Matching Items (134)
Description
The relationships between the properties of materials and their microstructures have been a central topic in materials science. The microstructure-property mapping and numerical failure prediction are critical for integrated computational material engineering (ICME). However, the bottleneck of ICME is the lack of a clear understanding of the failure mechanism as well as an efficient computational framework. To resolve these issues, research is performed on developing novel physics-based and data-driven numerical methods to reveal the failure mechanism of materials in microstructure-sensitive applications.
First, to explore the damage mechanism of microstructure-sensitive materials in general loading cases, a nonlocal lattice particle model (LPM) is adopted because of its intrinsic ability to handle the discontinuity. However, the original form of LPM is unsuitable for simulating nonlinear behavior involving tensor calculation. Therefore, a damage-augmented LPM (DLPM) is proposed by introducing the concept of interchangeability and bond/particle-based damage criteria. The proposed DLPM successfully handles the damage accumulation behavior in general material systems under static and fatigue loading cases.
Then, the study is focused on developing an efficient physics-based data-driven computational framework. A data-driven model is proposed to improve the efficiency of a finite element analysis neural network (FEA-Net). The proposed model, i.e., MFEA-Net, aims to learn a more powerful smoother in a multigrid context. The learned smoothers have good generalization properties, and the resulted MFEA-Net has linear computational complexity. The framework has been applied to efficiently predict the thermal and elastic behavior of composites with various microstructural fields. Finally, the fatigue behavior of additively manufactured (AM) Ti64 alloy is analyzed both experimentally and numerically. The fatigue experiments show the fatigue life is related with the contour process parameters, which can result in different pore defects, surface roughness, and grain structures. The fractography and grain structures are closely observed using scanning electron microscope. The sample geometry and defect/crack morphology are characterized through micro computed tomography (CT). After processing the pixel-level CT data, the fatigue crack initiation and growth behavior are numerically simulated using MFEA-Net and DLPM. The experiments and simulation results provided valuable insights in fatigue mechanism of AM Ti64 alloy.
ContributorsMeng, Changyu (Author) / Liu, Yongming (Thesis advisor) / Hoover, Christian (Committee member) / Li, Lin (Committee member) / Peralta, Pedro (Committee member) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2023
Description
The purpose of the overall project is to create a simulated environment similar to Google map and traffic but simplified for education purposes. Students can choose different traffic patterns and program a car to navigate through the traffic dynamically based on the changing traffic. The environment used in the project is ASU VIPLE (Visual IoT/Robotics Programming Language Environment). It is a visual programming environment for Computer Science education. VIPLE supports a number of devices and platforms, including a traffic simulator developed using Unity game engine. This thesis focuses on creating realistic traffic data for the traffic simulator and implementing dynamic routing algorithm in VIPLE. The traffic data is generated from the recorded real traffic data published at Arizona Maricopa County website. Based on the generated traffic data, VIPLE programs are developed to implement the traffic simulation based on dynamic changing traffic data.
ContributorsZhang, Zhemin (Author) / Chen, Yinong (Thesis advisor) / Wang, Yalin (Thesis advisor) / De Luca, Gennaro (Committee member) / Arizona State University (Publisher)
Created2022
Description
Little is known about how cognitive and brain aging patterns differ in older adults with autism spectrum disorder (ASD). However, recent evidence suggests that individuals with ASD may be at greater risk of pathological aging conditions than their neurotypical (NT) counterparts. A growing body of research indicates that older adults with ASD may experience accelerated cognitive decline and neurodegeneration as they age, although studies are limited by their cross-sectional design in a population with strong age-cohort effects. Studying aging in ASD and identifying biomarkers to predict atypical aging is important because the population of older individuals with ASD is growing. Understanding the unique challenges faced as autistic adults age is necessary to develop treatments to improve quality of life and preserve independence. In this study, a longitudinal design was used to characterize cognitive and brain aging trajectories in ASD as a function of autistic trait severity. Principal components analysis (PCA) was used to derive a cognitive metric that best explains performance variability on tasks measuring memory ability and executive function. The slope of the integrated persistent feature (SIP) was used to quantify functional connectivity; the SIP is a novel, threshold-free graph theory metric which summarizes the speed of information diffusion in the brain. Longitudinal mixed models were using to predict cognitive and brain aging trajectories (measured via the SIP) as a function of autistic trait severity, sex, and their interaction. The sensitivity of the SIP was also compared with traditional graph theory metrics. It was hypothesized that older adults with ASD would experience accelerated cognitive and brain aging and furthermore, age-related changes in brain network topology would predict age-related changes in cognitive performance.
