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Description
The management of underground utilities is a complex and challenging task due to the uncertainty regarding the location of existing infrastructure. The lack of accurate information often leads to excavation-related damages, which pose a threat to public safety. In recent years, advanced underground utilities management systems have been developed to improve the safety and efficiency of excavation work. This dissertation aims to explore the potential applications of blockchain technology in the management of underground utilities and reduction of excavation-related damage. The literature review provides an overview of the current systems for managing underground infrastructure, including Underground Infrastructure Management (UIM) and 811, and highlights the benefits of advanced underground utilities management systems in enhancing safety and efficiency on construction sites. The review also examines the limitations and challenges of the existing systems and identifies the opportunities for integrating blockchain technology to improve their performance. The proposed application involves the creation of a shared database of information about the location and condition of pipes, cables, and other underground infrastructure, which can be updated in real time by authorized users such as utility companies and government agencies. The use of blockchain technology can provide an additional layer of security and transparency to the system, ensuring the reliability and accuracy of the information. Contractors and excavation companies can access this information before commencing work, reducing the risk of accidental damage to underground utilities.
ContributorsAlnahari, Mohammed S (Author) / Ariaratnam, Samuel T (Thesis advisor) / El Asmar, Mounir (Committee member) / Czerniawski, Thomas (Committee member) / Arizona State University (Publisher)
Created2023
Description
In image classification tasks, images are often corrupted by spatial transformationslike translations and rotations. In this work, I utilize an existing method that uses
the Fourier series expansion to generate a rotation and translation invariant representation of closed contours found in sketches, aiming to attenuate the effects of distribution shift caused by the aforementioned transformations. I use this technique to
transform input images into one of two different invariant representations, a Fourier
series representation and a corrected raster image representation, prior to passing
them to a neural network for classification. The architectures used include convolutional neutral networks (CNNs), multi-layer perceptrons (MLPs), and graph neural
networks (GNNs). I compare the performance of this method to using data augmentation during training, the standard approach for addressing distribution shift, to see
which strategy yields the best performance when evaluated against a test set with
rotations and translations applied. I include experiments where the augmentations
applied during training both do and do not accurately reflect the transformations encountered at test time. Additionally, I investigate the robustness of both approaches
to high-frequency noise. In each experiment, I also compare training efficiency across
models. I conduct experiments on three data sets, the MNIST handwritten digit
dataset, a custom dataset (QD-3) consisting of three classes of geometric figures from
the Quick, Draw! hand-drawn sketch dataset, and another custom dataset (QD-345)
featuring sketches from all 345 classes found in Quick, Draw!. On the smaller problem space of MNIST and QD-3, the networks utilizing the Fourier-based technique to
attenuate distribution shift perform competitively with the standard data augmentation strategy. On the more complex problem space of QD-345, the networks using the
Fourier technique do not achieve the same test performance as correctly-applied data
augmentation. However, they still outperform instances where train-time augmentations mis-predict test-time transformations, and outperform a naive baseline model
where no strategy is used to attenuate distribution shift. Overall, this work provides
evidence that strategies which attempt to directly mitigate distribution shift, rather
than simply increasing the diversity of the training data, can be successful when
certain conditions hold.
ContributorsWatson, Matthew (Author) / Yang, Yezhou YY (Thesis advisor) / Kerner, Hannah HK (Committee member) / Yang, Yingzhen YY (Committee member) / Arizona State University (Publisher)
Created2023
Description
Animals have always been a source of inspiration for real-life problems. The octopus is one such animal that has a lot of untapped potential. The octopus’s arm is without solid joints or bone structure and despite this it can achieve many complicated movements with virtually infinite degrees of freedom. This ability is made possible through the unique morphology of the arm. The octopus’s arm is divided into transverse, longitudinal, oblique, and circular muscle groups and each one has a unique muscle fiber orientation. The octopus’s arm is classified as a hydrostat because it maintains a constant volume while contracting with the help of its different muscle groups. These muscle groups allow elongation, shortening, bending, and twisting of the arm when they work in combination with each other. To confirm the role of transverse and longitudinal muscle groups, an electromyography (EMG) recording of these muscle groups was performed while an amputated arm of an Octopus bimaculoides was stimulated with an electrical signal to induce movement. Statistical analysis was performed on these results to confirm the roles of each muscle group quantitatively. Octopus arm morphology was previously assumed to be uniform along the arm. Through a magnetic resonance imaging (MRI) study at the proximal, middle, and distal sections of the arm this notion was disproven, and a new pattern was discovered. Drawing inspiration from this finding and previous octopus arm prototypes, 4 bio-inspired designs were conceived and tested in finite element analysis (FEA) simulations. Four tests in elongation, shortening, bending, and transverse-assisted bending movements were performed on all designs to compare each design’s performance. The findings in this study have applications in engineering and soft robotics fields for use cases such as, handling fragile objects, minimally invasive surgeries, difficult-to-access areas that require squeezing through small holes, and other novel cases.
