Engineering Commons^™

Fault Location In Dc Microgrids Using Traveling Waves, Sajay Krishnan Paruthiyil

Electrical and Computer Engineering ETDs

In DC power systems, rapid fault location is crucial for maintaining reliable operation, particularly with the prevalence of DC-DC converters. This study investigates fault location techniques in DC systems utilizing Traveling Waves (TWs). Following data normalization, multi-resolution analysis employs discrete wavelet transform to capture high-frequency patterns of TW's wavelet coefficients. Parseval's theorem is utilized to quantify the energy of these coefficients. First, a curve-fitting technique is employed to estimate fault locations in DC microgrids. Then, two transfer learning approaches are proposed: first approach integrates Parseval energy curves into a Gaussian process estimator, while second employs feedforward neural network for fault …

Beyond Accuracy: Machine Learning Models For Predicting Presence Of Permanent Molar Caries In U.S. Children And Adolescents With Fairness Consideration, Pritam Deb, Lin Li, Christina R. Scherrer

Faculty Articles

Background

Although predictors of dental caries have been previously explored, a comprehensive understanding of factors influencing permanent‐molar decay in U.S. children and adolescents, especially with respect to racial and ethnic biases remains limited. This study aims to develop and evaluate machine‐learning (ML) models incorporating algorithmic fairness to predict caries in permanent molars.

Methods

Data from the National Health and Nutrition Examination Survey (NHANES) were analyzed, using the 2011–2014 cycles for training and validation and the 2015–2016 cycle for testing. The primary outcome was decayed, missing, and filled teeth (DMFT) in at least one permanent molar, dichotomized to represent the presence …

Computer Vision And Machine Learning Approaches For Defect Detection In 3d-Printed Cementitious Materials: A Systematic Review, Muhammad Ali Musarat, Ruben Paul Borg, Jingjie Wei, Carl James Debono, Kamal Khayat

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

3D printing is evolving at a fast pace in both the manufacturing and construction sectors. These advancements can greatly benefit these industries. However, the 3D printing of concrete structures presents some challenges due to defects in the 3D concrete printed elements. Hence, this study systematically reviews Artificial Intelligence (AI)-driven techniques, such as Computer Vision and Machine Learning, to identify surface defects that can occur in 3D-printed cementitious material structures. The adopted methodology was the PRISMA statement with the aim of reporting the systematic review and meta-analysis. Two well-known databases, Web of Science and Scopus, were utilised for data extraction of …

A Maintenance-Aware Machine Learning Framework For Network-Level Highway Pavement Condition Prediction, Jin Hwan Kim, Guk Gon Song, Youngguk Seo

Faculty Articles

This study develops and validates maintenance-aware machine learning models for predicting the Highway Pavement Condition Index (HPCI) on the Korean expressway network. Multiple regression and tree-based models were trained and tested using the pavement condition surveys archived in the Highway Pavement Management System (HPMS). A stacking regressor that integrates random forest, gradient boosting, and extreme gradient boosting as base learners exhibited the most robust predictions. Performance metrics indicated that the stacking ensemble achieved a mean absolute error of 0.21, a root mean square error of 0.31, and a coefficient of determination exceeding 0.73 on the testing dataset. Also, the residuals …

A Machine Learning-Based Apogee Prediction Methodology For Experimental Student Rockets, Price Hamilton Drawdy

Senior Honors Theses

The ability to predict the maximum altitude of a rocket (apogee) in real-time is incredibly useful for collegiate-level competition rockets. This project creates a machine learning-based real-time apogee prediction methodology. Three model types were tested: linear regression, random forest, and a 3-layer multi-layer perceptron (MLP) neural network. These models were trained on a large dataset of simulated flights. All models performed well on simulated test flights, with the linear regression model showing most promise for use on edge compute. More development and real-world testing are necessary to determine how applicable this method is for real-time operation. Nevertheless, this methodology provides …

Reward Representation Learning, Gregory M. Hyde

Dartmouth College Ph.D Dissertations

The \emph{Markov decision process} (MDP) has long served as the canonical model for sequential decision-making. However, it assumes that the reward function is Markov with respect to a given state representation---an assumption that often does not hold in practice. Instead, agents typically only perceive streams of observations and actions and must infer the latent structure according to which reward unfolds over time. From this perspective, reward prediction is initially non-Markov, reflecting a mismatch between the agent's current representation and the underlying structure of the environment.

