Other Engineering Commons^™

Diagnosis Of Disease Affecting Gait With A Body Acceleration‑Based Model Using Reflected Marker Data For Training And A Wearable Accelerometer For Implementation, Mohammad Ali Takallou, Farahnaz Fallahtafti, Mahdi Hassan, Ali Al‑Ramini, Basheer Qolomany, Iraklis I. Pipinos, Sara A. Myers, F. M. Alsaleem

Durham School of Architectural Engineering and Construction: Faculty Publications

This paper demonstrates the value of a framework for processing data on body acceleration as a uniquely valuable tool for diagnosing diseases that affect gait early. As a case study, we used this model to identify individuals with peripheral artery disease (PAD) and distinguish them from those without PAD. The framework uses acceleration data extracted from anatomical reflective markers placed in different body locations to train the diagnostic models and a wearable accelerometer carried at the waist for validation. Reflective marker data have been used for decades in studies evaluating and monitoring human gait. They are widely available for many …

Pathology Slide Segmentation, Mohamed Mohamed

Computer Science and Engineering Theses - Archive

The goal of this project was to create an image segmentation model that would extract an image of H&E pathology slides from real life scenarios. This was a part of a project that required the extraction of an image, querying of the image, and displaying the results. The baselines of the model were to be efficient, run on mobile devices, and be able to work with cameras of varying resolutions.

Application Of The Mixed Integer Nonlinear Programming Technique For The Economic Planning Of Transmission Networks In New Manner, Nader Shawky Abdelhakeem, Maged Gamal Lotfy, Mohamed Shibl Albags, Ahmed Saied Elzawawy

Mansoura Engineering Journal

In the recent years, the mathematical planning model of transmission networks based on full AC load flow has been developed. But, due to the model non-convexity and the existing of large number of local solutions, this model can only be used for small size networks as it consumes large unreasonable computation time in addition to its incapability to yield global solution in most cases. To overcome these problems, two new iterative solution procedures have been presented in this paper. In the first one, the objective function is changed to the minimization of the number of new lines required to satisfy …

Maximizing Mining Operations: Unlocking The Crucial Role Of Intelligent Fleet Management Systems In Surface Mining’S Value Chain, Arman Hazrathosseini, Ali Moradi Afrapoli

Earth and Environmental Sciences Faculty Publications

On the one side, the operational expenses of mining enterprises are showing an upward trend; and on the other side, conventional mining fleet management systems (FMSs) are falling short in addressing the high-dimensionality, stochasticity, and autonomy needed in increasingly complex operations. These major drivers for change have convinced researchers to search for alternatives including artificial-intelligence-enabled algorithms recommended by Mining 4.0. The present study endeavors to scrutinize this transition from a business management point of view. In other words, a literature review is carried out to gain insight into the evolutionary trajectory of mining FMSs and the need for intelligent algorithms. …

Enhancing Reservoir Modeling And Simulation Through Artificial Intelligence And Machine Learning: A Smart Proxy Modeling Approach, Andrew Timothy Jenkins

Graduate Theses, Dissertations, and Problem Reports (ETD)

The application of numerical reservoir simulation (NRS) has been a common approach within the oil and gas industry for decades, providing a means to model and forecast dynamic subsurface interactions, as a basis for reservoir management and development decisions. These techniques have expanded to application within carbon capture utilization and storage (CCUS) projects as domestic and global policy shift towards reducing carbon emissions while maintaining the energy needs of our modern society. NRS techniques have become a core process for permitting approval in Class VI (large-scale geological sequestration) wells due to the fundamental similarity of these types of subsurface processes. …

A Political Theory Of Engineered Systems And A Study Of Engineering And Justice Workshops, Dominic David Carrese

Dartmouth College Master’s Theses

Since there are good reasons to think that some engineered systems are socially undesirable—for example, internal combustion engines that cause climate change, algorithms that are racist, and nuclear weapons that can destroy all life—there is a well-established literature that attempts to identify best practices for designing and regulating engineered systems in order to prevent harm and promote justice. Most of this literature, especially the design theory and engineering justice literature meant to help guide engineers, focuses on environmental, physical, social, and mental harms such as ecosystem and bodily poisoning, racial and gender discrimination, and urban alienation. However, the literature that …

Stability Analysis Of Vertical Industrial Pressure Vessel Resting On Load Cells Subjected To A Seismic Load., Kameron W. Nez

College of Graduate Studies: Theses & Dissertations

Instability of structures, such as large vertical pressure vessels, is always an issue under the seismic load. In this current work, the acceleration that causes instability for a vertical pressure vessel has been determined experimentally using scaled models. This instability is caused by the tipping of the tall structure that is not completely fixed to the ground. This method is applied for three different sizes of pressure vessels, representing 50 CuFt to 1100 CuFt full scale models. These experimental results are compared with theoretical models. Using dimensional analysis, the accelerations that will cause the instability in the actual full-size models …

Adaptive Trust Management For Data Poisoning Attacks In Mec-Based Fl Infrastructures, Abeer Hamdy Dr.

