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Articles 181 - 210 of 525
Full-Text Articles in Engineering
Multi-Label Classification On Locally-Linear Data: Application To Chemical Toxicity Prediction, Xiu Huan Yap
Multi-Label Classification On Locally-Linear Data: Application To Chemical Toxicity Prediction, Xiu Huan Yap
Browse all Theses and Dissertations
Computational models may assist in identification and prioritization of large chemical libraries. Recent experimental and data curation efforts, such as from the Tox21 consortium, have contributed towards toxicological datasets of increasing numbers of chemicals and toxicity endpoints, creating a golden opportunity for the exploration of multi-label learning and deep learning approaches in this thesis. Multi-label classification (MLC) methods may improve model predictivity by accounting for label dependence. However, current measures of label dependence, such as correlation coefficient, are inappropriate for datasets with extreme class imbalance, often seen in toxicological datasets. In this thesis, we propose a novel label dependence measure …
Edge Processing Of Image For Uas Sense And Avoidance, Christopher J. Rave
Edge Processing Of Image For Uas Sense And Avoidance, Christopher J. Rave
Browse all Theses and Dissertations
Today there is a large market for Unmanned Aerial Systems. Although most current systems are remotely piloted by operators on the ground, increasingly, many of these systems will use some sort of automatic flight controller to help mitigate new challenges, due to their deployment at growing scale. These challenges include, but are not limited to, shortage of FAA-certified UAS pilots, transmission bandwidth and delay constraints and cyber security threats associated with wireless networking, profitability of operations constrained by energy capacity and efficiency and air dynamics planning, and etc. In order to address these rising challenges, this thesis is a part …
Arise - Augmented Reality In Surgery And Education, Sadan Suneesh Menon
Arise - Augmented Reality In Surgery And Education, Sadan Suneesh Menon
Browse all Theses and Dissertations
Human errors in healthcare can be fatal. Proper physical assessment of patients to avoid such errors is of paramount importance. Incorrect or insufficient assessment of the patient can cause treatment delays that may lead to negative outcomes. In this dissertation we introduce innovative technology to assist surgeons in patient assessment as well as during the training of nurses in order to enhance learning. Technological advancements have made it possible to visualize overlays of computer-generated 3D models on real-world surfaces. This technology is called augmented reality. Using Steady State Topography (SST) brain imaging to examine the brain activity of people who …
Leveraging Sequential Nature Of Conversations For Intent Classification, Shree Gotteti
Leveraging Sequential Nature Of Conversations For Intent Classification, Shree Gotteti
Browse all Theses and Dissertations
Conversations are more than just a sequence of text, it is where two or more participants interact in order to achieve their goals. Conversation Understanding (CU) requires all participants to understand each others intent. In the past decade, CU has been extended from automated human-human text processing to build automated conversational agents for human-machine interactions. Despite their popularity, these automated conversational agents (like Siri, Alexa, etc) can't handle more than one or two utterances, and they don't recognize conversations as intents. The development of approaches that extract intents behind an utterance is essential for the advancements of Question Answering (QA) …
Sample Mislabeling Detection And Correction In Bioinformatics Experimental Data, Soon Jye Kho
Sample Mislabeling Detection And Correction In Bioinformatics Experimental Data, Soon Jye Kho
Browse all Theses and Dissertations
Sample mislabeling or incorrect annotation has been a long-standing problem in biomedical research and contributes to irreproducible results and invalid conclusions. These problems are especially prevalent in multi-omics studies in which a large set of biological samples are characterized by multiple types of omics platforms at different times or different labs. While multi-omics studies have demonstrated tremendous value in understanding disease biology and improving patient outcomes, the complexity of these studies may increase opportunities for human error. Fortunately, the interrelated nature of the data collected in multi-omics studies can be exploited to facilitate the identification and, in some cases, correction …
Augmenting Structure/Function Relationship Analysis With Deep Learning For The Classification Of Psychoactive Drug Activity At Class A G Protein-Coupled Receptors, Hannah Willow Shows
Augmenting Structure/Function Relationship Analysis With Deep Learning For The Classification Of Psychoactive Drug Activity At Class A G Protein-Coupled Receptors, Hannah Willow Shows
Browse all Theses and Dissertations
G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used …
A Rebellion Framework With Learning For Goal-Driven Autonomy, Zahiduddin Mohammad
A Rebellion Framework With Learning For Goal-Driven Autonomy, Zahiduddin Mohammad
Browse all Theses and Dissertations
