EMF Shielding of Stepper Motors as a Means of Improving the Security of CNC Operations

This paper is a documentation on a study conducted exploring possible shield samples that can be used on standardized stepper motors. The cause for this study is the existence of a security vulnerability in computer numerical control (CNC) operations in which the stepper motors that are used in this operation emit a distinctive electromagnetic field (EMF). This distinctive EMF can be captured by an EMF reader and recorded by an outside party to reproduce the operation. For private CNC operators, this is a source for an unwanted leak of data and needs to be addressed. As a preventative measure, it was proposed to explore ways to passively shield the motors in operations. Therefore, twenty-seven different shields of varying degrees of freedom were used as candidates to test the effectiveness of these degrees of freedom. The degrees of freedom include the material, infill geometry, and overall thickness. Additionally, measurements were made at two different distances from the motor to observe the shielding effects in both close range and long range. Aside from the degrees of freedom, the experiment was thoroughly controlled for accuracy. Measurements from the experiments were then collected and compiled for visual and statistical analysis. During statistical analysis, it was discovered that the data collected failed to meet the requirements of a parametric analysis leading to the use of a nonparametric means instead. The statistical analysis concluded the degrees of freedom that were specifically explored were statistically not significant. The results in this study lead to other possible factors that may prove to be more effective in shielding EMF. These other possible factors may lead to further work to be conducted outside of the scope of this thesis

Computational Fluid Dynamics Analysis of the Blockage Accident in Wire-Wrapped Fuel Rod Bundles

The purpose for reducing CO2 emissions and enhancing the safety of nuclear reactors have led to increased interest in Liquid Metal Fast Reactors (LMFRs). These reactors offer high power density, low-pressure operation, and the ability to breed fissile material. However, LMFR fuel assemblies, comprising fuel pins enclosed in hexagonal ducts with wire-wrapped spacers, are susceptible to coolant flow blockages due to debris buildup, potentially leading to reduced heat transfer and fuel cladding damage. This PhD dissertation aims to conduct a comprehensive computational fluid dynamics (CFD) analysis of blockage accidents in wire-wrapped fuel rod bundles. The objectives include the preparation and validation of CFD models for both nominal (unblocked) conditions and various blockage scenarios, considering solid and porous blockages. Conjugate heat transfer modeling is also incorporated to simulate the cladding temperature. The proposed research activities encompass analyzing fluid flow behavior, pressure drop, velocity, turbulent structures, and temperature profiles for the different blockage configurations. Experimental data from wire-wrapped test facilities is used to validate the CFD models. These facilities have provided high-fidelity data of velocity and pressure drop for transition and turbulent flow regimes at nominal conditions, as well as for blockage scenarios with solid and porous obstructions. The CFD methodology involves solving the incompressible Navier-Stokes equations with the Reynolds Averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES) methods. The results demonstrate the accuracy of the proposed methodology in predicting friction factors and velocity profiles in both unblocked and blocked bundles after the comparison with the experimental data. The findings reveal that the presence of blockages in wire-wrapped fuel rod bundles significantly impact the thermal-hydraulic performance of LMFRs. The analyses show that solid blockages cause an increase in pressure drop and a decrease in velocity, while porous blockages have a lesser impact. The turbulence analysis reveals that the blockages lead to the formation of vortices and eddies, which can further impact the flow behavior and heat transfer. This research yields valuable insights into blockage accidents and contribute to gain more reliability in the use of CFD models for safety assessments of LMFRs

Improving the Ability of Activity Recognition Systems to Detect Activities of Daily Living Performed In-the-Wild

Failing to keep track of the performance of activities of daily living (ADLs) can lead to adverse health outcomes for people with health concerns. However, current recommended practices for keeping track are tedious and burdensome, making it easy for people to forget or stop managing their health. Using activity recognition systems to automatically detect and record ADL performance would address this issue, but most works in activity recognition focus on controlled or semi-naturalistic data in contrast to real world, in-the-wild data. As such, real world ADL recognition remains an open problem. Specifically, real world ADL recognition requires tackling several fundamental challenges for machine learning systems, and it is unclear if existing approaches would be robust to these challenges. We expect that semi-naturalistic data does not capture the diversity of all of the everyday activities such a system would encounter and that robust performance requires using in-the-wild data. In this work, we focus on quantifying the challenges associated with in-the-wild settings and investigating the design of in-the-wild ADL recognition systems. To achieve these goals, we conduct a series of analyses and machine learning experiments on two ADL datasets, one semi-naturalistic and one in-the-wild. First, we measure the class imbalance, interpersonal variability, and pairwise class overlap to motivate the difficulty of recognizing in-the-wild data. Second, we demonstrate the importance of training on negative samples, showing that training on NULL data results in more robust models than using unknown class rejection. Third, we investigate the design of in-the-wild ADL recognition systems, exploring both classical and deep learning methods as well as models with varying levels of context of the user���s hands. In doing so, we develop a recognition system that can recognize several ADLs with high event-based recall and precision with only the context of the dominant hand. These efforts represent a thorough investigation of a challenging open problem in human activity recognition. The results and insights serve as a meaningful step forward toward making robust in-the-wild ADL recognition a reality in order to make it easier for people to manage their health

