Engineering Commons^™

Improving The Accuracy Of Neighborhood Median Pixel Method (Nmpm) In Classifying Landsat-8 Oli Images By Optimizing The Scoring System’S Point Values, Abraham T. Magpantay, Proceso L. Fernandez Jr

Department of Information Systems & Computer Science Faculty Publications

The Neighborhood Median Pixel Method has previously been introduced as an image processing technique in remote sensing, developed to classify Landsat-8 OLI satellite image pixels into categories of vegetation, water, and built-up areas. This method relies on a lookup table based on the median pixel values within a pixel’s neighborhood and a scoring system that assigns point values for classification. While a 9x9 neighborhood size was originally proposed, a succeeding study suggested a 13x13 neighborhood for better classification accuracy. This study focuses on refining the scoring system used in the Neighborhood Median Pixel Method, particularly the original set of arbitrary …

Sampling Balanced High-Quality Data To Train An Automatic Mesh Generator, Jie Pan, Jingwei Huang, Gengdong Cheng, Yong Zeng

Engineering Management & Systems Engineering Faculty Publications

In real-world scenarios, high-quality data are often scarce and imbalanced, yet it is essential for the optimal performance of data-driven algorithmic models. Data synthesis methods are commonly used to address this issue; however, they typically rely heavily on the original dataset, which limits their ability to significantly improve performance. This article presents a quality function-based method for directly generating high-quality data and applies it to a mesh generation algorithm to demonstrate its efficiency and effectiveness. The proposed approach samples input-output pairs of the algorithm based on their feature spaces, selects high-quality samples using a defined quality function that evaluates the …

A Governance-Centric Framework For Strengthening Healthcare Cybersecurity: A Systems Perspective, Sujatha Alla, Sai Gireesh Komaragiri, Teresa Duvall, Satluk Karahan, Nagesh Bheesetty, Vijay Kumar Chattu

Engineering Management & Systems Engineering Faculty Publications

Healthcare systems face unprecedented security and privacy challenges due to increasing digitization and interconnectedness. This paper provides a comprehensive analysis of these challenges by examining various cyberattacks, defensive mechanisms, and governance frameworks within modern healthcare infrastructure. The research systematically categorizes prevalent security threats, such as ransomware, insider threats, and data breaches, identifying vulnerabilities specific to healthcare systems. Furthermore, the study evaluates current defensive strategies, including encryption techniques, access control systems, and intrusion detection tools, assessing their effectiveness against complex cyber threats. A key focus is placed on governance structures and their role in cybersecurity resilience. The research explores how regulatory …

Personalized Physics Learning Through Ai: Insights From Problem Generation, Chatbot Dialogues, And Intelligent Tutoring Systems, Atharva Dange

Physics Dissertations - Archive

Artificial intelligence (AI) is poised to transform science education, yet questions remain on how best to integrate these technologies into teaching and learning. This dissertation investigates the use of AI-driven tools in university physics courses through three complementary studies. In the first study, a generative language model (ChatGPT) was used to create novel physics homework problems aligned with course objectives. Analysis showed that, after expert vetting, AI-generated questions can foster higher-order problem-solving and reduce student reliance on solution memorization, though careful instructor oversight is required to ensure accuracy. The second study embedded an AI chatbot as a learning aid in …

Multiphysics Modeling Of Material Response To High-Intensity X-Ray And Laser Pulses: Heating, Ablation, And Plasma Expansion, Youssef Abouhussien

Theses and Dissertations

This dissertation presents a computational framework to investigate material response under high-intensity X-ray fluxes produced by an exo-atmospheric nuclear detonation and laser-material irradiations, with a focus on heating, ablation, and plasma expansion phenomena relevant to satellite vulnerability and high-energy-density environments. A hybrid Monte Carlo and Two-Temperature Model (MC-TTM) was developed to simulate X-ray and laser energy deposition and thermal relaxation in metals and semiconductors across a range of X-ray and laser pulse durations from femtoseconds to nanoseconds. Results demonstrate distinct thermal behavior between materials, with ablation thresholds and phase transitions captured in good agreement with experimental data.