Modeling an autonomous agent that decides for itself what actions to take to achieve its goals is a central objective of artificial intelligence. There are various approaches used to build autonomous agents including neural networks, state machines, utility functions, learning agents, and cognitive architectures. In this thesis, we focus on cognitive architectures. Our approach uses specific knowledge of the world, the goals they pursue, and the actions being performed. Most agents do what they are told (i.e., achieve the goals given to them by a human), but a genuinely autonomous agent does more. It can formulate its own goal or …
Partial Facial Re-Imaging Using Generative Adversarial Networks, Derek Desentz
Partial Facial Re-Imaging Using Generative Adversarial Networks, Derek Desentz
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Existing facial recognition software relies heavily on using neural networks to extract key facial features to accurately classify known individuals. Some of these key features include the shape, size, and distance between an individual’s eyes, nose, and mouth. When these key features cannot be extracted due to facial coverings, existing applications become inaccurate and unreliable. The accuracy and reliability of these technologies are growing concerns as the facial recognition market continues to grow at an exponential rate. In this thesis, we have developed a web-based application service that is able to take in a partially covered face image and generate …
Structural Analysis And Link Prediction Algorithm Comparison For A Local Scientific Collaboration Network, Denys Guriev
Structural Analysis And Link Prediction Algorithm Comparison For A Local Scientific Collaboration Network, Denys Guriev
Browse all Theses and Dissertations
Scientific collaboration between researchers is very common and much influential and ground-breaking research is performed by teams comprised of scientist from different fields and organizations. In this thesis, we analyze and model a small scientific collaboration network limited to two organizations: Wright State University and the Air Force Research Laboratory. Research paper co-authorship is used for establishing the network structure. We analyze several network properties and compare them to past results from analysis of larger and more diverse collaboration networks. We show that the two-organization network we explored exhibits properties similar to those of larger networks. Guided by advances in …
Bayesian Inspired Multi-Fidelity Optimization With Aerodynamic Design, Christopher Corey Fischer
Bayesian Inspired Multi-Fidelity Optimization With Aerodynamic Design, Christopher Corey Fischer
Browse all Theses and Dissertations
In most engineering design problems, there exist multiple models of varying fidelities for use in predicting a single system response such as Computational Fluid Dynamics (CFD) models constructed using Potential Flow, Euler equations, or full physics Navier Stokes implementation. Engineering design is constantly pushing the forefront of the field through imposing stricter and more complex constraints on system performance, thus elevating the need for use of high-fidelity models in the design process. Increasing fidelity level often correlates to an increase in cost (financial, computational time, and computational resources). Traditional design processes rely upon low-fidelity models for expedience and resource savings. …
Computational Simulation And Analysis Of Neuroplasticity, Madison E. Yancey
Computational Simulation And Analysis Of Neuroplasticity, Madison E. Yancey
Browse all Theses and Dissertations
Homeostatic synaptic plasticity is the process by which neurons alter their activity in response to changes in network activity. Neuroscientists attempting to understand homeostatic synaptic plasticity have developed three different mathematical methods to analyze collections of event recordings from neurons acting as a proxy for neuronal activity. These collections of events are from control data and treatment data, referring to the treatment of neuron cultures with pharmacological agents that augment or inhibit network activity. If the distribution of control events can be functionally mapped to the distribution of treatment events, a better understanding of the biological processes underlying homeostatic synaptic …
Deep Learning For Compressive Sar Imaging With Train-Test Discrepancy, Morgan R. Mccamey
Deep Learning For Compressive Sar Imaging With Train-Test Discrepancy, Morgan R. Mccamey
Browse all Theses and Dissertations
We consider the problem of compressive synthetic aperture radar (SAR) imaging with the goal of reconstructing SAR imagery in the presence of under sampled phase history. While this problem is typically considered in compressive sensing (CS) literature, we consider a variety of deep learning approaches where a deep neural network (DNN) is trained to form SAR imagery from limited data. At the cost of computationally intensive offline training, on-line test-time DNN-SAR has demonstrated orders of magnitude faster reconstruction than standard CS algorithms. A limitation of the DNN approach is that any change to the operating conditions necessitates a costly retraining …
Content Adaption And Design In Mobile Learning Of Wind Instruments, Neha Priyadarshani
