Engineering Commons^™

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 …

Cerebral Vasospasm Detection And Treatment Using Transcranial Doppler Signal Analysis And Adaptive Medication Pump Mechanism, Khaled Elzaafarany

All ETDs from UAB

In the United States, many patients pass away due to delayed detection of Cerebral Vasospasm (CV). Transcranial Doppler (TCD) is an efficient noninvasive, reliable device that can monitor the blood flow in various parts of the human brain like middle cerebral artery and basilar artery without any surgical intervention. It can help neurologists to diagnose many brain problems like edema, trauma, hemorrhage, and aneurysm. Unfortunately, continuous or even daily TCD monitoring is challenging due to the operator expertise and certification required in the form of a trained technologist, and certified physician to perform these studies. This barrier exists due to …

Genealogy Extraction And Tree Generation From Free Form Text, Timothy Sui-Tim Chu

Master's Theses

Genealogical records play a crucial role in helping people to discover their lineage and to understand where they come from. They provide a way for people to celebrate their heritage and to possibly reconnect with family they had never considered. However, genealogical records are hard to come by for ordinary people since their information is not always well established in known databases. There often is free form text that describes a person’s life, but this must be manually read in order to extract the relevant genealogical information. In addition, multiple texts may have to be read in order to create …

Demand Side Management In Smart Grid Using Big Data Analytics, Sidhant Chatterjee

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Smart Grids are the next generation electrical grid system that utilizes smart meter-ing devices and sensors to manage the grid operations. Grid management includes the prediction of load and and classification of the load patterns and consumer usage behav-iors. These predictions can be performed using machine learning methods which are often supervised. Supervised machine learning signifies that the algorithm trains the model to efficiently predict decisions based on the previously available data.

Smart grids are employed with numerous smart meters that send user statistics to a central server. The data can be accumulated and processed using data mining and machine …

Eigenblades: Application Of Computer Vision And Machine Learning For Mode Shape Identification, Alex W. La

Theses and Dissertations

On August 27, 2016, Southwest Airlines flight 3472 from New Orleans to Orlando had to perform an emergency landing when a fan blade separated from the engine hub and destroyed the cowling and punctured the fuselage. Initial findings from the metallurgical examination conducted in the National Transpiration Safety Board Materials Laboratory found that the fracture surface of the missing blade showed curving crack arrest lines consistent with fatigue crack growth. Fatigue is often cause by resonate vibrations. Modal analysis is a method that can model the natural frequencies and bending modes of turbomachinery blades. When performing modal analysis with finite …

Graph-Based Latent Embedding, Annotation And Representation Learning In Neural Networks For Semi-Supervised And Unsupervised Settings, Ismail Ozsel Kilinc

USF Tampa Graduate Theses and Dissertations

Machine learning has been immensely successful in supervised learning with outstanding examples in major industrial applications such as voice and image recognition. Following these developments, the most recent research has now begun to focus primarily on algorithms which can exploit very large sets of unlabeled examples to reduce the amount of manually labeled data required for existing models to perform well. In this dissertation, we propose graph-based latent embedding/annotation/representation learning techniques in neural networks tailored for semi-supervised and unsupervised learning problems. Specifically, we propose a novel regularization technique called Graph-based Activity Regularization (GAR) and a novel output layer modification called …

Machine Learning Based Method Of Moments (Ml-Mom), He Ming Yao, Li (Lijun) Jun Jiang, Yu Wei Qin

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a novel method by rethinking the method of moments (MoM) solving process into a machine learning training process. Based on the artificial neural network (ANN), the conventional MoM matrix is treated as the training data set, based on which machine learning training process becomes conventional linear algebra MoM solving process. The trained result is the solution of MoM. The multiple linear regression (MLR) is utilized to train the model. Amazon Web Service (AWS) is used as the computations platform to utilize the existing hardware and software resources for machine learning. To verify the feasibility of the proposed …

The Building Data Genome Project: An Open, Public Data Set From Non-Residential Building Electrical Meters, Clayton Miller, Forrest Meggers

Research Collection College of Integrative Studies

As of 2015, there are over 60 million smart meters installed in the United States; these meters are at the forefront of big data analytics in the building industry. However, only a few public data sources of hourly non-residential meter data exist for the purpose of testing algorithms. This paper describes the collection, cleaning, and compilation of several such data sets found publicly on-line, in addition to several collected by the authors. There are 507 whole building electrical meters in this collection, and a majority are from buildings on university campuses. This group serves as a primary repository of open, …

Information Theoretic Study Of Gaussian Graphical Models And Their Applications, Ali Moharrer

