Engineering Commons^™

Higher-Order Effects In Biaxial Flexure Of Gfrp I-Section Beams, Zia Razzaq, Faridoon Z. Razzaq

Civil & Environmental Engineering Faculty Publications

A theoretical study of Glass Fiber Reinforced Polymer (GFRP) beams subjected to biaxial bending moments is presented with a focus on the influence of higher-order effects on maximum normal stresses. It is shown that the biaxial bending type of loading causes a dramatic increase in the maximum normal stress for a GFRP beam when induced torsional effects are included. The study demonstrates that the traditional first-order theory can grossly underestimate the maximum normal stress in a GFRP beam. Based on the numerical results presented using a higher-order theory which also accounts for induced warping normal stresses, the maximum normal stress …

Strategies Automotive Manufacturing Managers Use To Deliver Work In Alignment With Project Milestones, Sashidhar Mr. Sankaranarayanan

Walden Dissertations and Doctoral Studies

Project managers working in manufacturing firms in the automotive industry frequently face challenges such as cost overruns, schedule delays, and scope changes. Project managers often lack effective strategies to ensure that different departments deliver their work in alignment with project milestones, which can negatively affect project success rates. Grounded in the balanced scorecard framework, the purpose of this qualitative single case study was to explore strategies managers in the automotive manufacturing industry used in their different departments to deliver their work in alignment with project milestones. The participants were three project managers who had successfully implemented strategies that reduced the …

Changes To Sea Surface Temperatures And Vertical Wind Shear And Their Influence On Tropical Cyclone Activity In The Caribbean And The Main Developing Region, Keneshia Hibbert, Equisha Glenn, Thomas M. Smith, Jorge E. González-Cruz

Publications and Research

Sea surface temperatures and vertical wind shear are essential to tropical cyclone formation. TCs need warm SSTs and low shear for genesis. Increasing SSTs and decreasing VWS influences storm development. This work analyzes SST and VWS trends for the Caribbean, surrounding region, and the Atlantic hurricane main developing region from 1982–2020. Storm intensity increases significantly during this period. Annual and seasonal trends show that regional SSTs in the MDR are warming annually at 0.0219 °C yr⁻¹ and, per season, 0.0280 °C yr⁻¹. Simultaneously, VWS decreases during the late rainfall season, at 0.056 m/s yr⁻¹ in the …

Board 201: A New Public Dataset For Exploring Engineering Longitudinal Development By Leveraging Curricular Analytics, David Reeping, Matthew Ohland, Kenneth Reid, Hossein Ebrahiminejad, Nahal Rashedi

School of Engineering Education Faculty Publications

Considering the increasing demand for engineering graduates, understanding what is limiting students from completing their degrees has been a consistent question posed in the literature. The nontrivial variance in pathways students take in obtaining an engineering degree, especially in cases where students abandon their studies, suggests that longitudinal datasets can hold a wealth of information to uncover factors contributing to attrition. Accordingly, this project uses existing data to explore curricular factors that create barriers for different students by leveraging a new framework for quantifying the impact of such factors, Curricular Analytics. Curricular Analytics uses network analysis to measure sequencing and …

Board 394: Sustaining And Scaling The Impact Of The Midfield Project At The American Society For Engineering Education (Year 1), Susan M. Lord, Matthew Ohland, Marisa K. Orr, Russell Long, Catherine E. Brawner, Joseph Roy

School of Engineering Education Faculty Publications

A substantial investment by the National Science Foundation (NSF), including awards from Engineering Education and Centers in the Engineering Directorate and the Division of Undergraduate Education in the Education and Human Resources Directorate, has led to the creation and study of the Multiple Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD). This large database of student records has yielded groundbreaking research on student pathways by a small interdisciplinary team of researchers. The team has shown that while individual engineering programs may have poor graduation rates, a multi-institutional view reveals that engineering programs as a whole graduate a larger fraction of …

Leveraging Campus-Scale Wi-Fi Data For Activity-Based Occupant Modeling In Urban Energy Applications, Martín Mosteiro-Romero, Clayton Miller, Matias Quintana, Adrian Chong, Rudi Stouffs