Content Adaption And Design In Mobile Learning Of Wind Instruments, Neha Priyadarshani
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People in today's world seek things that are simple to use. Learning is one of the most crucial aspects of the ongoing digital transformation. Everything is now accessible with a single click on mobile devices, making access to instructional materials faster, easier, and more comfortable. It takes time and effort to build abilities and become an expert in the fields of learning, training, and teaching; and music learning demands a great deal of both practice and mentoring. Initially, music teachers and band directors must maintain a steady attention and devote a significant amount of time to manually teaching materials. This …
Mathematical Formula Recognition And Automatic Detection And Translation Of Algorithmic Components Into Stochastic Petri Nets In Scientific Documents, Elisavet Elli Kostalia
Mathematical Formula Recognition And Automatic Detection And Translation Of Algorithmic Components Into Stochastic Petri Nets In Scientific Documents, Elisavet Elli Kostalia
Browse all Theses and Dissertations
A great percentage of documents in scientific and engineering disciplines include mathematical formulas and/or algorithms. Exploring the mathematical formulas in the technical documents, we focused on the mathematical operations associations, their syntactical correctness, and the association of these components into attributed graphs and Stochastic Petri Nets (SPN). We also introduce a formal language to generate mathematical formulas and evaluate their syntactical correctness. The main contribution of this work focuses on the automatic segmentation of mathematical documents for the parsing and analysis of detected algorithmic components. To achieve this, we present a synergy of methods, such as string parsing according to …
Evaluating The Performance Of Using Speaker Diarization For Speech Separation Of In-Person Role-Play Dialogues, Raveendra Medaramitta
Evaluating The Performance Of Using Speaker Diarization For Speech Separation Of In-Person Role-Play Dialogues, Raveendra Medaramitta
Browse all Theses and Dissertations
Development of professional communication skills, such as motivational interviewing, often requires experiential learning through expert instructor-guided role-plays between the trainee and a standard patient/actor. Due to the growing demand for such skills in practices, e.g., for health care providers in the management of mental health challenges, chronic conditions, substance misuse disorders, etc., there is an urgent need to improve the efficacy and scalability of such role-play based experiential learning, which are often bottlenecked by the time-consuming performance assessment process. WSU is developing ReadMI (Real-time Assessment of Dialogue in Motivational Interviewing) to address this challenge, a mobile AI solution aiming to …
Goal Management In Multi-Agent Systems, Venkatsampath Raja Gogineni
Goal Management In Multi-Agent Systems, Venkatsampath Raja Gogineni
Browse all Theses and Dissertations
Autonomous agents in a multi-agent system coordinate to achieve their goals. However, in a partially observable world, current multi-agent systems are often less effective in achieving their goals. In much part, this limitation is due to an agent's lack of reasoning about other agents and their mental states. Another factor is the agent's inability to share required knowledge with other agents and the lack of explanations in justifying the reasons behind the goal. This research addresses these problems by presenting a general approach for agent goal management in unexpected situations. In this approach, an agent applies three main concepts: goal …
Texture-Driven Image Clustering In Laser Powder Bed Fusion, Alexander H. Groeger
Texture-Driven Image Clustering In Laser Powder Bed Fusion, Alexander H. Groeger
Browse all Theses and Dissertations
The additive manufacturing (AM) field is striving to identify anomalies in laser powder bed fusion (LPBF) using multi-sensor in-process monitoring paired with machine learning (ML). In-process monitoring can reveal the presence of anomalies but creating a ML classifier requires labeled data. The present work approaches this problem by printing hundreds of Inconel-718 coupons with different processing parameters to capture a wide range of process monitoring imagery with multiple sensor types. Afterwards, the process monitoring images are encoded into feature vectors and clustered to isolate groups in each sensor modality. Four texture representations were learned by training two convolutional neural network …
Texture-Driven Image Clustering In Laser Powder Bed Fusion, Alexander H. Groeger
Texture-Driven Image Clustering In Laser Powder Bed Fusion, Alexander H. Groeger
Browse all Theses and Dissertations
The additive manufacturing (AM) field is striving to identify anomalies in laser powder bed fusion (LPBF) using multi-sensor in-process monitoring paired with machine learning (ML). In-process monitoring can reveal the presence of anomalies but creating a ML classifier requires labeled data. The present work approaches this problem by printing hundreds of Inconel-718 coupons with different processing parameters to capture a wide range of process monitoring imagery with multiple sensor types. Afterwards, the process monitoring images are encoded into feature vectors and clustered to isolate groups in each sensor modality. Four texture representations were learned by training two convolutional neural network …
