Engineering Commons^™

Exploring Age-Related Metamemory Differences Using Modified Brier Scores And Hierarchical Clustering, Chelsea Parlett-Pelleriti, Grace C. Lin, Masha R. Jones, Erik Linstead, Susanne M. Jaeggi

Engineering Faculty Articles and Research

Older adults (OAs) typically experience memory failures as they age. However, with some exceptions, studies of OAs’ ability to assess their own memory functions—Metamemory (MM)— find little evidence that this function is susceptible to age-related decline. Our study examines OAs’ and young adults’ (YAs) MM performance and strategy use. Groups of YAs (N = 138) and OAs (N = 79) performed a MM task that required participants to place bets on how likely they were to remember words in a list. Our analytical approach includes hierarchical clustering, and we introduce a new measure of MM—the modified Brier—in order to adjust …

Deformation Correlations And Machine Learning: Microstructural Inference And Crystal Plasticity Predictions, Michail Tzimas

Graduate Theses, Dissertations, and Problem Reports (ETD)

The present thesis makes a connection between spatially resolved strain correlations and material processing history. Such correlations can be used to infer and classify prior deformation history of a sample at various strain levels with the use of Machine Learning approaches. A simple and concrete example of uniaxially compressed crystalline thin films of various sizes, generated by two-dimensional discrete dislocation plasticity simulations is examined. At the nanoscale, thin films exhibit yield-strength size effects with noisy mechanical responses which create an interesting challenge for the application of Machine Learning techniques. Moreover, this thesis demonstrates the prediction of the average mechanical responses …

Machine Learning Methods For Functional Near Infrared Spectroscopy, Danushka Sandaruwan Bandara

Dissertations - ALL

Identification of user state is of interest in a wide range of disciplines that fall under the umbrella of human machine interaction. Functional Near Infra-Red Spectroscopy (fNIRS) device is a relatively new device that enables inference of brain activity through non-invasively pulsing infra-red light into the brain. The fNIRS device is particularly useful as it has a better spatial resolution than the Electroencephalograph (EEG) device that is most commonly used in Human Computer Interaction studies under ecologically valid settings. But this key advantage of fNIRS device is underutilized in current literature in the fNIRS domain.

We propose machine learning methods …

Localization Using Convolutional Neural Networks, Shannon D. Fong

Computer Engineering

With the increased accessibility to powerful GPUs, ability to develop machine learning algorithms has increased significantly. Coupled with open source deep learning frameworks, average users are now able to experiment with convolutional neural networks (CNNs) to solve novel problems. This project sought to train a CNN capable of classifying between various locations within a building. A single continuous video was taken while standing at each desired location so that every class in the neural network was represented by a single video. Each location was given a number to be used for classification and the video was subsequently titled locX. These …

Evaluating Load Adjusted Learning Strategies For Client Service Levels Prediction From Cloud-Hosted Video Servers, Ruairí De Fréin, Obinna Izima, Mark Davis

Conference papers

Network managers that succeed in improving the accuracy of client video service level predictions, where the video is deployed in a cloud infrastructure, will have the ability to deliver responsive, SLA-compliant service to their customers. Meeting up-time guarantees, achieving rapid first-call resolution, and minimizing time-to-recovery af- ter video service outages will maintain customer loyalty.

To date, regression-based models have been applied to generate these predictions for client machines using the kernel metrics of a server clus- ter. The effect of time-varying loads on cloud-hosted video servers, which arise due to dynamic user requests have not been leveraged to improve prediction …

Crude Oil Prices Forecasting: Time Series Vs. Svr Models, Xin James He

Journal of International Technology and Information Management

This research explores the weekly crude oil price data from U.S. Energy Information Administration over the time period 2009 - 2017 to test the forecasting accuracy by comparing time series models such as simple exponential smoothing (SES), moving average (MA), and autoregressive integrated moving average (ARIMA) against machine learning support vector regression (SVR) models. The main purpose of this research is to determine which model provides the best forecasting results for crude oil prices in light of the importance of crude oil price forecasting and its implications to the economy. While SVR is often considered the best forecasting model in …

