Engineering Commons^™

Transition Supply Chain 4.0 To Supply Chain 5.0: Innovations Of Industry 5.0 Technologies Toward Smart Supply Chain Partners, Mona Mohamed, Karam M. Sallam, Ali Wagdy Mohamed

Neutrosophic Systems with Applications

Industry 4.0 provides businesses with the tools they need to meet difficulties such as fluctuating demand and unstable markets. Additionally, Industry 4.0 refers to the connectivity of computers, various materials, and artificial intelligence (AI) with minimum involvement from humans in the decision-making process. Although Industry 4.0 has a significant potential for the expansion of the industrial sector, it faces several hurdles, including integration of technology, problems with human resources, problems with supply chains, and data security concerns. The human-centered approach that Industry 5.0 took meant that many of the problems that plagued Industry 4.0 could finally be solved. In the …

The Role Of Feedback Within Scrum For Engineering Department Operations, Massood Towhidnejad, Omar Ochoa, James J. Pembridge, Radu Babiceanu

Posters

The Scrum framework is built on the principles of inspection and adaptation. Feedback drives the inspection process, and the team adapts based on that feedback to optimize its performance and outcomes. Within engineering departments, Scrum requires departments to examine how and when feedback is obtained to ensure that the department is remaining agile. This poster illustrates the role of feedback within two Scrum teams, one focused on student success and the other focused on faculty rewards and incentives. The two cases emphasize the need for continuous introspection at team and department levels.

Wright State University's Celebration Of Student Research, Scholarship & Creative Activities From Thursday, October 26, 2023, Wright State University

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Abstract Books

The student abstract booklet is a compilation of abstracts from students' oral and poster presentations at Wright State University's Celebration of Student Research, Scholarship & Creative Activities on October 26, 2023.

Machine Learning Prediction Of Hea Properties, Nicholas J. Beaver, Nathaniel Melisso, Travis Murphy

College of Engineering Summer Undergraduate Research Program

High-entropy alloys (HEA) are a very new development in the field of metallurgical materials. They are made up of multiple principle atoms unlike traditional alloys, which contributes to their high configurational entropy. The microstructure and properties of HEAs are are not well predicted with the models developed for more common engineering alloys, and there is not enough data available on HEAs to fully represent the complex behavior of these alloys. To that end, we explore how the use of machine learning models can be used to model the complex, high dimensional behavior in the HEA composition space. Based on our …

Development Of User Interface And Testing Harness, Jacob Amezquita, William Albertini

College of Engineering Summer Undergraduate Research Program

No abstract provided.

Online Aircraft System Identification Using A Novel Parameter Informed Reinforcement Learning Method, Nathan Schaff

Doctoral Dissertations and Master's Theses

This thesis presents the development and analysis of a novel method for training reinforcement learning neural networks for online aircraft system identification of multiple similar linear systems, such as all fixed wing aircraft. This approach, termed Parameter Informed Reinforcement Learning (PIRL), dictates that reinforcement learning neural networks should be trained using input and output trajectory/history data as is convention; however, the PIRL method also includes any known and relevant aircraft parameters, such as airspeed, altitude, center of gravity location and/or others. Through this, the PIRL Agent is better suited to identify novel/test-set aircraft.

First, the PIRL method is applied to …

Spoken Language Processing And Modeling For Aviation Communications, Aaron Van De Brook

Doctoral Dissertations and Master's Theses

With recent advances in machine learning and deep learning technologies and the creation of larger aviation-specific corpora, applying natural language processing technologies, especially those based on transformer neural networks, to aviation communications is becoming increasingly feasible. Previous work has focused on machine learning applications to natural language processing, such as N-grams and word lattices. This thesis experiments with a process for pretraining transformer-based language models on aviation English corpora and compare the effectiveness and performance of language models transfer learned from pretrained checkpoints and those trained from their base weight initializations (trained from scratch). The results suggest that transformer language …

Alca: Comparing Icelandic Aluminum Emissions To The World Through Statistical Reasoning And Programming-Based Predictive Tools, Markus Sujansky

