Engineering Commons^™

A Machine Learning Approach For Identification Of Low-Head Dams, Salvador Augusto Vinay Mollinedo

Theses and Dissertations

Identifying Low-head dams (LHD) and creating an inventory become a priority as fatalities continue to occur at these structures. Because obstruction inventories do not specifically identify LHDs, and they are not assigned a hazard classification, there is not an official inventory of LHD. However, there is a multi-agency taskforce that is creating an inventory of LHD. All efforts have been performed by manually identifying LHD on Google Earth Pro (GE Pro). The purpose of this paper is to assess whether a machine learning approach can accelerate the national inventory. We used a machine learning approach to implement a high-resolution remote …

Principles For Using Remote Data Collection Devices And Deep Learning In Evaluating Social Impact Indicators Of Engineered Products For Global Development, Bryan J. Stringham

Theses and Dissertations

Evaluating the social impacts of engineered products, or effects products have on the daily lives of individuals, is critical to ensuring that products are having positive impacts while avoiding negative impacts and to learning how to improve product designs for a more positive social impact. One approach to quantifying a product's social impact is to use social impact indicators that combine user data in a meaningful way to give insight into the current social condition of an individual or population. However, determining social impact indicators relative to engineered products and individuals in developing countries can be difficult when there is …

Deep Learning Of Microstructures, Amir Abbas Kazemzadeh Farizhandi

Boise State University Theses and Dissertations

The internal structure of materials also called the microstructure plays a critical role in the properties and performance of materials. The chemical element composition is one of the most critical factors in changing the structure of materials. However, the chemical composition alone is not the determining factor, and a change in the production process can also significantly alter the materials' structure. Therefore, many efforts have been made to discover and improve production methods to optimize the functional properties of materials. The most critical challenge in finding materials with enhanced properties is to understand and define the salient features of the …

The Role Of Generative Adversarial Networks In Bioimage Analysis And Computational Diagnostics., Ahmed Naglah

Electronic Theses and Dissertations

Computational technologies can contribute to the modeling and simulation of the biological environments and activities towards achieving better interpretations, analysis, and understanding. With the emergence of digital pathology, we can observe an increasing demand for more innovative, effective, and efficient computational models. Under the umbrella of artificial intelligence, deep learning mimics the brain’s way in learn complex relationships through data and experiences. In the field of bioimage analysis, models usually comprise discriminative approaches such as classification and segmentation tasks. In this thesis, we study how we can use generative AI models to improve bioimage analysis tasks using Generative Adversarial Networks …

Data-Driven Deep Learning-Based Analysis On Thz Imaging, Haoyan Liu

Graduate Theses and Dissertations

Breast cancer affects about 12.5% of women population in the United States. Surgical operations are often needed post diagnosis. Breast conserving surgery can help remove malignant tumors while maximizing the remaining healthy tissues. Due to lacking effective real-time tumor analysis tools and a unified operation standard, re-excision rate could be higher than 30% among breast conserving surgery patients. This results in significant physical, physiological, and financial burdens to those patients. This work designs deep learning-based segmentation algorithms that detect tissue type in excised tissues using pulsed THz technology. This work evaluates the algorithms for tissue type classification task among freshly …

Iot In Smart Communities, Technologies And Applications., Muhammad Zaigham Abbas Shah Syed

Electronic Theses and Dissertations

Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing …

Benchmarking Of Embedded Object Detection In Optical And Radar Scenes, Vijaysrinivas Rajagopal

Masters Theses

A portable, real-time vital sign estimation protoype is developed using neural network- based localization, multi-object tracking, and embedded processing optimizations. The system estimates heart and respiration rates of multiple subjects using directional of arrival techniques on RADAR data. This system is useful in many civilian and military applications including search and rescue.

The primary contribution from this work is the implementation and benchmarking of neural networks for real time detection and localization on various systems including the testing of eight neural networks on a discrete GPU and Jetson Xavier devices. Mean average precision (mAP) and inference speed benchmarks were performed. …

Use Of Machine Learning And Natural Language Processing To Enhance Traffic Safety Analysis, Md Abu Sayed

Theses and Dissertations

Despite significant advances in vehicle technologies, safety data collection and analysis, and engineering advancements, tens of thousands of Americans die every year in motor vehicle crashes. Alarmingly, the trend of fatal and serious injury crashes appears to be heading in the wrong direction. In 2021, the actual rate of fatalities exceeded the predicted rate. This worrisome trend prompts and necessitates the development of advanced and holistic approaches to determining the causes of a crash (particularly fatal and major injuries). These approaches range from analyzing problems from multiple perspectives, utilizing available data sources, and employing the most suitable tools and technologies …

