Computational Engineering Commons^™

Numerical And Scaling Study On Application Of Inkjet Technology To Automotive Coating, Masoud Arabghahestani Dr.

Theses and Dissertations--Mechanical Engineering

A thorough literature review identified lack of precision control over quality of droplets generated by the currently available industrial sprayers and a growing need for higher quality droplets in the coating industry. Particularly, lack of knowledge and understanding in continuous inkjets (CIJ) and drop-on-demand (DOD) technologies is identified as significant. Motivated by these needs, this dissertation is dedicated to computational fluid dynamics (CFD) and scaling studies to improve existing inkjet technologies and develop new designs of efficient coating with single and/or multiple piezoelectric sensors to produce on-demand droplets. This dissertation study aims at developing a new DOD type coating technology, …

Ternary Flow Simulation Based On The Conservative Phase Field Lattice Boltzmann Method, Chunheng Zhao

Dissertations and Theses

In this thesis, we numerically investigated multi-phase fluid dynamics (2 and 3-phase flow) by solving the Navier-Stokes equations coupled with the conservative phase field (CPF) equations using the Lattice Boltzmann method (LBM). To effectively simulate the large-scale multi-phase physics, we developed an open-source software, IMEXLBM, which can be easily parallelized on both CPUs and GPUs without significant modifications to the code. We first validated various parts of this software and then used this method to study the interaction of rising bubbles with a static oil droplet as well as the engulfment of the water droplet on solids coated with a …

Face Representation Learning And Its Applications: From Image Editing To 3d Avatar Animation, Xudong Liu

Graduate Theses, Dissertations, and Problem Reports

Face representation learning is one of the most popular research topics in the computer vision community, as it is the foundation of face recognition and face image generation. Numerous representation learning frameworks have been integrated into applications in daily life, such as face recognition, image editing, and face tracking. Researchers have developed advanced algorithms for face recognition with successful commercial productions, for example, FaceID on the smartphone. The performance record on face recognition is constantly updated and becoming saturated with the help of large-scale datasets and advanced computational resources. Thanks to the robust representation in face recognition, in this dissertation, …

Bounded-Degree Plane Geometric Spanners: Connecting The Dots Between Theory And Practice, Matthew Alexander Graham

UNF Graduate Theses and Dissertations

The construction of bounded-degree plane geometric spanners has been a focus of interest since 2002 when Bose, Gudmundsson, and Smid proposed the first algorithm to construct such spanners. To date, eleven algorithms have been designed with various trade-offs in degree and stretch factor. We have implemented these sophisticated algorithms in C++ using the CGAL library and experimented with them using large synthetic and real-world pointsets. Our experiments have revealed their practical behavior and real-world efficacy. We share the implementations via GitHub for broader uses and future research.

We present a simple practical algorithm, named AppxStretchFactor, that can estimate stretch factors …

New Development Of Neutrosophic Probability, Neutrosophic Statistics, Neutrosophic Algebraic Structures, And Neutrosophic Plithogenic Optimizations, Florentin Smarandache, Yanhui Guo

Branch Mathematics and Statistics Faculty and Staff Publications

This collective book presents state-of-the-art papers on new topics related to neutrosophic theories, such as neutrosophic algebraic structures, neutrosophic triplet algebraic structures, neutrosophic extended triplet algebraic structures, neutrosophic algebraic hyperstructures, neutrosophic triplet algebraic hyperstructures, neutrosophic n-ary algebraic structures, neutrosophic n-ary algebraic hyperstructures, refined neutrosophic algebraic structures, refined neutrosophic algebraic hyperstructures, quadruple neutrosophic algebraic structures, refined quadruple neutrosophic algebraic structures, neutrosophic image processing, neutrosophic image classification, neutrosophic computer vision, neutrosophic machine learning, neutrosophic artificial intelligence, neutrosophic data analytics, neutrosophic deep learning, and neutrosophic symmetry, as well as their applications in the real world.

On Resource-Efficiency And Performance Optimization In Big Data Computing And Networking Using Machine Learning, Wuji Liu

Dissertations

Due to the rapid transition from traditional experiment-based approaches to large-scale, computational intensive simulations, next-generation scientific applications typically involve complex numerical modeling and extreme-scale simulations. Such model-based simulations oftentimes generate colossal amounts of data, which must be transferred over high-performance network (HPN) infrastructures to remote sites and analyzed against experimental or observation data on high-performance computing (HPC) facility. Optimizing the performance of both data transfer in HPN and simulation-based model development on HPC is critical to enabling and accelerating knowledge discovery and scientific innovation. However, such processes generally involve an enormous set of attributes including domain-specific model parameters, network transport …

The Evolution Of The Internet And Social Media: A Literature Review, Charles Alves De Castro, Isobel O'Reilly Dr, Aiden Carthy