Research Collection College of Integrative Studies

The widespread availability of open datasets in urban areas is transforming how urban energy systems are planned, simulated, and visualized. Urban energy models, however, require an understanding of urban dwellers, as their activities create the demands for energy in buildings. In this paper, we explore using campus-scale Wi-Fi data to identify typical occupant activity patterns as an input to an agent-based model of building occupants at the district scale. The data is taken from a Singaporean university’s Wi-Fi network at high resolution. Each record comprises a timestamp, a device identifier, the location of the device within the campus, and the …

Corrigendum: Human Mental Workload: A Survey And A Novel Inclusive Definition, Luca Longo, Christopher D. Wickens, Gabriella Hancock, P. A. Hancock

Articles

In the published article, the name of Gabriella Hancock was incorrectly written as “Gabriela M. Hancock.” The correct name is “Gabriella Hancock.” In the published article, there was also an error in the author list as published. Gabriella Hancock was listed as the last author, but should have been listed as third author. P. A. Hancock was listed as third author but should be listed as the last author. The corrected author list appears below. Luca Longo1, Christopher D.Wickens, Gabriella Hancock and P. A. Hancock. The authors apologize for this error and state that this does not change the scientific …

Subnetwork Ensembling And Data Augmentation: Effects On Calibration, A. Çağrı Demir, Simon Caton, Pierpaolo Dondio

Articles

Deep Learning models based on convolutional neural networks are known to be uncalibrated, that is, they are either overconfident or underconfident in their predictions. Safety-critical applications of neural networks, however, require models to be well-calibrated, and there are various methods in the literature to increase model performance and calibration. Subnetwork ensembling is based on the over-parametrization of modern neural networks by fitting several subnetworks into a single network to take advantage of ensembling them without additional computational costs. Data augmentation methods have also been shown to enhance model performance in terms of accuracy and calibration. However, ensembling and data augmentation …

Decision Making For Process Control Management In Control Rooms: A Survey Methodology And Initial Findings, Chidera Winifred Amazu, Ammar N. Abbas, Micaela Demichela, Davide Fissore

Articles

Control rooms and their operators are active elements in complex socio-technical systems such as process plants. Control room operators monitor process operations, respond to alarms, and manage process deviations until emergencies. The increase in automation of plants and equipment makes the operators less involved in manual process control or other physical roles while more exposed to cognitive load generated, for example, by increasing the number of alarms or potential system failures in abnormal situations. A shift in process control design and management techniques to holistically capture risks due to evolving process or monitoring capabilities and the related influencing factors is …

Persuasive Communication Systems: A Machine Learning Approach To Predict The Effect Of Linguistic Styles And Persuasion Techniques, Annye Braca, Pierpaolo Dondio

Articles

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

Detection Of Grape Clusters In Images Using Convolutional Neural Network, Mohammad Osama Shahzad, Anas Bin Aqeel, Waqar Shahid Qureshi

Articles

Convolutional Neural Networks and Deep Learning have revolutionized every field since their inception. Agriculture has also been reaping the fruits of developments in mentioned fields. Technology is being revolutionized to increase yield, save water wastage, take care of diseased weeds, and also increase the profit of farmers. Grapes are among the highest profit-yielding and important fruit related to the juice industry. Pakistan being an agricultural country, can widely benefit by cultivating and improving grapes per hectare yield. The biggest challenge in harvesting grapes to date is to detect their cluster successfully; many approaches tend to answer this problem by harvest …

Comparing And Extending The Use Of Defeasible Argumentation With Quantitative Data In Real-World Contexts, Lucas Rizzo, Luca Longo

Articles

Dealing with uncertain, contradicting, and ambiguous information is still a central issue in Artificial Intelligence (AI). As a result, many formalisms have been proposed or adapted so as to consider non-monotonicity. A non-monotonic formalism is one that allows the retraction of previous conclusions or claims, from premises, in light of new evidence, offering some desirable flexibility when dealing with uncertainty. Among possible options, knowledge-base, non-monotonic reasoning approaches have seen their use being increased in practice. Nonetheless, only a limited number of works and researchers have performed any sort of comparison among them. This research article focuses on evaluating the inferential …

Gated Deep Reinforcement Learning With Red Deer Optimization For Medical Image Classification, Narayanan Ganesh, Sambandan Jayalakshmi, Rama Chandran Narayanan, Miroslav Mahdal, Hossam Zawbaa, Ali Wagdy Mohamed

