Automotive Engineering Commons^™

Predictive Maintenance In Naval Vessel Propulsion Systems For Enhanced Marine Operations Using A Bigmm-Hmm Framework With Divergence-Based Clustering, Farshid Javadnejad, Hyoshin John Park, Samuel Kovacic, Andres Sousa-Poza

Engineering Management & Systems Engineering Faculty Publications

This study introduces a BiGMM-HMM Integration Framework designed to improve predictive maintenance strategies for naval vessel propulsion systems, addressing the need for efficient and reliable operation in marine engineering applications. The framework effectively manages multimodal sensor data by leveraging a unique combination of Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) in a bidirectional architecture. It analyses the dynamic interactions between sensors and subsystems. Two preprocessing methods are evaluated: Method 1 focuses on subsystem interactions, employing divergence-based root cause analysis to identify key sensor variables by clustering of sensors and subsystems. In contrast, Method 2 processes the entire dataset …

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the …

Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu

Computer Science Faculty Publications

Artificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated …

Computational Investigation Of Underbody Slant Angle Variation Effect On A Hatchback Vehicle, Nufile Uddin Ahmed

College of Graduate Studies: Theses & Dissertations

Electric Vehicles, despite many advantages over their gasoline-powered counterparts, have the following main challenges towards mass adoption – the charging infrastructure and the range of the vehicle. Among the myriads of factors affecting the range, the coefficient of drag and the required power to overcome it are noteworthy. This study aims to investigate the effect of rear underbody slant angle on a common, simplified hatchback-shaped Electric Vehicle. The rear underbody slant angles (upward) assessed were 0°, 5°, 10°, and 15° and they were subjected to three freestream velocities representative of urban (16 m/s), highway (25 m/s) and interstate (40 m/s) …

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 …

ต้นแบบระบบตรวจจับ คัดแยก และป้องกันภัยจากอากาศยานไร้คนขับขนาดเล็ก, รักษ์ตะวัน พูลสุวรรณ

Chulalongkorn University Theses and Dissertations (Chula ETD)

งานวิจัยนี้ได้นำเสนอการออกแบบและความเป็นไปได้ของระบบกระบวนการหลายขั้นตอนแบบครบวงจรสำหรับการตอบโต้โดรน (UAV) โดยใช้แพลตฟอร์ม Software-Defined Radio (SDR) ในกระบวนการแรก การตรวจจับใช้วิดีโอเรดาร์แบบคลื่นต่อเนื่องปรับความถี่ (FMCW) ที่มีความถี่พาหะ 2.4GHz สำหรับการตรวจจับและติดตามเป้าหมายทางอากาศ ระบบย่อยนี้ได้รับการออกแบบมาเพื่อระบุระยะทางและความเร็วของวัตถุที่เข้ามา ขั้นตอนที่สองเกี่ยวข้องกับการจำแนกสัญญาณคลื่นความถี่วิทยุ (RF) โดยใช้โครงข่ายประสาทเทียมแบบคอนโวลูชัน (CNN) เพื่อวิเคราะห์สัญญาณควบคุมและการถ่ายโอนข้อมูลที่ส่งระหว่างโดรนและผู้ควบคุมภายในย่านความถี่ ISM (2.4GHz) ขั้นตอนสุดท้ายคือการทำให้ภัยคุกคามที่ได้รับการยืนยันเป็นกลางโดยใช้เทคนิคการตอบโต้ทางอิเล็กทรอนิกส์สองวิธี วิธีแรกคือการปล่อยสัญญาณรบกวนเพื่อขัดขวางการเชื่อมโยงคำสั่งและการควบคุมในย่านความถี่ ISM (2.4GHz) เพื่อแยกโดรนออกจากผู้ควบคุมได้อย่างมีประสิทธิภาพ ในขณะเดียวกัน ระบบจะทำการหลอก GPS ภายในย่านความถี่ L1 โดยการส่งสัญญาณ GPS L1 จำลองในย่านความถี่ 1.575GHz เพื่อทำให้ตำแหน่งของโดรนสับสน ระบบนี้ได้ทดสอบกับโดรน DJI Mini 4K ที่มีพื้นที่หน้าตัดเรดาร์ 0.01 ตารางเมตร โดยมีผลลัพธ์คือระยะการตรวจจับ 20 เมตร และระยะการรบกวนและการหลอก GPS ที่ 50 เมตร ภายใต้ข้อจำกัดกำลังส่งที่ 20 dB ตามข้อจำกัดของย่านความถี่ ISM

Potential Of Lidar And Hyperspectral Sensing For Overcoming Challenges In Current Maritime Ballast Tank Corrosion Inspection, Sergio Pallas Enguita, Jiajun Jiang, Chung-Hao Chen, Samuel Kovacic, Richard Lebel