Multi-Cycle Dynamic Compaction of At-Speed Tests for Reduction in Test Data Volume, Test Application Time, and Power Supply Noise

The semiconductor industry has made great strides over the last few decades. Chip makers taking advantage of Moore���s law have pushed the limits of physics to shrink feature sizes on Silicon (Si) wafers. Delay test is an essential structural manufacturing test used to determine the maximal frequency at which the chip can run without incurring any functional failures. Small delay defects that were previously benign now manifest as delay faults due to the reduced timing margins. Another challenge is achieving better delay correlation with functional test, which is dominated by power supply noise (PSN). Differences in PSN between functional and structural tests can lead to differences in chip operating frequencies of 30% or more. Pseudo functional test (PFT), based on a multicycle clocking scheme, has better PSN correlation with functional test compared with traditional two-cycle at-speed test. This research focuses on the development of new test. methods for small delay defects, within the limits of affordable test generation cost, pattern count and power supply noise. First, this work proposes a new Dynamic Compaction algorithm to generate compacted test sets for K Longest paths per gate (KLPG) in scan based sequential circuits. The algorithm uses a greedy approach to compact paths with non-conflicting assignments together over multiple at-speed cycles during test generation. Reductions in pattern counts of as much as 67% are observed with the greedy approach. Second, compression is introduced to the test flow and the ATPG engine to observe the combined effect of compression and compaction. Multi-cycle at-speed test has around 60% reduction in pattern count over single-cycle at-speed test with similar compression rates. Higher compression rates are required for single cycle test to achieve similar test data volume and test application time which indicates more DFT effort required for single cycle at-speed tests. The compression framework is built inside the CodGen ATPG to make this process more seamless. Third, a simulation-based test relaxation algorithm is designed for CodGen ATPG as the patterns generated by the final justification SAT Engine assigns all the bits in the pattern. This algorithm implemented inside CodSim is able to produce similar don���t care bit density compared to the previous justification algorithms inside CodGen such as FAN and PODEM. Power supply noise (PSN) estimation using weighted switching activity (WSA) is then done for these partially specified patterns (with random or adjacent fill) to compare the single and multi-cycle test power. The multi-cycle test has very similar power profile and even lower in some cases to that of single-cycle test even though the patterns are more compacted. Finally, CodGen ATPG is improved to handle larger industrial designs by updating the SAT engine MiniSat that runs all binary justifications during the ATPG process. It is replaced with CaDiCaL a much modern SAT engine which has been able to provide significant speedup to the test generation process

Biological Ecosystem Inspired Approaches for Circular Economy Design and Quantification

A cornerstone of sustainable development is the idea of a circular economy, a closed loop system where resources are cycled through a system and waste generated and virgin resource use are minimized. This thesis proposes a new of design tools based on environmental network analysis (ENA) and new design metrics, Ns* and NS, as tools which can be used to design systems in accordance with circular economy principles. NS is the number of nodes which strongly connect to a cycle, thus participating in both donation and acceptance, and Ns* is the proportion of total actors connected to a cycle that have a strong connection to that cycle. To explore these tools, multiple engineered systems were evaluated to benchmark their performance against the performance of biological ecosystems and to investigate the ties between ENA metrics and circular economy strategies. Manufacturing floors were also assessed in their ability to be easily reconfigured and their performance based on ENA metrics. The manufacturing floors with the closest values to biological ecosystems also performed the best at reconfigurabilty. The results showed how the low data metrics were able to guide design decisions of an emerging technology through exploring the potential resource cycling routes in a hypothetical economy. Additionally, a carpet network model was used to understand how the design tools related to circular economy strategies. Ns* and NS were found to be integral in assessing collaboration between industries participating in cycling, while FCI showed to be a good indicator of resource cycling, waste diversion and a decreased reliance on raw materials

Image-Based PV Soiling Quantification and Defect Detection Using Machine Learning

