Research On Ship Track Prediction Based On Ipso-Cnn, Xiangen Bai, Nuo Chen, Xiaofeng Xu

Journal of Marine Science and Technology–Taiwan

With the rapid development of the global economy, international imports and exports have become increasingly important. Timely and effective tracking and prediction of ship trajectories are important in the face of complex maritime traffic. Based on AIS data, this study establishes a ship trajectory prediction model combining improved particle swarm optimization (IPSO) and a convolutional neural network (CNN) that uses the ship's historical navigation trajectory data to predict the future navigation trajectory. The historical ship trajectory data of the Port of Ningbo-Zhoushan were selected for the experiment, and the IPSO-CNN model experiment results were compared with those of other models. …

Pulsed Arc Additive Manufacturing Of A Functionally Graded Er2209 Duplex Stainless Steel And Hsla-100 Structure: Morphology, Characterization, And Mechanical Performance, Stevens G. Hill Jr

College of Graduate Studies: Theses & Dissertations

Wire arc additive manufacturing is a process well suited to the efficient production of large structures. Duplex stainless steel exhibits high corrosion resistance and good strength which can be highly beneficial for industrial use. However, its use is limited due to its cost and complexity in controlling microstructure to achieve desired properties. In many cases, it can be highly beneficial to manufacture a component which uses specialty steel grades such as duplex stainless steel only where necessary, and utilizes more affordable, commonly available steels for reinforcement or bulk structural support. A functionally graded material satisfies these requirements by providing a …

An Investigative Study Of Thermal Fluid Properties When Introducing Metallic Nanoparticles Through A Heat Exchanging System, Levi P. Mckinney

College of Graduate Studies: Theses & Dissertations

Heat-exchanging systems are essential in applications ranging from automobiles to air-conditioning units, and ongoing improvements aim to enhance efficiency while reducing system size. Conventional coolant upgrades often rely on ethylene glycol, which increases thermal stability and lowers freezing point but reduces water’s inherent heat-transfer capability. Advances in nanotechnology provide an alternative approach: suspending nanoparticles in base fluids can significantly modify thermal properties, with some formulations exhibiting conductivity increases of up to 60%. This thesis examines the thermal and tribological performance of three working fluids—distilled water, a 50:50 water–ethylene glycol mixture, and the same mixture enhanced with Al₂O₃ nanoparticles. Distilled water …

Experimental Study Of Defects In Coaxial Wire-Based Laser Metal Deposition, Remy Mathenia

Doctoral Dissertations

This work seeks to improve the usability and capability of coaxial wire-based laser metal deposition (LMD) through the experimental study of process parameters on output geometry, directional effects, and defect formation. Wire-based LMD is a directed energy deposition (DED) strategy that uses a focused laser heat source to melt and fuse metal wire as it is deposited. This process is used to build parts layer-by-layer until a desired geometry is accomplished. LMD enables the creation of complex components at a high build rate with low material and energy waste. This work focuses on the deposition of titanium wire in the …

Design Of Real-Time And Energy-Efficient Driver Assist Systems Using Electroencephalogram And Neuromorphic Computing, Nathan Alan Lutes

Doctoral Dissertations

Despite the technological breakthroughs in advanced driver assist systems, distracted driving persists as a major challenge to roadway safety. This investigation advances the body of knowledge towards a solution by developing an individualized driver-state detection method using electroencephalogram (EEG) and neuromorphic computing to provide a less invasive and more energy efficient ADAS solution. It furthermore explores the changes in brain functional connectivity under distracted conditions to better understand brain state information that could be used for neuro-feedback intervention systems. The first contribution introduces the concept of using Convolutional Spiking Neural Networks (CSNNs) for recognition of patterns with movement-intention predictive power …

Enhanced Optimization Of Mass Transfer For Carbon Capture And Wastewater Remediation In Algal Systems Through Algorithmic And Bioprocessing Techniques, Peter Ofuje Obidi

