Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Publication
- Publication Type
Articles 1 - 6 of 6
Full-Text Articles in Engineering
Machine Learning For High-Fidelity Prediction Of Cement Hydration Kinetics In Blended Systems, Rachel Cook, Taihao Han, Alaina Childers, Cambria Ryckman, Kamal Khayat, Hongyan Ma, Jie Huang, Aditya Kumar
Machine Learning For High-Fidelity Prediction Of Cement Hydration Kinetics In Blended Systems, Rachel Cook, Taihao Han, Alaina Childers, Cambria Ryckman, Kamal Khayat, Hongyan Ma, Jie Huang, Aditya Kumar
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
The production of ordinary Portland cement (OPC), the most broadly utilized man-made material, has been scrutinized due to its contributions to global anthropogenic CO2 emissions. Thus -- to mitigate CO2 emissions -- mineral additives have been promulgated as partial replacements for OPC. However, additives -- depending on their physiochemical characteristics -- can exert varying effects on OPC's hydration kinetics. Therefore -- in regards to more complex systems -- it is infeasible for semi-empirical kinetic models to reveal the underlying nonlinear composition-property (i.e., reactivity) relationships. In the past decade or so, machine learning (ML) has arisen as a promising, …
Physical-Based Training Data Collection Approach For Data-Driven Lithium-Ion Battery State-Of-Charge Prediction, Jie Li, Will Ziehm, Jonathan W. Kimball, Robert Landers, Jonghyun Park
Physical-Based Training Data Collection Approach For Data-Driven Lithium-Ion Battery State-Of-Charge Prediction, Jie Li, Will Ziehm, Jonathan W. Kimball, Robert Landers, Jonghyun Park
Electrical and Computer Engineering Faculty Research & Creative Works
Data-Driven approaches for State of Charge (SOC) prediction have been developed considerably in recent years. However, determining the appropriate training dataset is still a challenge for model development and validation due to the considerably varieties of lithium-ion batteries in terms of material, types of battery cells, and operation conditions. This work focuses on optimization of the training data set by using simple measurable data sets, which is important for the accuracy of predictions, reduction of training time, and application to online estimation. It is found that a randomly generated data set can be effectively used for the training data set, …
Fiber Optic Sensor Embedded Smart Helmet For Real-Time Impact Sensing And Analysis Through Machine Learning, Yiyang Zhuang, Qingbo Yang, Taihao Han, Ryan O'Malley, Aditya Kumar, Rex E. Gerald Ii, Jie Huang
Fiber Optic Sensor Embedded Smart Helmet For Real-Time Impact Sensing And Analysis Through Machine Learning, Yiyang Zhuang, Qingbo Yang, Taihao Han, Ryan O'Malley, Aditya Kumar, Rex E. Gerald Ii, Jie Huang
Electrical and Computer Engineering Faculty Research & Creative Works
Background: Mild traumatic brain injury (mTBI) strongly associates with chronic neurodegenerative impairments such as post-traumatic stress disorder (PTSD) and mild cognitive impairment. Early detection of concussive events would significantly enhance the understanding of head injuries and provide better guidance for urgent diagnoses and the best clinical practices for achieving full recovery. New method: A smart helmet was developed with a single embedded fiber Bragg grating (FBG) sensor for real-time sensing of blunt-force impact events to helmets. The transient signals provide both magnitude and directional information about the impact event, and the data can be used for training machine learning (ML) …
Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii
Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii
Masters Theses
“As the medical world becomes increasingly intertwined with the tech sphere, machine learning on medical datasets and mathematical models becomes an attractive application. This research looks at the predictive capabilities of neural networks and other machine learning algorithms, and assesses the validity of several feature selection strategies to reduce the negative effects of high dataset dimensionality. Our results indicate that several feature selection methods can maintain high validation and test accuracy on classification tasks, with neural networks performing best, for both single class and multi-class classification applications. This research also evaluates a proof-of-concept application of a deep-Q-learning network (DQN) to …
A Compact Wavelength Meter Using A Multimode Fiber, Ogbole Collins Inalegwu
A Compact Wavelength Meter Using A Multimode Fiber, Ogbole Collins Inalegwu
Masters Theses
“Wavelength meters are very important for precision measurements of both pulses and continuous-wave optical sources. Conventional wavelength meters employ gratings, prisms, interferometers, and other wavelength-sensitive materials in their design. Here, we report a simple and compact wavelength meter based on a section of multimode fiber and a camera. The concept is to correlate the multimodal interference pattern (i.e., speckle pattern) at the end-face of a multimode fiber with the wavelength of the input lightsource. Through a series of experiments, specklegrams from the end face of a multimode fiber as captured by a charge-coupled device (CCD) camera were recorded; the images …
Exposure Assessment Of Emerging Contaminants: Rapid Screening And Modeling Of Plant Uptake, Majid Bagheri
Exposure Assessment Of Emerging Contaminants: Rapid Screening And Modeling Of Plant Uptake, Majid Bagheri
Doctoral Dissertations
"With the advent of new chemicals and their increasing uses in every aspect of our life, considerable number of emerging contaminants are introduced to environment yearly. Emerging contaminants in forms of pharmaceuticals, detergents, biosolids, and reclaimed wastewater can cross plant roots and translocate to various parts of the plants. Long-term human exposure to emerging contaminants through food consumption is assumed to be a pathway of interest. Thus, uptake and translocation of emerging contaminants in plants are important for the assessment of health risks associated with human exposure to emerging contaminants. To have a better understanding over fate of emerging contaminants …