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- Genre: Doctoral Dissertation
- Creators: Liu, Yongming
- Resource Type: Text
Description
Ultrasound has become one of the most popular non-destructive characterization tools for soft materials. Compared to conventional ultrasound imaging, quantitative ultrasound has the potential of analyzing detailed microstructural variation through spectral analysis. Because of having a better axial and lateral resolution, and high attenuation coefficient, quantitative high-frequency ultrasound analysis (HFUA) is a very effective tool for small-scale penetration depth application. One of the QUS parameters, peak density had recently shown a promising response with the variation in the soft material microstructure. Acoustic scattering is arguably the most important factor behind different parametric responses in ultrasound spectra. Therefore, to evaluate peak density, acoustic scattering at different frequency levels was investigated. Analytical, computational, and experimental analysis was conducted to observe both single and multiple scattering in different microstructural setups. It was observed that peak density was an effective tool to express different levels of acoustic scattering that occurred through microstructural variation. The feasibility of the peak density parameter was further evaluated in ultrasound C-scan imaging. The study was also extended to detect the relative position of the imaged structure in the direction of wave propagation. For this purpose, a derivative parameter of peak density named mean peak to valley distance (MPVD) was developed to address the limitations of peak density. The study was then focused on detecting soft tissue malignancy. The histology-based computational study of HFUA was conducted to detect various breast tumor (soft tissue) grades. It was observed that both peak density and MPVD parameters could identify tumor grades at a certain level. Finally, the study was focused on evaluating the feasibility of ultrasound parameters to detect asymptotic breast carcinoma i.e., ductal carcinoma in situ (DCIS) in the surgical margin of the breast tumor. In that computational study, breast pathologies were modeled by including all the phases of DCIS. From the similar analysis mentioned above, it was understood that both peak density and MPVD parameters could detect various breast pathologies like ductal hyperplasia, DCIS, and calcification during intraoperative margin analysis. Furthermore, the spectral features of the frequency spectrums from various pathologies also provided significant information to identify them conclusively.
ContributorsPaul, Koushik (Author) / Ladani, Leila (Thesis advisor) / Razmi, Jafar (Committee member) / Holloway, Julianne (Committee member) / Li, Xiangjia (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2022
Description
Tire blowout often occurs during driving, which can suddenly disturb vehicle motions and seriously threaten road safety. Currently, there is still a lack of effective methods to mitigate tire blowout risks in everyday traffic, even for automated vehicles. To fundamentally study and systematically resolve the tire blowout issue for automated vehicles, a collaborative project between General Motors (GM) and Arizona State University (ASU) has been conducted since 2018. In this dissertation, three main contributions of this project will be presented. First, to explore vehicle dynamics with tire blowout impacts and establish an effective simulation platform for close-loop control performance evaluation, high-fidelity tire blowout models are thoroughly developed by explicitly considering important vehicle parameters and variables. Second, since human cooperation is required to control Level 2/3 partially automated vehicles (PAVs), novel shared steering control schemes are specifically proposed for tire blowout to ensure safe vehicle stabilization via cooperative driving. Third, for Level 4/5 highly automated vehicles (HAVs) without human control, the development of control-oriented vehicle models, controllability study, and automatic control designs are performed based on impulsive differential systems (IDS) theories.
Co-simulations Matlab/Simulink® and CarSim® are conducted to validate performances of all models and control designs proposed in this dissertation. Moreover, a scaled test vehicle at ASU and a full-size test vehicle at GM are well instrumented for data collection and control implementation. Various tire blowout experiments for different scenarios are conducted for more rigorous validations. Consequently, the proposed high-fidelity tire blowout models can correctly and more accurately describe vehicle motions upon tire blowout. The developed shared steering control schemes for PAVs and automatic control designs for HAVs can effectively stabilize a vehicle to maintain path following performance in the driving lane after tire blowout.
In addition to new research findings and developments in this dissertation, a pending patent for tire blowout detection is also generated in the tire blowout project. The obtained research results have attracted interest from automotive manufacturers and could have a significant impact on driving safety enhancement for automated vehicles upon tire blowout.