Articles

The brain is one of the most important and complex organs in the body, consisting of billions of individual cells. Uncontrolled growth and expansion of aberrant cell populations within or around the brain are the main causes of brain tumors. These cells have the potential to harm healthy cells and impair brain function [1]. Tumors can be detected using medical imaging techniques, which are considered the most popular and accurate way to classify different types of cancer, and this procedure is even more crucial as it is noninvasive [2]. Magnetic resonance imaging (MRI) is one such medical imaging technique that …

Schizo-Net: A Novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning On Electroencephalogram-Based Brain Connectivity Indices, Nitin Grover, Aviral Chharia, Rahul Upadhyay, Luca Longo

Articles

Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject’s interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuro-psychiatric patients from healthy subjects. The study presents Schizo-Net , a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain …

A Multidimensionality Reduction Approach To Rainfall Prediction, Menatallah Abdel Azeem, Prasanjit Dey, Soumyabrata Dev

Articles

The rainfall has an impact on various fields and industries, including transportation, construction, tourism, health, and wildlife preservation. Accurate rainfall prediction is essential for mitigating the negative impact of rainfall on these sectors. However, previous studies on rainfall prediction have been mainly based on datasets from North America, Europe, Australia, and Central Asia, covering different periods. This study proposes using weather datasets covering the past 5 to 10 years to capture recent patterns in weather data. Additionally, the curse of dimensionality can impact model performance and lead to overfitting. Therefore, this study proposes utilizing dimensionality reduction techniques to ensure that …

Nesnet: A Deep Network For Estimating Near-Surface Pollutant Concentrations, Prasanjit Dey, Bibhash Pran Das, Yee Hui Lee, Soumyabrata Dev

Articles

Atmospheric pollution has become a serious threat in recent years. The advancements and expansion of industrial activity and civilization have been the major catalysts. With serious consequences like climate change and global warming, the onset of which is already being observed, keeping a check on atmospheric pollutant levels is now more important than ever. Trace gases play a major role in atmospheric chemistry. Many of these are also regarded as major atmospheric pollutants. The concentration of gases, such as (SO2), (O3), (NO2), etc., are indicators of air quality. Therefore, in this study, we primarily concern ourselves with concentrations of NO2, …

How Visual Stimuli Evoked P300 Is Transforming The Brain–Computer Interface Landscape: A Prisma Compliant Systematic Review, Jai Kalra, Prashasti Mittal, Nirmiti Mittal, Abhishek Arora, Utkarsh Tewari, Aviral Chharia, Rahul Upadhyay, Vinay Kumar, Luca Longo

Articles

Non-invasive Visual Stimuli evoked-EEGbased P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics, and education. The current article systematically reviews 147 articles that were published between 2006-2021*. Articles that pass the pre-defined criteria are included in the study. Further, classification based on their primary focus, including article orientation, participants’ age groups, tasks given, databases, the EEG devices used in the studies, classification models, and application domain, is performed. The application-based classification considers …

Ontology-Based Case Study Management Towards Bridging Training And Actual Investigation Gaps In Digital Forensics, Hung Q. Ngo, Nhien-An Le-Khac

Articles

The training programs in digital forensics have contributed many case study models to guide digital forensic analyses. However, they only account for a small number of real cases and they are usually too abstract while actual cybercrime investigations are more diverse and complex. This gap leads to difficulties in giving immediate and straightforward actions for law enforcement during cybercrime investigations. In this paper, we propose an ontology-based knowledge map model, which is a foundation model for building a case study management system for Digital Forensic Intelligence (DFINT) and Open Source Intelligence (OSINT) in digital forensics. The main idea of this …

Comprehensive Fault Diagnostics And Performance Forecasting Of Wind Turbines Through Condition Monitoring Solutions, Shuo Zhang

Doctoral

The operation and maintenance (O&M) issues of wind turbines (WTs) are challenging because unplanned maintenance, caused by sudden component failures, can bring about durable downtimes and significant revenue losses. It is important to carry out effective fault diagnostics and prognostics schemes under the rapid development of wind power generation. Hence, Condition Monitoring (CM) solutions focus on measurements and detections of high-risk WT components, which could result in high failure rates and long downtimes. Machine Learning (ML) models have been commonly applied in CM to allocate imminent indications of failure or degradation for curtailing the O&M costs of WTs. By way …

