Computational Engineering Commons^™

Presence Of Atheromatous Plaques And Theirs Effects On The Blood Flow, Belhocine Mostefa Bm, Amrani Hichem Ah, Fedaoui Kamel Dr, Mazouz Hammoudi Mh

Emirates Journal for Engineering Research

The paper utilizes a finite element method to study both the blood flow and atheromatous plaques. Specifically, the COMSOL finite element package is employed to achieve a fluid model. COMSOL is a powerful finite element tool commonly used in various research and industrial domains to study multiphysics problems. The focus of the investigation is on the geometric aspects of the atheromatous plaques. The study considers different forms and arrangements of stenosis, taking into account the irregularities formed by various shapes of the plaques and the resulting flow patterns. The key findings of the research suggest that the pressure and velocity …

Star-Based Reachability Analysis Of Binary Neural Networks On Continuous Input, Mykhailo Ivashchenko

School of Computing: Dissertations, Theses, and Student Research

Deep Neural Networks (DNNs) have become a popular instrument for solving various real-world problems. DNNs’ sophisticated structure allows them to learn complex representations and features. However, architecture specifics and floating-point number usage result in increased computational operations complexity. For this reason, a more lightweight type of neural networks is widely used when it comes to edge devices, such as microcomputers or microcontrollers – Binary Neural Networks (BNNs). Like other DNNs, BNNs are vulnerable to adversarial attacks; even a small perturbation to the input set may lead to an errant output. Unfortunately, only a few approaches have been proposed for verifying …

Development And Simulation Of A Damage Assessment And Recovery Method For Critical Database Systems, Anthony Pham

Computer Science and Computer Engineering Undergraduate Honors Theses

With how much the world relies on technology and the critical database infrastructure that supports it, the infrastructures require efficient methods to detect and resolve suspicious database transactions, whether malicious or not. This paper focuses on an algorithm that detects and resolves malicious transactions in a database. The process begins with identifying suspicious transactions based on common patterns. When a transaction is flagged, the algorithm segments groups of suspicious transactions in separate log files, separating them for easy access. Within these segments, any dependent transactions that use data affected by the suspicious transactions will be stored there. After the transaction …

Enhancing Cybersecurity In Wireless Sensor Networks: Machine Learning, Blockchain And Future Perspectives, Ilemona Solomon Atawodi

Dissertations

Security in the Industrial Internet of Things encounters various security issues but the main issues can be broken down into three core issues: Availability, Integrity, and Confidentiality. Security challenges generally tend to be caused by a failure of the system in one of these areas or cause a failure in one of these areas. Therefore researching scalable solutions to these security issues is prudent to explore methods that could be applied to large-scale industrial IIoT with tens to hundreds of devices as well as small-scale systems on a tiny factory floor comprising of just a few devices. In our research, …

Context Dependent Training Data Selection For Automatic Target Detection., Tylman Michael

Electronic Theses and Dissertations

An Automatic Target Detection (ATD) algorithm is capable of identifying the location of targets of interest captured by Infra-Red imagery in vastly different contexts. ATD is often a precursor in a 2-stage methodology in order to ascertain the location and nature of a target in both military and civilian applications. In order to train an ATD algorithm, a large amount of data from varied sources is required. One drawback of this requirement is that some sources of data may harm the performance of the method in different contexts. This thesis explores utilizing an unsupervised method to identify a subset of …

Quantifying Hurricane Effects On Housing: Evaluating Damage, Loss, And Shelter Demands Using Historical And Simulated Storm Tracks, Adish Deep Shakya

All Theses

This research introduces an advanced framework which employs parametric wind field models for peak wind speeds, and building fragility curves, loss functions, and demographic data to estimate for estimating housing damage and loss. The uninhabitable units immediate displaced households, short-term and long-term shelter need households are determined. with a particular focus on those eligible for FEMA assistance. The framework's validity is reinforced by a high correlation in the analysis of recent hurricane events between estimated numbers of displaced households and actual FEMA aid recipients, where FEMA aids about 20-60% of the predicted long-term displaced households. A novel application of the …

Examining The Impact Of Customer Rfp Characteristics On Award Compliance, Laasya Ravipati

Data Science Undergraduate Honors Theses

In the context of intermodal transportation, understanding the dynamics of award compliance holds significant importance for operational efficiency and strategic decision-making. Award compliance refers to the percentage of awarded freight volume that is realized, indicating the extent to which contractual agreements are fulfilled. This analysis delves into the intricate relationship between customer characteristics and award compliance, aiming to provide valuable insights into the variability and predictability of compliance rates. By analyzing Request for Pricing (RFP) data and primary awarded freight volumes, the study seeks to address the need for more accurate volume estimations, crucial for sales planning, revenue projections, and …

Numerical Simulation Of Laser Induced Elastic Waves In Response To Short And Ultrashort Laser Pulses., Alireza Zarei

All Dissertations

In an era of intensified market competition, the demand for cost-effective, high-quality, high-performance, and reliable products continues to rise. Meeting this demand necessitates the mass production of premium products through the integration of cutting-edge technologies and advanced materials while ensuring their integrity and safety. In this context, Nondestructive Testing (NDT) techniques emerge as indispensable tools for guaranteeing the integrity, reliability, and safety of products across diverse industries.

