23671 research outputs found
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Design, structural analysis, and fabrication of electrostatic spray deposition system and study of tungsten carbide cobalt chromium powder coatings on aluminum 6061 substrate
No full textThesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Mechanical EngineeringDeposition techniques have been advanced to the extent that it is now becoming an important process to improve mechanical properties of the surface of the material by depositing composite powder particles. However, choosing an efficient and versatile procedure to coat thin films will be difficult and scaling that procedure to our requirements is also an important factor to be considered. In this work, the Electrostatic Spray Deposition system has been used which uses electrostatic forces to deposit powder particles onto the grounded substrate. This method offers advantages such as simple setup, low operational cost, minimal material waste, can be applied to complex geometries, and is scalable, making it suitable for protective coatings in biomedical, automotive, and paint industries applications. The ESD chamber has been designed and analyzed to ensure that it meets the requirements under operating conditions. Then the system was fabricated and tested using powder particles onto a substrate and the results demonstrate ESD’s capability for achieving moderately uniform coatings. Apart from these, laser sintering and furnace sintering have been performed using ESD coated substrates to understand ESD coatings
Quantum Bayesian Networks construction, prediction, and inference
No full textThesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of of Industrial, Systems and Manufacturing EngineeringIn recent years, quantum computing has garnered increasing attention for its potential to outperform classical methods in computational efficiency. While demonstrations of quantum supremacy remain rare, algorithms leveraging amplitude amplification have exhibited notable advantages over classical approaches, particularly for NP-hard problems found in optimization, uncertainty modeling, and machine learning.
This research explores the application of quantum computing to Bayesian Networks (BNs), widely used for modeling stochastic systems in probabilistic prediction, risk analysis, and system health monitoring—tasks that become computationally intensive at scale. We propose a method called C-QBN for designing quantum circuits that represent generic discrete BNs, with potential applicability to continuous variables via discretization. Efficient quantum representation of Bayesian Networks can facilitate the application of other quantum algorithms, for performing inference or prediction, for instance. To reduce quantum resource demands, we introduce AD-QBN, an improved version of C-QBN that minimizes multi-qubit gate usage, leading to simpler, more hardware-efficient circuits. Building upon this, we extend the approach to Dynamic Quantum Bayesian Networks (DQBNs), capable of modeling time-dependent systems by capturing relationships across and within time steps.
We validate these frameworks—C-QBN, AD-QBN, and DQBN—through multiple case studies, including stock prediction, risk assessment, and real-time health monitoring under uncertainty. Additionally, we examine the use of variational quantum circuits to approximate QBNs on Noisy Intermediate-Scale Quantum (NISQ) devices, offering a practical path forward while scalable quantum hardware remains in development.
All implementations are conducted in Python using IBM’s Qiskit simulator and are benchmarked against classical BN models
Department of Dental Hygiene Class of 2011
No full textFirst row (left to right): Davette McVoy, SADHA Vice President, 2009m SADHA President,2010-2011; Amber Billups, SADHA Secretary, 2010-2011; Erica Brock, SADHA Treasurer, 2010-2011; Amanda Ewertz, SADHA Class Representative, 2009-2011; Stephanie Ott, SADHA Class Representative, 2009-2011Second row (left to right): Jacki Adams, Sara Bayless, Valerie Braddy, Erin Brady, Carla Callarman, Whitney Campbell, Debra Coleman, Susan Doffing, Tiffany Grizzell, Mai HaThird row (left to right): Tiffany Heibert, Sara Humburg, Jen Koehn, Arlene Kotkoff, Julie Mathias, Savannah Milligan, Megan Neff, Nga Nguyen, Ivan Perez, Nicole PerrymanFourth row (left to right): Tina Powell, Amy Ramsour, Kati Rinehart, Sierra Smith, Jessica Stein, Katrina Stoecklein, Samanatha Thomas, Kelsey Weidner, Kelsey WoodDigitized by University Libraries' Technical Services Institutional Repository & Digitization group.Personal and non-profit use only
The implementation of artificial intelligence in robotics
No full textThesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of of Industrial, Systems and Manufacturing EngineeringThis research aims to address various unresolved technical challenges in industrial robotics and soft robotics using artificial intelligence and digital engineering tools. The first part of this study investigates the impact of cracks on the vibration characteristics of rigid robot links as cracks can degrade the performance and reliability of robotic systems. Using the finite element method (FEM), simulations were conducted on planar robot link models with and without artificial cracks of varying sizes, locations, and orientations. Their vibration responses were measured and then analyzed by using machine learning along with the Gramian Angular Summation Field (GASF) method that converts the vibration data into 2D images for crack detection. The results demonstrated 98.25% accuracy in crack detection, showcasing the feasibility of the proposed approach.
