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Full-Text Articles in Engineering
Triboinformatic Approaches For Surface Characterization: Tribological And Wetting Properties, Md Syam Hasan
Triboinformatic Approaches For Surface Characterization: Tribological And Wetting Properties, Md Syam Hasan
Theses and Dissertations
Tribology is the study of surface roughness, adhesion, friction, wear, and lubrication of interacting solid surfaces in relative motion. In addition, wetting properties are very important for surface characterization. The combination of Tribology with Machine Learning (ML) and other data-centric methods is often called Triboinformatics. In this dissertation, triboinformatic methods are applied to the study of Aluminum (Al) composites, antimicrobial, and water-repellent metallic surfaces, and organic coatings.Al and its alloys are often preferred materials for aerospace and automotive applications due to their lightweight, high strength, corrosion resistance, and other desired material properties. However, Al exhibits high friction and wear rates …
Full-Body Biomechanical Characterization Of Children With Hypermobile Ehlers-Danlos Syndrome During Gait And Activities Of Daily Living, Anahita Alahmoradiqashqai
Full-Body Biomechanical Characterization Of Children With Hypermobile Ehlers-Danlos Syndrome During Gait And Activities Of Daily Living, Anahita Alahmoradiqashqai
Theses and Dissertations
Hypermobile Ehlers-Danlos syndrome (hEDS) is an inherited connective tissue disorder, often under-diagnosed, and presenting with frequent chronic pain and severe musculoskeletal symptoms that can drastically reduce the quality of life during one’s life span. While there are limited quantitative approaches in the literature on adult movements, the biomechanics of movements during activities of daily living (ADLs) in children have not been investigated comprehensively. Therefore, the primary purpose of this dissertation was to characterize the biomechanics of the musculoskeletal system and investigate the biomechanics of hEDS by quantifying joint dynamics and muscle activations during ADLs and gait in the pediatric population. …
Detection Of Stealthy False Data Injection Attacks Against State Estimation In Electric Power Grids Using Deep Learning Techniques, Qingyu Ge
Theses and Dissertations
Since communication technologies are being integrated into smart grid, its vulnerability to false data injection is increasing. State estimation is a critical component which is used for monitoring the operation of power grid. However, a tailored attack could circumvent bad data detection of the state estimation, thus disturb the stability of the grid. Such attacks are called stealthy false data injection attacks (FDIAs). This thesis proposed a prediction-based detector using deep learning techniques to detect injected measurements. The proposed detector adopts both Convolutional Neural Networks and Recurrent Neural Networks, making full use of the spatial-temporal correlations in the measurement data. …
A Reinforcement Learning Approach To Sequential Acceptance Sampling As A Critical Success Factor For Lean Six Sigma, Hani A. Khalil
A Reinforcement Learning Approach To Sequential Acceptance Sampling As A Critical Success Factor For Lean Six Sigma, Hani A. Khalil
Theses and Dissertations
In the 21st century, globalization coupled with technological advancement and free trade has created competition among various businesses enterprises. This competition has led many businesses to adopt various management techniques such as acceptance sampling aimed at transforming their processes in order to remain competitive in the global market and adapt to new market demands. The successful implementation of acceptance sampling is highly dependent on what the academic literature refers to as acceptance sampling optimization. A literature review on the optimization of acceptance sampling has not shown any work that studied whether acceptance sampling and machine learning (ML) plans can be …
A Hierarchical, Fuzzy Inference Approach To Data Filtration And Feature Prioritization In The Connected Manufacturing Enterprise, Phillip Matthew Lacasse
A Hierarchical, Fuzzy Inference Approach To Data Filtration And Feature Prioritization In The Connected Manufacturing Enterprise, Phillip Matthew Lacasse
Theses and Dissertations
The current big data landscape is one such that the technology and capability to capture and storage of data has preceded and outpaced the corresponding capability to analyze and interpret it. This has led naturally to the development of elegant and powerful algorithms for data mining, machine learning, and artificial intelligence to harness the potential of the big data environment. A competing reality, however, is that limitations exist in how and to what extent human beings can process complex information. The convergence of these realities is a tension between the technical sophistication or elegance of a solution and its transparency …