Computational Engineering Commons^™

Evolution Of Computational Thinking Contextualized In A Teacher-Student Collaborative Learning Environment., John Arthur Underwood

LSU Doctoral Dissertations

The discussion of Computational Thinking as a pedagogical concept is now essential as it has found itself integrated into the core science disciplines with its inclusion in all of the Next Generation Science Standards (NGSS, 2018). The need for a practical and functional definition for teacher practitioners is a driving point for many recent research endeavors. Across the United States school systems are currently seeking new methods for expanding their students’ ability to analytically think and to employee real-world problem-solving strategies (Hopson, Simms, and Knezek, 2001). The need for STEM trained individuals crosses both the vocational certified and college degreed …

Edge-Cloud Iot Data Analytics: Intelligence At The Edge With Deep Learning, Ananda Mohon M. Ghosh

Electronic Thesis and Dissertation Repository

Rapid growth in numbers of connected devices, including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …

Using Case-Level Context To Classify Cancer Pathology Reports, Shang Gao, Mohammed Alawad, Noah Schaefferkoetter, Lynne Penberthy, Xiao-Cheng Wu, Eric B. Durbin, Linda Coyle, Arvind Ramanathan, Georgia Tourassi

Kentucky Cancer Registry Faculty Publications

Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence-for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We …

Gestión De Datos De Susceptibilidad Antimicrobiana De Bacterias Aisladas A Nivel Ocular, Mauro Estefan Ramirez Aranguren

Ingeniería en Automatización

El presente proyecto analizó, diseñó y puso en marcha un software web para consulta y respectiva gestión de datos. Este software tiene como finalidad promover y suministrar información en tiempo real a estudiantes y profesionales del programa de optometría, acerca del patrón de susceptibilidad antimicrobiana presentes a nivel ocular. Adicionalmente, brinda información acerca de las cepas presentes y si es posible la generación de multiresistencia a medicamentos. Cabe mencionar, que la información de campo en la que se fundamenta este trabajo fue proporcionada por las profesionales del programa de optometría de la Universidad de la Salle: Viviane Geraldine Rondón Correa …

Chaprates, Brinly Xavier, Micole Amanda Marietta, Nidhi Vedantam

Student Scholar Symposium Abstracts and Posters

On the Chapman campus, through taking and choosing various classes, there is a significant need for communication and feedback between students and peers, professors, tutors, and study groups. With this, we wanted to create an application that enables users from various majors to not only easily and effectively communicate with various people in their field, but one that also enables them to give and receive feedback on various classes through a rating system. We believe that the application will aid students in a myriad of specific ways, including being involved in study groups and getting tutoring help, determining which classes …

Evaluating Machine Learning Models For Semantic Segmentation Over Cloud Images For Classification, Harsh Nagarkar

Honors Theses

Due to the increasing number of available approaches nowadays, choosing the most accurate image semantic segmentation model has become hard. The purpose of this research is to find the best-performing image semantic segmentation model for Cloud classification. For the purpose of this study, a data set of cloud images from the Max Planck Institute for meteorology is used. These images were taken from the by two NASA space satellite.Three main models UNet, PSPNet and FPN were used in combination of 4 differ-ent encoder Inception-ResNet-v2, MobileNet-v2, ResNet-34, and ResNet 101. After training all the models in the Mississippi Center for Super …

Minet Magnetic Indoor Localization, Michael Drake

Honors Theses

Indoor localization is a modern problem of computer science that has no unified solution, as there are significant trade-offs involved with every technique. Magnetic localization, though less popular than WiFi signal based localization, is a sub-field that is rooted in infrastructure-free design, which can allow universal setup. Magnetic localization is also often paired with probabilistic programming, which provides a powerful method of estimation, given a limited understanding of the environment. This thesis presents Minet, which is a particle filter based localization system using the Earth's geomagnetic field. It explores the novel idea of state space limitation as a method of …