Augmenting Structure/Function Relationship Analysis With Deep Learning For The Classification Of Psychoactive Drug Activity At Class A G Protein-Coupled Receptors, Hannah Willow Shows
Augmenting Structure/Function Relationship Analysis With Deep Learning For The Classification Of Psychoactive Drug Activity At Class A G Protein-Coupled Receptors, Hannah Willow Shows
Browse all Theses and Dissertations
G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used …
Evaluating The Performance Of Using Speaker Diarization For Speech Separation Of In-Person Role-Play Dialogues, Raveendra Medaramitta
Evaluating The Performance Of Using Speaker Diarization For Speech Separation Of In-Person Role-Play Dialogues, Raveendra Medaramitta
Browse all Theses and Dissertations
Development of professional communication skills, such as motivational interviewing, often requires experiential learning through expert instructor-guided role-plays between the trainee and a standard patient/actor. Due to the growing demand for such skills in practices, e.g., for health care providers in the management of mental health challenges, chronic conditions, substance misuse disorders, etc., there is an urgent need to improve the efficacy and scalability of such role-play based experiential learning, which are often bottlenecked by the time-consuming performance assessment process. WSU is developing ReadMI (Real-time Assessment of Dialogue in Motivational Interviewing) to address this challenge, a mobile AI solution aiming to …
Mathematical Formula Recognition And Automatic Detection And Translation Of Algorithmic Components Into Stochastic Petri Nets In Scientific Documents, Elisavet Elli Kostalia
Mathematical Formula Recognition And Automatic Detection And Translation Of Algorithmic Components Into Stochastic Petri Nets In Scientific Documents, Elisavet Elli Kostalia
Browse all Theses and Dissertations
A great percentage of documents in scientific and engineering disciplines include mathematical formulas and/or algorithms. Exploring the mathematical formulas in the technical documents, we focused on the mathematical operations associations, their syntactical correctness, and the association of these components into attributed graphs and Stochastic Petri Nets (SPN). We also introduce a formal language to generate mathematical formulas and evaluate their syntactical correctness. The main contribution of this work focuses on the automatic segmentation of mathematical documents for the parsing and analysis of detected algorithmic components. To achieve this, we present a synergy of methods, such as string parsing according to …
Goal Management In Multi-Agent Systems, Venkatsampath Raja Gogineni
Goal Management In Multi-Agent Systems, Venkatsampath Raja Gogineni
Browse all Theses and Dissertations
Autonomous agents in a multi-agent system coordinate to achieve their goals. However, in a partially observable world, current multi-agent systems are often less effective in achieving their goals. In much part, this limitation is due to an agent's lack of reasoning about other agents and their mental states. Another factor is the agent's inability to share required knowledge with other agents and the lack of explanations in justifying the reasons behind the goal. This research addresses these problems by presenting a general approach for agent goal management in unexpected situations. In this approach, an agent applies three main concepts: goal …
Detecting Server-Side Web Applications With Unrestricted File Upload Vulnerabilities, Jin Huang
Detecting Server-Side Web Applications With Unrestricted File Upload Vulnerabilities, Jin Huang
Browse all Theses and Dissertations
Vulnerable web applications fundamentally undermine website security as they often expose critical infrastructures and sensitive information behind them to potential risks and threats. Web applications with unrestricted file upload vulnerabilities allow attackers to upload a file with malicious code, which can be later executed on the server by attackers to enable various attacks such as information exfiltration, spamming, phishing, and spreading malware. This dissertation presents our research in building two novel frameworks to detect server-side applications vulnerable to unrestricted file uploading attacks. We design the innovative model that holistically characterizes both data and control flows using a graphbased data structure. …
Progress, Georgia Southern University
Progress, Georgia Southern University
College of Engineering & Computing: News & Publications (2010-2023)
Welcome to the new online version of Progress, the magazine of the Allen E. Paulson College of Engineering and Computing. We are approaching 10,000 subscribers, and as we are the College of Engineering and Computing, we think online is the way to go! This issue features three of our first female engineering graduates, a recent student success story, two of our pre-eminent female faculty, as well as one of our most influential female industry/business collaborations. Welcome to the Women of Engineering and Computing issue of Progress!