Effect Of Sc On Recrystallization Resistance Of Aa7050, Keaton Schmidt

Dissertations, Master's Theses and Master's Reports

The extrusion process involves high temperatures and strains that can result in undesirable microstructures, especially along the surface. Extruded alloys tend to exhibit surface recrystallization during heat treating at regions of higher strains, which can lead to reduced fatigue strength and corrosion resistance. By adding Sc to AA7050, nano-sized dispersoids are formed with Sc cores and Zr shells that restrict recrystallization more than the base alloy that only utilizes Zr. Billets with varying Sc content and a control with only Zr were cast, and extrusions were made in order to compare surface microstructures at varying strains in the as-extruded and …

Exploring Ph Gradient Phenomena In Non-Linear Electrokinetic Microfluidic Devices, Azade Tahmasebi

Dissertations, Master's Theses and Master's Reports

Electrokinetic microfluidics is a versatile technology utilized within lab on a chip (LOC) devices for diagnostic and analytical applications; advantages include reduced resource demands, flexibility, and simplicity of use. Dielectrophoresis (DEP) is a precision nonlinear electrokinetic tool utilized within microfluidic microdevices to induce polarization and control bioparticle motions for applications that range from hemoglobin separations to cancer cell isolation and detection. Despite promising results, undesired side phenomena can occur in electrokinetic systems which impede reproducibility and accuracy. These unfavorable phenomena have not been comprehensively explored in the literature. Prior preliminary research suggests the fundamental phenomena originate from microelectrodes utilized in …

Evaluation Of Lidar Uncertainty And Applications Towards Slam In Off-Road Environments, Zachary D. Jeffries

Dissertations, Master's Theses and Master's Reports

Safe and robust operation of autonomous ground vehicles in all types of conditions and environment necessitates complex perception systems and unique, innovative solutions. This work addresses automotive lidar and maximizing the performance of a simultaneous localization and mapping stack. An exploratory experiment and an open benchmarking experiment are both presented. Additionally, a popular SLAM application is extended to use the type of information gained from lidar characterization, demonstrating the performance gains and necessity to tightly couple perception software and sensor hardware. The first exploratory experiment collects data from child-sized, low-reflectance targets over a range from 15 m to 35 m. …

Joint Probability Analysis Of Extreme Precipitation And Water Level For Chicago, Illinois, Anna Li Holey

Dissertations, Master's Theses and Master's Reports

A compound flooding event occurs when there is a combination of two or more extreme factors that happen simultaneously or in quick succession and can lead to flooding. In the Great Lakes region, it is common for a compound flooding event to occur with a high lake water level and heavy rainfall. With the potential of increasing water levels and an increase in precipitation under climate change, the Great Lakes coastal regions could be at risk for more frequent and severe flooding. The City of Chicago which is located on Lake Michigan has a high population and dense infrastructure and …

Automatic Optical Inspection-Based Pcb Fault Detection Using Image Processing, Shruti Rajiv Vaidya

Dissertations, Master's Theses and Master's Reports

Increased Printed Circuit Board (PCB) route complexity and density combined with the growing demand for low-scale rapid prototyping has increased the desire for Automated Optical Inspection (AOI) that reduces prototyping time and production costs by detecting defects early in the production process. Traditional defect detection method of human visual inspection is not only error prone but is also time-consuming given the growing complex and dense circuitry of modern-day electronics. Electric contact-based testing, either in the form of a bed of nails testing fixture or a flying probe system, is costly for low-rate rapid prototyping. An AOI is a non-contact test …

Quantifying The Evolution Of Strengthening Mechanisms For Commercially Produced Niobium And Titanium Hsla Steel Sheet, Isabella M.W. Jaszczak