Spam Detection Using Machine Learning And Deep Learning, Olubodunde Agboola

LSU Doctoral Dissertations

Text messages are essential these days; however, spam texts have contributed negatively to the success of this communication mode. The compromised authenticity of such messages has given rise to several security breaches. Using spam messages, malicious links have been sent to either harm the system or obtain information detrimental to the user. Spam SMS messages as well as emails have been used as media for attacks such as masquerading and smishing ( a phishing attack through text messaging), and this has threatened both the user and service providers. Therefore, given the waves of attacks, the need to identify and remove …

Neural Network Based Reactive Control Of Point Absorber Wave Energy Converters, Abdelmoamen Ali Nasser

Theses

The main objective of this work is to develop a neural-network-based Reactive Control (RC) system for wave energy converters. The ability to maximize the power output of WEC while maintaining operation constraints, which can be physical or thermal, is crucial to the development of deployable control strategies. Having a control method that is robust, which means it handles uncertainty and noise very well, is one of the main performance criteria in evaluating the method. Therefore, this work starts by deriving an averaged WEC model to be simulated in MATLAB/Simulink. Additionally, the concepts of resistive loading control and reactive control (approximate …

Deception Detection Across Domains, Languages And Modalities, Subhadarshi Panda

Dissertations, Theses, and Capstone Projects

With the increase of deception and misinformation especially in social media, it has become crucial to develop machine learning methods to automatically identify deception. In this dissertation, we identify key challenges underlying text-based deception detection in a cross-domain setting, where we do not have training data in the target domain. We analyze the differences between domains and as a result develop methods to improve cross-domain deception detection. We additionally develop approaches that take advantage of cross-lingual properties to support deception detection across languages. This involves the usage of either multilingual NLP models or translation models. Finally, to better understand multi-modal …

Artificial Neural Networks And Their Applications To Intelligent Fault Diagnosis Of Power Transmission Lines, Fatemeh Mohammadi Shakiba

Dissertations

Over the past thirty years, the idea of computing based on models inspired by human brains and biological neural networks emerged. Artificial neural networks play an important role in the field of machine learning and hold the key to the success of performing many intelligent tasks by machines. They are used in various applications such as pattern recognition, data classification, stock market prediction, aerospace, weather forecasting, control systems, intelligent automation, robotics, and healthcare. Their architectures generally consist of an input layer, multiple hidden layers, and one output layer. They can be implemented on software or hardware. Nowadays, various structures with …

Anonymization & Generation Of Network Packet Datasets Using Deep Learning, Spencer K. Vecile

Electronic Thesis and Dissertation Repository

Corporate networks are constantly bombarded by malicious actors trying to gain access. The current state of the art in protecting networks is deep learning-based intrusion detection systems (IDS). However, for an IDS to be effective it needs to be trained on a good dataset. The best datasets for training an IDS are real data captured from large corporate networks. Unfortunately, companies cannot release their network data due to privacy concerns creating a lack of public cybersecurity data. In this thesis I take a novel approach to network dataset anonymization using character-level LSTM models to learn the characteristics of a dataset; …

Learning With Limited Labeled Data For Image And Video Understanding, Razieh Kaviani Baghbaderani

Doctoral Dissertations

Deep learning-based algorithms have remarkably improved the performance in many computer vision tasks. However, deep networks often demand a large-scale and carefully annotated dataset and sufficient sample coverage of every training category. However, it is not practical in many real-world applications where only a few examples may be available, or the data annotation is costly and require expert knowledge. To mitigate this issue, learning with limited data has gained considerable attention and is investigated thorough different learning methods, including few-shot learning, weakly/semi supervised learning, open-set learning, etc.

In this work, the classification problem is investigated under an open-world assumption to …

Data-Driven Research On Engineering Design Thinking And Behaviors In Computer-Aided Systems Design: Analysis, Modeling, And Prediction, Molla Hafizur Rahman

Graduate Theses and Dissertations

Research on design thinking and design decision-making is vital for discovering and utilizing beneficial design patterns, strategies, and heuristics of human designers in solving engineering design problems. It is also essential for the development of new algorithms embedded with human intelligence and can facilitate human-computer interactions. However, modeling design thinking is challenging because it takes place in the designer’s mind, which is intricate, implicit, and tacit. For an in-depth understanding of design thinking, fine-grained design behavioral data are important because they are the critical link in studying the relationship between design thinking, design decisions, design actions, and design performance. Therefore, …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

Role Of Deep Learning Techniques In Non-Invasive Diagnosis Of Human Diseases., Hisham Abouelseoud Elsayem Abdeltawab