In parallel, a …

Leveraging Counterfactuals For Enhanced Natural Language Inference: A Multitask Knowledge Distillation Approach, Brian J. Davis

Browse all Theses and Dissertations

Natural-language inference (NLI) asks whether a hypothesis is entailed by, contradicts, or is neutral with respect to a premise. Modern transformers reach high raw accuracy on benchmarks such as SNLI, MNLI, and ANLI, yet they often rely on brittle lexical shortcuts and provide little insight into their decision process. This thesis shows that counterfactual-augmented knowledge distillation can simultaneously boost robustness and supply faithful, token-level explanations—without scaling model size. Four T5-v1_1 students (60M, 220M, 770M, 3B parameters) are trained under four curricula: (1) standard fine-tuning, (2) fine-tuning with free-text rationales, (3) multi-task distillation with naive counterfactuals, and (4) multi-task distillation with …

Optimizing Cloud Computing Resources For Operational Cost And Application Performance Using Machine Learning, Isaac K. Matthew

Browse all Theses and Dissertations

As AI-driven workloads accelerate the growth of cloud initiatives and spending, resource waste also increases due to persistent inefficiencies in cloud compute and infrastructure management. Overprovisioned resources and suboptimal configurations often lead to operational inefficiencies and unnecessary financial overhead. These challenges arise from the difficulty of anticipating resource demands in dynamic workloads and selecting suitable virtual machines to ensure optimal performance. Our research proposes a holistic, data-driven framework for managing cloud compute resources that reduces costs without compromising application performance. We integrate a predictive, model-driven, threshold-based autoscaling solution for cloud-native applications with an optimized instance right-sizing approach to select cost-effective …

Reducing Operator Training Time Through Virtual Reality: A Case Study On The Lpkf Protomat E44 Machine, Joshua C. Patel

Browse all Theses and Dissertations

This thesis presents the development of an immersive virtual reality (VR) simulation that replicates the operation of the LPKF ProtoMat E44 PCB milling machine. Aimed at reducing operator training time and improving procedural understanding, the simulation offers an interactive and realistic environment where users can safely engage with machine workflows and start-up sequences. The emphasis is on accurate representation, usability, and maintaining immersion to support intuitive learning. Although formal evaluation is outside the scope of this work, the system is designed to serve as a foundation for cost-effective, scalable training in technical and manufacturing contexts, offering a modern alternative to …

Scalable Real-Time Stream Clustering For Unbounded Text Streams, Nathaniel C. Crossman

Browse all Theses and Dissertations

Social media, AI systems, IoT sensors, and other platforms generate vast amounts of streaming data. Given this vast volume of information, techniques that can reduce and aggregate data into meaningful topics are essential. One such technique is the two-phase stream clustering approach. In the first, online micro-clustering phase, the system forms micro-clusters from the incoming data stream, incrementally merges new items into related existing micro-clusters, and prunes or fades micro-clusters as they become inactive, producing a constantly updating yet compact set of micro-clusters representing potential topics and subtopics of the stream. In the second, offline macro-clustering phase, these micro-clusters are …

Patient Subset Classification Using Encoded Embeddings And Knowledge Graph Retrieval-Augmented Generation, Benjamin A. Holmes

Browse all Theses and Dissertations

The widespread adoption of electronic medical records has created a vast reservoir of clinical data that can be leveraged to better understand how interventions relate to patient outcomes. Much of this information, however, exists as unstructured free-text, posing significant challenges for traditional statistical and machine-learning methods. Solving these challenges would allow the extraction of specific patient subpopulations (clinically relevant cohorts of individuals who share overlapping symptoms, risk factors, or diagnostic criteria), which could be used in precision medicine. Despite this promise, extracting these subpopulations from unstructured medical notes is an ongoing challenge due to the variability of clinical language and …

Generative Adversarial Networks (Gans) For High-Dimensional Biological Data, Harigovind Harikumar

