Engineering Commons^™

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 …

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 …

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 …

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 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, …

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 …

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 …

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 …

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, …

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 …

Intra-Hour Solar Forecasting Using Cloud Dynamics Features Extracted From Ground-Based Infrared Sky Images, Guillermo Terrén-Serrano

Electrical and Computer Engineering ETDs

Due to the increasing use of photovoltaic systems, power grids are vulnerable to the projection of shadows from moving clouds. An intra-hour solar forecast provides power grids with the capability of automatically controlling the dispatch of energy, reducing the additional cost for a guaranteed, reliable supply of energy (i.e., energy storage). This dissertation introduces a novel sky imager consisting of a long-wave radiometric infrared camera and a visible light camera with a fisheye lens. The imager is mounted on a solar tracker to maintain the Sun in the center of the images throughout the day, reducing the scattering effect produced …

Design, Analysis, And Optimization Of Traffic Engineering For Software Defined Networks, Mohammed Ibrahim Salman

Browse all Theses and Dissertations

Network traffic has been growing exponentially due to the rapid development of applications and communications technologies. Conventional routing protocols, such as Open-Shortest Path First (OSPF), do not provide optimal routing and result in weak network resources. Optimal traffic engineering (TE) is not applicable in practice due to operational constraints such as limited memory on the forwarding devices and routes oscillation. Recently, a new way of centralized management of networks enabled by Software-Defined Networking (SDN) made it easy to apply most traffic engineering ideas in practice. \par Toward creating an applicable traffic engineering system, we created a TE simulator for experimenting …

Deep Learning-Based Surrogate Models For Post-Earthquake Damage Assessment, Xinzhe Yuan

Doctoral Dissertations

"Seismic damage assessment is a critical step to enhance community resilience in the wake of an earthquake. This study aims to develop deep learning-based surrogate models for widely used fragility curves to achieve more accurate and rapid assessment in practice. These surrogate models are based on artificial neural networks trained from the labelled ground motions whose resulting damage classes on targeted structures are determined by nonlinear time history analyses. The development of various surrogate models is progressed in four phases. In Phase I, the multilayer perceptron (MLP) is used to develop multivariate seismic classifiers with up to 50 hand-crafted intensity …