Electronic Theses and Dissertations

Machine learning, a sub-discipline in the domain of artificial intelligence, concentrates on algorithms able to learn and/or adapt their structure (e.g., parameters) based on a set of observed data. The adaptation is performed by optimizing over a cost function. Machine learning obtained a great attention in the biomedical community because it offers a promise for improving sensitivity and/or specificity of detection and diagnosis of diseases. It also can increase objectivity of the decision making, decrease the time and effort on health care professionals during the process of disease detection and diagnosis. The potential impact of machine learning is greater than …

A Novel Computationally Efficient Ai-Driven Generative Inverse Design Framework For Accelerating Topology Optimization And Designing Lattice-Infused Structures, Darshil Patel

All Dissertations

Multiscale topology optimization (TO) provides an inverse design computational framework for designing globally and locally optimized hierarchical structures. Triply periodic minimal surfaces (TPMS), a subclass of parametrically-driven lattice structures, exhibit unique properties such as large surface area, significant volume densities, and good strength-to-weight ratio, which makes them favorable for novel engineering applications. The recent advances in additive manufacturing and its ability to fabricate high-resolution structures have spurred interest in multiscale TO and TPMS for computationally designing finer and high-resolution designs. While multiscale TO and TPMS bring transformative opportunities in various applications, their potential for everyday use remains idle due to …

Deep Learning Applications In Industrial And Systems Engineering, Winthrop Harvey

Graduate Theses and Dissertations

Deep learning - the use of large neural networks to perform machine learning - has transformed the world. As the capabilities of deep models continue to grow, deep learning is becoming an increasingly valuable and practical tool for industrial engineering. With its wide applicability, deep learning can be turned to many industrial engineering tasks, including optimization, heuristic search, and functional approximation. In this dissertation, the major concepts and paradigms of deep learning are reviewed, and three industrial engineering projects applying these methods are described. The first applies a deep convolutional network to the task of absolute aerial geolocalization - the …

Identifying And Discovering Curve Pattern Designs From Fragments Of Pottery, Jun Zhou

Theses and Dissertations

The surface of many cultural heritage objects, such as pottery sherds found in the Southeastern Woodlands, were embellished with curve patterns. The original full designs of these patterns reflect rich historical and cultural information. However, in practice, most objects are fragmentary, making the complete underlying designs unknowable at the scale of the sherd fragment. The challenge to reconstruct and study complete designs is stymied because 1) most pottery sherds contain only a small portion of the underlying full design, 2) curve patterns detected on a sherd are usually incomplete and noisy, and 3) in the case of a stamping application, …

Image Restoration Under Adverse Illumination For Various Applications, Lan Fu

Theses and Dissertations

Many images are captured in sub-optimal environment, resulting in various kinds of degradations, such as noise, blur, and shadow. Adverse illumination is one of the most important factors resulting in image degradation with color and illumination distortion or even unidentified image content. Degradation caused by the adverse illumination makes the images suffer from worse visual quality, which might also lead to negative effects on high-level perception tasks, e.g., object detection.

Image restoration under adverse illumination is an effective way to remove such kind of degradations to obtain visual pleasing images. Existing state-of-the-art deep neural networks (DNNs) based image restoration …

Automated Pre-Play Analysis Of American Football Formations Using Deep Learning, Jacob Deloy Newman

Theses and Dissertations

Annotation and analysis of sports videos is a time consuming task that, once automated, will provide benefits to coaches, players, and spectators. American football, as the most watched sport in the United States, could especially benefit from this automation. Manual annotation and analysis of recorded video of American football games is an inefficient and tedious process. Currently, most college football programs focus on annotating offensive formation. As a first step to further research for this unique application, we use computer vision and deep learning to analyze an overhead image of a football play immediately before the play begins. This analysis …

Remote Sensing Of High Latitude Rivers: Approaches, Insights, And Future Ramifications, Merritt E. Harlan

Doctoral Dissertations

High latitude rivers across the pan-Arctic domain are changing due to changes in climate and positive Arctic feedback loops. Understanding and contextualizing these changes is challenging due to a lack of data and methods for estimating and modeling river discharge, and mapping rivers. Remote sensing, and the availability of satellite imagery can provide ways to overcome these challenges. Through combining various forms of fieldwork, modeling, deep learning, and remote sensing, we contribute methodologies and knowledge to three key challenges associated with better understanding high latitude rivers. In the first chapter, we combine field data that can be rapidly deployed with …

Digitalization Of Construction Project Requirements Using Natural Language Processing (Nlp) Techniques, Fahad Ul Hassan

All Dissertations

Contract documents are a critical legal component of a construction project that specify all wishes and expectations of the owner toward the design, construction, and handover of a project. A single contract package, especially of a design-build (DB) project, comprises hundreds of documents including thousands of requirements. Precise comprehension and management of the requirements are critical to ensure that all important explicit and implicit requirements of the project scope are captured, managed, and completed. Since requirements are mainly written in a natural human language, the current manual methods impose a significant burden on practitioners to process and restructure them into …

Forecasting Salinity In The Laguna Madre Using Deep Learning, Martin J. Flores Jr.