ContributorsLi, Ao (Author) / Chen, Yan (Thesis advisor) / Berman, Spring (Committee member) / Kannan, Arunachala Mada (Committee member) / Liu, Yongming (Committee member) / Lin, Wen-Chiao (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2023
Description
Reliability growth is not a new topic in either engineering or statistics and has been a major focus for the past few decades. The increasing level of high-tech complex systems and interconnected components and systems implies that reliability problems will continue to exist and may require more complex solutions. The most heavily used experimental designs in assessing and predicting a systems reliability are the "classical designs", such as full factorial designs, fractional factorial designs, and Latin square designs. They are so heavily used because they are optimal in their own right and have served superbly well in providing efficient insight into the underlying structure of industrial processes. However, cases do arise when the classical designs do not cover a particular practical situation. Repairable systems are such a case in that they usually have limitations on the maximum number of runs or too many varying levels for factors. This research explores the D-optimal design criteria as it applies to the Poisson Regression model on repairable systems, with a number of independent variables and under varying assumptions, to include the total time tested at a specific design point with fixed parameters, the use of a Bayesian approach with unknown parameters, and how the design region affects the optimal design. In applying experimental design to these complex repairable systems, one may discover interactions between stressors and provide better failure data. Our novel approach of accounting for time and the design space in the early stages of testing of repairable systems should, theoretically, in the final engineering design improve the system's reliability, maintainability and availability.
ContributorsTAYLOR, DUSTIN (Author) / Montgomery, Douglas (Thesis advisor) / Pan, Rong (Thesis advisor) / Rigdon, Steve (Committee member) / Freeman, Laura (Committee member) / Iquebal, Ashif (Committee member) / Arizona State University (Publisher)
Created2023
Mechanical Behavior of Meteorites: Multiscale Characterization of the Strength and Failure Mechanism
Description
In this research, the chemical and mineralogical compositions, physical and mechanical properties, and failure mechanisms of two ordinary chondrite (OCs) meteorites Aba Panu (L3) and Viñales (L6), and the iron meteorite called Gibeon (IVA) were studied. OCs are dominated by anhydrous silicates with lesser amounts of sulfides and native Fe-Ni metals, while Gibeon is primarily composed of Fe-Ni metals with scattered inclusions of graphite and troilite. The OCs were investigated to understand their response to compressive loading, using a three-dimensional (3-D) Digital Image Correlation (DIC) technique to measure full-field deformation and strain during compression. The DIC data were also used to identify the effects of mineralogical and structural heterogeneity on crack formation and growth. Even though Aba Panu and Viñales are mineralogically similar and are both classified as L ordinary chondrites, they exhibit differences in compressive strengths due to variations in chemical compositions, microstructure, and the presence of cracks and shock veins. DIC data of Aba Panu and Viñales show a brittle failure mechanism, consistent with the crack formation and growth from pre-existing microcracks and porosity. In contrast, the Fe-Ni phases of the Gibeon meteorite deform plastically without rupture during compression, whereas during tension, plastic deformations followed by necking lead to final failure. The Gibeon DIC results showed strain concentration in the tensile gauge region along the sample edge, resulting in the initiation of new damage surfaces that propagated perpendicular to the loading direction. Finally, an in-situ low-temperature testing method of iron meteorites was developed to study the response of their unique microstructure and failure mechanism.
ContributorsRabbi, Md Fazle (Author) / Chattopadhyay, Aditi (Thesis advisor) / Garvie, Laurence A.J. (Thesis advisor) / Liu, Yongming (Committee member) / Fard, Masoud Yekani (Committee member) / Cotto-Figueroa, Desiree (Committee member) / Arizona State University (Publisher)
Created2023
Description
Existing machine learning and data mining techniques have difficulty in handling three characteristics of real-world data sets altogether in a computationally efficient way: (1) different data types with both categorical data and numeric data, (2) different variable relations in different value ranges of variables, and (3) unknown variable dependency.This dissertation developed a Partial-Value Association Discovery (PVAD) algorithm to overcome the above drawbacks in existing techniques. It also enables the discovery of partial-value and full-value variable associations showing both effects
of individual variables and interactive effects of multiple variables. The algorithm is compared with Association rule mining and Decision Tree for validation purposes. The results show that the PVAD algorithm can overcome the shortcomings of existing methods.
