Computational Engineering Commons^™

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

Department of Agricultural and Biological Systems Engineering: Faculty 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 …

Efficient Model-Data Integration For Flexible Modeling, Parameter Analysis & Visualization, And Data Management, Angela Gregory, Chao Chen, Rui Wi, Sarah Miller, Sajjad Ahmad, John W. Anderson, Hays Berrett, Karl Benedict, Dan Cadol, Sergiu M. Dascalu, Donna Delparte, Lynn Fenstermaker, Sarah Godsey, Frederick C. Harris Jr., James P. Mcnamara, Scott W. Tyler, John Savickas, Luke Sheneman, Mark Stone, Matthew A. Turner

Civil and Environmental Engineering and Construction Faculty Research

Due to the complexity and heterogeneity inherent to the hydrologic cycle, the modeling of physical water processes has historically and inevitably been characterized by a broad spectrum of disciplines including data management, visualization, and statistical analyses. This is further complicated by the sub-disciplines within the water science community, where specific aspects of water processes are modeled independently with simplification and model boundary integration receiving little attention. This can hinder current and future research efforts to understand, explore, and advance water science. We developed the Virtual Watershed Platform to improve understanding of hydrologic processes and more generally streamline model-data integration and …

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 …

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 …

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 …

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.

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 …

Coarse-Grained Dynamically Accurate Simulations Of Ionic Liquids At Vacuum-Interface, Tyler D. Stoffel

Theses and Dissertations--Mechanical and Aerospace Engineering

Ionic liquids, possessing improved properties in many areas of technical application, are excellent candidates as components in development of next-generation technology, including ultra-high energy batteries. If they are thus applied, however, extensive interfacial analysis of any selected ionic configuration will likely be required. Molecular dynamics (MD) provides an advantageous route by which this may be accomplished, but can fall short in observing some phenomena only present at larger time/length scales than it can simulate. Often times this is approached by coarse-graining (CG), with which scope of simulation can be significantly increased. However, coarse-grained MD systems are generally known to produce …

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 …

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, …

Optimal Feature Selection For Learning-Based Algorithms For Sentiment Classification, Zhaoxia Wang, Zhiping Lin

Research Collection School Of Computing and Information Systems

Sentiment classification is an important branch of cognitive computation—thus the further studies of properties of sentiment analysis is important. Sentiment classification on text data has been an active topic for the last two decades and learning-based methods are very popular and widely used in various applications. For learning-based methods, a lot of enhanced technical strategies have been used to improve the performance of the methods. Feature selection is one of these strategies and it has been studied by many researchers. However, an existing unsolved difficult problem is the choice of a suitable number of features for obtaining the best sentiment …

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 …

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 …

Nonhydrostatic Modeling Of Flow Interactions With Highly Flexible Vegetation, Navid Tahvildari, Ramin Familkhalili, Gangfeng Ma

Civil & Environmental Engineering Faculty Publications

Improving our understanding of the interactions between gravity waves, currents, and coastal vegetation, which are nonlinear in nature, enables coastal engineers and managers to better estimate hydrodynamic forces on coastal infrastructure and utilize natural elements to mitigate their impacts. Aquatic vegetation is ubiquitous in coastal waters and it is well-known that flow loses energy over vegetation. Computational modeling of wave-vegetation interaction has been the subject of numerous recent studies and many improvements have been achieved in reducing limitations applied on wave and vegetation behavior in these models. Mechanisms for highly flexible vegetation have been incorporated in a Boussinesq-type model and …

Quantitative Performance Assessment Of Lidar-Based Vehicle Contour Estimation Algorithms For Integrated Vehicle Safety Applications, David M. Mothershed

College of Graduate Studies: Theses & Dissertations

Many nations and organizations are committing to achieving the goal of `Vision Zero' and eliminate road traffic related deaths around the world. Industry continues to develop integrated safety systems to make vehicles safer, smarter and more capable in safety critical scenarios. Passive safety systems are now focusing on pre-crash deployment of restraint systems to better protect vehicle passengers. Current commonly used bounding box methods for shape estimation of crash partners lack the fidelity required for edge case collision detection and advanced crash modeling. This research presents a novel algorithm for robust and accurate contour estimation of opposing vehicles. The presented …

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 …

Speckle-Noise Reduction In Knee Articular Cartilage Ultrasound Image Using Anisotropic Diffusion, Ali Muhammad Shoaib

Student Works (2020-2029)

Knee arthritis is the most common type of arthritis which effects the people and may cause severe pain to the patient and can lead to joint effusion. Ultrasound (US) imaging is an appropriate and consistent substitute for other imaging techniques like magnetic resonance imaging or X-rays in the investigation or screening of knee injury. Nevertheless, one of the major problems in US images which make the analysis of these images hard is the presence of speckle noise. For the reduction of speckle noise, the performance of the anisotropic method is found much better over other approaches. In removing the speckle …

Ultra-Wideband Power Amplifier Design For Large Signal Application, Ragavan Krishnamoorthy

Student Works (2020-2029)

Broadband power amplifier design has become one of the most critical enabling block in today’s wireless communication technology. Numerous research efforts have been carried out throughout the years to establish high efficiency over wideband RF transmitter. This research work presents two approaches in achieving ultra-broadband power amplifier for RF transmitter. Firstly, a new technique for the design of ultra-broadband RF power amplifier is introduced, in which a combination of the reactance compensation and third-harmonic tuning are adopted. The design goal is to achieve 40 dBm (10W) output power across a wide frequency bandwidth operation. Theoretical design equations were developed to …

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 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, …

Attitudes Towards Teaching Computational Thinking And Computer Science: Insights From Educator Interviews And Focus Groups, Jorge Valenzuela

Journal of Computer Science Integration

In the last three years, integration of both computational thinking (CT) and computer science (CS) into K-12 instruction has become a focus of many schools throughout the Commonwealth of Virginia and the United States. With this new widespread demand, educational leaders and educators are focusing efforts on understanding the core concepts and practices of CT and CS, looking for logical connections for integrating across curriculum, and seeking strategies for implementing a wide variety of educational technology tools (apps and devices). This phenomenological research study was designed to gather depth information from 14 K-16 educators through both semi-structured interviews and two …