LSU Doctoral Dissertations

In many problems we are dealing with characterizing a behavior of a complex stochastic system or its response to a set of particular inputs. Such problems span over several topics such as machine learning, complex networks, e.g., social or communication networks; biology, etc. Probabilistic graphical models (PGMs) are powerful tools that offer a compact modeling of complex systems. They are designed to capture the random behavior, i.e., the joint distribution of the system to the best possible accuracy. Our goal is to study certain algebraic and topological properties of a special class of graphical models, known as Gaussian graphs. First, …

Predictive Power And Validity Of Connectome Predictive Modeling: A Replication And Extension, Michael Wang, Joaquin Goni, Enrico Amico

The Summer Undergraduate Research Fellowship (SURF) Symposium

Neuroimaging, particularly functional magnetic resonance imaging (fMRI), is a rapidly growing research area and has applications ranging from disease classification to understanding neural development. With new advancements in imaging technology, researchers must employ new techniques to accommodate the influx of high resolution data sets. Here, we replicate a new technique: connectome-based predictive modeling (CPM), which constructs a linear predictive model of brain connectivity and behavior. CPM’s advantages over classic machine learning techniques include its relative ease of implementation and transparency compared to “black box” opaqueness and complexity. Is this method efficient, powerful, and reliable in the prediction of behavioral measures …

Operating System Identification By Ipv6 Communication Using Machine Learning Ensembles, Adrian Ordorica

Graduate Theses and Dissertations

Operating system (OS) identification tools, sometimes called fingerprinting tools, are essential for the reconnaissance phase of penetration testing. While OS identification is traditionally performed by passive or active tools that use fingerprint databases, very little work has focused on using machine learning techniques. Moreover, significantly more work has focused on IPv4 than IPv6. We introduce a collaborative neural network ensemble that uses a unique voting system and a random forest ensemble to deliver accurate predictions. This approach uses IPv6 features as well as packet metadata features for OS identification. Our experiment shows that our approach is valid and we achieve …

Computer-Aided Detection Of Pathologically Enlarged Lymph Nodes On Non-Contrast Ct In Cervical Cancer Patients For Low-Resource Settings, Brian M. Anderson, Laurence E. Court, Ann Klopp, Stephen F. Kry, Jennifer Johnson, Erik Cressman, Arvind Rao, Jinzhong Yang

Dissertations and Theses (Open Access)

The mortality rate of cervical cancer is approximately 266,000 people each year, and 70% of the burden occurs in Low- and Middle- Income Countries (LMICs). Radiation therapy is the primary modality for treatment of locally advanced cervical cancer cases. In the absence of high quality diagnostic imaging needed to identify nodal metastasis, many LMIC sites treat standard pelvic fields, failing to include node metastasis outside of the field and/or to boost lymph nodes in the abdomen and pelvis. The first goal of this project was to create a program which automatically identifies positive cervical cancer lymph nodes on non-contrast daily …

Semantic Visualization For Short Texts With Word Embeddings, Van Minh Tuan Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Semantic visualization integrates topic modeling and visualization, such that every document is associated with a topic distribution as well as visualization coordinates on a low-dimensional Euclidean space. We address the problem of semantic visualization for short texts. Such documents are increasingly common, including tweets, search snippets, news headlines, or status updates. Due to their short lengths, it is difficult to model semantics as the word co-occurrences in such a corpus are very sparse. Our approach is to incorporate auxiliary information, such as word embeddings from a larger corpus, to supplement the lack of co-occurrences. This requires the development of a …

Prediction Of Graduation Delay Based On Student Characterisitics And Performance, Tushar Ojha

Electrical and Computer Engineering ETDs

A college student's success depends on many factors including pre-university characteristics and university student support services. Student graduation rates are often used as an objective metric to measure institutional effectiveness. This work studies the impact of such factors on graduation rates, with a particular focus on delay in graduation. In this work, we used feature selection methods to identify a subset of the pre-institutional features with the highest discriminative power. In particular, Forward Selection with Linear Regression, Backward Elimination with Linear Regression, and Lasso Regression were applied. The feature sets were selected in a multivariate fashion. High school GPA, ACT …

The Scalable And Accountable Binary Code Search And Its Applications, Qian Feng

Dissertations - ALL

The past decade has been witnessing an explosion of various applications and devices.

This big-data era challenges the existing security technologies: new analysis techniques

should be scalable to handle “big data” scale codebase; They should be become smart

and proactive by using the data to understand what the vulnerable points are and where

they locate; effective protection will be provided for dissemination and analysis of the data

involving sensitive information on an unprecedented scale.