Qlime-A Quadratic Local Interpretable Model-Agnostic Explanation Approach, Steven Bramhall, Hayley Horn, Michael Tieu, Nibhrat Lohia

SMU Data Science Review

In this paper, we introduce a proof of concept that addresses the assumption and limitation of linear local boundaries by Local Interpretable Model-Agnostic Explanations (LIME), a popular technique used to add interpretability and explainability to black box models. LIME is a versatile explainer capable of handling different types of data and models. At the local level, LIME creates a linear relationship for a given prediction through generated sample points to present feature importance. We redefine the linear relationships presented by LIME as quadratic relationships and expand its flexibility in non-linear cases and improve the accuracy of feature interpretations. We coin …

Performance Optimizations Of Nosql Databases In Distributed Systems, Tristyn Maalouf

Senior Honors Theses

Databases store information about a system and provide a mechanism for data to be accessed and manipulated. While advancements in the 1970s provided a relational database model that has persisted to this day, web-scale era mass data needs surfacing in the 1990s and the early 2000s revealed limitations in the scalability of the relational model. As systems grew and transitioned into distributed architectures to support mass data storage and parallel processing, a complete overhaul of distributed computing technologies evolved that fundamentally departed from the relational data model in favor of the NoSQL data model. The course of this research details …

Sensitivity Analysis Of Data-Driven Groundwater Forecasts To Hydroclimatic Controls In Irrigated Croplands, Alessandro Amaranto, Francesca Pianosi, Dimitri Solomatine, Gerald Corzo-Perez, Francisco Munoz-Arriola

Biological Systems Engineering: Papers and Publications

In the last decades, advancements in computational science have greatly expanded the use of artificial neural networks (ANNs) in hydrogeology, including applications on groundwater forecast, variable selection, extended lead-times, and regime-specific analysis. However, ANN-model performance often omits the sensitivity to ob- servational uncertainties in hydroclimate forcings. The goal of this paper is to implement a data-driven modeling framework for assessing the sensitivity of ANN-based groundwater forecasts to the uncertainties in observational inputs across space, time, and hydrological regimes. The objectives are two-folded. The first objective is to couple an ANN model with the PAWN sensitivity analysis (SA). The second objective …

Nonlinear Least Squares 3-D Geolocation Solutions Using Time Differences Of Arrival, Michael V. Bredemann

Mathematics & Statistics ETDs

This thesis uses a geometric approach to derive and solve nonlinear least squares minimization problems to geolocate a signal source in three dimensions using time differences of arrival at multiple sensor locations. There is no restriction on the maximum number of sensors used. Residual errors reach the numerical limits of machine precision. Symmetric sensor orientations are found that prevent closed form solutions of source locations lying within the null space. Maximum uncertainties in relative sensor positions and time difference of arrivals, required to locate a source within a maximum specified error, are found from these results. Examples illustrate potential requirements …

Perturbed Stress Field Of The Human Lens Capsule After Cataract Surgery, Kurt Ameku, Caleb Berggren, Ryan M. Pedrigi

UCARE Research Products

Current modeling of the human lens capsule has been focused on the mechanism of accommodation and its decline with age, but few studies have modeled the effects of cataract surgery and quantified the altered mechanical environment introduced by the procedure. The goal of this study is to develop the first fully 3-D finite element model of the post-surgical human lens capsule with an implanted device in order to characterize lens capsule mechanics after cataract surgery. The model demonstrates a highly perturbed stress field compared to the native state, which we hypothesize is the primary driving force behind the long-term errant …

The Effect Of Oxygen On Properties Of Zirconium Metal, Jie Zhao

Doctoral Dissertations

The influence of oxygen on the thermophysical properties of zirconium has been investigated using MSL-EML (Material Science Laboratory Electromagnetic Levitator) on ISS (International Space Station) in collaboration with NASA (National Aeronautics and Space Administration), ESA (European Space Agency), and DLR (German Aerospace Center). Zirconium samples with different oxygen concentrations was subjected to multiple melt cycles during which the thermophysical properties, such as density, viscosity and surface tension, have been measured at various undercooled and superheated temperatures. Also, there are melt cycles for verifying the solidification mechanism. Similar samples were found to show anomalous nucleation of the solid for certain ranges …