Iceland: Climate Change and The Arctic

Aluminum production is a process that is heavily reliant on large amounts of available

energy, and therefore lends itself to locations where energy production is abundant. Iceland, as a global leader in Geothermal and Hydropower energy, has experienced an industrial boom in the past 20-30 years, quickly becoming a top 10 worldwide aluminum producer. However, although the three main companies that operate their smelting processes in the country are powered by clean energy, they still import raw materials from all over the world, with source locations ranging from Brazil to Australia. In order to find out if the long distance …

Optimal Agricultural Land Use: An Efficient Neutrosophic Linear Programming Method, Maissam Jdid, Florentin Smarandache

Neutrosophic Systems with Applications

The increase in the size of the problems facing humans, their overlap, the division of labor, the multiplicity of departments, as well as the diversity of products and commodities, led to the complexity of business and the emergence of many administrative and production problems. It was necessary to search for appropriate methods to confront these problems. The science of operations research, with its diverse methods, provided the optimal solutions. It addresses many problems and helps in making scientific and thoughtful decisions to carry out the work in the best way within the available capabilities. Operations research is one of the …

Owner-Free Distributed Symmetric Searchable Encryption Supporting Conjunctive Queries, Qiuyun Tong, Xinghua Li, Yinbin Miao, Yunwei Wang, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Symmetric Searchable Encryption (SSE), as an ideal primitive, can ensure data privacy while supporting retrieval over encrypted data. However, existing multi-user SSE schemes require the data owner to share the secret key with all query users or always be online to generate search tokens. While there are some solutions to this problem, they have at least one weakness, such as non-supporting conjunctive query, result decryption assistance of the data owner, and unauthorized access. To solve the above issues, we propose an Owner-free Distributed Symmetric searchable encryption supporting Conjunctive query (ODiSC). Specifically, we first evaluate the Learning-Parity-with-Noise weak Pseudorandom Function (LPN-wPRF) …

Balanced Blended Space: Proposing A Universal Theoretical Framework For Combinative Reality, David Smith, Frederick Bianchi

Publications and Research

In today's fragmented societies, a unified framework for communication and collaboration across different realities is crucial. We introduce Balanced Blended Space (BBS) as a framework for describing combinative reality, encompassing virtual, physical, and conceptual realms, all intrinsically connected. Interactions within these environments shape our perceptual space. This paper outlines key axiomatic assumptions, criteria for a universal framework, and fundamental terminology. We identify deep symmetries enabling the BBS framework, including Cognitive and Computational Symmetry, Physical and Virtual Symmetry, Mediation Pathway Symmetry, Space-Time Symmetry, and Sensory Symmetry. We propose tests to determine its viability, emphasizing virtual intelligence as a collaborative partner. We …

Construction Aspects And Seismic Analysis Of Typical Buildings In Dolpo, Lhakpa Tsering

Independent Study Project (ISP) Collection

This study delves into the construction aspects and seismic analysis of typical buildings in the challenging terrain of Dolpo, situated within the Himalayan mountain range. Dolpo's unique geological and geographical characteristics, combined with its susceptibility to seismic activity, make it crucial to investigate and understand the dynamics of construction in this remote region. The research explores traditional and contemporary building practices, aiming to elucidate the interplay between construction methodologies and seismic resilience. Through a comprehensive examination of the geological context, structural design, and seismic vulnerability, this study contributes valuable insights to inform the development of robust and earthquake-resistant building strategies …

Neutrosophic Bicubic B-Spline Surface Interpolation Model For Uncertainty Data, Siti Nur Idara Rosli, Mohammad Izat Emir Zulkifly

Neutrosophic Systems with Applications

Dealing with the uncertainty data problem using neutrosophic data is difficult since certain data are wasted due to noise. To address this issue, this work proposes a neutrosophic set (NS) strategy for interpolating the B-spline surface. The purpose of this study is to visualize the neutrosophic bicubic B-spline surface (NBB-sS) interpolation model. Thus, the principal results of this study introduce the NBB-sS interpolation method for neutrosophic data based on the NS notion. The neutrosophic control net relation (NCNR) is specified first using the NS notion. The B-spline basis function is then coupled to the NCNR to produce the NBB-sS. This …

Rigid Body Constrained Motion Optimization And Control On Lie Groups And Their Tangent Bundles, Brennan S. Mccann

