Computational Engineering Commons^™

Intra-Hour Solar Forecasting Using Cloud Dynamics Features Extracted From Ground-Based Infrared Sky Images, Guillermo Terrén-Serrano

Electrical and Computer Engineering ETDs

Due to the increasing use of photovoltaic systems, power grids are vulnerable to the projection of shadows from moving clouds. An intra-hour solar forecast provides power grids with the capability of automatically controlling the dispatch of energy, reducing the additional cost for a guaranteed, reliable supply of energy (i.e., energy storage). This dissertation introduces a novel sky imager consisting of a long-wave radiometric infrared camera and a visible light camera with a fisheye lens. The imager is mounted on a solar tracker to maintain the Sun in the center of the images throughout the day, reducing the scattering effect produced …

Three-Dimensional Numerical Modeling Of Flow Hydrodynamics And Cohesive Sediment Transport In Enid Lake, Mississippi, Xiaobo Chao, A. K. M. Azad Hossain, Mohammad Z. Al-Hamdan, Yafei Jia, James V. Cizdziel

Faculty and Student Publications

Enid Lake is one of the largest reservoirs located in Yazoo River Basin, the largest basin in the state of Mississippi. The lake was impounded by Enid Dam on the Yocona River in Yalobusha County and covers an area of 30 square kilometers. It provides significant natural and recreational resources. The soils in this region are highly erodible, resulting in a large amount of fine-grained cohesive sediment discharged into the lake. In this study, a 3D numerical model was developed to simulate the free surface hydrodynamics and transportation of cohesive sediment with a median diameter of 0.0025 to 0.003 mm …

Development Of Guidelines For Collecting Transit Ridership Data, Hong Yang, Kun Xie, Sherif Ishak, Qingyu Ma, Yang Liu

Computational Modeling & Simulation Engineering Faculty Publications

Transit ridership is a critical determinant for many transit applications such as operation optimizations and project prioritization under performance-based funding mechanisms. As a result, the quality of ridership data is of utmost importance to both transit administrative agencies and transit operators. Many transit operators in Virginia report their ridership data to the Department of Rail and Public Transportation (DRPT) and the National Transit Database (NTD). However, with no specific guidelines available to transit agencies in Virginia for collecting ridership data, the heterogeneous mixture of diverse data collection methods and technologies has often raised concerns about the consistency and quality of …

Representation Learning For Chemical Activity Predictions, Mohamed S. Ayed

Dissertations, Theses, and Capstone Projects

Computational prediction of a phenotypic response upon the chemical perturbation on a biological system plays an important role in drug discovery and many other applications. Chemical fingerprints derived from chemical structures are a widely used feature to build machine learning models. However, the fingerprints ignore the biological context, thus, they suffer from several problems such as the activity cliff and curse of dimensionality. Fundamentally, the chemical modulation of biological activities is a multi-scale process. It is the genome-wide chemical-target interactions that modulate chemical phenotypic responses. Thus, the genome-scale chemical-target interaction profile will more directly correlate with in vitro and in …

Numerical Simulation On The Fire Performance Of Reinforced Concrete Flat Slab, Elias Ali

Summer Community of Scholars (RCEU and HCR) Project Proposals

No abstract provided.

Design And Development Of Active Flow Effectors, Konstantinos Kanistras

Summer Community of Scholars (RCEU and HCR) Project Proposals

No abstract provided.

Design And Development Of An Online Wind Tunnel, Konstantinos Kanistras

Summer Community of Scholars (RCEU and HCR) Project Proposals

No abstract provided.

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 …

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

Theses and Dissertations--Mechanical and Aerospace 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, …

Image Geo-Localization With Cross-Attention, Connor Greenwell

Theses and Dissertations--Computer Science

The problem of estimating the location from which un-geotagged photographs were captured has been well studied by the computer vision community in recent years. The central proposal of this thesis is to define a common framework within which existing approaches can be constructed and evaluated, and to introduce a new method under this framework which uses cross-attention between the query image and a database of satellite imagery with known geotags. Our experiments fit within three broad categories: 1) evaluating the ability of image localization approaches to generalize to unseen regions; 2) examining performance changes under various reference database resolutions, scales, …

Improving Feature Learning Capability And Interpretability Of Unsupervised Neural Networks, Chathurika S. Wickramasinghe Brahmana

Theses and Dissertations

The motivation for this dissertation is two-prong. Firstly, the current state of machine learning imposes the need for unsupervised Machine Learning (ML). Secondly, once such models are developed, a deeper understanding of ML models is necessary for humans to adapt and use such models.

Real-world systems generate massive amounts of unlabeled data at rapid speed, limiting the usability of state-of-the-art supervised machine learning approaches. Further, the manual labeling process is expensive, time-consuming, and requires the expertise of the data. Therefore, the existing supervised learning algorithms are unable to take advantage of the abundance of real-world unlabeled data. Thus, relying on …

Simulation-Based Optimization: Implications Of Complex Adaptive Systems And Deep Uncertainty, Andreas Tolk

VMASC Publications

Within the modeling and simulation community, simulation-based optimization has often been successfully used to improve productivity and business processes. However, the increased importance of using simulation to better understand complex adaptive systems and address operations research questions characterized by deep uncertainty, such as the need for policy support within socio-technical systems, leads to the necessity to revisit the way simulation can be applied in this new area. Similar observations can be made for complex adaptive systems that constantly change their behavior, which is reflected in a continually changing solution space. Deep uncertainty describes problems with inadequate or incomplete information about …

