 pages

Rollins, D. K. and A. Teh, “An extended data mining method for identifying differentially expressed assayspecific signatures in functional genomic studies,” BioData Mining 2010, 3:11.

Rollins, D. K., N. Bhandari, J. Kleinedler, K. Kotz, A. Strohbehn, L. Boland, M. Murphy, D. Andre, N. Vyas, G.Welk and W. Franke, "Freeliving inferential modeling of blood glucose level using only noninvasive inputs," Journal of Process Control 20 95107 (2010).

Rollins, D.K., K. Kotz and C. Stiehl, “Noninvasive Glucose Monitoring From Measured Inputs,” Proceeding of the UKACC International Conference on CONTROL 2010 710 September, Coventry, UK.

Rollins, D. K. and G. L. Larson, "Estimating a Minimum Set of PhysicallyBased Dynamic Parameters to Enhance Statistical Inference in BlockOriented Modeling," Computers and Chemical Engineering 32 494502 (2008).

Zhai, D., D. K. Rollins, and N. Bhandari, "Blockoriented Continuoustime Modeling or Nonlinear Systems under Sinusoidal Inputs," the International Journal of Modelling and Simulation 28(2) (2008).

Rollins, D. K., N. Bhandari and K. Kotz, “Critical Modeling Issues for Successful Feedforward Control of Blood Glucose in Insulin Dependent Diabetics,” Proceedings of the American Control Conference, Seattle, Washington (2008).

Rollins, D. K., D. J. Rollins and A. D. Jones, "SpatialTemporal Semiempirical Dynamic Modeling of Thermal Gradient CVI Processes," Chemical Engineering Research and Design 85(A10) 13901396 (2007).

Hardjasamudra, A., D. K. Rollins, N. Bhandari, and S. Chin, "Optimal Experimental Design for Wiener Systems," Chemical Engineering Communications 194, 656666 (2007).

Rollins, D. K, D. Zhai, A. L. Joe, J. W. Guidarelli, and R. Gonzalez, "A Novel Data Mining Method to Identify AssaySpecific Signatures in Functional Genomic Studies," BMC Bioinformatics, 7 377 (2006).Application to the Nitric Oxide Response in Escherichia coli (No. JSSAE2013100601),” Journal of Statistical Science and Application, 2(1), 118 (2014).

Hulting, S., D. K. Rollins, and N. Bhandari,"Optimal Experimental Design for Human Thermoregulatory System Identification," Chemical Engineering Research and Design 84(A11), 110 (2006).

Zhai, D., H. Wu., N. Bhandari, and D. K. Rollins, "ContinuousTime Hammerstein and Wiener Modeling Under Second Order Static Nonlinearity for Periodic Process Signals, "Computers & Chemical Engineering, 31, 112 (2006).

Rollins, D. K., L. Pacheco and N. Bhandari, “A Quantitative Measure to Evaluate Competing Designs for Nonlinear Dynamic Process Identification,” the Canadian Journal of Chemical Engineering, 84(4): 459468 (2006).

Rollins, D. K., N. Bhandari, S. Chin, T. M. Junge, and K. M. Roosa, "Optimal Deterministic" Transfer Function Modeling In the Presence of Serially Correlated Noise," Chemical Engineering Research and Design, 84(A1), 921 (2006).

Rollins, D. K., N. Bhandari, and S. Hulting, “Continuoustime Blockoriented Predictive Modeling of the Human Thermoregulatory System,” Chemical Engineering Science, 61, 15161527 (2006).

Devanathan, S., S. B. Vardeman and D. K. Rollins, “Likelihood and Bayesian Methods for Accurate Identification of Measurement Biases in Pseudo SteadyState Processes,” Chemical Engineering Research and Design, 83(A12),13911398 (2005).

Rollins, D. K, N. Bhandari, N. M. Matos, C. Sweeteng, and Stephen W. Mohn, “Continuoustime Dynamic Exogenous Modeling from Plant Data,” Proceedings of the Society of Plastics Engineers 63rd Annual Technical Conference, May 15, 2005, Boston Massachusetts, 2005.

Chin, S, N. Bhandari, and D. K. Rollins, “An Unrestricted Algorithm for Accurate Prediction of MIMO Wiener Processes,” Industrial and Engineering Chemistry Research, 43, pp. 70657074 (2004).

