CE Theses and Dissertations

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    Design and development of hydrogel for effective burn wound healing
    (Wichita State University, 2024-05) Paramshetty, Manju; Asmatulu, Eylem
    The creation and processing of intelligent hydrogels is becoming more and more of a focus in the field of material science research. As an exterior application, hydrogel compositions are quite successful in managing wound healing. Hydrogels developed for the healing of burn wounds tackle critical issues such controlling moisture, preventing infections, and promoting tissue regeneration. Hydrogels are more therapeutically effective when bioactive substances like growth factors and antibacterial chemicals are included, since this facilitates faster wound closure and tissue healing. Furthermore, regulated medication release and real-time monitoring of wound healing parameters are made possible by improvements in smart hydrogels, enabling customized treatment strategies. Burn wounds continue to be extremely difficult to treat, and burn injuries are a major global health problem. Our goal in this thesis is to create a unique hydrogel that would promote wound healing from burns, lessen pain and inflammation, and stop infections. The primary objective of this study is to investigate the effects of the natural and chemical ingredients in hydrogels recipe on the healing of burn wounds. Hydrogels may be created with customizable characteristics including porosity, swelling behavior, and degradation kinetics by a variety of synthetic techniques, such as physical and chemical cross linking. Hydrogel structure, content and properties may be evaluated by characterization methods including Fourier-transform infrared spectroscopy, Atomic force microscopy, X- ray diffraction, Thermogravimetric analysis and cytotoxicity, which guarantee the hydrogels’ appropriateness for use in burn wound treatments. In conclusion, design and development of hydrogel for effective burn wound healing offers an appropriate way to enhance the standard of care for burn victims and improve results.
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    Deep learning approaches for speech emotion recognition
    (Wichita State University, 2024-05) Srinivasan, Sriram; Kshirsagar, Shruti
    This thesis addresses the challenge of speech emotion recognition, focusing on contin- uous emotion estimation using deep learning techniques. Emotion detection plays a vital role in various domains, including healthcare, human-computer interaction, and affective com- puting. However, traditional approaches often struggle with accurately recognizing emotions across noise and reverberation, leading to limited diagnostic accuracy and applicability. To overcome these limitations, our study proposes a novel approach that integrates speech enhancement as a preprocessing step using advanced deep learning techniques. Our exper- imentation utilizes the AVEC 2018 challenge datasets, comprising audio/video recordings from diverse cultural backgrounds. The experimental pipeline involves several key components, including feature extrac- tion, model training, and data/speech enhancement techniques. We employ LSTM (Long Short-Term Memory) models for temporal dependency modeling and investigate the effec- tiveness of different hyperparameters, such as batch size, learning rate, and optimizer choice. We aim to evaluate the effectiveness of speech enhancement methods and explore the impact of various hyperparameters on emotion recognition performance. The results of our experi- ments demonstrate promising performance improvements when leveraging data/speech en- hancement techniques, such as single Spectral Enhancement (SSE) and Speech enhancement Generative adversarial network (SEGAN) show potential for capturing complex temporal relationships and contextual information, leading to enhanced emotion recognition capabilities. Overall, this research contributes to advancing the field of speech emotion recognition by providing insights into the effectiveness of different deep learning techniques and hyper- parameters. By improving emotion detection accuracy, our work lays the groundwork for future developments in healthcare monitoring technologies and human-computer interaction systems, ultimately enhancing patient outcomes and user experiences.
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    CNC machine control for high tolerance aerospace production
    (Wichita State University, 2024-05) Towner, Ridge Daniel; Moscoso-Kingsley, Wilfredo
    This research is motivated by the needs for advanced processes and control methods for CNC machines in the aerospace industry. Generally, more metal is removed from a workpiece than what is remaining in the machine after processing. This requires use of all types of tooling, cutters, drills, spindles, drive systems, actuators, controls, processes, etc. Countless decisions by analysts, engineers, planners, machinists, and managers, and the complex physics that govern machine tool movement, accuracy and repeatability demand post-machining quality control measurements be implemented. The ex-situ nature of the typical quality control systems increase cycle time and costs. In-situ quality control is proposed here as an alternative to reduce cycle time and cost, while increasing quality. For this purpose, a (CNC) machine tool was equipped with direct computer control coordinate measuring software and high accuracy contact probing. This special CNC is validated for its capability for self-health monitoring and in-situ part quality control. The validation conforms to international standards for performance evaluation of commercially available coordinate measuring machines (CMMs). The capability of the CNC system to perform CMM-type measurements is demonstrated via a case study. The equivalency between part dimensional measurements obtained directly from the use of the CNC machine tool as a CMM and from part dimensional measurements performed using commercially available CMMs is established via a correlation study. The self-health checks were run for a period of one-year and the measurements are analyzed statistically using standard SPC methods.
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    Verification of protection blinding in a real-world simulation model
    (Wichita State University, 2024-05) Rahman, Mohd Abrar; Aravinthan, Visvakumar
    The addition of renewable energy into the distribution comes with a large number of environmental benefits but does give rise to some risks associated with the protection system. Issues such as protection blinding, reverse flow, and sympathetic tripping are the most common that have been researched for the time over current relays. The coordination of these relays is at risk and verification of the relay settings on real world systems needs to be studied. This paper will build an urban Midwest utility Feeder distribution model in OpenDSS with the help of a local utility company and referencing one of their urban distribution models and verify if the standard protective relay settings are at risk of misoperating at the substation level. The study will be conducted on 3 different sized distributed energy resources (DER) and on two different locations on the same feeder, close to the substation and further away from the substation. Three different testing scenarios are conducted: normal flow with no faults on the system, faults only on the feeder that has the DER connected to it, and faults on neighboring feeders. Adjusting the DER size and location along with different fault types will allow to find the worst combination for protection blinding on the simulated model. The results show that the urban system in study is not at risk of protection blinding but is susceptible to sympathetic when the DER is large enough to back feed into the system. However, the placement of DER on the main feeder branch will avoid sympathetic tripping as the Switchgear feeder is the first protective device and is set high enough to avoid a misoperation. Placement of the DER on the Feeder branches does pose a threat and will cause a misoperation.
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    Acoustic hologram guided electrospinning
    (Wichita State University, 2024-05) Sack, Richard; Hakansson, Nils A.
    Electrospinning is an effective method to produce nanoscale fibers; however, the trajectory of fibers cannot be effectively controlled between the polymer source and ground plate. The resulting deposition of these fibers cannot be easily controlled and charged conductive fibers must avoid the ground plate. We demonstrate a novel system integrating acoustic holograms produced by a phased array of ultrasonic transducers to control the trajectory of electrospun nanofibers. The combination of electrospinning and acoustic holograms has not been found in academic research literature. Multiple designs were developed and tested until a final design produced the desired results. Fiber trajectories were controlled to deposit fibers in a specific location on the collection plate which correlated with simulations. Preliminary results also indicate that acoustic holograms improve fiber morphology at high flow rates by reducing the number of beads. These results lay a groundwork for subsequent work to improve the production speed of nanofibers, improve fiber morphology, efficiently produce highly aligned fibers, produce nanofiber yarns, and produce highly conductive nanofibers. This work is intended to open avenues for continued research and industrial nanofiber production. Future work research involves the introduction of higher frequency transducers, larger arrays, different transducer arrangements, and new ground plate designs.
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