ContributorsAhmadi, Salaheddin (Author) / Marvi, Hamidreza (Thesis advisor) / Fisher, Rebecca (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2023
Description
Open Information Extraction (OIE) is a subset of Natural Language Processing (NLP) that constitutes the processing of natural language into structured and machine-readable data. This thesis uses data in Resource Description Framework (RDF) triple format that comprises of a subject, predicate, and object. The extraction of RDF triples from natural language is an essential step towards importing data into web ontologies as part of the linked open data cloud on the Semantic web. There have been a number of related techniques for extraction of triples from plain natural language text including but not limited to ClausIE, OLLIE, Reverb, and DeepEx. This proposed study aims to reduce the dependency on conventional machine learning models since they require training datasets, and the models are not easily customizable or explainable. By leveraging a context-free grammar (CFG) based model, this thesis aims to address some of these issues while minimizing the trade-offs on performance and accuracy. Furthermore, a deep-dive is conducted to analyze the strengths and limitations of the proposed approach.
ContributorsSingh, Varun (Author) / Bansal, Srividya (Thesis advisor) / Bansal, Ajay (Committee member) / Mehlhase, Alexandra (Committee member) / Arizona State University (Publisher)
Created2023
Quantum Mechanical Study of the Electronic Structure and Thermoelectric Properties of Heusler Alloys
Description
Heusler alloys were discovered in 1903, and materials with half-metallic characteristics have drawn more attention from researchers since the advances in semiconductor industry [1]. Heusler alloys have found application as spin-filters, tunnel junctions or giant magnetoresistance (GMR) devices in technological applications [1]. In this work, the electronic structures, phonon dispersion, thermal properties, and electrical conductivities of PdMnSn and six novel alloys (AuCrSn, AuMnGe, Au2MnSn, Cu2NiGe, Pd2NiGe and Pt2CoSn) along with their magnetic moments are studied using ab initio calculations to understand the roots of half-metallicity in these alloys of Heusler family. From the phonon dispersion, the thermodynamic stability of the alloys in their respective phases is assessed. Phonon modes were also used to further understand the electrical transport in the crystals of these seven alloys. This study evaluates the relationship between materials' electrical conductivity and minority-spin bandgap in the band structure, and it provides suggestions for selecting constituent elements when designing new half-metallic Heusler alloys of C1b and L21 structures.
ContributorsPatel, Deep (Author) / Zhuang, Houlong (Thesis advisor) / Solanki, Kiran (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2023
Description
Air conditioning is a significant energy consumer in buildings, especially in humid regions where a substantial portion of energy is used to remove moisture rather than cool the air. Traditional dehumidification methods, which cool air to its dew point to condense water vapor, are energy intensive. This process unnecessarily overcools the air, only to reheat it to the desired temperature.This research introduces thermoresponsive materials as efficient desiccants to reduce energy demand for dehumidification. A system using lower critical solution temperature (LCST) type ionic liquids (ILs) as dehumidifiers is presented. Through the Flory-Huggins theory of mixtures, interactions between ionic liquids and water are analyzed. LCST ionic liquids demonstrate superior performance, with a coefficient of performance (COP) four times higher than non-thermoresponsive desiccants under similar conditions. The efficacy of ionic liquids as dehumidifiers is assessed based on properties like LCST temperature and enthalpic interaction parameter.
The research also delves into thermoresponsive solid desiccants, particularly polymers, using the Vrentas-Vrentas model. This model offers a more accurate depiction of their behaviors compared to the Flory-Huggins theory by considering elastic energy stored in the polymers. Moisture absorption in thin film polymers is studied under diverse conditions, producing absorption isotherms for various temperatures and humidities. Using temperature-dependent interaction parameters, the behavior of the widely-used thermoresponsive polymer (TRP) PNIPAAm and hypothetical TRPs is investigated. The parameters from the model are used as input to do a finite element analysis of a thermoresponsive dehumidifier. This model demonstrates the complete absorption-desorption cycle under varied conditions such as polymer absorption temperature, relative humidity, and air speed. Results indicate that a TRP with enhanced absorption capacity and an LCST of 50℃ achieves a peak moisture removal efficiency (MRE) of 0.9 at 75% relative humidity which is comparable to other existing thermoresponsive dehumidification systems. But other TRPs with even greater absorption capacity can produce MRE as high as 3.6. This system also uniquely recovers water in liquid form.
ContributorsRana, Ashish (Author) / Wang, Robert RW (Thesis advisor) / Green, Matthew MG (Committee member) / Milcarek, Ryan RM (Committee member) / Wang, Liping LW (Committee member) / Phelan, Patrick PP (Committee member) / Arizona State University (Publisher)
Created2023
Description
Advancements in three-dimensional (3D) additive manufacturing techniques have opened up new possibilities for healthcare systems and the medical industry, allowing for the realization of concepts that were once confined to theoretical discussions. Among these groundbreaking research endeavors is the development of intricate magnetic structures that can be actuated through non-invasive methods, including electromagnetic and magnetic actuation. Magnetic actuation, in particular, offers the advantage of untethered operation. In this study, a photopolymerizable resin infused with Fe3O4 oxide nanoparticles is employed in the printing process using the micro-continuous liquid interface production technique. The objective is to optimize the manufacturing process to produce microstructures featuring smooth surfaces and reduced surface porosity, and enhanced flexibility and magnetic actuation. Various intricate structures are fabricated to validate the printing process's capabilities. Furthermore, the assessment of the flexibilty of these 3D-printed structures is conducted in the presence of an external magnetic field using a homemade bending test setup, allowing for a comprehensive characterization of these components. This research serves as a foundation for the future design and development of micro-robots using micro-continuous liquid interface production technique.
