Systems Engineering Commons^™

Leveraging Physiological Signal Activity And Self-Report Data To Assess Students’ Trust In “My Friendly Mind” App And Its Impact On Their Mental Health Knowledge: A Mixed-Method Phase 1 Clinical Trial Focusing On Depression And Attention Deficit Hyperactivity Disorder From Human Factors Standpoint., Yeganeh Shahsavar

Graduate Theses, Dissertations, and Problem Reports (ETD)

Mental health issues have become a significant global public health concern, especially among younger generations. The growing number of mental health challenges, combined with limited access to quality care, makes the problem even worse. Studies reveal that over 70% of individuals worldwide in need of mental health services do not receive appropriate care. Digital health technologies have the potential to enhance mental health services by making them more accessible and affordable. Despite the increasing popularity of mental health mobile applications (mHealth), there remains a lack of robust evidence of their effectiveness and the level of user trust, particularly in areas …

Development Of Dynamic Mass-Energy-Thermodynamics Constrained Hybrid Neural Network Models For Process Systems Applications, Angan Mukherjee

Graduate Theses, Dissertations, and Problem Reports (ETD)

First-principles models can provide very good predictions even for cases when there are no data at all, or data are limited in certain range of operating conditions, or for cases where data collection is infeasible. However, the development of accurate first-principles models for complex nonlinear dynamic systems can be time consuming, computationally expensive, and may be infeasible for certain systems due to lack of sufficient knowledge (information). It is also challenging to adapt first-principles models for time-varying systems. Furthermore, it can be difficult, if not impossible, to develop accurate models for some complex phenomena that are poorly understood. On the …

Prediction Of Anomalous Events With Data Augmentation And Hybrid Deep Learning Approach, Ahmed Shoyeb Raihan

Graduate Theses, Dissertations, and Problem Reports (ETD)

In this study, we propose a novel anomaly detection framework designed specifically for Multivariate Time Series (MTS) data, addressing the prevalent challenges in analyzing such complex datasets. The detection of anomalies within MTS data is notably difficult due to the complex interplay of numerous variables, temporal dependencies, and the common issue of class imbalance, where one category significantly outnumbers another. Traditional deep learning (DL) approaches often fall short in simultaneously tackling these issues. Our framework is designed to address these challenges through a two-phased approach. Phase I employs Conditional Tabular Generative Adversarial Networks (CTGAN) to create strategic synthetic data, setting …

Milk Collection Problem: Integrating The Traveling Salesman And Set Covering Problem - A Case Study In West Virginia, Usa, Md Rabiul Hasan

Graduate Theses, Dissertations, and Problem Reports (ETD)

Route determination for perishable products is complex due to its unique characteristics, such as limited shelf-life regulatory requirements, or possibility of getting damaged. This research investigates a novel problem of collecting raw milk from a rural network of dairy farms. The research problem is grounded in a real scenario of milk collection in West Virginia, USA. The milk in this scenario is produced by small farms incapable of realizing transportation economies of density out in mostly rural areas throughout the state. Maximum coverage area and milk processing overhead costs are used to identify suitable locations for intermediate milk collection centers …

A Comparative Analysis Of Coastdown Testing Methods From An Electric Drive Unit Engagement Perspective Using A Student-Designed Parallel Hybrid Electric Vehicle, Dawson Everett Dunnuck

Graduate Theses, Dissertations, and Problem Reports (ETD)

Coastdown testing and road load determination are pivotal parts of the automotive design process. Vehicle manufacturers and independent companies perform and analyze road loads determined through a coastdown or similar method to determine a vehicle’s road load for modeling and EPA certification. For a traditional coastdown, the vehicle’s drivetrain must be disconnected through a clutch between the engine and the transmission while traveling at a high rate of speed to place the vehicle in neutral. This changes for hybrid and electric vehicles. Some hybrid, and most electric, vehicles delivered to customers do not have this clutch action to grant the …

Techno-Economic Analysis And Optimization Of Hydrogen And Mechanical Energy Storage Systems, Pavitra Senthamilselvan Sengalani

Graduate Theses, Dissertations, and Problem Reports (ETD)

The increasing significance of renewable energy sources is thrusting the load cycling of fossil-fueled power plants (FFPP), designed to operate under nominal-load conditions. Integration of energy storage systems (ESS) with the FFPPs such as hydrogen energy storage (HES) and mechanical energy storage facility such as compressed air energy storage (CAES) shows the potential to minimize the levelized cost of electricity during high demand scenarios and also minimize the negative impacts of off-design FFPP operation. The deployment of energy storage facilities at the FFPP level have considerable potential advantages as they can be exploited within the existing equipment items and facilities …

Accelerating Manufacturing Decisions Using Bayesian Optimization: An Optimization And Prediction Perspective, Taofeeq Olajire

Graduate Theses, Dissertations, and Problem Reports (ETD)

Manufacturing is a promising technique for producing complex and custom-made parts with a high degree of precision. It can also provide us with desired materials and products with specified properties. To achieve that, it is crucial to find out the optimum point of process parameters that have a significant impact on the properties and quality of the final product. Unfortunately, optimizing these parameters can be challenging due to the complex and nonlinear nature of the underlying process, which becomes more complicated when there are conflicting objectives, sometimes with multiple goals. Furthermore, experiments are usually costly, time-consuming, and require expensive materials, …

Dynamic Modeling, Data Reconciliation, Parameter Estimation, And Health Monitoring Of A Supercritical Power Plant, Katherine Grace Hedrick

Graduate Theses, Dissertations, and Problem Reports (ETD)