In this thesis, we advance the view that reward is not simply a signal to …

Increasing The Prediction Accuracy Of Plant Oil Production Processes Through Adjusting The Parameters Of Machine Learning Models, Umidjon Ruziev, M.K. Shodiev, A.T. Rajabov

Chemical Technology, Control and Management

Vegetable oil production is characterized by high variability in output indicators due to nonlinear interactions between raw material parameters, equipment modes, and heat and mass transfer conditions. Existing approaches to applying machine learning in this field, as a rule, do not account for the impact of hyperparameter adjustments on forecasting quality across specific technological stages. The article presents a systematic methodology for adjusting model parameters (Ridge regression, SVR, GBM, LSTM) applied to three key tasks: predicting residual oil content in oil cake, color index during bleaching, and free fatty acid content during deodorization. In a set of 1000 observations, including …

A Machine Learning Framework For Ddos Attack Detection In Sdn-Enabled Mobile Wireless Networks, Ishita Sharma, Satyam Agarwai, Shashi Shekhar Jha, Sumit Chakravarty

Faculty Articles

Attacks on network components and devices pose a significant threat to service continuity, necessitating robust detection mechanisms. This paper presents a Distributed Denial of Service (DDoS) attack detection framework tailored for heterogeneous mobile wireless networks within a Software-Defined Networking architecture. A two-tier model is proposed: localized attack detection at access points (APs) using a Multi-Layer Perceptron (MLP) classifier, and centralized detection under mobility at the controller using a Long Short-Term Memory (LSTM) model. The system incorporates novel traffic features such as flow count, speed of source IP, source and destination IP address entropy, proportion of bidirectional flows, and handover frequency, …

Machine Learning-Based Prediction And Experimental Validation Of The Pyrolysis Product Distribution In Tar-Rich Coals, Dong Shuai, Li Hongqiang, Wu Zhiqiang, Yu Zunyi, Lyu Zitao, Guo Wei, Yang Panxi, Fu Keming, Hao Xuanzhi, Liu Gen, Yang Bolun

Coal Geology & Exploration

Background Tar-rich coals serve as an important coal-based oil and gas resource in China, while their pyrolysis product distribution is governed by the coupling effects of coal properties and reaction conditions. Therefore, rapidly identifying the pyrolysis product distribution patterns holds great significance for the resource evaluation and experimental design of tar-rich coals.Methods Existing studies on tar-rich coals suffer from the insufficient integration of exclusive data and limited synergistic prediction capacities for multiple products. To address these issues, this study constructed a dedicated dataset involving proximate analysis, ultimate analysis, elemental molar ratios, maceral composition, and pyrolysis conditions by systematically collecting …

Physiobridge: Physiology-Constrained Self-Supervised Foundation Model For Cross-Device Ecg–Ppg Learning With Conformal Risk Control, Abbas Alzubaidi, Ali Al-Shuwaili, Ali Al-Bayaty

Electrical and Computer Engineering Faculty Publications and Presentations

Wearable and bedside sensors continuously generate electrocardiograms (ECG), photoplethysmograms (PPG), and related physiological waveforms that could enable earlier detection of deterioration and more personalized care. However, current deep learning pipelines in biomedical signal processing often remain taskand device-specific, degrade under domain shift (new hospitals, sensors, skin tones, motion), and provide limited uncertainty information for safety-critical decisions. We propose PhysioBridge, a foundation-model approach that learns a shared representation space for ECG and PPG via self-supervised pretraining and explicit physiology constraints, then supports downstream adaptation with distribution-free risk control. PhysioBridge introduces (i) multi-rate patch tokenization that preserves clinically meaningful morphology across heterogeneous …