Solar energy, a rapidly growing renewable energy source, has garnered significant global attention in recent years. Achieving high efficiency and maintaining the optimal performance of PV panels is crucial. In addition to the material properties and design of the solar cells, PV efficiency is also significantly affected by system losses and degradation. Soiling loss, an important system loss, cannot be improved solely through design modifications and requires periodic inspection and cleaning. In this thesis study, a novel image-based method for estimating soiling loss has been proposed, utilizing key feature extraction and linear regression techniques. Two datasets were collected for this purpose: an in-lab simulation dataset and a dataset obtained from an outdoor PV testing field. The proposed method was tested with both datasets using measured soiling loss/power loss as a gold standard. The method achieved an r-squared value of 0.98 and the root mean squared error of 0.01, which showed its significant potential for cost-effective soiling monitoring purposes. In addition to soiling loss, this study also addresses the problem of PV cell defects, which can come from degradation. A computer vision-based method is developed for detecting PV defects. The method utilized the State-of-the-Art (SOTA) object detection algorithm You Look Only Once V8 (YOLOV8), with U-net architecture and feature pyramid network to improve the accuracy. In addition, the model is compressed with Layer-Adaptive Magnitude-based Pruning to improve the computational efficiency, Additional improvement including the adoption of a better loss function inner-CIoU and the activation function MiSH. To test the proposed method, an open-source Electroluminescent PV defect dataset PVEL-AD was used. The method is compared with several existing algorithms in terms of accuracy and efficiency. The proposed method outperformed all reported work in accuracy and ranked No.2 only in efficiency. It reached mean Average Precision under IoU of 50% (mAP50) of 93.1%, and mean Average Precision under IoU from 50% to 95% (mAP50:95) of 68.7%, which improved about 8-15% comparing to the best existing algorithm. The model���s detection speed is 85.3 Frame per Second (FPS) which ranked in 2nd place among all the existing works. In addition, the model is trained on multiclass detection, the fastest method with FPS of 94.34 only trained on selected classes. In summary, the novel methods developed in this thesis provide effective tools for estimating soiling loss and detecting defects in PV panels. With improved efficiency and accuracy, these developments have the potential to significantly improve the overall efficiency and maintenance of solar energy systems

Crowding: An Exploration of the Effects of Methods and Sound

Visitation rates continue to rise in the United States��� protected areas like national parks and national forests. This has raised management concerns for both impacts on the environment and visitor experiences. The Satisfaction Model postulates that as use levels and encounters rise in parks and protected areas, there is a threshold where the visitor experience is negatively impacted by the additional visitors. Nevertheless, decades of research indicate this relationship is complicated and multifaceted. As a result, the Satisfaction Model has evolved to include norms, use patterns and research measurement techniques as concepts that impact responses to encountering other individuals. Therefore, the purpose of this dissertation was to explore how people respond to research techniques and environment conditions, specifically the soundscape, when visiting protected areas. Study one focused on starting point bias; a research bias where participant responses are systematically inflated or deflated due to research techniques. Research on crowding responses has frequently relied on visual methods where participants are shown a series of images with varying numbers of people visiting a protected area. The order in which the images are shown holds the potential to inflate or deflate results because the exposure to one treatment may impact responses to a subsequent treatment. This study���s findings revealed a starting point bias, but only on crowding ratings when moving from low setting density to high setting density. Furthermore, the soundscape has become a fruitful topic for research on visitor experiences. However, little is understood about how sound impacts crowding norms. Study two explored how anthropogenic sound types impact crowding and acceptability ratings of a setting density. Direct human sounds like voices and children playing were expected to be rated more favorably than mechanical sounds. However, the rank of crowding and acceptability ratings were mixed with direct human sounds generally being rated more harshly than mechanical sounds. Study three explored how sound loudness impacted crowding and acceptability ratings of the setting. Results indicated that loudness was only rated more harshly at the highest loudness levels. The studies are discussed in terms of their results and their impacts on theory and practice