The second part of this study explores the emerging field of soft robotics, which has garnered significant attention due to its potential to enhance flexibility, safety, and productivity in manufacturing. Soft robots, which are constructed from compliant materials such as silicones, exhibit superior adaptability in unstructured environments and facilitate safer human–robot interactions. Despite these advantages, challenges still persist in achieving precise motion control and stiffness compliance. This research investigates the motion of pneumatic soft robots under varying loading conditions using finite element analysis (FEA) and machine learning techniques. A novel asymmetric double-chamber soft robot design was proposed by demonstrating its ability to achieve a considerably larger reachable workspace compared to conventional single-chamber soft actuator designs. A machine learning model was established and then trained with simulation data to accurately predict the kinematics and workspace of the soft robot. It was able to demonstrate predictions with an R-squared value of 0.99 and a root mean square error (RMSE) of 0.783, providing valuable insights into optimizing such soft robot performances. In addition, comparative analysis of the workspaces of asymmetric double-chamber and single-chamber soft robots revealed that the double-chamber design offers approximately 185 times more reachable workspace than the single-chamber configuration
Department of Dental Hygiene Class of 2022
No full textFirst row (left to right): Mikayla Jellison, SADHA President; Kennedy Camp, SADHA Treasurer; Madeline Moyer, SADHA Secretary; Lauren Akin, SADHA Class Representative; Michelle Armstrong, SADHA Class RepresentativeSecond row (left to right): Lauren Baalman, Haylyn Bergkamp, Rachelle Bravo, Gabby Burciaga, Jade Burroughs, Lindsey Clark, Alyssa DeVous, Cassandra ForcumThird row (left to right): Katie Graves, Madison Hayes, Jaiden Hess, McKayla HooverFourth row (left to right): Morgan Jones, Samantha Koch, Gracie Musson, Stacee Patton, Rosy Pino, Delanie Randolph, Mayci Runyan, Maya ShuaibFifth row (left to right): Jennifer Stoddard, Maria Thompson, Katelyn Van, Marianna Vazquez, Haylie Warren, Shae Weatherson, Stefanie Webster, Shadden ZapataDigitized by University Libraries' Technical Services Institutional Repository & Digitization group.Personal and non-profit use only
Private entrepreneurs and public services: The role of endogenous capabilities
No full textAvailable for purchase as an individual chapter online or in the complete book in print or online.Public agencies are increasingly partnering with private entrepreneurs to deliver public services, particularly in developing countries with underdeveloped infrastructures. By sharing their resources and operations, both parties can create new capabilities in the process of value creation and devise new goals. Shared governance modes have shown to preserve the overall quality of services, but the complexity of this joint activity, coupled with the profit-maximizing orientation of private entrepreneurs, may lead to the development of new capabilities that conflict with social interests. We address these concerns by analyzing the endogenous emergence of capabilities in Public Private Partnerships (PPPs) and show how these can be plagued with rent-seeking and mission creep that reduce overall well-being. Drawing upon Penrose’s theory of firm growth, we show that PPPs are most likely to develop capabilities that go against the public interest when entrepreneurs are relatively efficient, contracts governing the provision are unclear as to the expansion of services, the attributes of the product being delivered are difficult to measure, and the service expertise is predominantly provided by the private sector
Department of Dental Hygiene Class of 2017
No full textFirst row (left to right): Ashley Dorenkamp, SADHA President; Tyanna Moore, SADHA Secretary; Elizabeth Strickland, SADHA Treasurer; Annabel Rodriguez, SADHA Class Representative; Gabriela Guerrero, SADHA Class RepresentativeSecond row (left to right): Brittany Balsters, Rachel Brewer, Naomi Burrell, Hattie-Joe Cauwels, Kaycee Cook; Fausto Dier, Ashley Frazier, Kate Garland, Hanna Heble, Ruth HembergerThird row (left to right): Syndi Hernandez, Jessica Johnson, Hallie Kuhlman, Estela Lujan, Karen Markhart, Aurora McCaffree, Madison Murphy, Jordan Newton, Kura Njie, Kaitlyn OrrFourth row (left to right): Ashley Renshaw, Vanessa Rosas, Kelly Seay, Denise Servis, Seth Sherwood, Taylor Tipton, Jessica Weimer, Rylee Yoder, Kelsey YoungerDigitized by University Libraries' Technical Services Institutional Repository & Digitization group.Personal and non-profit use only
Methods for mitigating bias of biometric systems
No full textThesis (Ph.D.)-- Wichita State University, College of Engineering, School of ComputingBiometric analysis systems, especially those employed for soft attribute classification tasks like gender or race estimation, frequently display significant biases against specific demographic groups. This inherent unfairness compromises the integrity and equity of algorithmic decision-making processes relying on these systems. While mitigating such bias is critically important for responsible AI deployment, existing techniques often encounter substantial limitations. These include poor generalizability across different datasets or conditions, a heavy reliance on labor-intensive and privacy-sensitive annotated demographic data, and an inherent conflict where efforts to maximize fairness can detrimentally affect overall classification accuracy, particularly impacting performance for well-represented groups.
Addressing this complex challenge, this research provides a detailed investigation into the manifestation of these biases within soft biometric algorithms. More importantly, it puts forth innovative mitigation frameworks, exploring techniques such as deep generative models and self-supervised learning paradigms. The central aim of this dissertation is to achieve a more effective balance between equity and utility. It seeks to substantially improve fairness metrics across diverse demographic groups while crucially avoiding significant detriment to the overall accuracy and generalization performance of the biometric system, paving the way for more trustworthy and equitable applications
Critiquing the community college stigma through the disability justice principles
No full textClick on the DOI link to access this article at the publishers website (may not be free).Educational hierarchies continue to proliferate in the U.S. reifying social inequities and benefitting a select few. Community colleges seek to disrupt this sorting mechanism with their non-elite admissions, varied educational offerings, and integrated student populations. As a consequence for challenging the status quo, community colleges are stigmatized as a haphazard, floundering mistake, but we contend the community college stigma is a tool for social stratification. The stigma, like all stigmas, reinforces and is reinforced by societal biases, and in this case, the value-laden narrative is linked with educational affiliation. By combatting the community college stigma, the many pervasive isms (i.e. racism, ableism, classism) are simultaneously destabilized. Therefore, as a collective of community college alumni and advocates, we critique the community college stigma via the disability justice framework in this article. We specifically engage the principles of intersectionality, collective access, interdependence, leadership of those most impacted, sustainability, and collective liberation to examine who the stigma harms and serves. We pose reflective questions to unsettle coded language and normalized practices that position community colleges and their students in need of external, education saviors. Further, we invite readers to acknowledge the importance of these locally situated, community-engaged institutions and to chart a transformative path forward by enacting community college values. © 2025 Taylor & Francis Group, LLC