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This thesis investigates the application of Generative AI models, mainly Generative Adversarial Network (GAN) models to high dimensional and low sample size biological datasets like Motion Sickness, Breast Cancer, Crohn, and Melanoma. We utilized and compared three generative AI frameworks: Vanilla GAN, Wasserstein GAN (WGAN), Locality-Sensitive Hashing GAN (LSH-GAN) and Omics GAN. To address the challenges associated with high-dimensionality and low sample size, which was leading to very poor outputs of biological synthetic samples, we came up with an approach to stop the model when it reaches its saturation level. That is, we printed the loss plots to see where …

Pixmix Attack: Implementation And Evaluation Of A Novel Pixel Injection On Digital Video Port (Dvp) Interface In Embedded Camera Systems With Pcb Hardware Trojan, Sayed Md Tashfi Nowroz

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Image sensors are at the heart of machine vision systems in robotics, industrial automation, and surveillance systems which ideally operate with minimal human supervision and only occasional maintenance. The image sensors convert visible light into electrical signals which are locally decoded to image on the printed circuit board (PCB) by an ordinary embedded processor System on Chip (SoC). This thesis investigates a critical vulnerability in such systems, targeting the communication protocol at the signal level during runtime. Specifically, it focuses on a novel attack in the Digital Video Port (DVP) protocol, possible to exploit with PCB-based hardware Trojans, to craft …

A Secure Ml-Assisted Framework For Resilient And Efficient Prediction Of Physiotherapy Sequence In Bilateral Carpal Tunnel Syndrome, Pratik Pandurang Kharat

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Bilateral idiopathic carpal tunnel syndrome (CTS) is a neuromuscular disorder characterized by compression of the median nerve at both wrists, leading to symptoms such as pain, numbness, tingling, and muscle weakness. Unlike unilateral cases, bilateral idiopathic CTS presents distinct therapeutic challenges due to the simultaneous involvement of both hands and the lack of an identifiable underlying cause. This study explores the application of machine learning techniques to predict the optimal sequence of physiotherapeutic interventions Stretching followed by Myofascial Mobilization (S/M) or the reverse (M/S) in female patients with bilateral idiopathic CTS and right hand dominance. Data were drawn from a …

Learning Under Data Scarcity: Reasoning And Negative Distillation For Texts And Graphs, Calvin T. Greenewald

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Modern machine learning (ML) models rely on large amounts of high-quality labeled data to achieve optimal performance. However, in many real-world domains, such as cyber security, acquiring sufficient labeled data is often infeasible due to cost, privacy concerns, and the rapid evolution of underlying phenomena. This challenge underscores the importance of learning under data scarcity. This thesis addresses this challenge by proposing distinct, modality-specific techniques for text and graph domains, which allow models to generalize effectively with minimal data. For text classification task, we incorporate distilled rationales from large language models and adversarial perturbations into the input space to improve …

Quantized Average Agreement Algorithms With Error Correction For Digraphs, Shuaib A. Mughal

Honors Undergraduate Theses

Multi-agent systems have become more and more prevalent as technology increasingly gets integrated into our daily lives. Some of these technological systems are large in size; for example, the smart grid where multiple devices are used to monitor and control different aspects of the energy grid. Another example is a team of autonomous systems deployed for a specific task. When these systems are spatially distributed, an important component of distributed algorithms is the ability for the agents to reach consensus on the global state of the system. Reaching agreement enables the spatially distributed agent make decisions or determine the next …

Crop2cloud Platform: Real-Time Data Integration For Agricultural Water Monitoring, Bryan Nsoh, Abia Katimbo, Kendall Dejonge, Wei-Zhen Liang, Hongzhi N. Guo, Yufeng Ge, Derek M. Heeren, Yeyin Shi, Xin Qiao, Daran R. Rudnick, Hope Njuki Nakabuye, Birru Girma, Isa Kabenge, Joshua Wanyama