Doctoral Dissertations

The scalability and industrial deployment of algal cultivation systems are limited by suboptimal mass transfer, constraining their effectiveness in carbon capture and wastewater remediation. This research investigated these challenges through integrated optimization methodologies that combine algorithmic frameworks with enhanced bioprocessing techniques to enhance efficiency, economy, and scalability. A System-of-Systems (SoS) meta-architecture was developed using genetic algorithms and fuzzy assessor functions to demonstrate a pathway toward cost reduction. Rigorous mechanical and chemical characterizations were quantitatively analyzed to reveal existing optimization strategies and further evaluated the best strategies to use in enhancing mass transfer for improved biomass yield. The work also integrates …

Data-Driven Mulitiscale Modeling Of Electrochemical Transport, Non-Linear Electrical Contact, And Additive Manufacturing Processes, Emmanuel Olugbade

Doctoral Dissertations

The growing demand for high-efficiency energy systems and advanced manufacturing technologies requires predictive frameworks that link atomic-scale physics with engineering-scale performance. This dissertation develops a unified multiscale modeling approach that integrates molecular dynamics, density functional theory, finite-element analysis, and machine learning to connect structure, transport, and performance across materials and processes. Depending on the interactions involved, the framework employs loose coupling for parameter transfer, tight coupling for two-way feedback, and hybrid coupling where machine-learning surrogates accelerate high-fidelity simulations while retaining physical interpretability. In electrochemical systems, an XGBoost-enhanced single-particle model reproduces P2D-level electrolyte potential dynamics at roughly one-hundredth the computational cost, …

Identification Of Corrosion Damage, Vibration, And Loose Conections In Aircraft Data Transmission Lines Using Reflected And Transmitted Signals; Formulation Of Vegetable Oil-Based Nanofluids As Cutting Fluids For Mql Machining, Saidanvardzhon Valiev

Doctoral Dissertations

This research presents a novel Frequency Domain Transmitometry (FDT) method that uses transmitted signals (S21) to detect and characterize aircraft data transmission lines (ADTL) corrosion damage without additional reflectometry circuitry. Corrosion experiments were conducted according to ASTM G85-A5 over 14 weeks. Combined FDT and reflected signals (S11) analyses revealed distinct three-peak signatures associated with damage and corrosion. The square root of the Area Under the Curve (AUC) of the FDT damage peak and the Full Width at Three-Quarter Maximum (FW3QM) of the S11 damage peak is correlated with corrosion propagation depth and width. S11 signals were further used for vibration …

A Framework For Biomimetic Robot Design Applied To The Development Of A Robotic Model Of Drosophila Melanogaster, Clarissa A. Goldsmith

Graduate Theses, Dissertations, and Problem Reports (ETD)

For decades, the field of biologically inspired robotics has leveraged insights from animal locomotion to improve the walking ability of legged robots. Recently, “biomimetic” robots have been developed to model how specific animals walk. By prioritizing biological accuracy to the target organism rather than the application of general principles from biology, these robots can be used to develop detailed biological hypotheses for animal experiments, ultimately improving our understanding of the biological control of legs while improving technical solutions. Much of this work involves biologically inspired walking controllers informed by the morphology and dynamics of the insect nervous system, which necessitate …

An Investigation Into Energy Consumption And V2g Potential Of An Electric School Bus Fleet, Rupesh Dahal

Graduate Theses, Dissertations, and Problem Reports (ETD)

The transition towards sustainable public transportation demands detailed evaluation of alternative fuel technologies. This thesis presents a comprehensive energy assessment and comparative analysis of diesel and electric school buses (ESBs) operating within the Monongalia County school district, its Vehicle-to-Grid (V2G) potential, and efficiency analysis of charging system. Utilizing real-world operational data collected from buses running identical routes, the study quantifies and compares their mileage specific energy consumption. The analysis reveals an average fuel economy of approximately 6.17 miles per gallon (MPG) for the conventional diesel school bus, contrasted with an average energy consumption rate of 0.46 miles per kilowatt-hour (miles/kWh) …