Robust Non-Invasive Cardiac Index Prediction Via Feature Integration And Data-Augmented Neural Networks, Chih-Hao Chang, Mei-Ling Chan, Yu-Hung Fang, Po-Lin Huang, Tsung-Yi Chen, Tsun-Kuang Chi, I Elizabeth Cha, Tzong-Rong Ger, Kuo-Chen Li, Shih-Lun Chen, Liang-Hung Wang, Jia-Ching Wang, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Concurrent with the rising consumption of ultra-processed, high-calorie diets and the decline in physical activity, obesity and related cardiovascular conditions among young adults have continued to increase, becoming an important global public health concern. This study integrates non-invasive Internet of Things (IoT) sensing devices, including the TERUMO ES-P2000 blood pressure monitor (Terumo Corp., Tokyo, Japan) and the PhysioFlow PF07 Enduro cardiac hemodynamic analyzer (Manatec Biomedical, Poissy, France), with an artificial neural network (ANN) for cardiac index (CI) prediction. Through appropriate data preprocessing and model training strategies, the generalization ability and stability of the proposed CI prediction model were significantly enhanced. …

Efficient Intrusion Detection For Iomt: Integrating Machine Learning, Feature Selection, And Fuzzy Logic, Ghaida Mansour Balhareth

Electronic Theses and Dissertations

The internet of medical things (IoMT) has transformed healthcare by enabling real-time patient monitoring, remote diagnoses, and effective data exchange among connected medical devices and clinical systems. The increasing reliance on interconnected medical equipment has also intensified cybersecurity risks, as resource-constrained devices and wireless communication channels are vulnerable to attacks such as man-in-the-middle, spoofing, data injection, and ransomware. Intrusion Detection Systems (IDSs) play a critical role in mitigating these threats; however, traditional IDS approaches often struggle with high-dimensional IoMT data, class imbalance, and uncertainty in traffic patterns, which can increase false alarms and reduce reliability in safety-critical environments. This dissertation …

Real-Time Data Collection System For Low-Cost Diesel Emissions Monitoring, Zachary Driskill

Theses and Dissertations

Diesel trucks are a major source of pollutants, including particulate matter (PM) and NO$_x$. Roadside emissions monitoring can identify high-emitting diesel vehicles, but conventional sensing systems rely on expensive, complex sensing instruments that limit widespread deployment. Low-cost sensors offer increased scalability due to their small size, low cost, and low power consumption, but they have lower accuracy and slower response time. This thesis presents the design and evaluation of a low-cost roadside emissions monitoring system, establishing the foundation for a scalable and widely deployable alternative. We apply machine learning calibration to roadside emission monitoring to improve the usability of low-cost …

Development Of A Novel Cfd Approach For Predicting Multiphase Flow Enhanced By Machine Learning, Abdulrahman Elazazi Mohamed

Thesis/ Dissertation Defenses

Accurate estimation of interface orientation is crucial for ensuring both the accuracy and robustness of Volume of Fluid (VOF) schemes in multiphase flow simulations, especially when using non-uniform Cartesian meshes. Conventional gradient reconstruction approaches, such as least-squares (LSQ) methods, often suffer from significant errors and strong oscillations on highly stretched grids. This thesis proposes a grid-transferable, learning-based methodology that predicts interface unit normal vectors directly from the local volume-fraction field on non-uniform structured Cartesian grids by means of a compact feedforward neural network.

The methodology extends earlier work originally developed for uniform grids, which is first reproduced to assess how …

Ai Method For Classification Of Diagnosis Of Near-Infrared Breast Lesion Images, Kaiquan Chen, Fangyang Shen, Honggang Wang, Zhengchao Dong, Jizhong Xiao, Ming Ma, Afroza Aktar, Christopher Chow, Wenxiong Zhang

Publications and Research

In near-infrared optical breast lesion screening and diagnosis systems, high-speed four-dimensional scanners can dynamically acquire tens of thousands of lesion images within a five-minute period. Currently, manual computer annotation is required to generate standard samples from these scanned breast lesion images, a process that depends heavily on physicians with clinical expertise. On average, a single physician can annotate only approximately ten samples per working day. As a result, this process is time-consuming and labor-intensive, and the collected samples often suffer from low accuracy, large variability, and limited diagnostic reliability. Several AI-based annotation tools, such as QuPath, HALO AI™, and X-AnyLabeling, …

Using Machine Learning Algorithms To Clarify Relationships Between Soil Properties And Lead Stomach Bioaccessibility, Shehan Wijesinghe, Dibyendu Sarkar, Hadeer Saleh, Khalid Mustafa, Smitha Rao, Rupali Datta