Computer Science

Federated Learning (FL) has emerged as a powerful paradigm, allowing multiple decentralized clients to collaboratively train a machine learning model without sharing their raw data. When combined with Multi-access Edge Computing (MEC), it enhances the utilization of computation and storage resources at the edge, enabling local data training on edge nodes. Such integration reduces latency and facilitates real time processing and decision-making while ensuring data privacy. However, this decentralized approach introduces security and trust challenges, as models can be compromised through data poisoning attacks, such as label flipping attacks. The trustworthiness of these edge nodes and the integrity of their …

Analyzing Imprecise Data From Wireless Temperature Sensor, Usama Afzal, Muhammad Aslam, Muhammad Ahmed Shehzad, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

Various sensors play an important role in the monitoring and development of robotic technology. We present a contemporary statistical analysis method for evaluating datasets generated by robotic systems. Specifically, this data set originates from the physical structure of the robot and is acquired by a wireless temperature sensor. The data collection process spans a temporal period of 1 to 10 hours during the operational period of the robot. The collected data is subjected to a rigorous analysis using neutrosophic methodology. To facilitate this, a modern neutrosophic formula has been devised, drawing on definitions established within the field. To benchmark the …

Hardening A Small Business Network, Madison Baxter

Williams Honors College, Honors Research Projects

Security is one of the most, if not the most, important facets of a network. Without security, proprietary data is vulnerable to theft by malicious actors or even just curious techies with too much time on their hands.

Building and implementing a network lays the foundation for a technically sound, robust environment. Network and device hardening minimizes security gaps on the network and streamlines functions and processes.

The objectives of this project were to stand up a functioning small business network, to harden the network and to offer recommendations to further secure the overall environment for a small business.

The …

Shape Inverse Prediction Of Magnetic Field-Actuated Soft Robots By Neural Network Machine Learning, Lineth J. Perez Monsalve

Theses and Dissertations

Soft robotics has drawn tremendous interest in recent years because the compliance and motion of soft robotics enable biocompatibility and versatility for many applications, such as human-machine interaction, wearable and assistive devices, and health monitoring. This study introduces a novel predictive modeling approach using neural networks for shape control of magnetic soft robots. The robots are made of silicone materials embedded with hard magnetic particles, which respond to the external magnetic field provided by a ring-type of permanent magnet. These robots, free from physical connections to external devices, i.e., non-tethered actuation, hold significant potential for applications in healthcare, such as …

Initial Solution Improvements To A Tethered Robotic Path Planner Using Random Geometric Graph Configurations And Biased Sampling, Austen J. Goddu

Dissertations, Master's Theses and Master's Reports

With NASA's ongoing efforts to establish a presence on the lunar surface to eventually move on to exploring mars, the development of intelligent robotic systems is more important than ever. The ability of robotics to explore hazardous and extreme terrain, coupled with long communication times from earth places an ever increasing need on more robust and efficient autonomy. Tethered robotics offer unique advantages to explore scientific targets both on the lunar and martian surfaces, capable of using their tether as a physical or metaphorical lifeline to allow the exploration of slopes, extreme dark regions, or areas in which wireless communication …

Machine-Learning-Assisted Design Of Deep Eutectic Solvents Based On Uncovered Hydrogen Bond Patterns, Usman Lame Abbas, Yuxuan Zhang, Joseph Tapia, Md Selim, Jin Chen, Jian Shi, Qing Shao

Markey Cancer Center Faculty Publications

Non-ionic deep eutectic solvents (DESs) are non-ionic designer solvents with various applications in catalysis, extraction, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation. The search for DES relies heavily on intuition or trial-and-error processes, leading to low success rates or missed opportuni- ties. Recognizing that hydrogen bonds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning (ML) models to discover new DES systems. We first analyze the …

Draft Makerspace Optimization And Procedure For The University Of Northern Iowa And Cedar Falls, Andrew Nii Anang

Graduate Research Papers

The University of Northern Iowa (UNI) Makcrspace is a dynamic, communal setting created to encourage members of the community at large to be creative, innovative and engage in hands-on learning. Access to a variety of hand and power tools as well as state-of-the-art technologies is available at this cutting-edge facility. The Makerspace promotes a culture of experimentation and entrepreneurship while offering assistance for multidisciplinary initiatives. The Makerspace incorporates real-world, project-based learning experience, greatly improving UNI's educational environment. It enhances the university's reputation for encouraging innovation and research by drawing in potential students and community members who are interested in cutting-edge …