Automatic Identification Of Animals In The Wild: A Comparative Study Between C-Capsule Networks And Deep Convolutional Neural Networks., Joel Kamdem Teto, Ying Xie

Master of Science in Computer Science Theses

The evolution of machine learning and computer vision in technology has driven a lot of

improvements and innovation into several domains. We see it being applied for credit decisions, insurance quotes, malware detection, fraud detection, email composition, and any other area having enough information to allow the machine to learn patterns. Over the years the number of sensors, cameras, and cognitive pieces of equipment placed in the wilderness has been growing exponentially. However, the resources (human) to leverage these data into something meaningful are not improving at the same rate. For instance, a team of scientist volunteers took 8.4 years, …

On The Feasibility Of Profiling, Forecasting And Authenticating Internet Usage Based On Privacy Preserving Netflow Logs, Soheil Sarmadi

USF Tampa Graduate Theses and Dissertations

Understanding Internet user behavior and Internet usage patterns is fundamental in developing future access networks and services that meet technical as well as Internet user needs. User behavior is routinely studied and measured, but with different methods depending on the research discipline of the investigator, and these disciplines rarely cross. We tackle this challenge by developing frameworks that the Internet usage statistics used as the main features in understanding Internet user behaviors, with the purpose of finding a complete picture of the user behavior and working towards a unified analysis methodology. In this dissertation we collected Internet usage statistics via …

A Comprehensive Framework To Replicate Process-Level Concurrency Faults, Supat Rattanasuksun

School of Computing: Dissertations, Theses, and Student Research

Concurrency faults are one of the most damaging types of faults that can affect the dependability of today’s computer systems. Currently, concurrency faults such as process-level races, order violations, and atomicity violations represent the largest class of faults that has been reported to various Linux bug repositories. Clearly, existing approaches for testing such faults during software development processes are not adequate as these faults escape in-house testing efforts and are discovered during deployment and must be debugged.

The main reason concurrency faults are hard to test is because the conditions that allow these to occur can be difficult to replicate, …

Machine Learning Based Computational Electromagnetic Analysis For Electromagnetic Compatibility, L. (Lijun) J. Jiang, H. M. Yao, H. H. Zhang, Y. W. Qin

Electrical and Computer Engineering Faculty Research & Creative Works

While machine learning is becoming a demanding request in every corner of modern technology development, we are trying to see if we could make computational electromagnetic algorithms compatible to machine learning methods. In this paper, we introduce two efforts in line with this direction: solving method of moments (MoM) can be seen as a training training process. Consequently, the artificial neural network (ANN) could be used to solve MoM naturally through training. Amazon Web Service (AWS) can be used as the computations platform to utilize the existing hardware and software resources for machine learning. Another effort regarding to the nonlinear …

Elm-Som: A Continuous Self-Organizing Map For Visualization, Renjie Hu, Venous Roshdibenam, Hans J. Johnson, Emil Eirola, Anton Akusok, Yoan Miche, Kaj Mikael Björk, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

This Paper Presents a Novel Dimensionality Reduction Technique: Elm-Som. This Technique Preserves the Intrinsic Quality of Self-Organizing Maps (Som): It is Nonlinear and Suitable for Big Data. It Also Brings Continuity to the Projection using Two Extreme Learning Machine (Elm) Models, the First One to Perform the Dimensionality Reduction and the Second One to Perform the Reconstruction. Elm-Som is Tested Successfully on Six Diverse Datasets. Regarding Reconstruction Error, Elm-Som is Comparable to Som While Bringing Continuity.