In this dissertation, I argue that the code search techniques can boost existing security

analysis techniques (vulnerability identification and memory analysis) in terms of scalability and …

Predictive Analytics In Cardiac Healthcare And 5g Cellular Networks, Dilranjan S. Wickramasuriya

USF Tampa Graduate Theses and Dissertations

This thesis proposes the use of Machine Learning (ML) to two very distinct, yet compelling, applications – predicting cardiac arrhythmia episodes and predicting base station association in 5G networks comprising of virtual cells. In the first scenario, Support Vector Machines (SVMs) are used to classify features extracted from electrocardiogram (EKG) signals. The second problem requires a different formulation departing from traditional ML classification where the objective is to partition feature space into constituent class regions. Instead, the intention here is to identify temporal patterns in unequal-length sequences. Using Recurrent Neural Networks (RNNs), it is demonstrated that accurate predictions can be …

Machine Learning Offers Predictive Insight Into The Silver Nanomaterial Protein Corona, Matthew Findlay

Bioengineering Senior Theses

The use of engineered nanomaterials (ENMs) in consumer and commercial products is increasing rapidly. The small size and high surface reactivity of ENMs gives them a range of attractive properties, and allows them to be incorporated into various materials. These properties make ENMs very appealing to modern industry, but also make ENMs toxic, causing serious health and environmental concerns. This toxicity is largely driven by the formation of a protein corona on the surface of ENMs. This protein corona is caused by proteins encountered in biological systems that bind to the surface of (ENMs). Despite the importance of the protein …

Real-Time Classification Of Biomedical Signals, Parkinson’S Analytical Model, Abolfazl Saghafi

USF Tampa Graduate Theses and Dissertations

The reach of technological innovation continues to grow, changing all industries as it evolves. In healthcare, technology is increasingly playing a role in almost all processes, from patient registration to data monitoring, from lab tests to self-care tools. The increase in the amount and diversity of generated clinical data requires development of new technologies and procedures capable of integrating and analyzing the BIG generated information as well as providing support in their interpretation.

To that extent, this dissertation focuses on the analysis and processing of biomedical signals, specifically brain and heart signals, using advanced machine learning techniques. That is, the …

Complementary Companion Behavior In Video Games, Gavin Scott

Master's Theses

Companion characters in are present in many video games across genres, serving the role of the player's partner. Their goal is to support the player's strategy and to immerse the player by providing a believable companion. These companions often only perform rigidly scripted actions and fail to adapt to an individual player's play-style, detracting from their usefulness. Behavior like this can also become frustrating to the player if the companions become more of a hindrance than they are a benefit. Other work, including this project's precursor, focused on building companions that mimic the player. These strategies customize the companion's actions …

Adaptive Region-Based Approaches For Cellular Segmentation Of Bright-Field Microscopy Images, Hady Ahmady Phoulady

USF Tampa Graduate Theses and Dissertations

Microscopy image processing is an emerging and quickly growing field in medical imaging research area. Recent advancements in technology including higher computation power, larger and cheaper storage modules, and more efficient and faster data acquisition devices such as whole-slide imaging scanners contributed to the recent microscopy image processing research advancement. Most of the methods in this research area either focus on automatically process images and make it easier for pathologists to direct their focus on the important regions in the image, or they aim to automate the whole job of experts including processing and classifying images or tissues that leads …

Classification Of Images Based On Pixels That Represent A Small Part Of The Scene. A Case Applied To Microaneurysms In Fundus Retina Images, Pablo F. Ordonez, Pablo F. Ordonez

Master of Science in Computer Science Theses

Convolutional Neural Networks (CNNs), the state of the art in image classification, have proven to be as effective as an ophthalmologist, when detecting Referable Diabetic Retinopathy (RDR). Having a size of less than 1\% of the total image, microaneurysms are early lesions in DR that are difficult to classify. The purpose of this thesis is to improve the accuracy of detection of microaneurysms using a model that includes two CNNs with different input image sizes, 60x60 and 420x420 pixels. These models were trained using the Kaggle and Messidor datasets and tested independently against the Kaggle dataset, showing a sensitivity of …

Feature Selection And Improving Classification Performance For Malware Detection, Carlos A. Cepeda Mora

Master of Science in Computer Science Theses

The ubiquitous advance of technology has been conducive to the proliferation of cyber threats, resulting in attacks that have grown exponentially. Consequently, researchers have developed models based on machine learning algorithms for detecting malware. However, these methods require significant amount of extracted features for correct malware classification, making that feature extraction, training, and testing take significant time; even more, it has been unexplored which are the most important features for accomplish the correct classification.