Doctoral Dissertations and Master's Theses

Rigid body motion requires formulations where rotational and translational motion are accounted for appropriately. Two Lie groups, the special orthogonal group SO(3) and the space of quaternions H, are commonly used to represent attitude. When considering rigid body pose, that is spacecraft position and attitude, the special Euclidean group SE(3) and the space of dual quaternions DH are frequently utilized. All these groups are Lie groups and Riemannian manifolds, and these identifications have profound implications for dynamics and controls. The trajectory optimization and optimal control problem on Riemannian manifolds presents significant opportunities for theoretical development. Riemannian optimization is an attractive …

Structure-Aware Image Translation-Based Long Future Prediction For Enhancement Of Ground Robotic Vehicle Teleoperation, Md Moniruzzaman, Alexander Rassau, Douglas Chai, Syed M. S. Islam

Research outputs 2022 to 2026

Predicting future frames through image-to-image translation and using these synthetically generated frames for high-speed ground vehicle teleoperation is a new concept to address latency and enhance operational performance. In the immediate previous work, the image quality of the predicted frames was low and a lot of scene detail was lost. To preserve the structural details of objects and improve overall image quality in the predicted frames, several novel ideas are proposed herein. A filter has been designed to remove noise from dense optical flow components resulting from frame rate inconsistencies. The Pix2Pix base network has been modified and a structure-aware …

Objectfusion: Multi-Modal 3d Object Detection With Object-Centric Fusion, Q. Cai, Y. Pan, T. Yao, Chong-Wah Ngo, T. Mei

Research Collection School Of Computing and Information Systems

Recent progress on multi-modal 3D object detection has featured BEV (Bird-Eye-View) based fusion, which effectively unifies both LiDAR point clouds and camera images in a shared BEV space. Nevertheless, it is not trivial to perform camera-to-BEV transformation due to the inherently ambiguous depth estimation of each pixel, resulting in spatial misalignment between these two multi-modal features. Moreover, such transformation also inevitably leads to projection distortion of camera image features in BEV space. In this paper, we propose a novel Object-centric Fusion (ObjectFusion) paradigm, which completely gets rid of camera-to-BEV transformation during fusion to align object-centric features across different modalities for …

Faster, Cheaper, And Better Cfd: A Case For Machine Learning To Augment Reynolds-Averaged Navier-Stokes, John Peter Romano Ii

Mechanical & Aerospace Engineering Theses & Dissertations

In recent years, the field of machine learning (ML) has made significant advances, particularly through applying deep learning (DL) algorithms and artificial intelligence (AI). The literature shows several ways that ML may enhance the power of computational fluid dynamics (CFD) to improve its solution accuracy, reduce the needed computational resources and reduce overall simulation cost. ML techniques have also expanded the understanding of underlying flow physics and improved data capture from experimental fluid dynamics.

This dissertation presents an in-depth literature review and discusses ways the field of fluid dynamics has leveraged ML modeling to date. The author selects and describes …

Deep-Learning-Based Classification Of Digitally Modulated Signals, John A. Snoap

Electrical & Computer Engineering Theses & Dissertations

This dissertation presents several novel deep-learning (DL)-based approaches for classifying digitally modulated signals, one method of which involves the use of capsule networks (CAPs) together with cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The classification performance and the generalization abilities of the proposed approach were tested using two distinct datasets that contained the same types of digitally modulated signals but had distinct generation parameters. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in …

Lithium Extraction From Aqueous Solution Using Magnesium Doped Lithium Ion-Sieve Composite, Ujjwal Pokharel

Civil & Environmental Engineering Theses & Dissertations

This study focused on the development of a magnesium doped ion-sieve composite to extract lithium from an aqueous solution using adsorption process. The extraction process will provide a sustainable alternative to the currently practiced solar evaporation/concentration method which is very slow (takes 24 months) and water intensive. Currently, most of the lithium in the world comes from the mining of lithium or from the evaporative extraction process from the brine. Additionally, the Salton Sea area geothermal brines are recognized as potentially important domestic sources of lithium. Lithium concentration in geothermal brines from the Salton Sea area is reported to be …

Optics Studies For Multipass Energy Recovery At Cebaf: Er@Cebaf, Isurumali Neththikumara