Assessing The Relationship Between Groundwater Nitrate Concentrations And Environmental Variables Through Repeat Sampling And Statistical Machine Learning: Dutch Flats, Nebraska, Martin Wells

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Nitrate-contaminated aquifers are common in landscapes dominated by agricultural land use. Health concerns related to consuming nitrate-contaminated groundwater are well documented and continued research aimed at decreasing concentrations is critical. A 1990s U.S. Geological Survey (USGS) study focused on groundwater characteristics in the Dutch Flats area of western Nebraska. Agricultural-related practices were determined to largely influence groundwater recharge and nitrate concentrations ([NO₃^-]). Since the conclusion of the USGS study, a transition to more efficient irrigation technology has been observed in this region. The emphasis of this 2016 study was to resample several well nests examined in 1998 …

Predict The Failure Of Hydraulic Pumps By Different Machine Learning Algorithms, Yifei Zhou, Monika Ivantysynova, Nathan Keller

The Summer Undergraduate Research Fellowship (SURF) Symposium

Pump failure is a general concerned problem in the hydraulic field. Once happening, it will cause a huge property loss and even the life loss. The common methods to prevent the occurrence of pump failure is by preventative maintenance and breakdown maintenance, however, both of them have significant drawbacks. This research focuses on the axial piston pump and provides a new solution by the prognostic of pump failure using the classification of machine learning. Different kinds of sensors (temperature, acceleration and etc.) were installed into a good condition pump and three different kinds of damaged pumps to measure 10 of …

Deep Neural Network Architectures For Modulation Classification Using Principal Component Analysis, Sharan Ramjee, Shengtai Ju, Diyu Yang, Aly El Gamal

The Summer Undergraduate Research Fellowship (SURF) Symposium

In this work, we investigate the application of Principal Component Analysis to the task of wireless signal modulation recognition using deep neural network architectures. Sampling signals at the Nyquist rate, which is often very high, requires a large amount of energy and space to collect and store the samples. Moreover, the time taken to train neural networks for the task of modulation classification is large due to the large number of samples. These problems can be drastically reduced using Principal Component Analysis, which is a technique that allows us to reduce the dimensionality or number of features of the samples …

Tactile Sensing And Position Estimation Methods For Increased Proprioception Of Soft-Robotic Platforms, Nathan Mcclain Day

Theses and Dissertations

Soft robots have the potential to transform the way robots interact with their environment. This is due to their low inertia and inherent ability to more safely interact with the world without damaging themselves or the people around them. However, existing sensing for soft robots has at least partially limited their ability to control interactions with their environment. Tactile sensors could enable soft robots to sense interaction, but most tactile sensors are made from rigid substrates and are not well suited to applications for soft robots that can deform. In addition, the benefit of being able to cheaply manufacture soft …

Hierarchical Bayesian Data Fusion Using Autoencoders, Yevgeniy Vladimirovich Reznichenko

Master's Theses (2009 -)

In this thesis, a novel method for tracker fusion is proposed and evaluated for vision-based tracking. This work combines three distinct popular techniques into a recursive Bayesian estimation algorithm. First, semi supervised learning approaches are used to partition data and to train a deep neural network that is capable of capturing normal visual tracking operation and is able to detect anomalous data. We compare various methods by examining their respective receiver operating conditions (ROC) curves, which represent the trade off between specificity and sensitivity for various detection threshold levels. Next, we incorporate the trained neural networks into an existing data …

Computational Analysis Of Developmental Disorders In Children, Siri Chandana Sambatur

Theses - ALL

Early developmental disorders are common in children between the ages of 3 through 17. These developmental disorders begin at early ages and affect the day-to-day activities of children. These disorders also impact the growth and lifestyle of children. Most of the time these developmental disorders co-exist in children. The main focus of our research lies in Autism Spectrum Disorder, Attention-Deficit/Hyperactivity Disorder, Deletion syndrome (22q) and their co-occurrences.