In this Thesis, it is created and analyzed a dataset of malware and clean files (goodware) from the static and dynamic features provided by the online …

Machine Learning Based Mom (Ml-Mom) For Parasitic Capacitance Extractions, He Ming Yao, Yu Wei Qin, Li (Lijun) Jun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

This paper is a rethinking of the conventional method of moments (MoM) using the modern machine learning (ML) technology. By repositioning the MoM matrix and unknowns in an artificial neural network (ANN), the conventional linear algebra MoM solving is changed into a machine learning training process. The trained result is the solution. As an application, the parasitic capacitance extraction broadly needed by VLSI modeling is solved through the proposed new machine learning based method of moments (ML-MoM). The multiple linear regression (MLR) is employed to train the model. The computations are done on Amazon Web Service (AWS). Benchmarks demonstrated the …

Estimating Short-Term Human Intent For Physical Human-Robot Co-Manipulation, Eric Christopher Townsend

Theses and Dissertations

Robots are increasingly becoming safer and more capable. In the past, the main applications for robots have been in manufacturing, where they perform repetitive, highly accurate tasks with physical barriers that separate them from people. They have also been used in space exploration where people are not around. Due to improvements in sensors, algorithms, and design, robots are beginning to be used in other applications like materials handling, healthcare, and agriculture and will one day be ubiquitous. For this to be possible, they will need to be able to function safely in unmodelled and dynamic environments. This is especially true …

A Review Of Approaches To Solving The Problem Of Bim Search: Towards Intelligence-Assisted Design, Hamed Khademi, Avril Behan

Conference papers

Due to the growing adoption of BIM and the rising popularity of cloud computing, BIM models are increasingly stored in central cloud repositories or Common Data Environments. Effective management and exploitation of these models creates the requirement for BIM retrieval systems. Thus far, the BIM industry has utilized general-purpose, text-based search techniques that operate on BIM metadata. This paper highlights the need for a domain-specific BIM search engine and reviews various approaches to address the problem of BIM search. Three main approaches were identified as context-, geometry-, and content-based BIM retrieval. For a comprehensive BIM retrieval system, all three approaches …

Internal Medicine, Keroles Hakem, Robert Trachy, Khanh Tran

Capstone Design Expo Posters

Our objective was to develop a model to predict the length of stay of patients using data from MCV. We conducted our analysis using a dataset of over 130,000 patients described by 66 features. The features contained clinical characteristics (e.g. diagnosis), facility characteristics (e.g. bed type), and socioeconomic characteristics (e.g. insurance type). Our study was focused on patients that stayed in the hospital. To cope with data imperfections, such as missing data, we applied data cleaning methods. Using learned domain knowledge, we identified 9 features to build our predictive models: admit source, primary insurance, discharge disposition, admit unit, iso result, …

Optimized Multilayer Perceptron With Dynamic Learning Rate To Classify Breast Microwave Tomography Image, Chulwoo Pack

Electronic Theses and Dissertations

Most recently developed Computer Aided Diagnosis (CAD) systems and their related research is based on medical images that are usually obtained through conventional imaging techniques such as Magnetic Resonance Imaging (MRI), x-ray mammography, and ultrasound. With the development of a new imaging technology called Microwave Tomography Imaging (MTI), it has become inevitable to develop a CAD system that can show promising performance using new format of data. The platform can have a flexibility on its input by adopting Artificial Neural Network (ANN) as a classifier. Among the various phases of CAD system, we have focused on optimizing the classification phase …

A Structural And Functional Analysis Of Human Brain Mri With Attention Deficit Hyperactivity Disorder, Arjun A. Watane

Honors Undergraduate Theses

Attention Deficit Hyperactivity Disorder (ADHD) affects 5-10% of children worldwide. Its effects are mainly behavioral, manifesting in symptoms such as inattention, hyperactivity, and impulsivity. If not monitored and treated, ADHD may adversely affect a child's health, education, and social life. Furthermore, the neurological disorder is currently diagnosed through interviews and opinions of teachers, parents, and physicians. Because this is a subjective method of identifying ADHD, it is easily prone to error and misdiagnosis. Therefore, there is a clear need to develop an objective diagnostic method for ADHD.

The focus of this study is to explore the use of machine language …

Using Natural Language Processing And Machine Learning Techniques To Characterize Configuration Bug Reports: A Study, Wei Wen

Theses and Dissertations--Computer Science

In this study, a tool is developed that achieves two purposes: (1) given bug reports, it identifies configuration bug reports from non-configuration bug reports; (2) once a bug report is identified to be a configuration bug report, the tool finds out what specific configuration option the bug report is associated.

This study starts with a review of related works that used machine learning tools to solve software bug and bug report related issues. It then discusses the natural language processing and machine learning techniques. Afterwards, the development process of the proposed tool is described in detail, including the motivation, the …

Quantitative Estimation Of Causality And Predictive Modeling For Precipitation Observation Sites And River Gage Sensors, Tri Vu Nguyen

LSU Master's Theses

This project seeks to investigate two questions: correlations from precipitation measurement sensors to river gage sensors, and predictive modeling of peak river gage heights during precipitation events. First, if correlations can be quantified, then a predictive model can be explored to predict peak water levels at river gage sensors, in response to precipitation inputs. Answering both research questions can provide early flood detection benefits and provide quantitative time assessments for flood risks. An extensive data-driven study was conducted across a geographical area of the U.S, spanning the time period 2008-2016 to identify river gage sensors that are closely correlated to …