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Full-Text Articles in Engineering
Integration Of Infrared Thermography And Deep Learning For Real-Time In-Situ Defect Detection And Rapid Elimination Of Defect Propagation In Material Extrusion, Asef Ishraq Sadaf
Integration Of Infrared Thermography And Deep Learning For Real-Time In-Situ Defect Detection And Rapid Elimination Of Defect Propagation In Material Extrusion, Asef Ishraq Sadaf
Electronic Theses and Dissertations
This study presents a novel approach to overcoming process reliability challenges in Material Extrusion (ME), a prominent additive manufacturing (AM) technique. Despite ME's advantages in cost, versatility, and rapid prototyping, it faces significant barriers to commercial-scale production, primarily due to quality issues such as overextrusion and underextrusion, which compromise final part performance. Traditional manual monitoring methods severely lack the capability to efficiently detect these defects and highlight the necessity for an efficient and real-time monitoring solution. Considering these challenges, an innovative and field-deployable infrared thermography-based in-situ real-time defect detection and feedback control system is proposed in this thesis. A novel …
Deep Learning Of Semantic Image Labels On Hdr Imagery In A Maritime Environment, Charles Montagnoli
Deep Learning Of Semantic Image Labels On Hdr Imagery In A Maritime Environment, Charles Montagnoli
Doctoral Dissertations and Master's Theses
Situational awareness in the maritime environment can be extremely challenging. The maritime environment is highly dynamic and largely undefined, requiring the perception of many potential hazards in the shared maritime environment. One particular challenge is the effect of direct-sunlight exposure and specular reflection causing degradation of camera reliability. It is for this reason then, in this work, the use of High-Dynamic Range imagery for deep learning of semantic image labels is studied in a littoral environment. This study theorizes that the use HDR imagery may be extremely beneficial for the purpose of situational awareness in maritime environments due to the …
Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data, Ali Al-Ramini, Mahdi Hassan, Farahnaz Fallahtafti, Mohammad Ali Takallou, Hafizur Rahman, Basheer Qolomany, Iraklis I. Pipinos, Fadi M. Alsaleem, Sara A. Myers
Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data, Ali Al-Ramini, Mahdi Hassan, Farahnaz Fallahtafti, Mohammad Ali Takallou, Hafizur Rahman, Basheer Qolomany, Iraklis I. Pipinos, Fadi M. Alsaleem, Sara A. Myers
Department of Mechanical and Materials Engineering: Faculty Publications
Peripheral artery disease (PAD) manifests from atherosclerosis, which limits blood flow to the legs and causes changes in muscle structure and function, and in gait performance. PAD is underdiagnosed, which delays treatment and worsens clinical outcomes. To overcome this challenge, the purpose of this study is to develop machine learning (ML) models that distinguish individuals with and without PAD. This is the first step to using ML to identify those with PAD risk early. We built ML models based on previously acquired overground walking biomechanics data from patients with PAD and healthy controls. Gait signatures were characterized using ankle, knee, …
Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data, Ali Al-Ramini, Mahdi Hassan, Farahnaz Fallahtafti, Mohammad Ali Takallou, Basheer Qolomany, Iraklis I. Pipinos, Fadi Alsaleem, Sara A. Myers
Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data, Ali Al-Ramini, Mahdi Hassan, Farahnaz Fallahtafti, Mohammad Ali Takallou, Basheer Qolomany, Iraklis I. Pipinos, Fadi Alsaleem, Sara A. Myers
Department of Mechanical and Materials Engineering: Faculty Publications
Peripheral artery disease (PAD) manifests from atherosclerosis, which limits blood flow to the legs and causes changes in muscle structure and function, and in gait performance. PAD is underdiagnosed, which delays treatment and worsens clinical outcomes. To overcome this challenge, the purpose of this study is to develop machine learning (ML) models that distinguish individuals with and without PAD. This is the first step to using ML to identify those with PAD risk early. We built ML models based on previously acquired overground walking biomechanics data from patients with PAD and healthy controls. Gait signatures were characterized using ankle, knee, …
Uncertainty-Aware Deep Learning For Prediction Of Remaining Useful Life Of Mechanical Systems, Samuel J. Cornelius
Uncertainty-Aware Deep Learning For Prediction Of Remaining Useful Life Of Mechanical Systems, Samuel J. Cornelius
Theses and Dissertations
Remaining useful life (RUL) prediction is a problem that researchers in the prognostics and health management (PHM) community have been studying for decades. Both physics-based and data-driven methods have been investigated, and in recent years, deep learning has gained significant attention. When sufficiently large and diverse datasets are available, deep neural networks can achieve state-of-the-art performance in RUL prediction for a variety of systems. However, for end users to trust the results of these models, especially as they are integrated into safety-critical systems, RUL prediction uncertainty must be captured. This work explores an approach for estimating both epistemic and heteroscedastic …
Deep Learning Segmentation Of Coronary Calcified Plaque From Intravascular Optical Coherence Tomography (Ivoct) Images With Application To Finite Element Modeling Of Stent Deployment, Yazan Gharaibeh, Pengfei Dong, David Prabhu, Chaitanya Kolluru, Juhwan Lee, Vlad Zimin, Hozhabr Mozafari, Hiram Bizzera, Linxia Gu, David Wilson
Deep Learning Segmentation Of Coronary Calcified Plaque From Intravascular Optical Coherence Tomography (Ivoct) Images With Application To Finite Element Modeling Of Stent Deployment, Yazan Gharaibeh, Pengfei Dong, David Prabhu, Chaitanya Kolluru, Juhwan Lee, Vlad Zimin, Hozhabr Mozafari, Hiram Bizzera, Linxia Gu, David Wilson
Department of Mechanical and Materials Engineering: Faculty Publications
Because coronary artery calcified plaques can hinder or eliminate stent deployment, interventional cardiologists need a better way to plan interventions, which might include one of the many methods for calcification modification (e.g., atherectomy). We are imaging calcifications with intravascular optical coherence tomography (IVOCT), which is the lone intravascular imaging technique with the ability to image the extent of a calcification, and using results to build vessel-specific finite element models for stent deployment. We applied methods to a large set of image data (>45 lesions and > 2,600 image frames) of calcified plaques, manually segmented by experts into calcified, lumen and …