Assessing And Predicting The Impact Of A Retrofitted Methane Mitigator On Non-Road Large Natural Gas Engines Using Artificial Neural Networks, Augustine Thomas Ojo

Graduate Theses, Dissertations, and Problem Reports (ETD)

Abstract

ASSESSING AND PREDICTING THE IMPACT OF A RETROFITTED METHANE MITIGATOR ON NON-ROAD LARGE NATURAL GAS ENGINES USING ARTIFICIAL NEURAL NETWORKS

Augustine Thomas Ojo

Methane emissions from the oil and gas sector present a pressing environmental and economic challenge, given methane’s high global warming potential and the considerable volumes of unutilized gas lost from industrial processes. This study investigates the impact of a retrofitted methane mitigation system, the Methane Mitigator (M²), on fuel consumption and engine-out emissions in non-road, four-stroke natural gas-powered, lean-burn engines and develops predictive Artificial Neural Network (ANN) models to estimate the resulting engine-out emissions and fuel …

Development Of A Mixed Reality Robotics Testing Environment, Haneen Hanoon

Graduate Theses, Dissertations, and Problem Reports (ETD)

Development of a Mixed Reality Robotics Testing Environment

Haneen Hanoon

This research presents the development of a Mixed Reality (MR) robotic framework that integrates real and virtual environments for experimental robotics, education, and visualization. The proposed system combines a motion-capture system, a simulator, and a projector to create a spatially aligned, interactive workspace where physical robots coexist with projected virtual objects. The framework enables real-robot motion to be tracked in real time and mirrored in the simulated environment, allowing projected obstacles, goals, and paths to be precisely aligned with the physical world.

A coordinate transformation and scaling process was implemented …

Development Of A Model To Estimate Engine Crankcase Methane Vent Emission Rates, Juan Pablo Rincon

Graduate Theses, Dissertations, and Problem Reports (ETD)

The primary focus of this research is the modeling and validation of crankcase methane emissions stemming from natural gas combustion engines commonly deployed in the natural gas compression industry. The models employed rely on equations predicting the flow rate and composition of losses, typically generated within reciprocating piston-cylinder systems. Since crankcase emissions originate in a piston–cylinder arrangement, multiple variables were considered in this study, including displaced volume, pressure, temperature, flow, and viscosity (with corrections applied to account for the thermal and compositional effects on the mixture of fluids). The initial data for this research was collected during three measurement campaigns …

Sensor Embedding And Thermal Monitoring Strategies For Laser-Foil-Printing Additive Manufacturing, Tunay Turk

Doctoral Dissertations

"This work investigates Laser Foil Printing (LFP), a novel metal additive manufacturing (AM) process that fabricates parts layer-by-layer using metal foils. To enhance LFP productivity, the process steps were optimized, and an 88.1% reduction in processing duration was achieved. A unique aspect of this study was the introduction of laser polishing as an edge polishing technique within the LFP process. This innovative approach, employing an overlapping spiral pattern, leverages selective laser remelting and erosion to effectively suppress elevated edges. To stabilize the molten melt pool, a discrete spot scanning strategy was implemented. The fabricated parts were subjected to detailed analysis, …

Additive Manufacturing Of Multifunctional Materials And Composites, John Michael Pappas

Doctoral Dissertations

"This research focused on developing fundamental knowledge of process-material interactions for additive manufacturing of multifunctional materials that are highly desirable in high-tech industries for potential weight and space savings. Multifunctional materials are very sensitive to defects, which severely hinder performance. Thus, defect formation was systematically studied to develop strategies and methodologies to efficiently fabricate high-performance materials and composites. Blown-powder-based laser additive manufacturing of transparent spinel ceramics was investigated to reduce porosity and cracking, defects that hindered transparency and mechanical properties. A systematic study of processing parameters revealed that laser power and powder flow rate had the largest effects on defect …