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Efficient water management is vital for sustainable agriculture, yet integrating real-time data for precise irrigation remains a challenge. This study designed the Crop2Cloud (C2C) platform, a system that leverages advanced sensors using Internet of Things (IoT), edge and cloud computing techniques, and computed Water Stress Indices (WSIs) and machine learning models (i.e., fuzzy logic), to provide scalable and real-time irrigation decisions. The C2C platform aggregates several data including Volumetric Water Content (VWC) from TDR sensors (Acclima Inc., US) installed at four multiple depths, canopy temperatures (Tc) measured by Infrared Radiometers (IRTs) (Apogee Instruments, US), as well as weather information and …

Cover Crops Can Reduce Greenhouse Gas Emissions From No-Till Maize In Southern Brazil: Insights From A Long-Term Field Experiment, Guilherme Rosa Da Silva, Adam J. Liska, Cimélio Bayer

Adam Liska Papers

Brazil is one of the countries that has the most agricultural area under no-till (NT) management. This research study aims to evaluate life-cycle greenhouse gas (GHG) emissions from maize (M) grain production in agroecosystems that used different cover crops under NT management in southern Brazil. The data for this study were from a long-term 41-year field experiment in southern Brazil. The long-term experiment evaluated the effects of fallow (F) and cover crops (oat (O), vetch (V), cowpea (B), pigeon pea (P), and lablab (L)) on nitrous oxide and methane emissions and soil carbon (C) sequestration in maize agroecosystems. Five cropping …

Investigations Of Conjugate Heat Transfer And Fluid Flow In Partitioned Porous Cavity Using Darcy-Forchheimer Model: Finite Element-Based Computations, Nasir Yasin, Shafee Ahmad, Muhammad Umair, Zahir Shah, Narcisa Vrinceanu, Ghadah Alhawael

Mathematics & Statistics Faculty Publications

The conjugate heat transfer and fluid flow has vast applications in thermal engineering, particularly for cooling in thermal devices, and automobile engines. This study investigates conjugate heat transfer in 2D enclosures, featuring thin solid fins attached to a porous bottom wall. The porous medium is considered isotropic and homogeneous by the Darcy-Forchheimer model, with fluid phases in local thermal equilibrium. The boundary conditions at the porous fluid interface ensure continuity of the velocities, stresses, temperature, and heat flux. The phenomenon is mathematically modelled by obtaining a set of partial differential equations. The finite element method (FEM) is used to perform …

Numerical Simulation Of The Fracture Resistance Of A Flame Deflector Under Impact And High Temperature By Using The Extended Finite-Element Method, Guangzhong Liu, Yuqing Feng, Tong Lin, Zhixin Xiong, Zhenting Chen

Journal of Marine Science and Technology–Taiwan

During the launch of rockets at sea, the flame deflector of the launch platform is subject to transient impact loads and high temperatures, which can result in the fracturing of the flame deflector, thereby endangering the platform and launch safety. In this study, dynamic crack propagation in a flame deflector under the aforementioned conditions was numerically investigated using the extended finite-element method (XFEM). The bulk of the flame deflector was modeled using shell elements, the local area in which the crack evolves was modeled using solid elements, and discontinuities were modeled using enrichment functions in the XFEM. The degrees of …

Overseas Agent Selection For International Logistics Companies: A Case Study, Hung-Ta Lin, Hua-An Lu

Journal of Marine Science and Technology–Taiwan

International logistics companies (ILCs) have aggressively participated in the supply chain systems of production industries as part of global trade development. They have adopted diversified and multinational models by expanding their overseas services to align with the various characteristics of regional economic patterns. Assigning specific local agents for their forwarding operations has been one of the key strategies for overseas services. Notably, the performance of these selected agents has significantly influenced service quality in their respective regions. In collaboration with the top management of an ILC, this study proposes an evaluation framework comprising 4 criteria and 16 subcriteria for selecting …

Cfrp Retrofitting For Enhancing Damping In Cushion Mounts For Marine Applications, Chih-Lung Hou, Guang-Min Luo, Sheng-Yuan Chen

