Derrick K. Rollins
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View article: Two-Stage Wiener-Physically-Informed-Neural-Network (W-PINN) AI Methodology for Highly Dynamic and Highly Complex Static Processes
Two-Stage Wiener-Physically-Informed-Neural-Network (W-PINN) AI Methodology for Highly Dynamic and Highly Complex Static Processes Open
The objective of this work is the development of a highly effective Wiener-Physically-Informed-Neural-Network (W-PINN) modeling methodology for systems and processes with highly nonlinear dynamic and highly nonlinear static behavior. This …
View article: Theoretically Based Dynamic Regression (TDR)—A New and Novel Regression Framework for Modeling Dynamic Behavior
Theoretically Based Dynamic Regression (TDR)—A New and Novel Regression Framework for Modeling Dynamic Behavior Open
The theoretical modeling of a dynamic system will have derivatives of the response (y) with respect to time (t). Two common physical attributes (i.e., parameters) of dynamic systems are dead-time (θ) and lag (τ). Theoretical dynamic modeli…
View article: A powerful AI grain moisture sensor approach with demonstration on a real in-bin grain dryer
A powerful AI grain moisture sensor approach with demonstration on a real in-bin grain dryer Open
View article: Physically-Informed AI Closed-Loop Sensor Glucose Forecasting Modeling Methodology with Application to Type 1 Diabetes
Physically-Informed AI Closed-Loop Sensor Glucose Forecasting Modeling Methodology with Application to Type 1 Diabetes Open
The objective of this work is the development of a sufficiently accurate, cause-and-effect, forecast modeling methodology for closed-loop control of sensor glucose concentration (SGC). The forecast horizon is 60 minutes across eleven cases…
View article: A Powerful Ai Grain Moisture Sensor Approach with Demonstration on a Real In-Bin Grain Dryer
A Powerful Ai Grain Moisture Sensor Approach with Demonstration on a Real In-Bin Grain Dryer Open
View article: Rational design of oral drugs targeting mucosa delivery with gut organoid platforms
Rational design of oral drugs targeting mucosa delivery with gut organoid platforms Open
View article: Prediction of Slope Failure through Integrating Statistical Design of Experiments and Artificial Neural Networks
Prediction of Slope Failure through Integrating Statistical Design of Experiments and Artificial Neural Networks Open
View article: Modeling Urban Road Scenarios to Evaluate Intersection Visibility
Modeling Urban Road Scenarios to Evaluate Intersection Visibility Open
Road safety is key to sustainable mobility. Rapid technological advances have allowed several road safety-related analyses, previously performed in situ, to be conducted virtually. These virtual analyses benefit understanding of how roads …
View article: Use of Discrete-Time Forecast Modeling to Enhance Feedback Control and Physically Unrealizable Feedforward Control with Applications
Use of Discrete-Time Forecast Modeling to Enhance Feedback Control and Physically Unrealizable Feedforward Control with Applications Open
When the manipulated variable (MV) has significantly large time delay in changing the control variable (CV), use of the currently measured CV in the feedback error can result in very deficient feedback control (FBC). However, control strat…
View article: TNFα regulates intestinal organoids from mice with both defined and conventional microbiota
TNFα regulates intestinal organoids from mice with both defined and conventional microbiota Open
View article: Principal Component Neural Networks for Modeling, Prediction, and Optimization of Hot Mix Asphalt Dynamics Modulus
Principal Component Neural Networks for Modeling, Prediction, and Optimization of Hot Mix Asphalt Dynamics Modulus Open
The dynamic modulus of hot mix asphalt (HMA) is a fundamental material property that defines the stress-strain relationship based on viscoelastic principles and is a function of HMA properties, loading rate, and temperature. Because of the…
View article: Predicting Dynamic Modulus of Asphalt Mixture Using Data Obtained from Indirect Tension Mode of Testing
Predicting Dynamic Modulus of Asphalt Mixture Using Data Obtained from Indirect Tension Mode of Testing Open
Understanding stress-strain behavior of asphalt pavement under repetitive traffic loading is of critical importance to predict pavement performance and service life. For viscoelastic materials, the stress-strain relationship can be represe…
View article: Predicting Dynamic Modulus of Asphalt Mixture Using Data Obtained from\n Indirect Tension Mode of Testing
Predicting Dynamic Modulus of Asphalt Mixture Using Data Obtained from\n Indirect Tension Mode of Testing Open
Understanding stress-strain behavior of asphalt pavement under repetitive\ntraffic loading is of critical importance to predict pavement performance and\nservice life. For viscoelastic materials, the stress-strain relationship can be\nrepr…
View article: Modeling rutting susceptibility of asphalt pavement using principal component pseudo inputs in regression and neural networks
Modeling rutting susceptibility of asphalt pavement using principal component pseudo inputs in regression and neural networks Open
Permanent deformation is a major load-associated distress occurring in flexible pavement systems and increases with load repetitions affecting road roughness, serviceability, and the international roughness index (IRI). Early detection of …