Engineering Commons^™

Novel Systems Engineering Framework Analysis Of Photovoltaic Models And Equations, Peter R. Michael

USF Tampa Graduate Theses and Dissertations

This dissertation analyzes Photovoltaic PV equations and models for silicon based systems from a Systems Engineering framework. Background information includes an introduction, a summary of the state of PV use, a brief history of photovoltaics, and the detailed derivation of equations that enable the finding of the PV parameters contained in the PV models.The novel inquiry, leveraging systems engineering frameworks, includes three areas useful in analyzing PV equations and models. The first is a statistical verification of common simplifications of PV equations at the primary conditions of short circuit, open circuit, and maximum power. Additional analysis shows other simplifications that …

Novel Systems Engineering Framework Analysis Of Photovoltaic Models And Equations, Peter R. Michael

USF Tampa Graduate Theses and Dissertations

This dissertation analyzes Photovoltaic PV equations and models for silicon based systems from a Systems Engineering framework. Background information includes an introduction, a summary of the state of PV use, a brief history of photovoltaics, and the detailed derivation of equations that enable the finding of the PV parameters contained in the PV models.The novel inquiry, leveraging systems engineering frameworks, includes three areas useful in analyzing PV equations and models. The first is a statistical verification of common simplifications of PV equations at the primary conditions of short circuit, open circuit, and maximum power. Additional analysis shows other simplifications that …

Cyber-Physical Multi-Robot Systems In A Smart Factory: A Networked Ai Agents Approach, Zixiang Nie

USF Tampa Graduate Theses and Dissertations

This dissertation focuses on addressing the technical challenges of non-stationarity in smart factories through the use of cyber-physical AI agents. Industry 4.0 and smart manufacturing with smart factories as a central role, have a growing demand for Just-in-Time (JIT) and on-demand production, as well as mass customization—all while maintaining high productivity, resource efficiency and resilience. This research positions Multi-Robot Systems (MRS)-driven smart factories. The heterogeneous production and transportation robots in an MRS collaborate to form multiple real-time adjusted production flows achieving the flexibility to accommodate such on-demand, mass customization.

However, the implementation of MRS introduces new sets of challenges, including …

Enhancing Smart Grid Security And Reliability Through Graph Signal Processing And Energy Data Analytics, Md Abul Hasnat

USF Tampa Graduate Theses and Dissertations

Situational awareness in a large, dynamic, and complex cyber-physical critical infrastructure, such as a smart grid, is vital for ensuring its smooth and uninterrupted operation. With the evolving realities of the modern-day smart grids, new challenges associated with the situational awareness of these systems are emerging that demand intelligent and efficient solutions. This dissertation intends to address several problems for enhancing situational awareness by studying the dynamic interaction among the components of the smart grids through energy data analytics using various data-driven, machine learning, and graph signal processing (GSP) techniques. The presented work provides valuable insight into the data-driven analysis …

Deep Learning Enhancement And Privacy-Preserving Deep Learning: A Data-Centric Approach, Hung S. Nguyen

USF Tampa Graduate Theses and Dissertations

Deep Learning and its applications have become attractive to a lot of research recentlybecause of its capability to capture important information from large amounts of data. While most of the work focuses on finding the best model parameters, improving machine learning performance from data perspective still needs more attention. In this work, we propose techniques to enhance the robustness of deep learning classification by tackling data issue. Specifically, our data processing proposals aim to alleviate the impacts of class-imbalanced data and non- IID data in deep learning classification and federated learning scenarios. In addition, data pre-processing strategies such that dimensionality …

Fabric-Based Organic Electrochemical Transistor Towards Wearable Ph Sensing Electronics, Nestor Osvaldo Marquez Rios

USF Tampa Graduate Theses and Dissertations

Wearable electronics interest has attracted the attention of a few sectors because of their applicability in different areas like healthcare, military, and our daily basis. Still building circuits on fabrics to make these wearable electronics is challenging. As transistors are the building blocks of electronic circuits and most of the biosensors, recently, fiber-shaped electrochemical transistors have been studied extensively for a lot of applications including bioelectronics. Fiber-based devices are getting popular in different applications due to their low fabrication cost, lightweight, and flexibility without losing their properties. Additionally, they are potentially suitable for making sensors on garments.

In this work, …

Multiple Access Techniques Enabling Diverse Wireless Services, Mehmet Mert Şahin

USF Tampa Graduate Theses and Dissertations

The growing interest in diverse wireless applications such as virtual reality (VR), digital twin, ultra-massive broadband communication (u-MBB) has led the cellular industry to look for new multiple accessing schemes and new signal processing techniques for utilization of next-generation wireless technologies. Throughout the PhD duration, different studies are performed in regards to dual-functional and interference robust multiple accessing and control of electromagnetic radiation for cognitive radio systems. Firstly, a novel concept on non-orthogonal multiple access (NOMA), which is the coexistence of different waveform structures in the same resource elements is proposed. Secondly, a novel JCR waveform is proposed to serve …

Development And Implementation Of Telemetry Devices To Identify And Characterize Sources Of Intraocular Pressure Variability In Rats, Christina M. Nicou

USF Tampa Graduate Theses and Dissertations

Eye health depends partially on intraocular pressure (IOP) as abnormal levels can lead to ocular tissue damage. Glaucoma is a neurodegenerative disease that affects nearly 80 million people worldwide [1]. It is associated with elevated IOP, which can lead to irreversible blindness. Relatively little is known about IOP dynamics and the physiological factors that affect it as IOP is typically monitored using tonometry. Tonometry is a common tool used by clinicians and researchers to measure IOP noninvasively. It provides a good estimate of IOP mean but not variance because data collection takes time. Readings can also be influenced by subject …

Process Automation And Robotics Engineering For Industrial Processing Systems, Drake Stimpson

USF Tampa Graduate Theses and Dissertations

Automation in industrial systems applications has emerged as the fundamental solution for improving quality, production rate, and efficiency of a process. Much of the recent popularity surrounding the transition of processes from manually operated tasks to automated systems can be attributed to the concept of Industry 4.0, which outlines the fundamental guidelines for integrating cyber-physical systems into industrial processes. Due to rapid advancement of technology in robotics and automation as well as the increase in accessibility of resources to this technology, the capability to develop automated systems has become feasible for small-scale enterprise. This work presents a two-part initiative to …

Deep Reinforcement Learning Based Optimization Techniques For Energy And Socioeconomic Systems, Salman Sadiq Shuvo

USF Tampa Graduate Theses and Dissertations

Optimization, which refers to making the best or most out of a system, is critical for an organization's strategic planning. Optimization theories and techniques aim to find the optimal solution that maximizes/minimizes the values of an objective function within a set of constraints. Deep Reinforcement Learning (DRL) is a popular Machine Learning technique for optimization and resource allocation tasks. Unlike the supervised ML that trains on labeled data, DRL techniques require a simulated environment to capture the stochasticity of real-world complex systems. This uncertainty in future transitions makes the planning authorities doubt real-world implementation success. Furthermore, the DRL methods have …