Dissertations, Master's Theses and Master's Reports

Strength uniformity along the coil length of commercially produced high-strength, low-alloy (HSLA) steel hot-rolled sheet is crucial to avoid the downgrading of product that does not meet strength specifications. In addition to contributing to precipitation strengthening through the growth of niobium-titanium carbides ((Nb,Ti)-C), niobium hinders austenite recrystallization and refines ferrite grain size. The potency of these strengthening mechanisms relies heavily on the austenite to ferrite transformation kinetics of the hot-rolling process. While niobium’s effect on precipitation strengthening, Hall-Petch strengthening, dislocation strengthening, and solute strengthening have all been studied in literature independently, the interactions of these mechanisms with each other and …

Neuromorphic Computing Applications In Robotics, Noah Zins

Dissertations, Master's Theses and Master's Reports

Deep learning achieves remarkable success through training using massively labeled datasets. However, the high demands on the datasets impede the feasibility of deep learning in edge computing scenarios and suffer from the data scarcity issue. Rather than relying on labeled data, animals learn by interacting with their surroundings and memorizing the relationships between events and objects. This learning paradigm is referred to as associative learning. The successful implementation of associative learning imitates self-learning schemes analogous to animals which resolve the challenges of deep learning. Current state-of-the-art implementations of associative memory are limited to simulations with small-scale and offline paradigms. Thus, …

Optimizing The Extrudability Of 6082 Aluminum By Varying The Magnesium And Silicon Concentration, Eli A. Harma

Dissertations, Master's Theses and Master's Reports

Alloy 6082 aluminum is used for high-volume manufacturing in the automotive industry due to its high strength, impact performance, and corrosion resistance. However, given these improved properties, the alloy has decreased formability compared to other 6xxx series alloys, especially in the extrusion process. Controlling the dynamic recovery and recrystallization properties by changing the additions of Mg and Si can improve the hot deformation properties. Five alloys of varying Mg and Si concentrations between 0.6 to 1.2wt% Mg and 0.7 to 1.3wt% Si were made with constant concentrations of Cr, Fe, and Mn and the same homogenization heat treatment. The proposed …

Capturing Microstructural Heterogeneity And Predicting Local Transport Phenomena In Pemfc Catalyst Layers: A Comprehensive Network Modeling Approach, Shahriar Alam

Dissertations, Master's Theses and Master's Reports

A unique network architecture that captures the microstructural heterogeneity and predicts the local transport properties of PEMFC catalyst layers is proposed. Separate networks containing numerous cylindrical elements and nodes are generated that represent the solid and pore phase of the catalyst layer. Transport resistances are assigned to the elements while the nodes are volumeless. The networks are interlinked through nodes where local properties are stored. The generated computational grid's macroscopic behaviors (percolation behavior, gas diffusivity, and ion conductivity) will be matched against the experimental data for validation. Diffusion-like transport equations are applied to the networks that provide local water balance, …

An Active Voice Coil Negative Stiffness Vibration Isolator With Application To Mobile 3d Printing, Lucas M. Schloemp

Dissertations, Master's Theses and Master's Reports

Equipment whose performance degrades when exposed to base vibration is often deployed in vibration-rich environments. Using 3D printers in mobile applications, such as vehicles and ships, are typical examples where the extruder and bedplate are easily excited by base vibration. A versatile active vibration isolator is considered in this thesis constructed using a voice coil actuator and a laser displacement feedback sensor to achieve a wide range of dynamic response characteristics, including negative stiffness. This approach permits transmissibility shaping to meet band-limited base isolation requirements without active damping vibration control. A combination of simulation and hardware validation is used to …

Programming The Bistable Dynamic Vibration Absorbers Of A 1d-Metastructure For Adaptive Broadband Vibration Absorption, Shantanu H. Chavan

Dissertations, Master's Theses and Master's Reports

This research addresses a critical challenge in structural engineering—achieving comprehensive vibration control and energy dissipation in meta-structures. Departing from the limitations of passive structures with fixed bandgaps, we propose an innovative approach utilizing active meta-structures capable of dynamically tuning their bandgaps. The primary goal is to introduce an efficient method for programming meta-structures with multiple variable bandgaps, thereby enabling effective vibration attenuation across a broad frequency spectrum.

The methodology involves transforming passive resonators into bistable adaptable Dynamic Vibration Regulators (DVRs) through a sophisticated switching mechanism. This adaptation sets the stage for numerous unique combinations by independently switching each resonator. A …