For both cognitive and brain aging, autistic traits and sex interacted to predict trajectories, such that older men with high autistic traits were most at risk for poorer outcomes. In men with autism, variability in SIP scores across time points trended toward predicting cognitive aging trajectories. Findings also suggested that autistic traits are more sensitive to differences in brain aging than diagnostic group and that the SIP is more sensitive to brain aging trajectories than other graph theory metrics. However, further research is required to determine how physiological biomarkers such as the SIP are associated with cognitive outcomes.
ContributorsSullivan, Georgia (Author) / Braden, Blair (Thesis advisor) / Kodibagkar, Vikram (Thesis advisor) / Schaefer, Sydney (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Vanadium-dioxide-based devices show great switchability in their optical properties due to its dramatic thermochromic phase transition from insulator to metal, but generally have concerns due to its relatively high transition temperature at 68 °C. Doping the vanadium dioxide with tungsten has been shown to reduce its transition temperature at the cost lower optical property differences between its insulating and metallic phases. A recipe is developed through parametric experimentation to fabricate tungsten-doped vanadium dioxide consisting of a novel dual target co-sputtering deposition, a furnace oxidation process, and a post-oxidation annealing process. The transmittance spectra of the resulting films are measured via Fourier-transform infrared spectroscopy at different temperatures to confirm the lowered transition temperature and analyze their thermal-optical hysteresis behavior through the transition temperature range. Afterwards, the optical properties of undoped sputtered vanadium films are modeled and effective medium theory is used to explain the effect of tungsten dopants on the observed transmittance decrease of doped vanadium dioxide. The optical modeling is used to predict the performance of tungsten-doped vanadium dioxide devices, in particular a Fabry-Perot infrared emitter and a nanophotonic infrared transmission filter. Both devices show great promise in their optical properties despite a slight performance decrease from the tungsten doping. These results serve to illustrate the excellent performance of the co-sputtered tungsten-doped vanadium dioxide films.
ContributorsChao, Jeremy (Author) / Wang, Liping (Thesis advisor) / Wang, Robert (Committee member) / Tongay, Sefaattin (Committee member) / Arizona State University (Publisher)
Created2022
Description
As robots become increasingly integrated into the environments, they need to learn how to interact with the objects around them. Many of these objects are articulated with multiple degrees of freedom (DoF). Multi-DoF objects have complex joints that require specific manipulation orders, but existing methods only consider objects with a single joint. To capture the joint structure and manipulation sequence of any object, I introduce the "Object Kinematic State Machines" (OKSMs), a novel representation that models the kinematic constraints and manipulation sequences of multi-DoF objects. I also present Pokenet, a deep neural network architecture that estimates the OKSMs from the sequence of point cloud data of human demonstrations. I conduct experiments on both simulated and real-world datasets to validate my approach. First, I evaluate the modeling of multi-DoF objects on a simulated dataset, comparing against the current state-of-the-art method. I then assess Pokenet's real-world usability on a dataset collected in my lab, comprising 5,500 data points across 4 objects. Results showcase that my method can successfully estimate joint parameters of novel multi-DoF objects with over 25% more accuracy on average than prior methods.