The second part of this dissertation focuses on knee point detection on noisy data. This extended research topic was inspired during the investigation into categorization for numeric data, which corresponds to Step 1 of the PVAD algorithm. A new mathematical definition of knee point on discrete data is introduced. Due to the unavailability of ground truth data or benchmark data sets, functions used to generate synthetic data are carefully selected and defined. These functions are subsequently employed to create the data sets for this experiment. These synthetic data sets are useful for systematically evaluating and comparing the performance of existing methods. Additionally, a deep-learning model is devised for this problem. Experiments show that the proposed model surpasses existing methods in all synthetic data sets, regardless of whether the samples have single or multiple knee points.
The third section presents the application results of the PVAD algorithm to real-world
data sets in various domains. These include energy consumption data of an Arizona State University (ASU) building, Computer Network, and ASU Engineering Freshmen Retention. The PVAD algorithm is utilized to create an associative network for energy consumption modeling, analyze univariate and multivariate measures of network flow variables, and identify common and uncommon characteristics related to engineering student retention after their first year at the university. The findings indicate that the PVAD algorithm offers the advantage and capability to uncover variable relationships.
ContributorsFok, Ting Yan (Author) / Ye, Nong (Thesis advisor) / Iquebal, Ashif (Committee member) / Ju, Feng (Committee member) / Collofello, James (Committee member) / Arizona State University (Publisher)
Created2023
Description
Environmental problems are more abundant because of the rapid increase in urbanization, climate change, and population growth leading to the depletion of natural resources and endangerment of some species. The availability of infrastructure as well as socio-economic factors facilitate the illicit trade of wildlife through supply chain networks, adding further threats to species. Ecosystem conservation and protection of wildlife from illegal trade and poaching is fundamental to guarantee the survival of endangered species. Conservation efforts require a landscape approach that incorporates spatial features for the effective functionality of the selected reserve. This dissertation studies combinatorial optimization problems with application to two classes of societal problems: landscape conservation and disruption of illicit supply chains. The first and second chapter propose a mixed-integer formulation to model the reserve design problem with budget and ecological constraints. The first uses the radius of the smallest circle enclosing the selected areas as a metric of compactness. An extension of the model is proposed to solve the multi reserve design problem and the reserve expansion problem. The solution approach includes warm start heuristic, separation problem and cuts to improve model performance. The enhanced model outperforms the linearized and the equivalent nonlinear model. The second chapter uses the Reock’s metric as a metric of compactness. The solution approach includes warm start heuristic, knapsack based separation problem to inject solutions, and cuts to improve model performance. The enhanced model outperforms the default model. The third chapter proposes an integer programming model to solve the wildlife corridor design problem with minimum width requirement and a budget constraint. A separation algorithm is proposed to identify boundary patches and violations in the corridor width. A branch-and-cut approach is proposed to induce the corridor width and is tested on real-life landscape. The fourth chapter proposes an integer programming formulation to model the disruption of illicit supply chain problem. The proposed model enforces that at least x paths must be disrupted for an Origin-Destination pair to be disrupted and at least y arcs must be disrupted for a path to be disrupted. The proposed model is tested on real-life road networks.