Michigan Tech Publications

Featured Application: This paper has important applications in environmental health risk assessment and urban soil remediation planning. By demonstrating how machine learning can predict lead bioaccessibility in lead paint-contaminated soil, the study provides a scalable, cost-effective alternative to laboratory-based extraction methods. Such models could support rapid screening of contaminated sites, helping prioritize high-risk areas for intervention and allocate remediation resources more efficiently. This model is expected to advance data-driven decision-making for managing lead-contaminated soils and protect vulnerable urban populations from exposure. Lead contamination in urban soils, primarily from deteriorating lead-based paint, poses a significant health risk in the United States. …

Roadmap On Artificial Intelligence-Augmented Additive Manufacturing, Ali Zolfagharian, Liuchao Jin, Qi Ge, Wei-Hsin Liao, Andrés Díaz Lantada, Francisco Franco Martínez, Tianyu Zhang, Tao Liu, Charlie C.L. Wang, Mohammad Hossein Mosallanejad, Reza Ghanavati, Abdollah Saboori, Alejandro De Blas De Miguel, William Solórzano-Requejo, Yi Cai, Xiangyang Dong, Huangyi Qu, Najmeh Samadiani, Guangyan Huang, Austin Downey, Yanzhou Fu, Lang Yuan

Faculty Publications

Artificial intelligence-augmented additive manufacturing (AI2AM) represents a transformative frontier in digital fabrication, where artificial intelligence (AI) is embedded not as a peripheral tool, but as a central framework driving intelligent, adaptive, and autonomous additive manufacturing (AM) systems. The objective of this Roadmap is to present a comprehensive vision of the state-of-the-art developments in AI2AM while charting the future trajectory of this rapidly emerging field. As AM applications continue to expand across diverse sectors, conventional design and control strategies face growing limitations in scalability, quality assurance, and material complexity. AI uses tools like computer vision, generative design, and large language models …

Machine Learning For Real-Time Body Movement Classification Using Eeg And Vr Technologies, Aiden H. Behler, Robin Ghosh

Undergraduate Research

This project investigates the feasibility of real-time full-body movement classification using electroencephalography (EEG) integrated with virtual reality (VR) technologies. The primary objective is to develop and evaluate machine learning models for predicting human body movements using EEG data alone, with the long-term goal of reducing or eliminating reliance on wearable motion trackers. Currently, several machine learning algorithms have been tested, but classification accuracy remains modest, indicating the complexity of the task. Ongoing work focuses on optimizing preprocessing, feature selection, and model architectures to improve performance. The system architecture combines synchronized neural and motion data collected within a VR environment. EEG …

Development Of A Novel Cfd Approach For Predicting Multiphase Flow Enhanced By Machine Learning, Abdulrahman Elazazi Salem

Theses

Accurate estimation of interface orientation is crucial for ensuring both the accuracy and robustness of Volume of Fluid (VOF) schemes in multiphase flow simulations, especially when using non-uniform Cartesian meshes. Conventional gradient reconstruction approaches, such as least-squares (LSQ) methods, often suffer from significant errors and strong oscillations on highly stretched grids. This work proposes a grid-transferable, learning-based methodology that predicts interface unit normal vectors directly from the local volume-fraction field on nonuniform structured Cartesian grids using a feedforward neural network. The methodology extends earlier work originally developed for uniform grids, which is first reproduced to assess how performance deteriorates in …

The Developing Role Of Ai In Modern Engineering Research, Rianna Pais

The Cardinal Edge

No abstract provided.

Roadmap: Integrating Artificial Intelligence In Structural Health Monitoring Systems, Simon Laflamme, Erik Blasch, Flippo Ubertini, Zheng Liu, John Wertz, Christine Knott, Matthew Cherry, Eric Lindgren, Fu-Kuo Chang, Amrita Kumar, Jack Poole, Keith Worden, Austin Downey, Jie Wei, Patrick F. Musgrave, Adrian S. Wong, Guiseppe Quaranta, Marco Martino Rosso, Giuseppe Carlo Marano, Yu Chen, Et. Al.