Nano-Micro Engineering For Advanced Energy Devices, Tazdik Patwary Plateau

Doctoral Dissertations

The development of advanced materials and manufacturing techniques for energy storage devices is continuously growing. The research emphasizes micro-engineering, nano-engineering, and advanced materials science, with the goal of enhancing the performance and scalability of manufacturing methods of different materials processing with a view of achieving sustainability. In the micro-engineering domain, novel methods for fabricating thick electrodes with short diffusion paths are introduced, enabling higher mass loading and improved battery performance. Additionally, innovative strategies for optimizing the performance of carbon nanofibers (CNFs) as anode materials in LIBs are explored in the nano-engineering domain. These studies address key challenges such as inter-bonding …

Ranking Of Non-Destructive Testing Methods For Ductile Iron Castings For Industry 4.0, Michael W. Jones

College of Graduate Studies: Theses & Dissertations

Non-destructive testing techniques provide a unique solution to provide quality assurance of parts rapidly and efficiently without altering the functional properties of the workpiece. The metal casting industry requires an elevated level of quality assurance, as castings are critical components to several industries. The wrong selection of non-destructive testing methods could increase production costs, quality costs, and potentially permit non-conforming castings to enter the market. The aim of this research is to develop a ranking model for non-destructive testing techniques for ductile iron castings within the context of the fourth industrial revolution. The ranking model analyzes the effectiveness of the …

Trustworthiness And Trust: Identifying Factors That Drive Successful Human-Ai Interaction In Nuclear Power Plant Applications, Yusuke Yamani, Austin Jackson, Casey Kovesdi, Jeffrey Joe, Jeremy Mohon

Psychology Faculty Publications

Emerging technologies such as artificial intelligence (AI) and machine learning are rapidly evolving and promising tools for efficient and continued safe operations of the U.S. nuclear power plants (NPPs). Emerging AI techniques like large language models (LLMs) are one such technology that may help personnel at existing NPPs perform work more efficiently. For example, operators may query the current operational status of a power plant via a chat interface, leveraging LLMs to access plant-related information in an interactive manner rather than manually collecting various sensor data for surveillance or work order tasks. This is a fundamental shift in the way …

Design, Fabrication, & Laboratory Testing Of A Strain Gage Instrumentation System For Marine Applications, Salem C. Homrighausen

UNF Graduate Theses and Dissertations

A strain gage-based instrumentation system was designed and fabricated to measure the moment present at the base of a mock, diesel-engine snorkel for a submerged vehicle designed to operate in the surf zone. Afterwards, a lab experiment was designed and executed to determine the accuracy of the instrument and a 2-dimensional, matrix-vector equation was formulated to relate pairs of voltages, Vx and Vy, to moments, Mx and My for the experiment. Three versions of this model were built. The first model was created using traditional statics equations governing strain, stress, moment, force. The second and third models were built directly …

Continuing Development Of An Unmanned Vehicle For Conducting Scientific And Engineering Operations In The Littoral Zone, Peter S. Bohn

UNF Graduate Theses and Dissertations

The beach and near shore are among the most economically and environmentally important regions on earth, yet they remain poorly understood due to the challenges of surveying the surf zone and obtaining other important data during high-energy events. Traditional methods such as wading surveys, small watercraft, and specialized vehicles like the coastal research amphibious buggy (CRAB) are limited by safety risks, high mobilization costs, and restricted operating windows. The work conducted in this effort addresses these limitations by continuing development of a remotely operated vehicle (ROV) intended to be operational in harsh surf conditions to collect high-resolution bathymetric and hydrodynamic …

Identification Of Fiducial Points In Seismocardiographic Cycles Using Manual And Automated Annotation Methods, Jasmine-Vy T. Truong