A Literature Review On Combining Heuristics And Exact Algorithms In Combinatorial Optimization, Hesamoddin Tahami, Hengameh Fakhravar

Engineering Management & Systems Engineering Faculty Publications

There are several approaches for solving hard optimization problems. Mathematical programming techniques such as (integer) linear programming-based methods and metaheuristic approaches are two extremely effective streams for combinatorial problems. Different research streams, more or less in isolation from one another, created these two. Only several years ago, many scholars noticed the advantages and enormous potential of building hybrids of combining mathematical programming methodologies and metaheuristics. In reality, many problems can be solved much better by exploiting synergies between these approaches than by “pure” classical algorithms. The key question is how to integrate mathematical programming methods and metaheuristics to achieve such …

Combining Green Metrics And Digital Twins For Sustainability Planning And Governance Of Smart Buildings And Cities, Casey R. Corrado, Suzanne M. Delong, Emily G. Holt, Edward Y. Hua, Andreas Tolk

VMASC Publications

Creating a more sustainable world will require a coordinated effort to address the rise of social, economic, and environmental concerns resulting from the continuous growth of cities. Supporting planners with tools to address them is pivotal, and sustainability is one of the main objectives. Modeling and simulation augmenting digital twins can play an important role to implement these tools. Although various green best practices have been utilized over time and there are related attempts at measuring green success, works in the published literature tend to focus on addressing a single problem (e.g., energy efficiency), and a comprehensive approach that takes …

Coastal Housing Recovery In A Postdisaster Environment: A Supply Chain Perspective, Rafael Diaz, Joshua Behr, Beatriz Acerp

VMASC Publications

In coastal cities, the damage stemming from hurricanes and severe tropical storms may reduce housing stock. Accelerating the reconstruction of housing stock becomes crucial in minimizing the time that residents are displaced. It is critical to understand how to better coordinate all available resources for an effective and balanced reconstruction where nongovernmental organizations (NGOs), government agencies, volunteers, and many other complex resources converge. This study combines Monte Carlo and beta models to assess supply, costs, and recovery times. The models capture the immediate and long-term costs of reconstruction activities under highly uncertain conditions using embedded stochastic components on projected materials, …

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.

A Simulation-Based Disaster Management Framework To Analyze Housing Recovery: The Case Of Hampton Roads, Usa, Rafael Diaz, Joshua Behr, Beatriz Acero, Bridget D. Giles, Juita-Elena Wie Yusuf

VMASC Publications

In the aftermath of a catastrophic weather event, housing recovery and reconstruction activities are highly complex. Coordinating housing recovery activities is generally challenging because complex supply chains converge simultaneously in a highly uncertain environment. In this environment, anticipating and quantifying the extent of potential damage and determining the actions that must be taken to rebuild the housing stock may assist regions in allocating resources for reconstruction. This paper presents the development of a comprehensive disaster management framework to assist decision-makers in predicting the impact of projected natural disasters on housing. This framework uses a simulation-based approach to quantify likely regional …

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

Graduate Theses, Dissertations, and Problem Reports (ETD)

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, …

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 …

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 …

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. …

Characterization Of Bacterial Nanocellulose - Graphite Nanoplatelets Composite Films, Bili Darnanto Susilo, Heru Suryanto, Aminnudin Aminnudin

Journal of Mechanical Engineering Science and Technology (JMEST)

Bacterial cellulose (BC) was synthesized from pineapple peel extract media with addition of fermentation agent bacteria Acetobacter xylinum. BC was disintegrated from the pellicle into bacterial nanocellulose (BNC) by using a high-pressure homogenizer (hph) machine, which has a three-dimensional woven nanofibrous network. The synthesis of composite films started when BNC, graphite nanoplatelets, and cetyltrimethylammonium bromide (CTAB) were homogenized using an ultrasonic homogenizer then baked on a glass mold at a temperature of 80 degrees Celcius for 14h. A scanning electron microscope (SEM) was used to analyze its morphology. X-Ray diffraction spectra were used to analyze the composite films structure. The …

Implementation Of Internet Of Things (Iot) In A Plastic Blow Moulding Machine And Its Performance Measurement, Muhammad F. Faqih, Mahros Darsin, Aris Z. Muttaqin, Danang Yudistiro

Journal of Mechanical Engineering Science and Technology (JMEST)

Efficiency and effectiveness are indispensable things in the production process. Accurate use of existing resources and the shorter cycle time of production are of particular concern to optimize the production process. This research aims to implement automation to a conventional blow molding. An advanced attempt was carried out to use the Internet of Things (IoT) to increase its efficiency while maintaining the quality of the products. The use of the nodeMCU microcontroller and the blynk application allows the operator to operate the machine without having to come into or having direct contact with the machine. The performance of automation and …

Design Of Shielded Metal Arc Welding Parameters For Optimum Tensile Strength Using Taguchi Method, Moh. Dedy Setiawan, Yanuar Rohmat A. P. Pradana, Suprayitno Suprayitno

Journal of Mechanical Engineering Science and Technology (JMEST)

Shielded Metal Arc Welding (SMAW), an arc welding process, is widely used in applications. In practice, SMAW is widely applied to the welding process on hollow square pipe. Performance expected from this welding is the tensile strength of weld joint. The tensile strength is influenced by parameters process which have possibility for an optimization process to become ‘robust’. Robust is a design which less sensitive to the effect of uncertain quantities or noise factors. Taguchi method is the most efficient optimization method which accommodates the noise factors effect and requires less experiment. This study is focusing on optimizing the welding …