ContributorsRavishankar, Shreyas (Author) / Sefair, Jorge A (Thesis advisor) / Escobedo, Adolfo R (Committee member) / Grubesic, Anthony (Committee member) / Iquebal, Ashif (Committee member) / Arizona State University (Publisher)
Created2023
Description
Over the past few decades, medical imaging is becoming important in medicine for disease diagnosis, prognosis, treatment assessment and health monitoring. As medical imaging has progressed, imaging biomarkers are being rapidly developed for early diagnosis and staging of disease. Detecting and segmenting objects from images are often the first steps in quantitative measurement of these biomarkers. While large objects can often be automatically or semi-automatically delineated, segmenting small objects (blobs) is challenging. The small object of particular interest in this dissertation are glomeruli from kidney magnetic resonance (MR) images. This problem has its unique challenges. First of all, the size of glomeruli is extremely small and very similar with noises from images. Second, there are massive of glomeruli in kidney, e.g. over 1 million glomeruli in human kidney, and the intensity distribution is heterogenous. A third recognized issue is that a large portion of glomeruli are overlapping and touched in images. The goal of this dissertation is to develop computational algorithms to identify and discover glomeruli related imaging biomarkers. The first phase is to develop a U-net joint with Hessian based Difference of Gaussians (UH-DoG) blob detector. Joining effort from deep learning alleviates the over-detection issue from Hessian analysis. Next, as extension of UH-DoG, a small blob detector using Bi-Threshold Constrained Adaptive Scales (BTCAS) is proposed. Deep learning is treated as prior of Difference of Gaussian (DoG) to improve its efficiency. By adopting BTCAS, under-segmentation issue of deep learning is addressed. The second phase is to develop a denoising convexity-consistent Blob Generative Adversarial Network (BlobGAN). BlobGAN could achieve high denoising performance and selectively denoise the image without affecting the blobs. These detectors are validated on datasets of 2D fluorescent images, 3D synthetic images, 3D MR (18 mice, 3 humans) images and proved to be outperforming the competing detectors. In the last phase, a Fréchet Descriptors Distance based Coreset approach (FDD-Coreset) is proposed for accelerating BlobGAN’s training. Experiments have shown that BlobGAN trained on FDD-Coreset not only significantly reduces the training time, but also achieves higher denoising performance and maintains approximate performance of blob identification compared with training on entire dataset.
ContributorsXu, Yanzhe (Author) / Wu, Teresa (Thesis advisor) / Iquebal, Ashif (Committee member) / Yan, Hao (Committee member) / Beeman, Scott (Committee member) / Arizona State University (Publisher)
Created2022
Description
Sequential event prediction or sequential pattern mining is a well-studied topic in the literature. There are a lot of real-world scenarios where the data is released sequentially. People believe that there exist repetitive patterns of event sequences so that the future events can be predicted. For example, many companies build their recommender system to predict the next possible product for the users according to their purchase history. The healthcare system discovers the relationships among patients’ sequential symptoms to mitigate the adverse effect of a treatment (drugs or surgery). Modern engineering systems like aviation/distributed computing/energy systems diagnosed failure event logs and took prompt actions to avoid disaster when a similar failure pattern occurs. In this dissertation, I specifically focus on building a scalable algorithm for event prediction and extraction in the aviation domain. Understanding the accident event is always the major concern of the safety issue in the aviation system. A flight accident is often caused by a sequence of failure events. Accurate modeling of the failure event sequence and how it leads to the final accident is important for aviation safety. This work aims to study the relationship of the failure event sequence and evaluate the risk of the final accident according to these failure events. There are three major challenges I am trying to deal with. (1) Modeling Sequential Events with Hierarchical Structure: I aim to improve the prediction accuracy by taking advantage of the multi-level or hierarchical representation of these rare events. Specifically, I proposed to build a sequential Encoder-Decoder framework with a hierarchical embedding representation of the events. (2) Lack of high-quality and consistent event log data: In order to acquire more accurate event data from aviation accident reports, I convert the problem into a multi-label classification. An attention-based Bidirectional Encoder Representations from Transformers model is developed to achieve good performance and interpretability. (3) Ontology-based event extraction: In order to extract detailed events, I proposed to solve the problem as a hierarchical classification task. I improve the model performance by incorporating event ontology. By solving these three challenges, I provide a framework to extract events from narrative reports and estimate the risk level of aviation accidents through event sequence modeling.