ContributorsGUPTA, ANMOL (Author) / Gopalan, Nakul (Thesis advisor) / Zhang, Yu (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2024
Description
In today's data-driven world, privacy is a significant concern. It is crucial to preserve the privacy of sensitive information while visualizing data. This thesis aims to develop new techniques and software tools that support Vega-Lite visualizations while maintaining privacy. Vega-Lite is a visualization grammar based on Wilkinson's grammar of graphics. The project extends Vega-Lite to incorporate privacy algorithms such as k-anonymity, l-diversity, t-closeness, and differential privacy. This is done by using a unique multi-input loop module logic that generates combinations of attributes as a new anonymization method. Differential privacy is implemented by adding controlled noise (Laplace or Exponential) to the sensitive columns in the dataset. The user defines custom rules in the JSON schema, mentioning the privacy methods and the sensitive column. The schema is validated using Another JSON Validation library, and these rules help identify the anonymization techniques to be performed on the dataset before sending it back to the Vega-Lite visualization server. Multiple datasets satisfying the privacy requirements are generated, and their utility scores are provided so that the user can trade-off between privacy and utility on the datasets based on their requirements. The interface developed is user-friendly and intuitive and guides users in using it. It provides appropriate feedback on the privacy-preserving visualizations generated through various utility metrics. This application is helpful for technical or domain experts across multiple domains where privacy is a big concern, such as medical institutions, traffic and urban planning, financial institutions, educational records, and employer-employee relations. This project is novel as it provides a one-stop solution for privacy-preserving visualization. It works on open-source software, Vega-Lite, which several organizations and users use for business and educational purposes.
ContributorsSekar, Manimozhi (Author) / Bryan, Chris (Thesis advisor) / Wang, Yalin (Committee member) / Cao, Zhichao (Committee member) / Arizona State University (Publisher)
Created2024
Description
Image denoising, a fundamental task in computer vision, poses significant challenges due to its inherently inverse and ill-posed nature. Despite advancements in traditional methods and supervised learning approaches, particularly in medical imaging such as Medical Resonance Imaging (MRI) scans, the reliance on paired datasets and known noise distributions remains a practical hurdle. Recent progress in noise statistical independence theory and diffusion models has revitalized research interest, offering promising avenues for unsupervised denoising. However, existing methods often yield overly smoothed results or introduce hallucinated structures, limiting their clinical applicability. This thesis tackles the core challenge of progressing towards unsupervised denoising of MRI scans. It aims to retain intricate details without smoothing or introducing artificial structures, thus ensuring the production of high-quality MRI images. The thesis makes a three-fold contribution: Firstly, it presents a detailed analysis of traditional techniques, early machine learning algorithms for denoising, and new statistical-based models, with an extensive evaluation study on self-supervised denoising methods highlighting their limitations. Secondly, it conducts an evaluation study on an emerging class of diffusion-based denoising methods, accompanied by additional empirical findings and discussions on their effectiveness and limitations, proposing solutions to enhance their utility. Lastly, it introduces a novel approach, Unsupervised Multi-stage Ensemble Deep Learning with diffusion models for denoising MRI scans (MEDL). Leveraging diffusion models, this approach operates independently of signal or noise priors and incorporates weighted rescaling of multi-stage reconstructions to balance over-smoothing and hallucination tendencies. Evaluation using benchmark datasets demonstrates an average gain of 1dB and 2% in PSNR and SSIM metrics, respectively, over existing approaches.
ContributorsVora, Sahil (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Zhou, Yuxiang (Committee member) / Arizona State University (Publisher)
Created2024
Description
Organic electronics have remained a research topic of great interest over the past few decades, with organic light emitting diodes (OLEDs) emerging as a disruptive technology for lighting and display applications. While OLED performance has improved significantly over the past decade, key issues remain unsolved such as the development of stable and efficient blue devices. In order to further the development of OLEDs and increase their commercial potential, innovative device architectures, novel emissive materials and high-energy hosts are designed and reported.