ContributorsJha, Ujjawal (Author) / Chen, Xiangfan (Thesis advisor) / Li, Xiangjia (Committee member) / Jin, Kailong (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2023
Description
Instruction tuning of language models has demonstrated the ability to enhance model generalization to unseen tasks via in-context learning using a few examples. However, typical supervised learning still requires a plethora of training data for downstream or “Held-in” tasks. Often in real-world situations, there is a scarcity of data available for finetuning, falling somewhere between few shot inference and fully supervised finetuning. In this work, I demonstrate the sample efficiency of instruction tuned models over various tasks by estimating the minimal training data required by downstream or “Held-In” tasks to perform transfer learning and match the performance of state-of-the-art (SOTA) supervised models. I conduct experiments on 119 tasks from Super Natural Instructions (SuperNI) in both the single task learning / Expert Modelling (STL) and multi task learning (MTL) settings. My findings reveal that, in the STL setting, instruction tuned models equipped with 25% of the downstream train data surpass the SOTA performance on the downstream tasks. In the MTL setting, an instruction tuned model trained on only 6% of downstream training data achieve SOTA, while using 100% of the training data results in a 3.69% points improvement (ROUGE-L 74.68) over the previous SOTA. I conduct an analysis on T5 vs Tk-Instruct by developing several baselines to demonstrate that instruction tuning aids in increasing both sample efficiency and transfer learning. Additionally, I observe a consistent ∼ 4% performance increase in both settings when pre-finetuning is performed with instructions. Finally, I conduct a categorical study and find that contrary to previous results, tasks in the question rewriting and title generation categories suffer from instruction tuning.
ContributorsGupta, Himanshu (Author) / Baral, Chitta Dr (Thesis advisor) / Mitra, Arindam Dr (Committee member) / Gopalan, Nakul Dr (Committee member) / Arizona State University (Publisher)
Created2023
Description
The evolution of technology, including the proliferation of the Internet of Things (IoT), advanced sensors, intelligent systems, and more, has paved the way for the establishment of smart homes. These homes bring a new era of automation with interconnected devices, offering increased services. However, they also introduce data security and device management challenges. Current smart home technologies are susceptible to security violations, leaving users vulnerable to data compromise, privacy invasions, and physical risks. These systems often fall short in implementing stringent data security safeguards, and the user control process is complex. In this thesis, an approach is presented to improve smart home security by integrating private blockchain technology with situational awareness access control. Using blockchain technology ensures transparency and immutability in data transactions. Transparency from the blockchain enables meticulous tracking of data access, modifications, and policy changes. The immutability of blockchain is utilized to strengthen the integrity of data, deterring, and preventing unauthorized alterations. While the designed solution leverages these specific blockchain features, it consciously does not employ blockchain's decentralization due to the limited computational resources of IoT devices and the focused requirement for centralized management within a smart home context. Additionally, situational awareness facilitates the dynamic adaptation of access policies.
The strategies in this thesis excel beyond existing solutions, providing fine-grained access control, reliable transaction data storage, data ownership, audibility, transparency, access policy, and immutability. This approach is thoroughly evaluated against existing smart home security improvement solutions.
ContributorsLin, Zhicheng (Author) / Yau, Stephen S. (Thesis advisor) / Baek, Jaejong (Committee member) / Ghayekhloo, Samira (Committee member) / Arizona State University (Publisher)
Created2023
Description
The advancement of cloud technology has impacted society positively in a number of ways, but it has also led to an increase in threats that target private information available on cloud systems. Intrusion prevention systems play a crucial role in protecting cloud systems from such threats. In this thesis, an intrusion prevention approach todetect and prevent such threats in real-time is proposed. This approach is designed for network-based intrusion prevention systems and leverages the power of supervised machine learning with Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) algorithms, to analyze the flow of each packet that is sent to a cloud system through the network. The innovations of this thesis include developing a custom LSTM architecture, using this architecture to train a LSTM model to identify attacks and using TCP reset functionality to prevent attacks for cloud systems. The aim of this thesis is to provide a framework for an Intrusion Prevention System. Based on simulations and experimental results with the NF-UQ-NIDS-v2 dataset, the proposed system is accurate, fast, scalable and has a low rate of false positives, making it suitable for real world applications.
ContributorsGianchandani, Siddharth (Author) / Yau, Stephen (Thesis advisor) / Zhao, Ming (Committee member) / Lee, Kookjin (Committee member) / Arizona State University (Publisher)
Created2023