ContributorsZhao, Xinyu (Author) / Yan, Hao (Thesis advisor) / Liu, Yongming (Committee member) / Ju, Feng (Committee member) / Iquebal, Ashif (Committee member) / Arizona State University (Publisher)
Created2022
Description
Additive manufacturing consists of successive fabrication of materials layer upon layer to manufacture three-dimensional items. Several key problems such as poor quality of finished products and excessive operational costs are yet to be addressed before it becomes widely applicable in the industry. Retroactive/offline actions such as post-manufacturing inspections for defect detection in finished products are not only extremely expensive and ineffective but are also incapable of issuing corrective action signals during the building span. In-situ monitoring and optimal control methods, on the other hand, can provide viable alternatives to aid with the online detection of anomalies and control the process. Nevertheless, the complexity of process assumptions, unique structure of collected data, and high-frequency data acquisition rate severely deteriorates the performance of traditional and parametric control and process monitoring approaches. Out of diverse categories of additive manufacturing, Large-Scale Additive Manufacturing (LSAM) by material extrusion and Laser Powder Bed Fusion (LPBF) suffer the most due to their more advanced technologies and are therefore the subjects of study in this work. In LSAM, the geometry of large parts can impact the heat dissipation and lead to large thermal gradients between distance locations on the surface. The surface's temperature profile is captured by an infrared thermal camera and translated to a non-linear regression model to formulate the surface cooling dynamics. The surface temperature prediction methodology is then combined into an optimization model with probabilistic constraints for real-time layer time and material flow control. On-axis optical high-speed cameras can capture streams of melt pool images of laser-powder interaction in real-time during the process. Model-agnostic deep learning methods offer a great deal of flexibility when facing such unstructured big data and thus are appealing alternatives to their physical-related and regression-based modeling counterparts. A configuration of Convolutional Long-Short Term Memory (ConvLSTM) auto-encoder is proposed to learn a deep spatio-temporal representation from sequences of melt pool images collected from experimental builds. The unfolded bottleneck tensors are then further mined to construct a high accuracy and low false alarm rate anomaly detection and monitoring procedure.
ContributorsFathizadan, Sepehr (Author) / Ju, Feng (Thesis advisor) / Wu, Teresa (Committee member) / Lu, Yan (Committee member) / Iquebal, Ashif (Committee member) / Arizona State University (Publisher)
Created2022
Description
Cellular metamaterials arouse broad scientific interests due to the combination of host material and structure together to achieve a wide range of physical properties rarely found in nature. Stochastic foam as one subset has been considered as a competitive candidate for versatile applications including heat exchangers, battery electrodes, automotive, catalyst devices, magnetic shielding, etc. For the engineering of the cellular foam architectures, closed-form models that can be used to predict the mechanical and thermal properties of foams are highly desired especially for the recently developed ultralight weight shellular architectures. Herein, for the first time, a novel packing three-dimensional (3D) hollow pentagonal dodecahedron (HPD) model is proposed to simulate the cellular architecture with hollow struts. An electrochemical deposition process is utilized to manufacture the metallic hollow foam architecture. Mechanical and thermal testing of the as-manufactured foams are carried out to compare with the HPD model. Timoshenko beam theory is utilized to verify and explain the derived power coefficient relation. Our HPD model is proved to accurately capture both the topology and the physical properties of hollow stochastic foam. Understanding how the novel HPD model packing helps break the conventional impression that 3D pentagonal topology cannot fulfill the space as a representative volume element. Moreover, the developed HPD model can predict the mechanical and thermal properties of the manufactured hollow metallic foams and elucidating of how the inevitable manufacturing defects affect the physical properties of the hollow metallic foams. Despite of the macro-scale stochastic foam architecture, nano gradient gyroid lattices are studied using Molecular Dynamics (MD) simulation. The simulation result reveals that, unlike homogeneous architecture, gradient gyroid not only shows novel layer-by-layer deformation behavior, but also processes significantly better energy absorption ability. The deformation behavior and energy absorption are predictable and designable, which demonstrate its highly programmable potential.
ContributorsDai, Rui (Author) / Nian, Qiong (Thesis advisor) / Jiao, Yang (Committee member) / Kwon, Beomjin (Committee member) / Liu, Yongming (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2021