Browsing by Author "Olawoyin, Richard"

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    Crowding Reduction and Waiting Time Analysis in Health-Care System Using Machine Learning
    (2022-01-01) Hijry, Hassan Mohmmed; Olawoyin, Richard; McDonald, Gary; Edward, William; Debnath, Debatosh
    In the hospital setting, the emergency room (ER) offers timely emergency care for patients and is considered the busiest department because of the urgency of cases. Emergency rooms have the highest number of patients overcrowding within any hospital; more than 50 of the patients admitted to the hospital come through the ER. Healthcare management is continuously trying to minimize wait times and optimize the hospitals allocated resources, but most ERs still suffer from the overcrowding crisis due to the stochastic arrival and random arrival distribution. Advanced techniques, such as machine learning algorithms, are useful for determining real life queue scenarios and patient flow (e.g., waiting time in queue and length of stay), which are considered measures of ER overcrowding. As such, we began by building a model to predict patient length of stay through predictive input factors such as patient age, mode of arrival, and patient’s type of condition using three machine learning algorithms (e.g., artificial neural networks (ANN), linear regression, and logistic regression). The best model accuracy ANN resulted in an increase of 19.5 compared to the performance from previous studies. Then, the Deep Learning Model was applied for historical queueing variables to vi predict patient waiting time in a system alongside, or in place of, queueing theory (QT). Four optimization algorithms (SGD, Adam, RMSprop, and AdaGrade) were applied and compared to find the best model with the lowest mean absolute error. The results showed that the SGD algorithm achieved better prediction accuracy than the traditional approach and reduced the use of assumptions. Moreover, the model decreased the error reduction by 24 when compared to prior literature. Lastly, we proposed a model to predict the patient waiting time based on the lab test results. Multi-algorithms were implemented by using real-life COVID-19 test results data recorded during the pandemic. Among the eight proposed models, the results showed that decision tree regression performed better for predicting waiting times. Based on experiments performed in the research, this dissertation provides a guideline for waiting time analysis in the queue—not only in healthcare, but also in other sectors, considering model understandability and the feature extraction process.
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    Disturbance Accommodating Control of an Automotive Transmission Torque Converter Clutch System
    (2024-01-01) Perkins, Todd Addison; Das, Manohar; Kobus, Chris; Olawoyin, Richard; Arefifar, S. Ali; Tomlin, Kasaundra
    Modern automotive drivelines are facing increasing pressure to improve overall efficiency while maintaining vehicle comfort and performance. This thesis presents several new ways to control the torque converter clutch in an automotive automatic transmission in such a way they improve driveline system efficiency while maintaining its comfort. This efficiency improvement is accomplished by extending the clutch’s application conditions to a lower speed where the environment is harsher than is possible with the current class of controllers.
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    Environmental Health Risk Perception of Hydraulic Fracturing in the US
    (Cogent Environmental Science, 2016-07-08) Olawoyin, Richard; McGlothlin, Charles; Conserve, Donaldson F.; Ogutu, Jack
    The advent of new technologies such as directional drilling (D2) and the hydraulic fracturing technique (HFtech) has made it possible to enhance energy production from petroleum reserves. The procedures involved have however aroused public sentiments and triggered the debate on the economic importance of petroleum recovery processes. Public perceptions of the environmental health consequences of these processes have been fuzzy. Public survey was conducted using the United States as a case study to foster the development of the most effective policy relative to environmental health sustainability and energy independence. Participants (n = 1243) were surveyed on the prevalence and concerns for HFtech in proxy communities in 2015. Key to the perception inquiry was the knowledge of respondents on HFtech and the concerns relative to the exploration processes. Ordinal logistic regression and Poisson regression (Pλ) were used to interpret the responses obtained from the participants. The study determined mixed public view for HFtech based on the analyses conducted. Young men, on average, had the least degree of concerns, while older residents (60+ years old) are more inclined to have friends who support HFtech in the communities (p-value = 0.082). Through this study, a clearer global profile of perceived public risks can be developed in countries using HFtech, in determining risk acceptability and proper governance for shale gas development. The detailed survey carried out is important for the development of effective strategies for managing risky decisions to emerging energy development issues while balancing the need for a sustainable environment.