Zhai, D., D.K. Rollins, Sr., and N. Bhandari, “Compact Blockoriented Continuoustime Dynamic Modeling for Nonlinear Systems under Sinusoidal Input Sequences,” Proceedings of the IASTED Intelligent Systems and Control Conference, Honolulu, Hawaii, pp. 295300 (2004).

Rollins, D. K. and N. Bhandari, “Constrained MIMO Dynamic DiscreteTime Modeling Exploiting Optimal Experimental Design,” Journal of Process Control, 14(6), pp. 671683 (2004).

Bhandari, N. and D. K. Rollins, “Continuoustime Hammerstein Nonlinear Modeling Applied to Distillation,” AIChE Journal, 50(2), pp. 530533 (2004).

Bhandari, N. and D. K. Rollins, “A ContinuousTime MIMO Wiener Modeling Method,”Industrial and Engineering Chemistry Research, 42(22), pp. 55835595 (2003).

Rollins, D. K, N. Bhandari and L. Pacheco, “Experimental Designs That Maximize Information For Nonlinear Dynamic Processes,” Proceedings of FOCAPO, Coral Springs, Florida, pp. 463466, January 2003.

Bhandari, N. and D. K. Rollins, “Continuoustime modeling versus discretetime modeling for blockoriented nonlinear dynamic systems.” Proceedings of International Symposium on Process Systems Engineering and Control, Bombay, India, January 2003.

Rollins, D. K., N. Bhandari, N., A. M. Bassily, G. M. Colver and S. Chin, “A ContinuousTime Nonlinear Dynamic Predictive Modeling Method For Hammerstein Processes,” Industrial and Engineering Chemistry Research, 42(4), pp. 861872 (2003).

Rollins, D. K. and S. Devanathan, "Measurement Bias Detection in Linear Dynamic Systems," Computers & Chemical Engineering, 26(9), pp. 12011211, October (2002).

Rollins, D. K., “A ContinuousTime Hammerstein Approach Working With Statistical Experimental Design,” Proceedings of The Life of a Process Model: From Conception to Action Workshop, London, England (2002).

Kongsjahju, R. and D. K. Rollins, “Accurate Identification of Biased Measurements Under Serial Correlation,” IChemE Transactions Part A – Chemical Engineering Research and Design, 78, pp.10101017, October (2000).

Devanathan, S., D. K. Rollins and S.B. Vardeman, “A New Approach for Improved Identification of Measurement Bias,” Computers and Chemical Engineering, 24(12), pp. 27552764, December (2000).

Rollins, D. K., and N. Bhandari, “Accurate Predictive Modeling of Response Variables Under Dynamic Conditions Without the Use of Past Response Data,” ISA Transactions  The Science and Engineering of Measurement and Automation, 39, pp. 2934 (2000).

Chen, Victoria C. P. and D. K. Rollins, "Issues Regarding Artificial Neural Network Modeling for Reactors and Fermenters," Bioprocess and Biosystems Engineering, 22:85 (2000).

Bhandari N., and D. K. Rollins, “Superior Semiempirical Dynamic Predictive Modeling That Addresses Interactions,” IASTED, Proceeding of Intelligent Systems and Control, Santa Barbara, California, pp. 316321, October, 1999.

Rollins, D. K., M. McNaughton, C.M. SchulzeHewett, “Accurate SemiPredictive Modeling of an Underdamped Process,“ ISA Transactions – The Science and Engineering of Measurement and Automation, 38, pp. 279290 (1999).

Chen, V. C. P., M. Melendez, and D. K. Rollins, “The Problem of Too Much Power in a Statistical Hypothesis Test,” ISA Transactions  The Science and Engineering of Measurement and Automation, 37, pp. 329336 (1998).

Rollins, D. K., J. M. Liang, and P. Smith "Accurate Simplistic Predictive Modeling of Nonlinear Dynamic Processes," ISA Transactions, The Science and Engineering of Measurement and Automation, 36(4), 293 (1998).

Rietz, C. A. and D. K. Rollins, “Implementation of a MPC Technique on a DCS,” Proceedings of the 1998 American Control Conference, Invited Paper, pp. 29512955.