Electrical & Computer Engineering Faculty Publications

Corrosion in maritime ballast tanks is a major driver of maintenance costs and operational risks for maritime assets. Inspections are hampered by complex geometries, hazardous conditions, and the limitations of conventional methods, particularly visual assessment, which struggles with subjectivity, accessibility, and early detection, especially under coatings. This paper critically examines these challenges and explores the potential of Light Detection and Ranging (LiDAR) and Hyperspectral Imaging (HSI) to form the basis of improved inspection approaches. We discuss LiDAR’s utility for accurate 3D mapping and providing a spatial framework and HSI’s potential for objective material identification and surface characterization based on spectral …

High-Fidelity Soh Prediction In Lithium-Ion Batteries Using Hybrid Ml Networks, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

Accurate and efficient prediction of lithium-ion battery state of health (SOH) is critical for ensuring reliability in electric vehicles, grid storage, and aerospace systems. Traditional SOH estimation methods often struggle with nonlinear degradation behaviors and lack sensitivity to subtle electrochemical signals, limiting their real-world deployment. To address these challenges, this study examines hybrid deep learning models that integrate differential capacity (dQ/dV) analysis to enhance predictive accuracy. Four hybrid architectures - hybrid CNN-LSTM multihead, CNN extractor for LSTM, DNN-LSTM, and DNN Bi-LSTM - were developed and evaluated using the NASA randomized battery usage dataset, offering a realistic benchmark under diverse operational …

Intelligent Systems For Synergistic Optimization Of Vehicle Dynamics And Energy Consumption In Connected And Automated Vehicles, Aman Poovalappil

Dissertations, Master's Theses and Master's Reports

This dissertation focuses on the development and validation of on-board, real-time estimation algorithms and the investigation of energy-saving strategies for modern light-duty vehicles, including connected and automated vehicles (CAVs). The accurate estimation of key vehicle parameters is critical for enhancing energy efficiency, enabling advanced driver-assistance systems (ADAS), and optimizing vehicle performance and energy efficiency. First, this research introduces a novel real-time algorithm to dynamically learn vehicle mass using readily available sensor data. Based on longitudinal vehicle dynamics, a sensitivity analysis was conducted to identify the conditions under which mass estimation is most robust. The algorithm was extensively validated through on-road …

Valve Train Design And Material Testing, Luis Luna

Publications and Research

In automotive engineering, CAD software like SolidWorks enables engineers to design and simulate mechanical components efficiently, reducing the need for extensive prototyping. This project examines the rocker arm's performance and durability within a valve train system. Components like pushrods, camshafts, and rocker arms control air intake and exhaust timing. Due to its high-impact motion and exposure to elevated temperatures, the rocker arm endures considerable wear. This study uses SolidWorks and Finite Element Analysis (FEA) to analyze the rocker arm's stress, deformation, and wear potential during camshaft movement. Material testing and motion analysis results provide insights into improving the durability of …

Carbon Nanotube-Enhanced Nanolubricants: Stability And Performance Improvements For Automotive Use, Sherif Elsoudy

Mechanical Engineering

Nanotechnology has been a promising technology over the years. Carbon nanotubes (CNTs) are one of the principal technologies that are developed in the nanotechnology sector. This study investigated the implementation of CNTs as additives in automotive lubricants, focusing on their impact on performance metrics. The dispersion of CNTs in lubricants is influenced by several factors, the most critical of which is the dispersion stability. Key findings include improved dispersion stability of CNTs in lubricants through adding surfactants like oleic acid, which effectively managed the aggregation issues. The CNT-enhanced lubricants exhibited superior tribological, thermophysical and rheological properties compared to the bare …

Faulty Perception Correction Of Autonomous Vehicles In Real-Life Driving Scenarios, Mark Omwansa

Dissertations

Driving is one of the most popular modes of transportation in the world. The United States Department of Transportation’s (USDOT) Federal Highway Administration (FHWA) reported 2.8 trillion vehicle-miles traveled (VMT) in 2020, and the National Highway Traffic Association (NHTSA) recorded 3.2 trillion VMT in 2019. Also recorded in the NHTSA report were 39,096 fatalities and 2.7 million injuries due to traffic accidents, costing the economy an estimated $242 billion. Most of these recorded accidents can be attributed to human error or misjudgment. It is for this reason that governments and the automotive industry are looking at autonomous vehicle (AV) technologies …

