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Laterally Heterogeneous Seismic Anisotropy Investigated By Shear Wave Splitting Analyses, Yan Jia

Doctoral Dissertations

"Numerous geophysical studies suggest that seismic anisotropy is a nearly ubiquitous property of the Earth’s crust and upper mantle. In this study, we utilize the shear wave splitting technique to investigate the piercing-point-dependent azimuthal anisotropy beneath the northeastern edge of the Sichuan Basin in central China, and the spatial and temporal variations of anisotropy near the 2019 M7.1 Ridgecrest earthquake in California, respectively. A clear back azimuthal dependence of the splitting parameters and the lack of a 90° or 180° periodicity of azimuthal variation in the observed fast orientations provide strong evidence for the existence of piercing-point-dependent anisotropy beneath the …

Mantle Flow And Transition Zone Discontinuities Beneath The Carribean Plate: Constraints From Shear Wave Splitting And Receiver Function Analyses, Tu Xue

Doctoral Dissertations

"Azimuthal anisotropy quantified by teleseismic SKS, SKKS, PKS (“XKS”) and local S wave splitting parameters is used to investigate lithospheric deformation and asthenospheric flow beneath the boundary zone of the North American and Caribbean plates and adjacent areas. A total of 4915 XKS and 1202 pairs of local S wave splitting parameters were obtained at 24 broad band seismic stations. The XKS observations can be divided into two groups based on the spatial distribution of the resulting fast polarization orientations. Those observed on the Caribbean Plate are mostly WNW-ESE which are roughly trench-parallel. In contrast, the fast orientations observed on …

Application Of Machine Learning In Geophysics: Ranking Teleseismic Shear Wave Splitting Measurements And Classifying Different Types Of Earthquakes, Yanwei Zhang

Doctoral Dissertations

"During the past decades, applications of Machine Learning have been explosively developed to solve various academic and industrial problems, and over-human performance has been shown in diverse areas. In geophysical research, Machine Learning, especially Convolutional Neural Network (CNN), has been applied in numerous studies and demonstrated considerable potential. In this study, we applied CNN to solve two geophysical problems, ranking teleseismic shear splitting (SWS) measurements and classifying different types of earthquakes.

For ranking teleseismic SWS measurements, we utilized a CNN-based method to automatically select reliable SWS measurements. The CNN was trained by human-verified teleseismic SWS measurements and tested using synthetic …

Optimization Of Energy Storage Scheduling In Electricity Markets, Jian Liu

Doctoral Dissertations

"In the existing literature, merchants' trading actions are usually assumed not to affect market prices; however, a large-scale energy storage merchant’s actions can affect market prices. This work examined two electricity merchant scenarios: one with only energy storage and the other with both energy storage and renewable power plants. We approximated market impact via a linear function of the electricity traded by the merchant. This study began by applying dynamic programming to the optimal economic dispatch policy of electricity merchants and it considered the market impact, physical characteristics of storage systems, and the uncertainty of renewable energy sources. Then, this …

Disorder Effects In Frustrated Magnets And Absorbing State Transitions, Xuecheng Ye

Doctoral Dissertations

"Correlation, topology, and disorder can fundamentally affect the properties of interacting many-particle systems. After a short introduction which covers the basic concepts of phase transitions and scaling as well as the physics of Josephson junctions, the dissertation focuses on three separate projects.

The first project is motivated by the stripe and nematic phases observed e.g. in cuprate superconductors and iron pnictides. To understand the effects of disorder on such phases, we have investigated the behavior of the diluted J₁-J₂ Ising model. Spinless impurities generate a random-field disorder for the spin-density (stripe) order parameter, which destroys the stripe …

Depositional Conditions, Stratigraphic Evolution, And Allo- And Autogenic Controls Of Lower Permian Non-Marine Carbonate Rocks, Lucaogou Low-Order Cycle, Bogda Mountains, Nw China, Yiran Lu

Doctoral Dissertations

"The overall objective of this study is to interpret the depositional conditions of lacustrine carbonate deposits of the lower Permian Lucaogou low-order cycle in the greater Turpan-Junggar continental rift basin, NW China. The depositional environments of ten carbonate lithofacies in the Tarlong-Taodonggou half graben and the Zhaobishan section were interpreted. Allo- and autogenic factors were identified. They controlled characteristics and distribution pattern of carbonate deposits in regional and local scale, respectively. Allogenic factors include the amount and direction of regional siliciclastic input, basin geometry, cyclic humidity/aridity oscillation, temperature, and regional tectonics. Autogenic factors include local siliciclastic input, local topography, local …

Deep Learning-Based Surrogate Models For Post-Earthquake Damage Assessment, Xinzhe Yuan

Doctoral Dissertations

"Seismic damage assessment is a critical step to enhance community resilience in the wake of an earthquake. This study aims to develop deep learning-based surrogate models for widely used fragility curves to achieve more accurate and rapid assessment in practice. These surrogate models are based on artificial neural networks trained from the labelled ground motions whose resulting damage classes on targeted structures are determined by nonlinear time history analyses. The development of various surrogate models is progressed in four phases. In Phase I, the multilayer perceptron (MLP) is used to develop multivariate seismic classifiers with up to 50 hand-crafted intensity …

Adsorption Of Arsenic Onto River Sediments, Leticia Augusta Dos Santos Ferreira

Masters Theses

“Previous studies have noted the relationship between shallow groundwater rich in sodium (Na) and bicarbonate (HCO₃) and elevated levels of dissolved arsenic. However, most experimental work on arsenic adsorption in the presence of HCO₃ and differing Na/Ca ratios has proven difficult to extrapolate to natural systems because of differences in tested mineral compositions and component concentrations. In this study, I performed a series of adsorption experiments using river sediments to evaluate the influence of HCO₃ and monovalent/divalent cations on the extent of arsenic adsorption onto natural sediment in groundwater.