Journal of Marine Science and Technology–Taiwan

Reducing vibration transmission and dynamic response is critical for data processing devices, precision instruments, and machinery in environments with frequent shocks, such as naval applications. These devices are often mounted to ships with cushion mounts. This study presents a cost-effective and rapid method for retrofitting mounts with carbon fiber reinforced polymers; the method greatly improved damping and effectively reduced vibration transmission. Specimens were produced and subjected to experimental modal analysis, and finite element models were developed for comparison. The frequency response functions and modes of the structural components from both methods were compared to assess the improvement in the damping …

Application Of Genetic Algorithm In Container Vessel Stowage Planning With Carbon Tax Considerations, Ming-Feng Yang, Wei-Hao Su, Ko-Meng Hu, Yu-Hsuan Li

Journal of Marine Science and Technology–Taiwan

This study developed a method for optimizing stowage planning for container vessels, a crucial aspect of international trade logistics. Over 80% of global trade depends on containerized transportation; thus, effective stowage planning is essential for minimizing transportation costs and enhancing operational efficiency. In the proposed hybrid optimization approach, integer programming is combined with a genetic algorithm to generate optimal stowage plans. The key factors considered in this method include load capacity limits, stacking constraints, and carbon tax regulations. The proposed method involves maximizing space utilization while minimizing logistics costs, with particular emphasis on reducing port dwell times. The findings of …

Research On Multi-Ship Collision Avoidance Decision-Making Based On Improved Velocity Obstacle Method Using Dynamic Elliptical Ship Domains, Tang Xinwei, Li Weifeng

Journal of Marine Science and Technology–Taiwan

The velocity obstacle method offers collision avoidance solutions for critical multi-ship encounter scenarios. This thesis introduces a Reciprocal Velocity Obstacle (RVO) approach based on dynamic elliptical ship domains for multi-ship collision avoidance. Initially, the concept of dynamic elliptical ship domains is presented, building upon the original static elliptical ship domain Velocity Obstacle (VO) method. Subsequently, multi-ship encounter scenarios are simulated in both virtual environments and real nautical charts, comparing the two methods. The analysis results demonstrate the improved method's excellent performance in both cases, underscoring its advantages. This research provides more effective collision avoidance strategies for multi-ship encounters.

Exploring The Red Sea Crisis’S Supply Chain Disruption Impacts On The Shipping Industry, Po-Hsing Tseng, Nick Pilcher

Journal of Marine Science and Technology–Taiwan

Since October 7, 2023, the Houthi militia in Yemen has continuously attacked Israel and its nearby ships in the Red Sea. This crisis has impacted on global ship operations, freight rates, and shipping networks, since this conflict seriously affects the international trade between Europe and Asia and brings many unpredictable butterfly effects (e.g. supply chain disruption, and high transportation costs in shipping and air cargo). This papero explores its impact on the supply chain and suggests potential solutions;, and to consider perceptions on who should be responsible for the crisis, and who will actually shoulder the responsibility for the crisis. …

Dna Barcoding In Species Identification Of Flying Fish (Exocoetidae) And Halfbeaks (Hemiramphidae) Larvae: A Comparative Study Of The Database Over A Decade, Hui-Ling Ko, Kwang-Tsao Shao, Ching-Yi Chen, Yen-Wei Chang

Journal of Marine Science and Technology–Taiwan

The families Exocoetidae and Hemiramphidae comprise migratory species, whose identification based on their external morphological characteristics can be challenging because of their similarities. DNA barcoding has been used in taxonomic and evolutionary studies to address these issues. The morphological characteristics of fish larvae change rapidly and often lack distinctive features, posing a challenge for species-level identification. This study used DNA barcoding to identify flying fish and half-beaked larvae. Using plankton nets, sampling was conducted seasonally between 2010 and 2013 in Nanwan, Taiwan. A total of 903 individuals were collected, of which 111 were identified as flying and half-beaked fish based …

Operational Risk Evaluation Of Oil Tankers Via Multivariate Gaidai Reliability Framework Incorporating Memory Effects, Oleg Gaidai, Shicheng He, Jinlu Sheng, Yan Zhu, Alaa Elsayed, Mahmoud El-Wazery