Various NDT techniques, including ultrasonic testing, computed tomography, thermography, and acoustic emissions, have long served as cornerstones for inspecting materials and structures. Among these, ultrasonic testing stands out as the most prevalent method, …

Developing General Purpose Apps To Automate Image Analysis Of Wave-Augmented-Varicose-Explosion Atomization And Other Multi-Phase Interfacial Flows, Ethan Newkirk

Senior Honors Theses

Atomization involves disrupting a flow of contiguous liquid into small droplets ranging from one submicron to several hundred microns (micrometers) in diameter through the processes of exerting sufficient forces that disrupt the retaining surface tensions of the liquid. Understanding this phenomenon requires high-speed imaging from physical models or rigorous multiphase computational fluid dynamics models. We produce a MATLAB application that utilizes various methods of image analysis to quickly analyze and store mathematical data from detailed image analyses. We present a user with numerous tools and capabilities that provide results that deviate from 1.8% to 8.9% of the original image sequence …

Automated Brain Tumor Classifier With Deep Learning, Venkata Sai Krishna Chaitanya Kandula

Electronic Theses, Projects, and Dissertations

Brain Tumors are abnormal growth of cells within the brain that can be categorized as benign (non-cancerous) or malignant (cancerous). Accurate and timely classification of brain tumors is crucial for effective treatment planning and patient care. Medical imaging techniques like Magnetic Resonance Imaging (MRI) provide detailed visualizations of brain structures, aiding in diagnosis and tumor classification^[8].

In this project, we propose a brain tumor classifier applying deep learning methodologies to automatically classify brain tumor images without any manual intervention. The classifier uses deep learning architectures to extract and classify brain MRI images. Specifically, a Convolutional Neural Network (CNN) …

Data Engineering: Building Software Efficiency In Medium To Large Organizations, Alessandro De La Torre

Whittier Scholars Program

The introduction of PoetHQ, a mobile application, offers an economical strategy for colleges, potentially ushering in significant cost savings. These savings could be redirected towards enhancing academic programs and services, enriching the educational landscape for students. PoetHQ aims to democratize access to crucial software, effectively removing financial barriers and facilitating a richer educational experience. By providing an efficient software solution that reduces organizational overhead while maximizing accessibility for students, the project highlights the essential role of equitable education and resource optimization within academic institutions.

Stiffness And Strength-Based Concurrent Shape And Fiber Path Optimization Of Continuous Fiber Composites, Zhelong He, Ali Tamijani

Publications

A computer-implemented method of optimizing a computer model including a shape and a fiber path for a continuous fiber composite can include initializing a fixed finite element mesh. The method can also include creating an updated version of the shape and the fiber path by iterating updates of the shape and the fiber path using the fixed finite element mesh. The method can also include initializing an adapted finite element mesh on the updated version of the shape and the fiber path. The method can also include creating an optimized version of the shape and the fiber path by iterating …

Development Of On-The-Fly Quasi-Steady State Approximation For Chemical Kinetics In Cfd, Abhinav Balamurugan

Doctoral Dissertations and Master's Theses

This study analyzes the feasibility of On-The-Fly Quasi-Steady-State Approximation (OTF-QSSA) application for solving chemical kinetics within Computational Fluid Dynamics (CFD) simulations, aiming to reduce the computational demand of detailed mechanisms. An algorithm that dynamically identifies and designates Quasi-Steady-State (QSS) species at specific grid locations and instances during the simulation was developed. With this information, our method pseudo-delays the advancement of concentrations for these QSS species—effectively setting their rate of concentration change to zero for a set number iteration before updating using the detailed mechanism and thereby omitting the computationally intensive processes typically required for their calculation during those skipped iteration. …

Computational Modeling And Analysis Of Facial Expressions And Gaze For Discovery Of Candidate Behavioral Biomarkers For Children And Young Adults With Autism Spectrum Disorder, Megan Anita Witherow

Electrical & Computer Engineering Theses & Dissertations

Facial expression production and perception in autism spectrum disorder (ASD) suggest the potential presence of behavioral biomarkers that may stratify individuals on the spectrum into prognostic or treatment subgroups. High-speed internet and the ease of technology have enabled remote, scalable, affordable, and timely access to medical care, such as measurements of ASDrelated behaviors in familiar environments to complement clinical observation. Machine and deep learning (DL)-based analysis of video tracking (VT) of expression production and eye tracking (ET) of expression perception may aid stratification biomarker discovery for children and young adults with ASD. However, there are open challenges in 1) facial …