Honors Undergraduate Theses

There is currently a need for complementary methods for non-invasive cardiac monitoring. Seismocardiography (SCG), the measurement of cardiac-induced vibrations at the chest surface, has shown potential clinical utility. Improving the reliability of detecting fiducial points of electrocardiography (ECG) and SCG, which collectively capture the electro-mechanical cardiac activities, could expand ECG/SCG utility as a low-cost, accessible tool for clinical assessment. This study identifies commonly accepted criteria for fiducial point detection in SCG and ECG through an extensive literature review and signal processing techniques. The previous criteria were evaluated to identify their strengths and weaknesses. Based on the findings, an improved set …

Digital Twins For Flexible Manufacturing, Drew Brandon Sander

Theses and Dissertations

With the establishment of Digital Twins as an enabling technology for Industry 4.0, it is time to transition from the previous foundations to works that implement and highlight the capabilities Digital Twins present. The Industry 4.0 paradigm has recentered the focus on customizability, leading to reemerging goals of flexibility within manufacturing. Empowered through new cyber-physical systems, new enabling technologies are driving the revolution. To identify how Digital Twins as an enabling technology contribute to flexibility within manufacturing, a comprehensive investigation is conducted along with a detailed case study. A systematic literature review is conducted to analyze quality Digital Twin implementations …

Novel Redox Oxygen Active Materials For Solid Oxide Cells And Thermochemical Hydrogen Production, Jiaxin Lu

Theses and Dissertations

This dissertation investigates novel materials designed to significantly enhance the performance and durability of oxygen electrodes for solid oxide cells and redox active materials for thermochemical hydrogen production. The first part of this work evaluates Ta-doped BaCoO₃₋δ (BCT) and its structural analog SrCoO₃₋δ (SCT), focusing on their catalytic performance in oxygen reduction and evolution reactions. Among BCT compositions (x = 0.1, 0.3, 0.5), the BCT10 (x = 0.1) composition exhibited the highest oxygen vacancy concentration, electronic conductivity, and lowest polarization resistance. However, SCT10 surpassed BCT10 in these critical performance parameters and also demonstrated greater stability over extended operational periods. Notably, …

Advances In Battery Modeling And Management Systems: A Comprehensive Review Of Techniques, Challenges, And Future Perspectives, Seyed Saeed Madani, Yasmin Shabeer, Ananthu Shibu Nair, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, Shi Xue Dou, Khay See, Saad Mekhilef, Françios Allard

Electrical & Computer Engineering Faculty Publications

Energy storage systems (ESSs) and electric vehicle (EV) batteries depend on battery management systems (BMSs) for their longevity, safety, and effectiveness. Battery modeling is crucial to the operation of BMSs, as it enhances temperature control, fault detection, and state estimation, thereby maximizing efficiency and preventing malfunctions. This paper thoroughly examines the most recent advancements in battery and BMS modeling, including data-driven, thermal, and electrochemical methods. Advanced modeling approaches are explored, including physics-based models that incorporate mechanical stress and aging effects, as well as artificial intelligence (AI)-driven state estimation. New technologies that facilitate data-driven decision-making, real-time monitoring, and simplified systems include …

Safety And Sustainability In The Domestic Ferry Sector : A Pci Framework For Esg-Aligned Maritime Governance, Mirza Zeeshan Baig

World Maritime University Ph.D. Dissertations

The domestic ferry sector is a significant component of maritime transportation. It strengthens social ties, supports economic growth and connects communities. Despites its importance, this industry faces ongoing challenges. These include fragmented governance, operational adequacies, safety risks and environmental concerns. The study deals with these issues by combining a systems-based approach with governance and change management theories. It examines the interplay between human, technical and organizational aspects. The study integrates the rule-based and rights-based maritime governance along with the Lewin’s three stage change management model (Unfreeze, Change, and Refreeze).