Guidelines For Using Streetlight Data For Planning Tasks, Hong Yang, Mecit Cetin, Qingyu Ma

Computational Modeling & Simulation Engineering Faculty Publications

The Virginia Department of Transportation (VDOT) has purchased a subscription to the StreetLight (SL) Data products that mainly offer origin-destination (OD) related metrics through crowdsourcing data. Users can manipulate a data source like this to quickly estimate origin-destination trip tables. Nonetheless, the SL metrics heavily rely on the data points sampled from smartphone applications and global positioning services (GPS) devices, which may be subject to potential bias and coverage issues. In particular, the quality of the SL metrics in relation to meeting the needs of various VDOT work tasks is not clear. Guidelines on the use of the SL metrics …

Dynamic Procedural Music Generation From Npc Attributes, Megan E. Washburn

Master's Theses

Procedural content generation for video games (PCGG) has seen a steep increase in the past decade, aiming to foster emergent gameplay as well as to address the challenge of producing large amounts of engaging content quickly. Most work in PCGG has been focused on generating art and assets such as levels, textures, and models, or on narrative design to generate storylines and progression paths. Given the difficulty of generating harmonically pleasing and interesting music, procedural music generation for games (PMGG) has not seen as much attention during this time.

Music in video games is essential for establishing developers' intended mood …

Deal: Differentially Private Auction For Blockchain Based Microgrids Energy Trading, Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen

Publications

Modern smart homes are being equipped with certain renewable energy resources that can produce their own electric energy. From time to time, these smart homes or microgrids are also capable of supplying energy to other houses, buildings, or energy grid in the time of available self-produced renewable energy. Therefore, researches have been carried out to develop optimal trading strategies, and many recent technologies are also being used in combination with microgrids. One such technology is blockchain, which works over decentralized distributed ledger. In this paper, we develop a blockchain based approach for microgrid energy auction. To make this auction more …

Incipient Deformation Of Small Volumes Of Fcc Metals, Mahdi Bagheripoor

Electronic Thesis and Dissertation Repository

In the area of mechanics of materials, the classic theories cannot describe the material behaviour as the volume of deformation or sample size is small enough to be compared with the size scales of the imperfections of the crystal. So, there has been a great deal of interest in investigating the plasticity of micron and nano-sized materials, in the last 20 years. As a Ph.D. research project, the deformation mechanism at small scales of fcc metals is studied based on dislocations behaviour. The effect of main parameters that haven’t been studied in detail, including, crystal orientation, pre-existing faults, grain boundaries, …

Analysis Of Pro-Drop Errors In L2 English By L1 Spanish Speakers, Marcos R. Ynoa

Dissertations, Theses, and Capstone Projects

Although educators and linguists have described and studied L2 learner errors, often from the perspective of positive and negative transfer (Krashen, 1981), there is relatively little empirical data on the frequencies of L2 error types. Learner error rankings do exist (Donahue, 2001) (Cambridge learner corpus; (Nicholls, 1999)), but these rankings are often too general and may overlook specifics when it comes to particular language learner groups. The goal of our work is to develop a tool that can be used to explore error patterns, address educator needs, and help answer research questions in L2 language learning. The tool we have …

Computational Modeling Of Laminated Veneer Bamboo (Lvb) Dowel Joints, Niloufar Khoshbakht

Doctoral Dissertations

Laminated veneer bamboo (LVB) is a sustainable building material that has been gaining interest in the construction industry of late. As a relatively new product, little is known about its connection performance, specifically, its failure behavior in dowel type joints and possible similarities it may have to engineered wood products in terms of failure mechanisms. Research is needed to aid in the understanding of LVB dowel connection failure behavior and to quantify the failure mechanism and key factors associated with LVB dowel connection strength. Modeling, as conducted in this research, is a valuable tool to help devise safe standards and …