OLEDs employing step-wide graded-doped emissive layers were designed to improve charge balance and center the exciton formation zone leading to improved device performance. A red OLED with a peak efficiency of 16.9% and an estimated LT97 over 2,000 hours at 1,000 cd/m2 was achieved. Employing a similar structure, a sky-blue OLED was demonstrated with a peak efficiency of 17.4% and estimated LT70 over 1,300 hours at 1,000 cd/m2. Furthermore, the sky-blue OLEDs color was improved to CIE coordinates of (0.15, 0.25) while maintaining an efficiency of 16.9% and estimated LT70 over 600 hours by incorporating a fluorescent sensitizer. These devices represent literature records at the time of publication for efficient and stable platinum phosphorescent OLEDs.
A newly developed class of emitters, metal-assisted delayed-fluorescence (MADF), are demonstrated to achieve higher-energy emission from a relatively low triplet energy. A green MADF device reaches a peak efficiency of 22% with an estimated LT95 over 350 hours at 1,000 cd/m2. Additionally, a blue charge confined OLED of PtON1a-tBu demonstrated a peak efficiency above 20%, CIE coordinated of (0.16, 0.27), and emission onset at 425 nm.
High triplet energy hosts are required for the realization of stable and efficient deep blue emission. A rigid “M”-type carbazole/fluorene hybrid called mDCzPF and a carbazole/9-silafluorene hybrid called mDCzPSiF are demonstrated to have high triplet energies ET=2.88 eV and 3.03 eV respectively. Both hosts are demonstrated to have reasonable stability and can serve as a template for future material design. The techniques presented here demonstrate alternative approaches for improving the performance of OLED devices and help to bring this technology closer to widespread commercialization.
OLEDs employing step-wide graded-doped emissive layers were designed to improve charge balance and center the exciton formation zone leading to improved device performance. A red OLED with a peak efficiency of 16.9% and an estimated LT97 over 2,000 hours at 1,000 cd/m2 was achieved. Employing a similar structure, a sky-blue OLED was demonstrated with a peak efficiency of 17.4% and estimated LT70 over 1,300 hours at 1,000 cd/m2. Furthermore, the sky-blue OLEDs color was improved to CIE coordinates of (0.15, 0.25) while maintaining an efficiency of 16.9% and estimated LT70 over 600 hours by incorporating a fluorescent sensitizer. These devices represent literature records at the time of publication for efficient and stable platinum phosphorescent OLEDs.
A newly developed class of emitters, metal-assisted delayed-fluorescence (MADF), are demonstrated to achieve higher-energy emission from a relatively low triplet energy. A green MADF device reaches a peak efficiency of 22% with an estimated LT95 over 350 hours at 1,000 cd/m2. Additionally, a blue charge confined OLED of PtON1a-tBu demonstrated a peak efficiency above 20%, CIE coordinated of (0.16, 0.27), and emission onset at 425 nm.
High triplet energy hosts are required for the realization of stable and efficient deep blue emission. A rigid “M”-type carbazole/fluorene hybrid called mDCzPF and a carbazole/9-silafluorene hybrid called mDCzPSiF are demonstrated to have high triplet energies ET=2.88 eV and 3.03 eV respectively. Both hosts are demonstrated to have reasonable stability and can serve as a template for future material design. The techniques presented here demonstrate alternative approaches for improving the performance of OLED devices and help to bring this technology closer to widespread commercialization.
ContributorsKlimes, Kody George (Author) / Li, Jian (Thesis advisor) / Adams, James (Committee member) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2019
Description
The residential building sector accounts for more than 26% of the global energy consumption and 17% of global CO2 emissions. Due to the low cost of electricity in Kuwait and increase of population, Kuwaiti electricity consumption tripled during the past 30 years and is expected to increase by 20% by 2027. In this dissertation, a framework is developed to assess energy savings techniques to help policy-makers make educated decisions. The Kuwait residential energy outlook is studied by modeling the baseline energy consumption and the diffusion of energy conservation measures (ECMs) to identify the impacts on household energy consumption and CO2 emissions.