With the introduction of a larger portion of renewable sources of power coming onto the U.S. power grid in recent decades, the operational strategy of coal-fired power plants has changed significantly to focus more on flexibility in response to the changing energy market. This has naturally led to different operational challenges. Many of these challenges are focused on the boilers within these plants, as they are producing more emissions and experiencing increased damage during load-following, which in turn leads to increased costs from penalties for not achieving emission standards or maintenance costs as boilers accumulate damage from the cycling behavior. …

Framework For Data Acquisition And Fusion Of Camera And Radar For Autonomous Vehicle Systems, Clay Edward Vincent

Graduate Theses, Dissertations, and Problem Reports (ETD)

The primary contribution is the development of the data collection testing methodology for autonomous driving systems of a hybrid electric passenger vehicle. As automotive manufacturers begin to develop adaptive cruise control technology in vehicles, progress is being made toward the development of fully-autonomous vehicles. Adaptive cruise control capability is classified into five levels defined by the Society of Automotive Engineering. Some vehicles under development have attained higher levels of autonomy, but the focus of most commercial development is Level 2 autonomy. As the level of autonomy increases, the sensor technology becomes more advanced with a sensor suite which includes radar, …

Top-Down & Bottom-Up Approaches To Robot Design, Dylan Michael Covell

Graduate Theses, Dissertations, and Problem Reports (ETD)

This thesis presents a study of different engineering design methodologies and demonstrates their effectiveness and limitations in actual robot designs. Some of these methods were blended together with focus on providing an easily interpreted project design flow while implementing more bottom-up, or feedback, elements into the design methodology. Typically design methods are learned through experience, and design taught in academia aims to shape and formalize previous experience. Usually, inexperienced engineers are taught approaches resembling the Verein Deutscher Ingenieure (VDI) 2221 process. This method presented by the Association of German Engineers in 2006 is regarded as the general system design process. …

Identification Of Moving Bottlenecks In Production Systems, Funmilayo Mofoluwasola Adeyinka

Graduate Theses, Dissertations, and Problem Reports (ETD)

Manufacturing sector have been plagued by bottlenecks from time immemorial, leading to loss of productivity and profitability, various research effort has been expended towards identifying and mitigating the effects of bottlenecks on production lines. However, traditional approaches often fail in identifying moving bottlenecks. The current data boom and giant strides made in the machine learning field proffers an alternative means of using the large volume of data generated by machines in identifying bottlenecks. In this study, a hierarchical agglomerative clustering algorithm is used in identifying potential groups of bottlenecks within a serial production line.

A serial production line with five …

Evaluation Of Lidar Systems For Rock Mass Discontinuity Identification In Underground Stone Mines From 3d Point Cloud Data, Mario Alejandro Bendezu De La Cruz

Graduate Theses, Dissertations, and Problem Reports (ETD)

The goal of this thesis is to compare the accuracy and precision of the discontinuity identification results obtained by three active remote sensing technologies along with a point cloud processing program, and the results obtained by conventional methods. For this research, the active remote sensing devices were terrestrial LIDAR, mobile LiDAR with Simultaneous localization, and mapping (SLAM), and LIDAR/Camera on an autonomous UAV. The open-source point cloud data processing programs Discontinuity Set Extractor (DSE) and the Cloud Compare were used to process point cloud data.

The results of this research found that it is possible to identify certain geological structures …

Modeling, Optimization And Uncertainty Quantification Of Solids-Based Co2 Capture Technologies, Anca Ostace

Graduate Theses, Dissertations, and Problem Reports (ETD)

Solids-based carbon dioxide (CO₂)adsorption, where CO₂ is adsorbed under high pressure/low temperature and desorbed at low pressure/high temperature, is a low energy consumption capture technology with high carbon uptake efficiency. Chemical looping combustion (CLC), where a solid oxygen carrier (OC) transports oxygen between two high-temperature reactors – a fuel and an air reactor –, is a low energy penalty, fuel-flexible technology with inherent CO₂ capture for power/heat and syngas production.

In this work, state-of-the-art equation-oriented (EO) mathematical models of a fixed-bed (FxB) contactor for CO₂ adsorption and moving-bed (MB) reactors for gas-, and solid-fueled CLC …

Subsurface Analytics: Contribution Of Artificial Intelligence And Machine Learning To Reservoir Engineering, Reservoir Modeling, And Reservoir Management, Shahab D. Mohaghegh

Faculty & Staff Scholarship

Subsurface Analytics is a new technology that changes the way reservoir simulation and modeling is performed. Instead of starting with the construction of mathematical equations to model the physics of the fluid flow through porous media and then modification of the geological models in order to achieve history match, Subsurface Analytics that is a completely AI-based reservoir simulation and modeling technology takes a completely different approach. In AI-based reservoir modeling, field measurements form the foundation of the reservoir model. Using data-driven, pattern recognition technologies; the physics of the fluid flow through porous media is modeled through discovering the best, most …

Energy Efficiency Evaluation Of Blower Heater Non-Purge Compressed Air Dryers, Alexandra M. Botts

Graduate Theses, Dissertations, and Problem Reports (ETD)

There are several compressed air dryers available for industrial use including, refrigerant, desiccant, and membrane. This research focuses on twin tower regenerative closed loop desiccant dryers, specifically: blower heater non-purge (BHNP) with and without cooling water pumps, Compressed-air Heater Purge (CHP), Blower Heater Purge (BHP), and Pressure Swing Heaterless (PSH). These styles of dryers are used mainly in industries that require extremely dry air such as, food manufacturing, medical air, or sensitive technology manufacturers. The research was conducted by collecting and analyzing real time current draw data on air compressors and associated dryers at eight different facilities (13 compressor systems) …