Articles

This article reviews and analyses factors impacting the evolution of the internet, the web, and social media channels, charting historic trends and highlight recent technological developments. The review comprised a deep search using electronic journal databases. Articles were chosen according to specific criteria with a group of 34 papers and books selected for complete reading and deep analysis. The 34 elements were analysed and processed using NVIVO 12 Pro, enabling the creation of dimensions and categories, codes and nodes, identifying the most frequent words, cluster analysis of the terms, and creating a word cloud based on each word's frequency. The …

Numerical Simulation Of A Cryogenic Spray, Neel Kishorkumar Shah

Doctoral Dissertations and Master's Theses

Cryogenic sprays have many applications in modern engineering. Cooling of electronic equipment subject to high heat flows, surgical ablation of gastrointestinal mucosae or orbital maneuvering are a few examples of their versatility. However, the atomization of a cryogenic liquid is a complex process. During such an event, aerodynamic effects associated with secondary atomization are further affected by thermodynamic flashing. A better understanding of the characteristics of cryogenic sprays is then necessary to allow for improved design and optimization in applications. The overarching objective of this study is to document such characteristics. The numerical simulation was performed over cryogenic nitrogen spray …

Development Of A Model For Control Of A Flexible Production Sewage System, Shalala Jafarova

Scientific-technical journal

This article discusses the development of a production module management model for one area of the technological process. New modeling methods are used for this purpose. Mathematical modeling and research is one of the key issues in the early stages of designing automated and automated systems operating in uncertain or fuzzy environments. Efficient modeling devices are used to solve these problems, taking into account the specific features of the process. The article builds the management model of the production module and obtains the results.

Verification Of Acoustic Dissipation In Two-Phase Dilute Dispersed Flow Models In Computational Fluid Dynamics, Brennan Reeder

Theses and Dissertations

With existing numerical models for fluid particle systems in CHEM, the acoustic-particle interactions associated with two-phase dilute dispersed flow can be captured and the particle model can be validated using experimental and analytical data and verified using numerical techniques. The experimental and analytical data come from Zink and Delsasso and provides data for particles of diameters 5 to 15 microns for frequencies between 500Hz to 13600Hz. In the particle number density measurements by Zink and Delsasso there was a 10% estimated error range. Using the fourth order skew symmetric flux in CHEM and the built in Eulerian and Lagrangian particle …

Knowtext: Auto-Generated Knowledge Graphs For Custom Domain Applications, Bojan Bozic, Jayadeep Kumar Sasikumar, Tamara Matthews

Articles

While industrial Knowledge Graphs enable information extraction from massive data volumes creating the backbone of the Semantic Web, the specialised, custom designed knowledge graphs focused on enterprise specific information are an emerging trend. We present “KnowText”, an application that performs automatic generation of custom Knowledge Graphs from unstructured text and enables fast information extraction based on graph visualisation and free text query methods designed for non-specialist users. An OWL ontology automatically extracted from text is linked to the knowledge graph and used as a knowledge base. A basic ontological schema is provided including 16 Classes and Data type Properties. The …

Machine-Learning-Based Approach To Decoding Physiological And Neural Signals, Elnaz Lashgari

Computational and Data Sciences (PhD) Dissertations

In recent years, machine learning algorithms have been developing rapidly, becoming increasingly powerful tools in decoding physiological and neural signals. The aim of this dissertation is to develop computational tools, and especially machine learning techniques, to identify the most effective methods for feature extraction and classification of these signals. This is particularly challenging due to the highly non-linear, non-stationery, and artifact- and noise-prone nature of these signals.

Among basic human-control tasks, reaching and grasping are ubiquitous in everyday life. I investigated different linear and non-linear dimensionality reduction techniques for feature extraction and classification of electromyography (EMG) during a reach-grasp-lift task. …

Quantum State Estimation And Tracking For Superconducting Processors Using Machine Learning, Shiva Lotfallahzadeh Barzili

Computational and Data Sciences (PhD) Dissertations

Quantum technology has been rapidly growing; in particular, the experiments that have been performed with superconducting qubits and circuit QED have allowed us to explore the light-matter interaction at its most fundamental level. The study of coherent dynamics between two-level systems and resonator modes can provide insight into fundamental aspects of quantum physics, such as how the state of a system evolves while being continuously observed. To study such an evolving quantum system, experimenters need to verify the accuracy of state preparation and control since quantum systems are very fragile and sensitive to environmental disturbance. In this thesis, I look …

One Step At A Time: Robotics Lab Team Combines Talents In Quest For The Perfect Synthetic Tendon, Laura Meader

Colby Magazine

When Caitrin Eaton arrived at Colby a year ago, she named her new robotics lab C3PO. The name grabs students’ attention, but they’re drawn to the lab for another reason: Eaton’s cutting-edge research unites engineering and animal physiology to help robots walk more smoothly than Hollywood’s stiff-legged C-3PO robot.