Towards Algorithmic Justice: Human Centered Approaches To Artificial Intelligence Design To Support Fairness And Mitigate Bias In The Financial Services Sector, Jihyun Kim

CMC Senior Theses

Artificial Intelligence (AI) has positively transformed the Financial services sector but also introduced AI biases against protected groups, amplifying existing prejudices against marginalized communities. The financial decisions made by biased algorithms could cause life-changing ramifications in applications such as lending and credit scoring. Human Centered AI (HCAI) is an emerging concept where AI systems seek to augment, not replace human abilities while preserving human control to ensure transparency, equity and privacy. The evolving field of HCAI shares a common ground with and can be enhanced by the Human Centered Design principles in that they both put humans, the user, at …

Mouralherwaqh Coastal Wetland Road Crossing Da'luk, Romel Robinson Ii

Cal Poly Humboldt theses and projects

The integration of Indigenous and Western science plays an essential role in Tribally led collaborations for land management. This process of woven sciences is rooted in reciprocal relations and partnerships guided by Tribal Nations. Our cohort was invited by Wiyot Tribal Representatives to investigate a culvert located within the wetlands of Mouralherwaqh— a parcel of land reacquired by the Wiyot Tribe in 2022. This document seeks to share our experience and analysis as part of the Wiyot Tribe’s broader journey in navigating ecocultural restoration projects within Mouralherwaqh. The four community interests we investigated for the wetland crossing included a resized …

Analysis Of Shoreline Erosion At Cumberland Island Using Advanced Circulation (Adcirc) Modeling, Christopher Harrigan

UNF Graduate Theses and Dissertations

This study investigates shoreline erosion at Brickhill Bluff along the Brickhill River in Cumberland Island National Seashore using ADCIRC. Two scenarios were modeled during this research: bed stresses during regular tidal cycles and bed stresses during worst-case storm (i.e., hurricane) conditions. Velocities from these models were used to compute bed shear stresses and these stresses were compared to site’s sediment critical shear stress. Results appear to show that a meander in the Brickhill River causes relatively high (i.e., on the order of 0.27 ��/��² ) shear stresses during regular tidal cycles and even higher (i.e., on the order of …

Application Of High-Resolution Fiber Optic Data To Enhance Completion Design, Christian J. Pacheco

Graduate Theses, Dissertations, and Problem Reports (ETD)

MS Dissertation Defense

By

Christian Pacheco

Title: Application of High-Resolution Fiber Optic Data to Enhance Completion Design Major: Petroleum and Natural Gas Engineering Date: Thursday, April 18, 2024 Time: 4:00 PM Place: 141 Engineering Science Building

Abstract

Well stimulation is a technique that has been used in the industry for decades, and with the ability to drill wells horizontally the practice has become more valuable and effective than ever before. Its use is consistently being optimized and completions design plays a crucial role in the recovery of hydrocarbons. Several different downhole tools and measurements have been used to optimize these …

Enhancing Pipeline Simulations Through Artificial Intelligence And Machine Learning: A Smart Proxy Modelling Approach, Afeez Shittu

Graduate Theses, Dissertations, and Problem Reports (ETD)

ABSTRACT

Enhancing pipeline simulations is essential for improving operational efficiencies and effectively managing risks in the oil and gas industry. Traditional pipeline simulators, relying heavily on mathematical modeling assumptions, often face limitations due to their high energy and computational demands. This thesis addresses these challenges by introducing an innovative approach that integrates artificial intelligence (AI) and machine learning (ML) through a smart proxy model, offering a more efficient, cost-effective, and flexible alternative to conventional full-physics models used in pipeline simulation software.

The primary aim of this research is to develop and implement a smart proxy model capable of accurately predicting …

Machine Learning-Driven Quantification Of Co₂ Plume Dynamics At Ibdp Sites Using Microseismic Data, Ikponmwosa Bright Iyegbekedo

Graduate Theses, Dissertations, and Problem Reports (ETD)

This thesis delves into the utilization of machine learning methodologies to quantify the spatial extent of CO₂ plumes by leveraging microseismic data obtained from the Illinois Basin Decatur Project (IBDP) site spanning November 2011 to June 2018. This initiative, focused on the geological sequestration of carbon dioxide, furnishes a unique and comprehensive dataset comprising well logs, microseismic activity records, and CO₂ injection metrics, all crucial for quantifying the subsurface CO₂ saturation plume dynamics. The primary objective is to forecast the temporal evolution of CO₂ saturation plumes in the subsurface, a critical undertaking for ensuring both the environmental integrity and operational …