Physics Theses & Dissertations

Energy recovery linacs (ERLs), focus on recycling the kinetic energy of electron beam for the purpose of accelerating a newly injected beam within the same accelerating structure. The rising developments in the super conducting radio frequency technology, ERL technology has achieved several noteworthy milestones over the past few decades. In year 2003, Jefferson Lab has successfully demonstrated a single pass energy recovery at the CEBAF accelerator. Furthermore, they conducted successful experiments with IR-FEL demo and upgrades, as well as the UV FEL driver. This multi-pass, multi-GeV range energy recovery demonstration proposed to be carried out at CEBAF accelerator at Jefferson …

An Effective Model For Dynamic Properties Of Local Soils And Their Influence On Seismic Response Of A Typical Reinforced Concrete Building, Kaveh Zehtab

Civil & Environmental Engineering Theses & Dissertations

This dissertation presents an effective model to determine the dynamic properties of local soils and their impact on the seismic response of a typical mid-height reinforced concrete building. The results of the study suggest that the use of global models and empirical relationships for the dynamic properties of soils may not consider the effects of unique stress conditions on the cyclic response of the foundation soil. The study also investigates the seismic response of a site in central Adapazarı, Turkey, where four- to seven-story buildings suffered significant damage during the 1999 Kocaeli earthquake. A new model is presented that combines …

Machine Learning Approach To Activity Categorization In Young Adults Using Biomechanical Metrics, Nathan Q. C. Holland

Mechanical & Aerospace Engineering Theses & Dissertations

Inactive adults often have decreased musculoskeletal health and increased risk factors for chronic diseases. However, there is limited data linking biomechanical measurements of generally healthy young adults to their physical activity levels assessed through questionnaires. Commonly used data collection methods in biomechanics for assessing musculoskeletal health include but are not limited to muscle quality (measured as echo intensity when using ultrasound), isokinetic (i.e., dynamic) muscle strength, muscle activations, and functional movement assessments using motion capture systems. These assessments can be time consuming for both data collection and processing. Therefore, understanding if all biomechanical assessments are necessary to classify the activity …

Transition Supply Chain 4.0 To Supply Chain 5.0: Innovations Of Industry 5.0 Technologies Toward Smart Supply Chain Partners, Mona Mohamed, Karam M. Sallam, Ali Wagdy Mohamed

Neutrosophic Systems with Applications

Industry 4.0 provides businesses with the tools they need to meet difficulties such as fluctuating demand and unstable markets. Additionally, Industry 4.0 refers to the connectivity of computers, various materials, and artificial intelligence (AI) with minimum involvement from humans in the decision-making process. Although Industry 4.0 has a significant potential for the expansion of the industrial sector, it faces several hurdles, including integration of technology, problems with human resources, problems with supply chains, and data security concerns. The human-centered approach that Industry 5.0 took meant that many of the problems that plagued Industry 4.0 could finally be solved. In the …

A Novel Method Of Decision Making Based On Plithogenic Contradictions, Nivetha Martin, Florentin Smarandache, Sudha S

Neutrosophic Systems with Applications

Plithogenic decision-making models are evolved integrating the Plithogenic modelling approach with various methods of multi-criteria decision-making (MCDM). The earlier Plithogenic based decision methods are primarily based on the degrees of appurtenance. This paper introduces a novel Plithogenic ranking genre of decision-making paradigm based on degrees of contradiction. The method of Decision Making on Plithogenic Contradictions (DMPC) developed in this research work is indigenous and unique as the modeling procedure doesn’t resemble any of the decision methods. This simple and logical approach proposed in this paper is applied in making optimal decisions on supplier selection. The proposed contradiction based Plithogenic model …

Heart Disease Prediction Under Machine Learning And Association Rules Under Neutrosophic Environment, Ahmed A. El-Douh, Songfeng Lu, Ahmed Abdelhafeez, Ahmed M. Ali, Alber S. Aziz

Neutrosophic Systems with Applications

Early identification and precise prediction of heart disease have important implications for preventative measures and better patient outcomes since cardiovascular disease is a leading cause of death globally. By analyzing massive amounts of data and seeing patterns that might aid in risk stratification and individualized treatment planning, machine learning algorithms have emerged as valuable tools for heart disease prediction. Predictive modeling is considered for many forms of heart illness, such as coronary artery disease, myocardial infarction, heart failure, arrhythmias, and valvar heart disease. Resource allocation, preventative care planning, workflow optimization, patient involvement, quality improvement, risk-based contracting, and research progress are …