At the core of the study is the development of a …

Advanced Air Preheater Sealing Systems For Enhanced Efficiency And Reliability In Thermal Power Plants, Pavan Kumar Ravulaparthy

Theses and Dissertations

Global electricity generation still relies heavily on thermal power plants, which consume vast amounts of fuel and emit significant amounts of carbon dioxide. A major but often overlooked source of inefficiency in these plants lies within the rotary regenerative air preheater (RAPH), a critical heat-exchange component where air leakage can exceed 20% in aging units. This leakage increases fuel consumption, parasitic fan power, and CO₂ emissions, while degrading boiler reliability.

This research introduces advanced adaptive sealing systems designed to mitigate these losses by dynamically compensating for rotor eccentricity, thermal distortion, and wear under harsh operating conditions. Brush seals, composed of …

Implementation Of Quantized Artificial Neural Networks With Spintronic Stochastic Computing, Saadi Sabyasachi Mr.

Theses and Dissertations

Artificial intelligence or machine learning is going through a rapid expansion. It also incurs significant costs for power and device footprints. Various approaches are being explored to design energy and hardware efficient machine learning models. Stochastic computing has been proposed for efficient machine learning implementation. It requires a source of random number generation which poses some practical challenges. So spintronic solutions such as magnetic tunnel junction has been used for random number generation. Again, spintronic random number generation to implement high precision circuit is prone to device-to-device variations. Hence we designed quantized artificial neural network with spintronic stochastic computing which …

Reverse Geometric Dispersion In The Beaufort Sea: Dissecting Sound Propagation At Long Ranges In Ducted Profiles, Jessica Desrochers

Open Access Dissertations

Acoustic propagation in the Arctic is evolving as surface temperatures increase and ice melts, resulting in a changing sound propagation environment. This study investigates the acoustic arrival structure in the Canada Basin and its connection to the underlying sound speed profiles. Two Seagliders were deployed in the summer of 2016 and 2017, diving in a sawtooth pattern collecting conductivity, temperature, and depth (CTD) data as part of the Canada Basin Acoustic Glider Experiment (CABAGE). These data are used to calculate sound speed profiles used to predict and understand acoustic arrival structures.

The warming of the Pacific Summer Water and surface …

Gas-Generating Reactive Materials: Design, Evaluation And Effect Of Morphology On Their Ignition And Combustion, Purvam Mehulkumar Gandhi

Dissertations

This work investigates optimizing gas-generating reactive materials, focusing on particle morphology's effect on ignition and combustion behavior. Metal-based gas-generating energetic materials (EMs) are promising alternatives to replace traditional CHNO compounds. Design of advanced metal-based EMs requires consistent ways of evaluating how their characteristics affect their ignition and combustion. Many relevant evaluation approaches exist; however, the results may be difficult to compare directly across different studies. Commonly, experimentalists ignite metal-based EMs in enclosed chambers and report pressures, P. However, direct comparison of these pressures is hindered by variations in the chamber volume, V, and the EM mass, m. To standardize the …

Assessing Potential Energy Sustainability Of Micro-Hydropower System Through A Sustainable Indicator Approach, Icmi Alif Safitri, Muhammad Sulaiman, Rachmawan Budiarto

Journal of Environmental Science and Sustainable Development

The development of micro-scale renewable energy power generation systems, such as micro-hydropower (MHP), often needs more consideration for sustainability during the planning stages. Many small-scale renewable energy systems have shorter-than-expected lifespans, jeopardizing Indonesia's Net Zero Emission Target for 2060 or earlier. Despite this, only some studies have examined the long-term viability of installed renewable energy generation projects, particularly MHP plants not managed by state-owned companies or PLN (Pembangkit Listrik Negara). The primary objectives of this research are to evaluate the feasibility of retrofitting and assess the potential sustainability of a decommissioned MHP in Banjarnegara, Central Java. A cash flow analysis …