Multigrid For The Nonlinear Power Flow Equations, Enrique Pereira Batista
Multigrid For The Nonlinear Power Flow Equations, Enrique Pereira Batista
Mathematics Theses and Dissertations
The continuously changing structure of power systems and the inclusion of renewable
energy sources are leading to changes in the dynamics of modern power grid,
which have brought renewed attention to the solution of the AC power flow equations.
In particular, development of fast and robust solvers for the power flow problem
continues to be actively investigated. A novel multigrid technique for coarse-graining
dynamic power grid models has been developed recently. This technique uses an
algebraic multigrid (AMG) coarsening strategy applied to the weighted
graph Laplacian that arises from the power network's topology for the construction
of coarse-grain approximations to …
Emocolor : Fine-Grained Emotion Recognition From Skin Color Information, Maria Guadalupe Jimenez Velasco
Emocolor : Fine-Grained Emotion Recognition From Skin Color Information, Maria Guadalupe Jimenez Velasco
Open Access Theses & Dissertations
In everyday human-to-human communication, emotions play a fundamental role. Emotions represent the affective behavior of humans that is multi-modal, subtle, and complex. Previous approaches based on conventional computer vision explicitly used shape information. Modern approaches based on deep learning implicitly exploit all information available in the image, but by their nature make it difficult to assess the contributions of each source of information. In addition, skin color as a unimodal technique to recognize emotions has been explored to recognize only three coarse-grained emotions in valence space.To the best of our knowledge, this work presents the first approach to fine-grained emotion …
A Modeling Framework For Urban Growth Prediction Using Remote Sensing And Video Prediction Technologies: A Time-Dependent Convolutional Encoder-Decoder Architecture, Ahmed Hassan Jaad
A Modeling Framework For Urban Growth Prediction Using Remote Sensing And Video Prediction Technologies: A Time-Dependent Convolutional Encoder-Decoder Architecture, Ahmed Hassan Jaad
Civil and Environmental Engineering Theses and Dissertations
Studying the growth pattern of cities/urban areas has received considerable attention during the past few decades. The goal is to identify directions and locations of potential growth, assess infrastructure and public service requirements, and ensure the integration of the new developments with the existing city structure. This dissertation presents a novel model for urban growth prediction using a novel machine learning model. The model treats successive historical satellite images of the urban area under consideration as a video for which future frames are predicted. A time-dependent convolutional encoder-decoder architecture is adopted. The model considers as an input a satellite image …
Learning Deep Architectures For Power Systems Operation And Analysis, Mahdi Khodayar
Learning Deep Architectures For Power Systems Operation And Analysis, Mahdi Khodayar
Electrical Engineering Theses and Dissertations
With the rapid increase in size and computational complexities of power systems, the need for powerful computational models to capture strong patterns from energy datasets is emerged. In this thesis, we provide a comprehensive review on recent advances in deep neural architectures that lead to significant improvements in classification and regression problems in the area of power engineering. Furthermore, we introduce our novel deep learning methodologies proposed for a large variety of applications in this area. First, we present the interval deep probabilistic modeling for wind speed forecasting. Incorporating the Rough Set Theory into deep neural networks, we create an …
Machine Learning Applications In Power Systems, Xinan Wang
Machine Learning Applications In Power Systems, Xinan Wang
Electrical Engineering Theses and Dissertations
Machine learning (ML) applications have seen tremendous adoption in power system research and applications. For instance, supervised/unsupervised learning-based load forecasting and fault detection are classic ML topics that have been well studied. Recently, reinforcement learning-based voltage control, distribution analysis, etc., are also gaining popularity. Compared to conventional mathematical methods, ML methods have the following advantages: (i). better robustness against different system configurations due to its data-driven nature; (ii). better adaption to system uncertainties; (iii). less dependent on the modeling accuracy and validity of assumptions. However, due to the unique physics of the power grid, many problems cannot be directly solved …
Finding Data Races In Software Binaries With Symbolic Execution, Nathan D. Jackson
Finding Data Races In Software Binaries With Symbolic Execution, Nathan D. Jackson
Browse all Theses and Dissertations
Modern software applications frequently make use of multithreading to utilize hardware resources better and promote application responsiveness. In these applications, threads share the program state, and synchronization mechanisms ensure proper ordering of accesses to the program state. When a developer fails to implement synchronization mechanisms, data races may occur. Finding data races in an automated way is an already challenging problem, but often impractical without source code or understanding how to execute the program under analysis. In this thesis, we propose a solution for finding data races on software binaries and present our prototype implementation BINRELAY. Our solution makes use …