Análisis De Imágenes Aéreas Como Apoyo Para El Seguimiento De Cultivos De Pasto Para Consumo Bovino, Cristian Hernando Cristancho Toloza

Ingeniería en Automatización

En el presente trabajo de grado se desarrolló una herramienta computacional mediante el uso de imágenes aéreas del cultivo con la finalidad de generar un informe en el cual se incluya datos de relevancia. Este trabajo se realizó empleando una metodología de trabajo que abarca desde la toma de imágenes empleando en este caso una cámara Mapir Survey 3 montada en un drone DJI Mavic Pro, pasando por su procesado y generación de orto mosaicos empleando la herramienta Open Drone Map, ya en la última parte se llevó a cabo el desarrollo de un plugin para el sistema de información …

Multiscale Modeling Of Carbon Fibers/Graphene Nanoplatelets/Epoxy Hybrid Composites For Aerospace Applications, Hashim Al Mahmud

Dissertations, Master's Theses and Master's Reports

Significant research effort has been dedicated for decades to improve the mechanical properties of aerospace polymer-based composite materials. Lightweight epoxy-based composite materials have increasingly replaced the comparatively heavy and expensive metal alloys used in aeronautical and aerospace structural components. In particular, carbon fibers (CF)/graphene nanoplatelets (GNP)/epoxy hybrid composites can be used for this purpose owing to their high specific stiffness and strength. Therefore, this work has been completed to design, predict, and optimize the effective mechanical properties of CF/GNP/epoxy composite materials at different length scales using a multiscale modeling approach. The work-flow of modeling involves a first step of using …

A Reduced Model For Microbial Electrolysis Cells, Dina Aboelela, Moustafa A. Soliman, Ibrahim Ashour

Chemical Engineering

Microbial electrolysis cells (MECs) are breakthrough technology of cheap hydrogen production with high efficiency. In this paper differential-algebraic equation (DAE) model of a MEC with an algebraic constraint on current was studied, simulated and validated by implementing the model on continuous-flow MECs. Then sensitivity analysis for the system was effectuated. Parameters which have the predominating influence on the current density and hydrogen production rate were defined. This sensitivity analysis was utilized in modeling and validation of the batch-cycle of MEC. After that parameters which have less influence on MEC were eliminated and simplified reduced model was obtained and validated. Finally, …

A Reduced Model For Microbial Fuel Cell, Dina Aboelela, Moustafa A. Soliman, Ibrahim Ashour

Chemical Engineering

Microbial fuel cells (MFCs) are a group of microbial electrochemical cells (bioreactors) that are used to generate energy from organic waste found in wastewater. MFCs represent a promising method of waste disposal and production of electricity. Scaling up the use of MFCs requires extensive analysis and detailed grasp of the required processes. The current work aimed to study a model of an MFC, and find the optimum parameters needed for maximum energy production. The process was simulated and validated on continuous-flow MFCs with a Columbic efficiency of 162% and 35% COD removal. Sensitivity analysis of the model was performed. The …

A Half- Yearly E-Newsletter Of The Department Of Computer Science And Engineering, Manipal Institute Of Technology - Jan 2020, Ashalatha Nayak

Faculty work

No abstract provided.