Most child psychologists and pediatricians diagnose these disorders in children through parent-based surveys. Our research uses three different parent-based reports: (1) Autism Diagnostic Interview (ADI), (2) Behavioral Assessment Schedule for Children (BASC), and …

Machine Learning Models For Context-Aware Recommender Systems, Yogesh Jhamb

Engineering Ph.D. Theses

The mass adoption of the internet has resulted in the exponential growth of products and services on the world wide web. An individual consumer, faced with this data deluge, is expected to make reasonable choices saving time and money. Organizations are facing increased competition, and they are looking for innovative ways to increase revenue and customer loyalty. A business wants to target the right product or service to an individual consumer, and this drives personalized recommendation. Recommender systems, designed to provide personalized recommendations, initially focused only on the user-item interaction. However, these systems evolved to provide a context-aware recommendations. Context-aware …

More Accurate Process Understanding From Process Characterization Studies Using Monte Carlo Simulation, Regularized Regression, And Classification Models, Cary Opel, Cerintha J. Hui, Patrick Y. Yang, Daniel J. Tien, Gayle E. Derfus, Rajesh Krishnan

Cell Culture Engineering XVI

Establishment of an appropriate control strategy with defined operating ranges (OR) predicted to meet a target product profile is a critical component of commercializing new biologics under the Quality by Design (QbD) approach. Process characterization (PC) studies are performed to expand process understanding by achieving two main goals: 1) determining which process parameters have significant effects on quality attributes and 2) establishing models describing the relationships between these critical process parameters (CPP) and critical quality attributes (CQA). Risk assessment and design of experiments (DOE) techniques are effectively deployed in the industry to identify parameters to study and build process understanding. …

Training Machine Learning Agents In A 3d Game Engine, Diego Calderon

Computer Science and Computer Engineering Undergraduate Honors Theses

Artificial intelligence (AI) and video games benefit from each other. Games provide a challenging domain for testing learning algorithms, and AI provides a framework to designing and implementing intelligent behavior, which reinforces meaningful play. Medium and small studios, and independent game developers, have limited resources to design, implement, and maintain agents with reactive behavior. In this research, we trained agents using machine learning (ML), aiming to find an alternative to expensive traditional algorithms for intelligent behavior used in video games. We use Unity as a game engine to implement the environments and TensorFlow for the neural network training.

Baseline Data From Servo Motors In A Robotic Arm For Autonomous Machine Fault Diagnosis, Jacob Brown

Mechanical Engineering Undergraduate Honors Theses

Fault diagnosis can prolong the life of machines if potential sources of failure are discovered and corrected before they occur. Supervised machine learning, or the use of training data to enable machines to discover these faults on their own, makes failure prevention much easier. The focus of this thesis is to investigate the feasibility of creating datasets of various faults at both the component and system level for a servomotor and a compatible robotic arm, such that this data can be used in machine learning algorithms for fault diagnosis. The faults induced at the component level in different servomotors include: …

Machine Learning For Omics Data Analysis., Ameni Trabelsi

Electronic Theses and Dissertations

In proteomics and metabolomics, to quantify the changes of abundance levels of biomolecules in a biological system, multiple sample analysis steps are involved. The steps include mass spectrum deconvolution and peak list alignment. Each analysis step introduces a certain degree of technical variation in the abundance levels (i.e. peak areas) of those molecules. Some analysis steps introduce technical variations that affect the peak areas of all molecules equally while others affect the peak areas of a subset of molecules with varying degrees. To correct these technical variations, some existing normalization methods simply scale the peak areas of all molecules detected …

Evolution Of Mg Az31 Twin Activation With Strain: A Machine Learning Study, Andrew D. Orme

Undergraduate Honors Theses

Machine learning is being adopted in various areas of materials science to both create predictive models and to uncover correlations which reveal underlying physics. However, these two aims are often at odds with each other since the resultant predictive models generally become so complex that they can essentially be described as a black box, making them difficult to understand. In this study, complex relationships between microstructure and twin formation in AZ31 magnesium are investigated as a function of increasing strain. Supervised machine learning is employed, in the form of J-48 decision trees. In one approach, strain is incorporated as an …

An Unsupervised Cluster: Learning Water Customer Behavior Using Variation Of Information On A Reconstructed Phase Space, Michele Rae Bizub Malinowski

Dissertations (1934 -)

The unsupervised clustering algorithm described in this dissertation addresses the need to divide a population of water utility customers into groups based on their similarities and differences, using only the measured flow data collected by water meters. After clustering, the groups represent customers with similar consumption behavior patterns and provide insight into ‘normal’ and ‘unusual’ customer behavior patterns. This research focuses upon individually metered water utility customers and includes both residential and commercial customer accounts serviced by utilities within North America. The contributions of this dissertation not only represent a novel academic work, but also solve a practical problem for …