Batch adsorption (kinetics, equilibrium, and metal loading) …

A Convolutional Neural Network (Cnn) For Defect Detection Of Additively Manufactured Parts, Musarrat Farzana Rahman

Masters Theses

“Additive manufacturing (AM) is a layer-by-layer deposition process to fabricate parts with complex geometries. The formation of defects within AM components is a major concern for critical structural and cyclic loading applications. Understanding the mechanisms of defect formation and identifying the defects play an important role in improving the product lifecycle. The convolutional neural network (CNN) has been demonstrated to be an effective deep learning tool for automated detection of defects for both conventional and AM processes. A network with optimized parameters including proper data processing and sampling can improve the performance of the architecture. In this study, for the …

Experimental Optimization Of Simulated Ring Rolling Operation For Heavy Rail Industry, Jacob M. Summers

Masters Theses

“Industrially cast AISI 1070 steel wheel pre-forms from Amsted Rail Co. were experimentally hot rolled to simulate the conditions for industrial wheel rolling. Ring rolling of near net shape castings can improve location specific properties by decreasing segregation, closing porosity, and reducing grain size without the use of multiple forging operations in a traditional forging line. As-cast wheel sections were subjected to thermomechanical processing routes using a 2-high rolling mill in a temperature range of 830°C to 1200°C. The goal being to simulate the ring rolling process and optimize benefits of mechanical properties of the as-rolled steel. Charpy V- and …

Several Problems In Nonlinear Schrödinger Equations, Tim Van Hoose

Masters Theses

“We study several different problems related to nonlinear Schrödinger equations….

We prove several new results for the first equation: a modified scattering result for both an averaged version of the equation and the full equation, as well as a set of Strichartz estimates and a blowup result for the 3d cubic problem.

We also present an exposition of the classical work of Bourgain on invariant measures for the second equation in the mass-subcritical regime”--Abstract, page iv.

Pressure Relief Wells: Analysis Of Subsurface Heterogeneity To Evaluate Relief Well Locations For Mississippi River Levees, Emma Marie Young

Masters Theses

“When designing pressure relief well systems, it is imperative to understand what major geomorphology and heterogenies features are present, such as buried oxbow lakes, especially when the feature is parallel to the source, such as the Mississippi River. When present, there is a notable greater increase in head pressures, especially on the landward tow of the levee. This can cause erosional features that originally thought of to have been protected from by installing pressure relief wells. When comparing the effective hydraulic conductivities of horizontal clay layers and vertical clay layers spanning the length of the model, little to no noticeable …

Continuous And Discrete Models For Optimal Harvesting In Fisheries, Nagham Abbas Al Qubbanchee

Masters Theses

"This work focuses on the logistic growth model, where the Gordon-Schaefer model is considered in continuous time. We view the Gordon-Schaefer model as a bioeconomic equation involved in the fishing business, considering biological rates, carrying capacity, and total marginal costs and revenues. In [25], the authors illustrate the analytical solution of the Schaefer model using the integration by parts method and two theorems. The theorems have many assumptions with many different strategies. Due to the nature of the problem, the optimal control system involves many equations and functions, such as the second root of the equation. We concentrate on Theorem …

Numerical Investigations Of 2-D Magnetic Nozzles On Pulsed Plasma Plumes, Joshua Daniel Burch

Masters Theses

"This research presents studies of a novel type of magnetic nozzle that allows for three-dimensional (3-D) steering of a plasma plume. Numerical simulations were performed using Tech-X's USim® software to quantify the nozzle's capabilities. A2-D planar magnetic nozzle was applied to plumes of a nominal pulsed inductive plasma (PIP) source with discharge parameters similar to those of Missouri S&T's Missouri Plasmoid Experiment (MPX). Argon and xenon plumes were considered. Simulations were verified and validated through a mesh convergence study as well as comparison with available experimental data. Periodicity was achieved over the simulation run time and phase angle samples were …

Using Lake Sediment Chemistry In The Exploration For Nickel Laterite Deposits In Tropical Regions, Gabriela Yvonne Ramirez

Masters Theses

"Developing reliable geochemical indicators to guide the search for ore deposits is a long-standing goal. This particularly applies to tropical regions that are often heavily vegetated which hampers boots-on-the ground exploration efforts. Here we present the preliminary results of a study that investigates the usefulness of lake sediment chemistry in the exploration for nickel laterite ore deposits in tropical regions using Lake Izabal, Guatemala, as a case study. Lake Izabal’s catchment area contains Ni laterite deposits along its northern shore, whereas no Ni-laterite discoveries have been made in the south except for a Ni mine in the southern region that …