Systematic Model-Based Design Assurance And Property-Based Fault Injection For Safety Critical Digital Systems, Athira Varma Jayakumar

Theses and Dissertations

With advances in sensing, wireless communications, computing, control, and automation technologies, we are witnessing the rapid uptake of Cyber-Physical Systems across many applications including connected vehicles, healthcare, energy, manufacturing, smart homes etc. Many of these applications are safety-critical in nature and they depend on the correct and safe execution of software and hardware that are intrinsically subject to faults. These faults can be design faults (Software Faults, Specification faults, etc.) or physically occurring faults (hardware failures, Single-event-upsets, etc.). Both types of faults must be addressed during the design and development of these critical systems. Several safety-critical industries have widely adopted …

Heterogeneous Uncertainty Quantification For Reliability-Based Design Optimization, Mingyang Li

Dissertations, Master's Theses and Master's Reports

Uncertainty is inherent to real-world engineering systems, and reliability analysis aims at quantitatively measuring the probability that engineering systems successfully perform the intended functionalities under various sources of uncertainties. In this dissertation, heterogeneous uncertainties including input variation, data uncertainty, simulation model uncertainty, and time-dependent uncertainty have been taken into account in reliability analysis and reliability-based design optimization (RBDO). The input variation inherently exists in practical engineering system and can be characterized by statistical modeling methods. Data uncertainty occurs when surrogate models are constructed to replace the simulations or experiments based on a set of training data, while simulation model uncertainty …

Life Cycle Information Models With Parameter Uncertainty Analysis To Facilitate The Use Of Life-Cycle Assessment Outcomes In Pavement Design Decision-Making, Chaitanya Ganesh Bhat

Dissertations, Master's Theses and Master's Reports

The objective of this dissertation is to develop Life Cycle Information Models (LCIMs) to promote consistent and credible communication of potential environmental impacts quantified through Life-Cycle Assessment (LCA) methodology. The introduction of Life Cycle Information Models (LCIMs) will shift the focus of pavement LCA stakeholders to collect reliable foreground data and adapt to consistent background data present within LCIMs. LCA methodology requires significant Life Cycle Inventory (LCI) data to model real world systems and quantify potential environmental impacts. The lack of guidance in ISO standards on consistently compiling LCI data and defining protocols for modeling lowers the reliability of LCA …

Zechipc: Time Series Interpolation Method Based On Lebesgue Sampling, Luis Miralles-Pechuán, Muhammad Atif Qureshi, Matthieu Bellucci, Brian Mac Namee

Books/Book Chapters

In this paper, we present an interpolation method based on Lebesgue sampling that could help to develop systems based time series more efficiently. Our methods can transmit times series, frequently used in health monitoring, with the same level of accuracy but using much fewer data. Our method is based in Lebesgue sampling, which collects information depending on the values of the signal (e.g. the signal output is sampled when it crosses specific limits). Lebesgue sampling contains additional information about the shape of the signal in-between two sampled points. Using this information would allow generating an interpolated signal closer to the …

Valve Health Identification Using Sensors And Machine Learning Methods, Muhammad Atif Qureshi, Luis Miralles-Pechuán, Wood, Galway Technology Park, Parkmore, Galway, Ireland, Brian Mac Namee

Books/Book Chapters

Predictive maintenance models attempt to identify developing issues with industrial equipment before they become critical. In this paper, we describe both supervised and unsupervised approaches to predictive maintenance for subsea valves in the oil and gas industry. The supervised approach is appropriate for valves for which a long history of operation along with manual assessments of the state of the valves exists, while the unsupervised approach is suitable to address the cold start problem when new valves, for which we do not have an operational history, come online.

For the supervised prediction problem, we attempt to distinguish between healthy and …

Automatic Chest X-Rays Analysis Using Statistical Machine Learning Strategies, Hermann Yepdjio Nkouanga

All Master's Theses

Tuberculosis (TB) is a disease responsible for the deaths of more than one million people worldwide every year. Even though it is preventable and curable, it remains a major threat to humanity that needs to be taken care of. It is often diagnosed in developed countries using approaches such as sputum smear microscopy and culture methods. However, since these approaches are rather expensive, they are not commonly used in poor regions of the globe such as India, Africa, and Bangladesh. Instead, the well known and affordable chest x-ray (CXR) interpretation by radiologists is the technique employed in those places. Nevertheless, …