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    Modeling and Control of Heat Transfer in a Single Vertical Ground Heat Exchanger for a Geothermal Heat Pump System
    (2023-01-01) Salhein, Khaled Asharef Assudani; Zohdy, Mohamed A; Kobus, Christopher J; Aloi, Daniel N; Olawoyin, Richard; Schmidt, Darrell P
    The Ground Heat Exchanger (GHE) is regarded as the most critical component of a Geothermal Heat Pump System (GHPS) due to its direct contact with the Earth and ability to benefit from its relatively steady temperature. A GHE can attain the maximum benefit from the Earths heat when the water temperature reaches the ground, which occurs when the water velocity is moderate, allowing the heat exchanger to balance. Optimally, controlling water velocity is crucial such that the water reaches the desired temperature. Therefore, in this dissertation, I proposed a novel mathematical model of heat transfer behavior between the water inside the underground pipe and the surrounding ground for heating and cooling modes in a GHE. The proposed dynamic model was applied to three case studies of GHPS at Oakland University, the University Politècnica de València, and Oklahoma State University in heating and cooling modes to assess its validity and further enhance the performance of the GHE by determining the optimum velocity range. The results revealed the optimal water velocity ranges for three GHPSs. Model Predictive Control (MPC) was designed to optimize the GHE’s output temperature by controlling the water velocity, which can reduce the power consumption used for the water circulation pump and therefore maximize efficiency. In this dissertation, I also introduced an Improved Grey Prediction Model (IGM (1,1)). The proposed IGM (1,1) model was based on optimizing the current predicted value by subtracting the error prediction between the previous accumulated time response of the GM (1,1) model and the previous background value throughout the prediction length. The IGM (1,1) model was applied to perform the GHPS’s output temperature prediction eight hours in advance at Oklahoma State University, the University Politècnica de València, and Oakland University, respectively. Thus, the IGM (1,1) model outperformed the traditional GM (1,1) model for all used datasets.
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    Robust Non-Linear Lyapunov Deep Learning Control Design For Chaotic Systems
    (2022-11-07) Mahmoud, Amr Salah; Zohdy, Mohamed; Dean, Brian; Schmidt, Darrell; Olawoyin, Richard
    Despite their operational success, machine learning controllers lack theoretical guarantees in terms of system stability. In contrast, classic model-based controller design uses principled approaches such as Linear Quadratic Regulator (LQR) to synthesize stable controllers with verifiable proofs. In addition, deep learning controllers encounter feedback timing bottlenecks that increase exponentially with the system complexity. Deep learning is also dependent on the quality and diversity of the dataset to produce unbiased findings; therefore, the prediction of deep learning is not guaranteed. As a result, in this research, we develop and implement a guaranteed stability solution for safety critical and chaotic systems through the integration of Lyapunov Stability theory and deep machine learning. Three control methods are researched, leading to the development of the Deep Lyapunov-stable controller: the deep learning methodology, the Lyapunov control function, and controller parameters. In this research, we provide a generic method for synthesizing a Deep Lyapunov-stable control and a way to simultaneously confirm its stability. A unique Lyapunov control function is devised and shown to be effective in managing Duffing, Van der Pol, and Zohdy-Harb nonlinear systems, but with restrictions on the system's oscillation frequency, initial conditions and disturbances. Subsequently, Dynamic Lyapunov Deep Learning is introduced to alleviate the Lyapunov control’s shortcomings. Developing a deep learning architecture in combination with a customized Lyapunov control resolves the temporal delay and Lyapunov parameters calibration concern. Different datasets are also presented before establishing the one with the best accuracy. In addition to the dataset, the architecture of the deep learning model has a significant effect on the model's accuracy. A process for relearning is intended to accommodate the introduction of new system dynamics. Based on the correlation study, we also designed an optimization technique to improve the integration of the deep learning layer and controller layer. The proposed integration of Deep Learning and Lyapunov Control, referred to as Lyapunov Deep Learning (LDL) control, is applied in MATLAB / SIMULINK to the magnetic levitation chaotic nonlinear system to demonstrate its effectiveness in addressing sudden changes in system behavior, the environment, and demands in comparison to other methods of control.