Faculty Publications

Advances in computing and machine learning (ML) methods have led to a rapid rise in artificial intelligence (AI) research and applications in many fields. AI research benefitted from advances in computation hardware, collection and distribution of large data sets, and proliferation of software techniques. AI techniques include ML for provable results, deep learning for data exploration, reinforcement learning for control, and active learning for adaptive systems. Likewise, AI algorithms can handle large amounts of data, construct unknown representations, and provide a direct link between data and classification for decision making. These unmatched capabilities have been seen as a path to …

Predictive Analysis Of Greenhouse Gas Emissions From Electric Vehicle Charging In The United States, Mahyar Amirgholy, Faysal A. Chowdhoury, Chenyu Wang, S Nikhila Kanigiri

Faculty Articles

Electric vehicles (EVs) emit substantially fewer air pollutants than conventional internal combustion engine vehicles. However, the continuous increase in electricity demand from the power grid for EV charging, resulting from the growing adoption and total vehicle miles traveled, leads to higher greenhouse gas emissions from the power sector. This study presents a predictive analysis of energy sector greenhouse gas emissions from EV charging at the regional level across the United States under various projection scenarios of technology costs, fuel prices, demand growth, and electricity sector policies. The predictive modeling of greenhouse gas emissions from EV charging is performed using a …

Machine Learning-Based Upscaling Of Rock Permeability From Pore Scale To Core Scale: Effect Of Training Dataset Size And Sub-Core Volumes, Yaotian Guo, Fei Jiang, Takeshi Tsuji, Yoshitake Kato, Mai Shimokawara, Lionel Esteban, Mojtaba Seyyedi, Marina Pervukhina, Maxim Lebedev, Ryuta Kitamura

Research outputs 2022 to 2026

Permeability characterizes the capacity of porous formations to conduct fluids, thereby governing the performance of carbon capture, utilization, and storage (CCUS), hydrocarbon extraction, and subsurface energy storage. A reliable assessment of rock permeability is therefore essential for these applications. Direct estimation of permeability from low-resolution CT images of large rock samples offers a rapid approach to obtain permeability data. However, the limited resolution fails to capture detailed pore-scale structural features, resulting in low prediction accuracy. To address this limitation, we propose a convolutional neural network (CNN)-based upscaling method that integrates high-precision pore-scale permeability information into core-scale, low-resolution CT images. In …

Orthogonal Time Frequency Space Modulation For Underwater Acoustic Communication Systems: A Review, Bevek Subba, Quoc Viet Phung, Stefan Lachowicz, Walid K. Hasan, Muhammad Haziq, Daryoush Habibi, Iftekhar Ahmad

Research outputs 2022 to 2026

Underwater Acoustic Communication (UAC) has garnered significant attention due to its applications in marine science, defence, and exploration. With the vast majority of the Earth's surface covered by water, there is a growing demand for reliable and effective communication techniques in underwater environments. However, the underwater acoustic channel is the most challenging channel due to its harsh characteristics, which significantly hinder the reliability and performance of UAC systems. Orthogonal Time Frequency Space (OTFS) modulation has emerged as a promising solution for next-generation UAC systems to address high Doppler and high mobility scenarios. It has demonstrated tolerance to fast time-varying channel …

Development Of A Framework For Identifying Asphalt Pavement Cracking Distresses Using Machine Learning, Dingxin Cheng

Mineta Transportation Institute

Asphalt pavement cracking is one of the most critical distresses affecting pavement performance and service life. When pavement deteriorates, it can lead to safety hazards, higher vehicle maintenance costs, and expensive repairs for cities and states—making early detection essential for everyone who relies on the roadway system. To address this challenge, the research team developed a prototype cracking identification system that integrates a customized machine learning model with computer vision algorithms. High-resolution images collected from drones or ground-based cameras are processed within the system to automatically detect and classify major cracking types. The core of the framework utilizes the You …

Machine Learning–Driven Prediction Of Dementia From Mri And Clinical Features: A Comparative Analysis Of Ensemble And Baseline Models, Sarah Raad Hameed, Zainab Muhannad Nahid, Rawan Ahmed Abdulmahdi