Journal of Marine Science and Technology–Taiwan

The retreat of Arctic Sea ice may exert a major impact on economy，potentially transforming the nature of commerce between Asia, Europe, and the Americas. Projections indicate a 40% reduction in transit distance and a 30% decrease in voyage time between Europe and northwest Asia compared to traditional routes such as the Suez Canal. However, even during summer navigation, fragmented floating ice persists, generating stochastic ice loads on vessel bows and hulls through complex ship-to-ice interaction. For structural design, statistical extrapolation methods are required to accurately assess excessive areal bow/hull stresses. This study proposes a novel multi-modal structural damage risk assessment …

Key Steering Characteristics For Training Remote Operators Of Maritime Autonomous Surface Ships In Collision Avoidance, Soyeong Lee, Ik-Hyun Youn

Journal of Marine Science and Technology–Taiwan

Maritime Autonomous Surface Ships (MASS) require remote operators (ROs) who need to be trained in collision avoidance (CA) maneuvers. In order to develop a training system, this study investigated differences in the steering characteristics of expert and novice navigators such as the timing of the initial rudder operation, rudder angle at a relative bearing 0°, heading at the maximum rate of turn to starboard, and, heading at the closest point of approach. The experts exhibited relatively consistent and controlled steering patterns characterized by earlier rudder adjustments and smoother heading transitions while novices demonstrated delayed reactions and greater variability in heading …

Subsurface Origin Of Tex86 Signals Revealed By Cross-Seasonal Gdgt Distributions In Suspended Particulate And Sediment Samples From The Southern East China Sea, Hung-Lin Tsai, Da-Chen Lin

Journal of Marine Science and Technology–Taiwan

The TetraEther indeX of 86 carbon atoms (TEX₈₆) proxy, which is based on isoprenoid glycerol dialkyl glycerol tetraethers (isoGDGTs) produced by Thaumarchaeota in marine environments, is widely used to reconstruct past sea surface temperatures (SSTs). However, evidence suggests that this proxy often instead reflects subsurface conditions, particularly in dynamic marginal seas. Therefore, we conducted a cross-seasonal study of glycerol dialkyl glycerol tetraether (GDGT) distributions in the southern East China Sea (ECS), which is characterized by strong monsoonal and current-driven hydrographic variability. We analyzed surface sediments and the high-resolution vertical profiles of suspended particulate matter (SPM) collected from 2016 …

The Current Status And Challenges Of Efforts To Generate Blue Carbon Credits In Okinawa Prefecture, Takashi Tamura, Wei-Chung Chen, Takafumi Sasaki

Journal of Marine Science and Technology–Taiwan

In Okinawa Prefecture, the Mozuku aquaculture industry is keen on establishing Blue Carbon Credits. This enthusiasm stems from various perspectives, including climate change mitigation, improvement of fishing grounds, industry maintenance, and enhancement of fishing ground surveillance functions. However, the industry faces several challenges. One significant challenge is the lack of a clear remaining coefficient for Mozuku, which complicates the creation of credits. Additionally, there is a shortage of manpower within the fisheries cooperative, further exacerbating the problem. To address these challenges, it is crucial to secure human resources that can promote system understanding and establish a financial base for undertaking …

In-Situ And Laboratory Sound Velocities Of Unconsolidated Sediments In The Western South Korea Plateau Of The East Sea, Kiju Park, Gil Young Kim, Yuri Kim, Bo Yeon Yi, Nyeon Keon Kang, Gwang Soo Lee

Journal of Marine Science and Technology–Taiwan

The in-situ velocity of unconsolidated shelf sediments in the western South Korea Plateau of the East Sea was directly measured using the Korea Institute of Geoscience and Mineral Resources (KIGAM) Seafloor Acoustic Probe. Physical properties, including laboratory velocity, were also measured for core samples collected from the same location. The laboratory measurements were conducted at a temperature of 23℃ and under atmospheric pressure. Owing to the in-situ temperature and pressure (0.5–1.5℃ and 100–180 atm, respectively) at the sampling site, the laboratory velocity was adjusted to the in-situ conditions. In addition, a comparison with a theoretical prediction model (Biot-Stoll model) was …