Open-Cast Mining Deformations Monitoring Using Sentinel-1 Sar Data (Sbas Technique), Mahvash Naddaf Sangani, Seyed Reza Hosseinzadeh, José Francisco Martín Duque, Mahnaz Jahadi Toroghi, Kapil Kumar Malik

Journal of Sustainable Mining

Land surface deformation created by mining activities can have negative impacts on the environment. Measuring them can be a tool for managing the environmental impacts of mining. Synthetic Aperture Radar Interferometry is a remote sensing method for measuring deformations. The main aim of this research is to investigate the deformation phenomenon on a region scale and extend our understanding of it to all mining deformation areas across the country. This paper used Small Baseline Subset Interferometric Synthetic Aperture Radar technology to obtain deformations information in the Sangan mine based on mining activities. We used 48 scenes of Single Look Complex(SLC) …

Numerical Modelling Of Uniaxial Compressive Strength Laboratory Tests, Phu Minh Vuong Nguyen, Andrzej Walentek, Petr Waclawik, Kamil Soucek, Michał Antoniuk

Journal of Sustainable Mining

In the last decades, numerical modelling has been widely used to simulate rock mass behaviour in geo-engineering issues. The only disadvantage of numerical modelling is the reliability of required input data (e.g. mechanical parameters), which is not always fully provided due to the complexity of rock mass, project budget, available test methods or human errors. On the other hand, it was proven in many cases that numerical modelling is a helpful tool for solving such complex problems, especially when coupled with the results of laboratory and in-situ tests. This paper presents an attempt to determine the proper numerical constitutive model …

Enhanced Cell Viability And Migration Of Primary Bovine Annular Fibrosus Fibroblast-Like Cells Induced By Microsecond Pulsed Electric Field Exposure, Prince M. Atsu, Connor Mowen, Gary L. Thompson Iii

Henry M. Rowan College of Engineering Departmental Research

This study is the first to report the enhancement of cell migration and proliferation induced by in vitro microsecond pulsed electric field (μsPEF) exposure of primary bovine annulus fibrosus (AF) fibroblast-like cells. AF primary cells isolated from fresh bovine intervertebral disks (IVDs) are exposed to 10 and 100 μsPEFs with different numbers of pulses and applied electric field strengths. The results indicate that 10 μs-duration pulses induce reversible electroporation, while 100 μs pulses induce irreversible electroporation of the cells. Additionally, μsPEF exposure increased AF cell proliferation up to 150% while increasing the average migration speed by 0.08 μm/min over 24 …

A Multitask Learning Framework For Pilot Decontamination In 5g Massive Mimo, Crallet Victor

Tanzania Journal of Engineering and Technology (TJET)

Reference signals enable the acquisition of channel state information (CSI) for purposes such as channel estimation, beam selection, precoding, and symbol detection in 5G massive multiple-input multiple output (MAMIMO) systems. Eventually, as more and more users and cells are added, orthogonal reference signals become few which leads to pilot contamination. Pilot contamination limits the performance and occurs when non-orthogonal reference signals occupy time-frequency resources that are alike. Learning-based techniques have been proposed to alleviate it. However, each can only learn to perform a single task namely pilot assignment, power allocation, pilot design, or de-noising for pilot decontamination. In addition, each …

A Radial Basis Function Neural Network Algorithm For The Simultaneous Retrieval Of Two Meteorological Parameters From Solar Radiation, Nicholas W. Nzala, Nicolausi Ssebiyonga, Dennis Muyimbwa, Taddeo Ssenyonga

Tanzania Journal of Science

Local meteorological parameters are key in understanding the frequency of occurrence of extreme weather conditions such as floods, and droughts, among others. In this study, we present a method for simultaneous retrieval of two weather parameters. The method is based on already measured monthly average values of weather parameters from 2011 to 2016, which were used to train a Feed-forward radial basis function neural network (RBFNN) to obtain a fast and accurate method to compute global solar radiation for specified weather parameters pair. In inverse modelling, a multidimensional unconstrained non-linear optimization was employed to retrieve the weather parameters pair. The …