The energy resources and power generation in Kuwait were studied. The characteristics of the residential buildings along with energy codes of practice were investigated and four building archetypes were developed. Moreover, a baseline of end-use electricity consumption and demand was developed. Furthermore, the baseline energy consumption and demand were projected till 2040. It was found that by 2040, energy consumption would double with most of the usage being from AC. While with lighting, there is a negligible increase in consumption due to a projected shift towards more efficient lighting. Peak demand loads are expected to increase by an average growth rate of 2.9% per year. Moreover, the diffusion of different ECMs in the residential sector was modeled through four diffusion scenarios to estimate ECM adoption rates. ECMs’ impact on CO2 emissions and energy consumption of residential buildings in Kuwait was evaluated and the cost of conserved energy (CCE) and annual energy savings for each measure was calculated. AC ECMs exhibited the highest cumulative savings, whereas lighting ECMs showed an immediate energy impact. None of the ECMs in the study were cost effective due to the high subsidy rate (95%), therefore, the impact of ECMs at different subsidy and rebate rates was studied. At 75% subsidized utility price and 40% rebate only on appliances, most of ECMs will be cost effective with high energy savings. Moreover, by imposing charges of $35/ton of CO2, most ECMs will be cost effective.
The energy resources and power generation in Kuwait were studied. The characteristics of the residential buildings along with energy codes of practice were investigated and four building archetypes were developed. Moreover, a baseline of end-use electricity consumption and demand was developed. Furthermore, the baseline energy consumption and demand were projected till 2040. It was found that by 2040, energy consumption would double with most of the usage being from AC. While with lighting, there is a negligible increase in consumption due to a projected shift towards more efficient lighting. Peak demand loads are expected to increase by an average growth rate of 2.9% per year. Moreover, the diffusion of different ECMs in the residential sector was modeled through four diffusion scenarios to estimate ECM adoption rates. ECMs’ impact on CO2 emissions and energy consumption of residential buildings in Kuwait was evaluated and the cost of conserved energy (CCE) and annual energy savings for each measure was calculated. AC ECMs exhibited the highest cumulative savings, whereas lighting ECMs showed an immediate energy impact. None of the ECMs in the study were cost effective due to the high subsidy rate (95%), therefore, the impact of ECMs at different subsidy and rebate rates was studied. At 75% subsidized utility price and 40% rebate only on appliances, most of ECMs will be cost effective with high energy savings. Moreover, by imposing charges of $35/ton of CO2, most ECMs will be cost effective.
ContributorsAlajmi, Turki (Author) / Phelan, Patrick E (Thesis advisor) / Kaloush, Kamil (Committee member) / Huang, Huei-Ping (Committee member) / Wang, Liping (Committee member) / Hajiah, Ali (Committee member) / Arizona State University (Publisher)
Created2019
Description
Unsupervised learning of time series data, also known as temporal clustering, is a challenging problem in machine learning. This thesis presents a novel algorithm, Deep Temporal Clustering (DTC), to naturally integrate dimensionality reduction and temporal clustering into a single end-to-end learning framework, fully unsupervised. The algorithm utilizes an autoencoder for temporal dimensionality reduction and a novel temporal clustering layer for cluster assignment. Then it jointly optimizes the clustering objective and the dimensionality reduction objective. Based on requirement and application, the temporal clustering layer can be customized with any temporal similarity metric. Several similarity metrics and state-of-the-art algorithms are considered and compared. To gain insight into temporal features that the network has learned for its clustering, a visualization method is applied that generates a region of interest heatmap for the time series. The viability of the algorithm is demonstrated using time series data from diverse domains, ranging from earthquakes to spacecraft sensor data. In each case, the proposed algorithm outperforms traditional methods. The superior performance is attributed to the fully integrated temporal dimensionality reduction and clustering criterion.
ContributorsMadiraju, NaveenSai (Author) / Liang, Jianming (Thesis advisor) / Wang, Yalin (Thesis advisor) / He, Jingrui (Committee member) / Arizona State University (Publisher)
Created2018