Theses and Dissertations

Salinity is an important metric in the Laguna Madre for establishing the long term health of the local ecological population. By utilizing Deep Learning (DL) techniques, the predicted and forecasted salinity in the Laguna Madre is generated from data provided by the Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua satellite.

Currently, only one other DL model has been used to forecast Sea Surface Salinity (SSS), being a Recurrent Neural Network (RNN). However, the RNN model requires the prediction of a full area of salinity to function.

As such, several model architectures were tested, with the best one, being a Multi-input MPNN, utilized …

Utilizing Shockwave Theory And Deep Learning To Estimate Intersection Traffic Flow And Queue Length Based On Connected Vehicle Data., Abdulmaged Algomaiah

Electronic Theses and Dissertations

The development of Connected Vehicles (CV) facilitates the use of trajectory data to estimate queue length and traffic volume at signalized intersections. The previously proposed models involved additional information that may require conducting different types of data collection. Also, most models need higher market penetration rate or more than a vehicle per cycle to provide adequate estimation. This is mainly because a significant number of the estimation models utilized only queued vehicles. However, the state of motion in non-queued vehicles, particularly slowed-down vehicles, provides much information about the traffic characteristics. There is a lack of exploiting the information from slowed-down …

Modeling And Analysis Of Subcellular Protein Localization In Hyper-Dimensional Fluorescent Microscopy Images Using Deep Learning Methods, Yang Jiao

UNLV Theses, Dissertations, Professional Papers, and Capstones

Hyper-dimensional images are informative and become increasingly common in biomedical research. However, the machine learning methods of studying and processing the hyper-dimensional images are underdeveloped. Most of the methods only model the mapping functions between input and output by focusing on the spatial relationship, whereas neglect the temporal and causal relationships. In many cases, the spatial, temporal, and causal relationships are correlated and become a relationship complex. Therefore, only modeling the spatial relationship may result in inaccurate mapping function modeling and lead to undesired output. Despite the importance, there are multiple challenges on modeling the relationship complex, including the model …

Machine Learning Based Surrogate Model For Hurricane Storm Surge Forecasting In The Laguna Madre, Cesar E. Davila Hernandez

Theses and Dissertations

Texas coastal communities are at constant risk of hurricane impacts every storm season. It is especially important to model and predict storm surge variations during hurricane and storm events. Traditionally, hurricane storm surge predictions have been the result of numerical hydrodynamics based simulations. This type of simulations often requires high amounts of computational resources and complex ocean modelling efforts. Recently, machine learning techniques are being explored and are gaining popularity in hydrologic and ocean engineering modelling fields based on their performance to model nonlinear relationships and low computational requirements for prediction. Advances in machine learning and artificial intelligence (A.I.) demand …

Deep Learning Object-Based Detection Of Manufacturing Defects In X-Ray Inspection Imaging, Juan C. Parducci

Mechanical & Aerospace Engineering Theses & Dissertations

Current analysis of manufacturing defects in the production of rims and tires via x-ray inspection at an industry partner’s manufacturing plant requires that a quality control specialist visually inspect radiographic images for defects of varying sizes. For each sample, twelve radiographs are taken within 35 seconds. Some defects are very small in size and difficult to see (e.g., pinholes) whereas others are large and easily identifiable. Implementing this quality control practice across all products in its human-effort driven state is not feasible given the time constraint present for analysis.

This study aims to identify and develop an object detector capable …

A Deep Reinforcement Learning Approach With Prioritized Experience Replay And Importance Factor For Makespan Minimization In Manufacturing, Jose Napoleon Martinez

LSU Doctoral Dissertations

In this research, we investigated the application of deep reinforcement learning (DRL) to a common manufacturing scheduling optimization problem, max makespan minimization. In this application, tasks are scheduled to undergo processing in identical processing units (for instance, identical machines, machining centers, or cells). The optimization goal is to assign the jobs to be scheduled to units to minimize the maximum processing time (i.e., makespan) on any unit.

Machine learning methods have the potential to "learn" structures in the distribution of job times that could lead to improved optimization performance and time over traditional optimization methods, as well as to adapt …