AUIQ Technical Engineering Science

Early detection of dementia remains a pressing challenge in clinical neuroscience, as delayed diagnosis limits therapeutic impact and healthcare planning. Leveraging the Open Access Series of Imaging Studies (OASIS) cross-sectional dataset of 436 participants, this study developed a robust machine learning pipeline integrating sociodemographic, clinical, and neuroimaging-derived features. Preprocessing included removal of highly sparse variables (Delay), median imputation of partially missing but clinically essential measures (SES, MMSE, CDR, Educ), Winsorization of extreme values, and skewness correction. The target Clinical Dementia Rating (CDR) was binarized (0 = no dementia, ≥ 0.5 = dementia) to align with clinically actionable screening. Categorical features …

Hybrid Data-Driven Cement-Stabilized Soil Design: An Integration Of Machine Learning, Multi-Objective Optimization, And Life Cycle Assessment, Chikezie Chimere Onyekwena, Yunli Li, Ikenna J. Okeke, Ubani Obinna Uzodimma, Monday Uchenna Okoronkwo, Wenping Wu

Chemical and Biochemical Engineering Faculty Research & Creative Works

Soil stabilization is crucial in geotechnical engineering, yet conventional methods are often time-consuming, resource-intensive, and environmentally unsustainable. Despite growing interest in Machine Learning (ML) and optimization tools for mix design, few studies integrate these methods with decision-making techniques and environmental assessment to support practical implementation. This study proposes a hybrid data-driven framework for predicting strength, optimizing mix compositions, and evaluating environmental impacts via life cycle assessment of cement-stabilized soft soils. Six ML models were evaluated, and the top-performing eXtreme Gradient Boosting (XGB) model was further improved using the Grey Wolf Optimizer (GWO). The optimized XGB-GWO model, integrated with a polynomial …

Machine Learning In Peak Demand Forecasting: Foundations, Trends, And Insights, Shuang Dai, Fanlin Meng, Hongsheng Dai, Qian Wang, Xizhong Chen, Wenlei Bai, Peizhi Shi, Richard Allmendinger, Yuchen Zhang, Jian Liu

Electrical and Computer Engineering Faculty Research & Creative Works

Peak demand forecasting involves predicting the maximum electricity demand within a specific period, which plays a key role in maintaining the efficiency and stability of power systems. The rapid evolution of power systems, driven by advanced metering infrastructure, local energy applications such as electric vehicles, and the increasing adoption of intermittent renewable energy, has introduced greater randomness and reduced predictability in peak demand. Given the pressing need to address more diverse implementation requirements across different contexts, accurate and reliable peak demand forecasting has become increasingly important. To the best of our knowledge, this study is the first to provide a …

Ai-Driven Stratified Modeling For Early Liver Disease Detection: A Comparative Study Of Ensemble And Conventional Machine Learning Classifiers, Ghadeer Murtadha Ali, Ali Aqeel Hadi, Mustafa Abdulkareem Abbas, Abdullah Alkarar Mohammad, Ahmed Fadhil Abdulhussein

AUIQ Technical Engineering Science

Background: Early prediction of liver disease remains challenging in routine clinical diagnostics due to the multifactorial nature of hepatic dysfunction and the limited discriminative capacity of conventional laboratory-only assessments.

Objective: This study aims to develop and rigorously evaluate a robust machine learning framework for binary liver disease classification, emphasizing predictive stability, diagnostic balance (sensitivity–specificity), and statistical reproducibility across repeated experiments.

Methodology: A structured dataset of 1,700 records with 11 features representing demographic, behavioral, genetic, and clinical determinants was used to train and compare five supervised models: CatBoost, AdaBoost, Random Forest, Support Vector Machine (SVM), and Decision Tree. Performance was assessed …

Ai-Driven Automatic Fault Detection Systems: Revolutionizing Modern Smart Grids, Aravind Sanikommu

Student Theses and Dissertations

The increasing complexity of current power systems, resulting from the integration of distributed generators and renewable energy sources, necessitates intelligent and adaptive fault detection schemes. Traditional protection using impedance and phasor analysis is usually weak when operating in nonlinear and transient operating conditions. Consequently, the tools of Data-driven fault classification and decision-making have gained strength under artificial intelligence (AI) and machine learning (ML) to improve grid reliability. This thesis is a proposal of an automatic fault detection and classification system based on AI applied to a smart mini-grid setting built in MATLAB/Simulink. A complete set of voltage and current data …