Kongsjahju, R. and D. K. Rollins, “Enhancement of Gross Error Detection When Data are Serially Correlated,” Proceeding of the 1998 FOCAPO meeting, pp. 386390, July (1998).

Walker, J. J. and D. K. Rollins, "Detecting Powder Mixture Segregation for Multicomponent Mixtures," Chemical Engineering Science, 53(4), 651655 (1998).

Manuell, L., M. Bascuñana, and D. K. Rollins, “Statistical Fault Detection for Automatically Controlled Processes,” Proceedings of the ADCHEM ‘97 International Symposium on Advanced Control of Chemical Processes, pp. 458463 (1997).

Walker, J. J. and D. K. Rollins, "Detecting Powder Mixture Segregation for NonNormal Measurement Errors," Powder Technology, 92, 915, (1997).

Kuiper, S. D., D. K. Rollins and Victoria C. P. Chen, "Gross Error Detection Strategies When Constraints Are Bilinear,"Proceedings of ADCHEM ‘97 International Symposium on Advanced Control of Chemical Processes, pp. 289294 (1997).

Liang, J. M., D. K. Rollins, and Victoria C. P. Chen, "A Comparative Study Between Linear Regression and Artificial Neural Networks in Modeling an Industrial Grain Dryer," Proceedings of The International Society for Measurement and Control Conference (ISA/96, Chicago), pp. 235243, (1996).

Chen, Victoria C. P., Jennifer Heldt, Kelly McGylnn, and Derrick Rollins, "Critical Issues in Data Collection and Conditions When Fitting Predictive Neural Network Models for Dynamic Processes," Proceedings of The International Society for Measurement and Control Conference (ISA/96, Chicago), pp 279291, (1996).

Garth, A. D. N., Victoria C. P. Chen, Jun Zhu, and D. K. Rollins, "Evaluation of Model Discrimination Techniques in Artificial Neural Networks With Application to Grain Drying," Proceedings of the Artificial Neural Networks in Engineering (ANNIE ‘96) 6, pp. 939950 (1996).

Chen, Victoria C. P. and D. K. Rollins, "Gross Error Detection and Power Analysis for a Real Chemical Process," Proceedings of The International Society for Measurement and Control Conference (ISA/96, Chicago), pp 187202, (1996).

Rollins, D. K., Y. Cheng, and Victoria C. P. Chen, "Detection of Equipment Faults in Automatically Controlled Processes," AIChE J., 42, 642 (1996).

Rollins, D. K., D. L. Faust, and D. L. Jabas, "A Superior Approach to Indices in Determining Mixture Segregation," Powder Technology, 84, 277 (1995).

Rollins, D. K., Y. Cheng and S. Devanathan, "Intelligent Selection of Hypothesis Tests to Enhance Gross Error Identification," Computers & Chemical Engineering Journal, 20(5), 517530 (1996).

Rollins, D. K., J. F. Davis, and Jennifer Walker "Application of Multiresponse Estimation to a Wetted Wall Column Model," AIChE J., October, 41(10), 2327 (1995).

Sofekun, O. A., D. K. Rollins and L. K. Doraiswamy, "A Random Particle Model for Catalyst Dilution," Chemical Engineering Science, 49(16), 2611 (1994).

Rollins, D. K. and J. F. Davis, "Gross Error Detection When VarianceCovariance Matrices Are Unknown," AIChE J., 39(8), 1335 (1993).

Rollins, D. K., and S. Devanathan, "Unbiased Estimation in Dynamic Data Reconciliation," AIChE J., 39, 1330 (1993).

Rollins, D. K., and S. Devanathan, "Computational Issues in Gross Error Detection and Data Reconciliation," Proc. of FOCAPO Meeting, July, 1993.

Rollins, D. K. and J. F. Davis, "Unbiased Estimation of Gross Errors In Process Measurements," AIChE J., 38, 563 (1992).

Rollins, D. K., and S. D. Roelfs, "Gross Error Detection When Constraints Are Bilinear," AIChE J., 38, 1295 (1992).

Rollins, D. K. and K. S. Knaebel, "Applicability of Cullinan's Equation for Predicting Liquid Binary Mutual Diffusion Coefficients," AIChE J., 37, 470 (1991).