Rces: Rapid Cues Exploratory Search Using Taxonomies For Covid-19, Wei Li, Rishi Choudhary, Arjumand Younus, Bruno Ohana, Nicole Baker, Brendan Leen, Muhammad Atif Qureshi

Articles

To assist the COVID-19 focused researchers in life science and healthcare in understanding the pandemic, we present an exploratory information retrieval system called RCES. The system employs a previously developed EVE (Explainable Vector-based Embedding) model using DBpedia and an adopted model using MeSH taxonomies to exploit concept relations related to COVID-19. Various expansion methods are also developed, along with explanations and facets that collectively form rapid cues for a valuable navigational and informed user experience.

Integrating Compound Flood Conditions Through 2d Hydraulic Modeling For Simulating Flood Risk Processes In Coastal Cities, Francisco Pena Guerra Mr.

FIU Electronic Theses and Dissertations

Low elevation coastal karst environments are highly vulnerable to flooding conditions due to climate change. Trends in rising global temperatures have increased the frequency and intensity of extreme precipitation, hydrometeorological phenomena and sea level rise, exacerbating the impact of pluvial, fluvial, coastal and groundwater flood hazards. Compound flooding events amplify flood hazards and pose a higher threat to residents and infrastructure in unison compared to independent phenomena. Recent advancements in coupling hydrologic and hydraulic modeling frameworks have improved our ability to account for the combined effects of extreme pluvial, fluvial, and coastal flood hazards. This innovation in the hydroinformatics field …

Benchmarking Small-Dataset Structure-Activity-Relationship Models For Prediction Of Wnt Signaling Inhibition, Mahtab Kokabi

Masters Theses

Quantitative structure-activity relationship (QSAR) models based on machine learning algorithms are powerful tools to expedite drug discovery processes and therapeutics development. Given the cost in acquiring large-sized training datasets, it is useful to examine if QSAR analysis can reasonably predict drug activity with only a small-sized dataset (size < 100) and benchmark these small-dataset QSAR models in application-specific studies. To this end, here we present a systematic benchmarking study on small-dataset QSAR models built for prediction of effective Wnt signaling inhibitors, which are essential to therapeutics development in prevalent human diseases (e.g., cancer). Specifically, we examined a total of 72 two-dimensional (2D) QSAR models based on 4 best-performing algorithms, 6 commonly used molecular fingerprints, and 3 typical fingerprint lengths. We trained these models using a training dataset (56 compounds), benchmarked their performance on 4 figures-of-merit (FOMs), and examined their prediction accuracy using an external validation dataset (14 compounds). Our data show that the model performance is maximized when: 1) molecular fingerprints are selected to provide sufficient, unique, and not overly detailed representations of the chemical structures of drug compounds; 2) algorithms are selected to reduce the number of false predictions due to class imbalance in the dataset; and 3) models are selected to reach balanced performance on all 4 FOMs. These results may provide general guidelines in developing high-performance small-dataset QSAR models for drug activity prediction.

Natural Language Processing For Lexical Corpus Analysis, Abram Kaufman Handler

Doctoral Dissertations

People have been analyzing documents by reading keywords in context for centuries. Traditional approaches like paper concordances or digital keyword-in-context viewers display all occurrences of a single word from a corpus vocabulary amid immediately surrounding tokens or characters, to show readers how individual lexical items are used in bodies of text. We propose that these common tools are one particular application of a more general approach to analyzing documents, which we define as lexical corpus analysis. We then propose new natural language processing techniques for lexically-focused corpus investigation, and demonstrate how such methods can be used to create new user-facing …

Cost-Efficient Resource Provisioning For Cloud-Enabled Schedulers, Lurdh Pradeep Reddy Ambati

Doctoral Dissertations

Since the last decade, public cloud platforms are rapidly becoming de-facto computing platform for our society. To support the wide range of users and their diverse applications, public cloud platforms started to offer the same VMs under many purchasing options that differ across their cost, performance, availability, and time commitments. Popular purchasing options include on-demand, reserved, and transient VM types. Reserved VMs require long time commitments, whereas users can acquire and release the on-demand (and transient) VMs at any time. While transient VMs cost significantly less than on-demand VMs, platforms may revoke them at any time. In general, the stronger …

Security And Privacy Analysis Of Wearable Health Device, Abm Kamrul Islam Riad

Symposium of Student Scholars

Wearable technology allows for consumers to record their healthcare data for either personal or clinical use via portable devices. As advancements in this technology continue to rise, the use of these devices has become more widespread. In this paper, we examine the significant security and privacy features of three health tracker devices: Fitbit, Jawbone and Google Glass. We also analyze the devices' strength and how the devices communicate via its Bluetooth pairing process with mobile devices. We explore possible malicious attacks through Bluetooth networking. The outcomes of this analysis illustrate how these devices allow third parties to access sensitive information, …