Revolutionizing Feature Selection: A Breakthrough Approach For Enhanced Accuracy And Reduced Dimensions, With Implications For Early Medical Diagnostics, Shabia Shabir Khan, Majid Shafi Kawoosa, Bonny Bannerjee, Subhash C. Chauhan, Sheema Khan

Research Symposium

Background: The system's performance may be impacted by the high-dimensional feature dataset, attributed to redundant, non-informative, or irrelevant features, commonly referred to as noise. To mitigate inefficiency and suboptimal performance, our goal is to identify the optimal and minimal set of features capable of representing the entire dataset. Consequently, the Feature Selector (Fs) serves as an operator, transforming an m-dimensional feature set into an n-dimensional feature set. This process aims to generate a filtered dataset with reduced dimensions, enhancing the algorithm's efficiency.

Methods: This paper introduces an innovative feature selection approach utilizing a genetic algorithm with an ensemble crossover operation …

A 4-Node Shell Finite Element Based On Assumed Bending And Membrane Strains For Static Analysis Of Plates And Shells, Sifeddine Abderrahmani

Emirates Journal for Engineering Research

In this paper the development of a new rectangular flat shell element is proposed. This element is called SBRPK-SBRIE. This element is used in the numerical analysis of thin structures based on the strain approach with linear elastic behavior. Combining bending and membrane elements yields the proposed element.The strain-based rectangular finite element for the thin plate bending element denoted SBRPK, and the strain-based membrane element denoted SBRIE. Several numerical examples have been conducted to assess the accuracy and reliability of the developed element compared with the theoretical results and other finite elements. Obtained results show its good performance compared to …

Weakly Supervised Attention-Based Recognition Under Spectral, Turbulence, And Resource Variations, Kshitij Naresh Nikhal

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

While supervised optimization paradigms are ubiquitous across diverse recognition systems, the risk of over-fitting and increasing bias have limited their applicability.

This dissertation focuses on unsupervised learning—learning without precisely curated data—and argues that unsupervised learning methods can enable both discriminability and generalizability. Through the use of attention-based machine learning and advanced clustering, unsupervised methods are able to focus on fine-grained information in images without any explicit supervision. The dissertation introduces a domain-bridging framework for tasks like cross-spectrum matching and long-range recognition, utilizing intra-domain clustering and inter-domain matching to generate pseudo-labels. Additionally, a hash-based network is proposed to accelerate the search …

Path-Bigbird: An Ai-Driven Transformer Approach To Classification Of Cancer Pathology Reports, Mayanka Chandrashekar, Isaac Lyngaas, Heidi A. Hanson, Shang Gao, Xiao Cheng Wu, John Gounley

School of Public Health Faculty Publications

PURPOSE: Surgical pathology reports are critical for cancer diagnosis and management. To accurately extract information about tumor characteristics from pathology reports in near real time, we explore the impact of using domain-specific transformer models that understand cancer pathology reports. METHODS: We built a pathology transformer model, Path-BigBird, by using 2.7 million pathology reports from six SEER cancer registries. We then compare different variations of Path-BigBird with two less computationally intensive methods: Hierarchical Self-Attention Network (HiSAN) classification model and an off-the-shelf clinical transformer model (Clinical BigBird). We use five pathology information extraction tasks for evaluation: site, subsite, laterality, histology, and behavior. …

Artificial Neural Network Modeling Applied For Predicting Reformate Yield And Research Octane Number In The Reforming Process, Badiea S. Babaqi, Abdelrigeeb Ali Al-Gathe, Mohd S. Takriff, Hassimi Abu Hasan, Mohammed H. Al-Douh

Hadhramout University Journal of Natural & Applied Sciences

The prediction model of the continuous catalytic regeneration reforming process was developed for expecting the reformate yield and research octane number using an Artificial Neural Network technique (ANN) to improve the process performance. The proposed model includes temperatures, pressures, and hydrogen to hydrocarbon molar ratio as input parameters while the output of the process represents reformate yield and research octane number. The ANN model was carried out to estimate the process behavior based on the Levenberg-Marquardt Algorithm, which included the nine input parameters, two hidden layers (10-5 neurons), and two parameters as network outputs. The results obtained were that the …

Cardiac Active Tension Modeling Via Genetic Algorithm-Optimized Fractional Order Systems, Afnan Khaled Elhamshari

Theses and Dissertations

Developing a computational model to model cardiac activity has been increasingly important in recent decades. Accurate cell-level active tension modeling for cardiomyocytes is critical to understanding cardiac functionality on a patient-specific basis and developing an effective in-silico cardiac model. However, cell-level models in the literature fail to account for viscoelasticity and inter-patient variations in active tension. This research proposes a genetic algorithm-optimized, fractional order system to model cell-level active tension by extending Land’s state-of-the-art model of cardiac contraction. The model features the (left) Caputo derivative of six state variables that identify the mechanistic origins of viscoelasticity in a myocardial cell …