Biological Roles of Bone Morphogenetic Protein 1 (BMP1) in Periodontium

This thesis explores the pivotal role of Bone Morphogenetic Protein 1 (BMP1) in the development and maintenance of the periodontium, employing Bmp1 conditional knockout (cKO) mouse models. The research involved creating a unique lineage of mice with a targeted deletion of the Bmp1 gene in dental follicle cells during early embryonic development, specifically in the progenitor cells destined to differentiate into alveolar bone osteoblasts, cementoblasts, and periodontal ligament (PDL) fibroblasts, known as Osr2-Cre;Bmp1^flox/flox mice. This study provides a detailed comparative analysis of the alveolar bone, cementum, and PDL structures in these Bmp1 cKO mice against normal control mice through various analytical methods, including plain x-ray radiography, histological examination, and immunohistochemical (IHC) analysis. Our findings reveal significant disruptions in PDL collagen fiber organization and bone matrix development, leading to substantial alveolar bone loss in Bmp1 cKO mice at both 6 and 24 weeks of age. Histological and IHC analyses highlighted a reduction in PDL integrity, abnormal protein distributions, and significant decreases in Dentin Matrix Protein 1 (DMP1) levels, suggesting BMP1's essential role in collagen synthesis and the overall mineralization process. Notably, irregular distribution patterns of periostin and fibrillin, underscore the profound impact of BMP1 deletion on dental morphology and periodontal health. The study conclusively demonstrates that BMP1 is critical for the structural integrity and functional maintenance of the periodontium, influencing various proteins involved in the development and health of the periodontal ligament and alveolar bone. The observed defects in Bmp1 cKO mice highlight the importance of BMP1 in collagen network maintenance and alveolar bone formation, with significant implications for periodontal disease pathology and treatment strategies. This research underlines the complex interplay between BMP1 and other proteins in periodontal development, providing invaluable insights into the mechanisms underpinning periodontal health and disease

Crowding: An Exploration of the Effects of Methods and Sound

Visitation rates continue to rise in the United States��� protected areas like national parks and national forests. This has raised management concerns for both impacts on the environment and visitor experiences. The Satisfaction Model postulates that as use levels and encounters rise in parks and protected areas, there is a threshold where the visitor experience is negatively impacted by the additional visitors. Nevertheless, decades of research indicate this relationship is complicated and multifaceted. As a result, the Satisfaction Model has evolved to include norms, use patterns and research measurement techniques as concepts that impact responses to encountering other individuals. Therefore, the purpose of this dissertation was to explore how people respond to research techniques and environment conditions, specifically the soundscape, when visiting protected areas. Study one focused on starting point bias; a research bias where participant responses are systematically inflated or deflated due to research techniques. Research on crowding responses has frequently relied on visual methods where participants are shown a series of images with varying numbers of people visiting a protected area. The order in which the images are shown holds the potential to inflate or deflate results because the exposure to one treatment may impact responses to a subsequent treatment. This study���s findings revealed a starting point bias, but only on crowding ratings when moving from low setting density to high setting density. Furthermore, the soundscape has become a fruitful topic for research on visitor experiences. However, little is understood about how sound impacts crowding norms. Study two explored how anthropogenic sound types impact crowding and acceptability ratings of a setting density. Direct human sounds like voices and children playing were expected to be rated more favorably than mechanical sounds. However, the rank of crowding and acceptability ratings were mixed with direct human sounds generally being rated more harshly than mechanical sounds. Study three explored how sound loudness impacted crowding and acceptability ratings of the setting. Results indicated that loudness was only rated more harshly at the highest loudness levels. The studies are discussed in terms of their results and their impacts on theory and practice

The How and Why of Realness: Reconceptualizing Identity Management and Examining Relationships with Authenticity and Motivation in a U.S.-Based LGBTQ2+ Sample

In this dissertation I examined whether the relationship between the identity management behaviors of LGBTQ+ employees and workplace outcomes (work engagement and affective well-being) is mediated by authenticity. Furthermore, I considered whether autonomous and controlled motivations for identity management behaviors moderate this relationship. In doing so, I add to identity management research that presumes but rarely tests the mediating effects of authenticity and I question the presumption that LGBTQ+ employees are always motivated to share their identities for autonomous reasons and hide them for controlled reasons. Additionally, I explored an alternative approach to measuring identity management behaviors, dividing them into verbal and nonverbal behaviors. Using a correlational design, I collected survey-based quantitative data from 456 LGBTQ+ identifying workers through the online data collection platform Prolific during the months of October and November 2023. Analyses conducted using the PROCESS macros indicated that the relationship between closed communication (e.g., verbal concealment and nonverbal suppression) and outcomes was mediated by authenticity, but this was not the case for open communication (e.g., verbal disclosure and nonverbal expression). There was an interaction effect between autonomous motivation and identity communication behaviors, such that for individuals with lower levels of autonomy, engaging in more open communication behaviors and/or more closed communication behaviors predicted diminished feelings of authenticity as well as reduced outcomes. Based on exploratory path analyses, the division of open communication into verbal and nonverbal behaviors better represented the true population model than combining these behaviors into one variable, however, there was no difference in model fit when closed communication was divided into verbal and nonverbal behaviors compared to a single-variable measure. I conclude by discussing theoretical and practical implications of these findings and propose future directions for this research