Low-Cost Vehicle Controller Testing System, Kevin R. Jung

Electrical Engineering

This project aims to create a system to facilitate easier evaluation of PCBs designed for low-voltage vehicle applications with an emphasis on accessibility for student teams in collegiate design series' such as Formula SAE. Student teams or smaller vehicle electronics manufacturers often need to verify designs and validate functionality during both development and manufacturing. An inexpensive, small, and portable, yet capable, system for taking measurements and simulating inputs would allow designers to put their boards into vehicle-representative conditions and environments without needing to connect to actual vehicle hardware. Additionally, while systems exist off-the-shelf that could fulfill the requirements needed by …

Piston Crown Design, Kurt L. Lippmann, Liam T. Janssen, Alex Rangel, Larry J. Davis

Mechanical Engineering

This project investigates the effects of dimpling a piston crown, and its impact on engine combustion dynamics. Through computational methods, the effects of piston crown dimpling on flow characteristics were evaluated. Theoretical results were verified with experimental methods. Experimental methods included characterization of exhaust gas content, engine horsepower and torque, and exhaust opacity.

Striking A Balance: Market Shock & Responses In Automotive Components Manufacturing, Emma Lane Mcgahey

All Theses

This thesis examines the effects of extreme market shocks on supply chain dynamics within the automotive industry. Through an analysis of demand data from an automotive manufacturer to its component suppliers (January 2018 to May 2024), the study investigates the relationship between market shocks and supply chain responses, providing insights into how auto components inventory management handles downstream responses to market shocks. With supporting public data—from FRED, BLS, and the U.S. Census Bureau resources—we explore two primary relationships: the impact of market shocks on the Average Standard Deviation of Demand (SDO) and the effect of demand variability on expedited pricing …

Model Reference Adaptive Control For Mobile Manipulators And Beyond, Srivatsan Srinivasan

All Dissertations

In recent years, robotics has expanded into various sectors, including manufacturing, transportation, and household services, making the integration of autonomy a critical area of research. This shift aims to ensure safety and enhance the utility of autonomous systems. Traditionally, robotic applications focused separately on mobility, like automated guided vehicles, and manipulation, such as serial-chain arms in manufacturing. Today, however, we see a merging of these capabilities in the growing field of mobile manipulator robots that combine movement with purposeful interactive functionalities.

A typical mobile manipulator is a robotic arm mounted on a wheeled base. This thesis focuses on advancing control …

Low Carbon, High Cooling Potential Alcohol Fuels In A High Compression Ratio Spark Ignition Engine, John Gandolfo

All Dissertations

Even though most of the effort towards implementing low-carbon alternative fuels has been directed towards heavy-duty vehicles that are difficult to electrify, the high-autoignition resistance of these fuels make them challenging to combust in compression ignition engines. However, several fuel candidates, such as ethanol and methanol, are ideal fuels for spark ignition engines. With the demand for electric vehicles slowing down and increasing recognition of the merits of hybrid powertrains, there is a need to maximize the performance of these fuels for spark ignition engines, which are likely to remain the combustion strategy of choice for hybrids due to their …

Data-Driven Quality Improvement For Sustainability In Automotive Packaging Systems, Tyler Mcknight

Morehead State Theses and Dissertations

A thesis presented to the faculty of the College of Science and Engineering at Morehead State University in partial fulfillment of the requirements for the Degree Master of Science by Tyler McKnight on November 25, 2024.

Sdf-1Α Mediates Primary Tumor Escape In Glioblastoma Through Activation Of Mesenchymal Transitions, Charles T. Froman-Glover

The Cardinal Edge

Glioblastoma (GBM), a highly aggressive primary brain tumor originating in glial cells, poses a significant challenge due to its rapid growth and invasive nature within healthy brain tissue.

Current treatments involve surgical resection, chemotherapy, and radiation. These treatments alone are not enough to cure this disease, and a better understanding of the mechanics of the tumor's micro-environment is imperative to furthering the field of cancer research. This research focuses on understanding the tumor microenvironment's impact, specifically investigating the role of stromal cell-derived factor 1 (SDF-1) mechanics on GBM aggressiveness. SDF-1 is known to facilitate disease progression by facilitating chemotaxis toward …

Transferred-Learning Intrusion Detection System For Internet Of Vehicles, Tan Nguyen

Undergraduate Research Symposium Posters

The growing connectivity of modern vehicles, particularly within the Internet of Vehicles (IoV), has significantly increased the need for robust cybersecurity solutions. Building upon the work of Yang and Shami (2022), who proposed a transfer learning and optimized convolutional neural network (CNN)-based Intrusion Detection System (IDS) for IoV, this research seeks to validate their results and explore potential enhancements. The original IDS demonstrated exceptional performance, with detection rates surpassing 99.25% on benchmark datasets. In this study, we first replicate their experiments using the Car-Hacking dataset to confirm the effectiveness of the proposed model and then evaluate its ability to detect …