Enhanced Grain Partitioning Of X-Ray Microtomography Segmented Images, Nicholas C. Skrivanos Ii

LSU Master's Theses

In the field of petroleum engineering, rock samples are often taken from wells during the drilling process. Grain partitioning of digital three-dimensional microtomography segmented images obtained from these samples provides valuable in-situ properties and statistics that allow for accurate particle and structure characterization. This information can be used directly in detailed production and reservoir analysis, and can also be used to generate realistic packing models for advanced simulation. Additionally, the partitioned image can be used as a building block for realistic hydraulic fracture modeling. This technology has applications in other fields as well, such as core analysis in soil sciences …

Predicting The Vote Using Legislative Speech, Aditya Budhwar

Master's Theses

As most dedicated observers of voting bodies like the U.S. Supreme Court can attest, it is possible to guess vote outcomes based on statements made during deliberations or questioning by the voting members. In most forms of representative democracy, citizens can actively petition or lobby their representatives, and that often means understanding their intentions to vote for or against an issue of interest. In some U.S. state legislators, professional lobby groups and dedicated press members are highly informed and engaged, but the process is basically closed to ordinary citizens because they do not have enough background and familiarity with the …

Integrated Strategies For Sustainable Wastewater-Based Algal Biofuel Production And Environmental Mitigation In The Us, Javad Roostaei

Wayne State University Dissertations

Integration of algae cultivation with wastewater treatment has received increasing interest as a cost-effective strategy for biofuel production. However, there has been no full assessment of algal biofuel production with wastewater on macro-scale by taking into account wastewater resources, land availability, CO2 emission resources, and geographic variation. This research addressed and evaluated the use of wastewater for algae cultivation, in terms of modeling and laboratory experiments. The first goal of this research was to develop a spatially explicit lifecycle model, by integrating life cycle assessment (LCA), and Geographic Information Systems (GIS) analysis, for the evaluation of the environmental and economic …

Data Fusion Techniques For Biomedical Informatics And Clinical Decision Support, Peng Guo

Doctoral Dissertations

"Data fusion can be used to combine multiple data sources or modalities to facilitate enhanced visualization, analysis, detection, estimation, or classification. Data fusion can be applied at the raw-data, feature-based, and decision-based levels. Data fusion applications of different sorts have been built up in areas such as statistics, computer vision and other machine learning aspects. It has been employed in a variety of realistic scenarios such as medical diagnosis, clinical decision support, and structural health monitoring. This dissertation includes investigation and development of methods to perform data fusion for cervical cancer intraepithelial neoplasia (CIN) and a clinical decision support system. …

Computational Intelligence Methods For Predicting Fetal Outcomes From Heart Rate Patterns, Vinayaka Nagendra Harikishan Gude Divya Sampath

Masters Theses

"In this thesis, methods for evaluating the fetal state are compared to make predictions based on Cardiotocography (CTG) data. The first part of this research is the development of an algorithm to extract features from the CTG data. A feature extraction algorithm is presented that is capable of extracting most of the features in the SISPORTO software package as well as late and variable decelerations. The resulting features are used for classification based on both U.S. National Institutes of Health (NIH) categories and umbilical cord pH data. The first experiment uses the features to classify the results into three different …

Monitoring Of Hybrid Manufacturing Using Acoustic Emission Sensor, Haythem Gaja

Doctoral Dissertations

"The approach of hybrid manufacturing addressed in this research uses two manufacturing processes, one process builds a metal part using laser metal deposition, and the other process finishes the part using a milling machining. The ability to produce complete functioning parts in a short time with minimal cost and energy consumption has made hybrid manufacturing popular in many industries for parts repair and rapid prototyping. Monitoring of hybrid manufacturing processes has become popular because it increases the quality and accuracy of the parts produced and reduces both costs and production time. The goal of this work is to monitor the …