Juggling A Devil-Stick: Hybrid Orbit Stabilization Using The Impulse Controlled Poincaré Map, Nilay Kant, Ranjan Mukherjee

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The control design for juggling a devil-stick between two symmetric configurations is proposed. Impulsive forces are applied to the devil-stick at the two configurations; and impulse of the force and its point of application are modeled as the control inputs. The dynamics of the devil-stick is described by a single return Poincaré map, and it is shown that the control objective of juggling can be achieved by stabilizing a hybrid orbit. The impulse controlled Poincaré map (ICPM) approach, recently proposed for stabilization of continuous-time orbits of underactuated systems, is extended to achieve asymptotic stabilization of the hybrid orbit. The applicability …

Feature Learning For Optimal Control With B-Spline Representations, Vinay Kenny, Sixiong You, Chaoying Pei, Ran Dai

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The paper develops a feature learning-based method to solve optimal control problems using B-splines to approximate the optimal solutions. The feature learning-based optimal control method can quickly generate near-optimal trajectories for general optimal control problems subject to system dynamics and constraints. The steps in the proposed method are as follows: Firstly, by representing the state and control variables with B-spline functions, the optimal control problem is converted into an approximate nonlinear programming (NLP) problem, where parameters of the B-splines are identified as features of the optimal solution. Secondly, for a specific problem with designated inputs, a dataset of the optimal …

Improving Virtual Synchronous Generator Control In Microgrids Using Fuzzy Logic Control, Oroghene Oboreh-Snapps, Rui Bo, Buxin She, Fangxing Fran Li, Hantao Cui

Electrical and Computer Engineering Faculty Research & Creative Works

Virtual synchronous generators (VSG) are designed to mimic the inertia and damping characteristics of synchronous generators (SG), which can improve the frequency response of a microgrid. Unlike synchronous generators whose inertia and damping are restricted by the physical characteristics of the SG, VSG parameters can be more flexibly controlled to adapt to different disturbances. This paper therefore proposes a fuzzy logic controller designed to adaptively set the parameters of the VSG during a frequency event to ensure an improved frequency nadir and rate of change of frequency (ROCOF) response. The proposed control method is implemented and tested on the power …

Monetization For Content Generation And User Engagement On Social Media Platforms: Evidence From Paid Q&A, Jonathan Hua Ye, Cecil Eng Huang Chua

Business and Information Technology Faculty Research & Creative Works

Social media platforms want to increase their valuation in terms of total content quantity and user engagement. Monetization is often used to induce user content generation. However, research documents that while monetization increases the quantity of specific kinds of content, it does not necessarily increase the total content quantity or user engagement (i.e., platform value). Furthermore, the impact of monetization may depend on the social status of content creators. This article investigates paid question and answer (paid Q&A). Based on expectancy theory and relevant research, this article hypothesizes the effects of introducing paid Q&A on both total content quantity and …

A Novel Echo State Network Autoencoder For Anomaly Detection In Industrial Iot Systems, Fabrizio De Vita, Giorgio Nocera, Dario Bruneo, Sajal K. Das

Computer Science Faculty Research & Creative Works

The Industrial Internet of Things (IIoT) technology had a very strong impact on the realization of smart frameworks for detecting anomalous behaviors that could be potentially dangerous to a system. In this regard, most of the existing solutions involve the use of Artificial Intelligence (AI) models running on Edge devices, such as Intelligent Cyber Physical Systems (ICPS) typically equipped with sensing and actuating capabilities. However, the hardware restrictions of these devices make the implementation of an effective anomaly detection algorithm quite challenging. Considering an industrial scenario, where signals in the form of multivariate time-series should be analyzed to perform a …

Noise Resilient Learning For Attack Detection In Smart Grid Pmu Infrastructure, Prithwiraj Roy, Shameek Bhattacharjee, Sahar Abedzadeh, Sajal K. Das

Computer Science Faculty Research & Creative Works

Falsified data from compromised Phasor Measurement Units (PMUs) in a smart grid induce Energy Management Systems (EMS) to have an inaccurate estimation of the state of the grid, disrupting various operations of the power grid. Moreover, the PMUs deployed at the distribution layer of a smart grid show dynamic fluctuations in their data streams, which make it extremely challenging to design effective learning frameworks for anomaly-based attack detection. In this paper, we propose a noise resilient learning framework for anomaly-based attack detection specifically for distribution layer PMU infrastructure, that show real time indicators of data falsifications attacks while offsetting the …

Cansafe: An Mtd Based Approach For Providing Resiliency Against Dos Attack Within In-Vehicle Networks, Ayan Roy, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

Trending towards autonomous transportation systems, modern vehicles are equipped with hundreds of sensors and actuators that increase the intelligence of the vehicles with a higher level of autonomy, as well as facilitate increased communication with entities outside the in-vehicle network. However, increase in a contact point with the outside world has exposed the controller area network (CAN) of a vehicle to remote security vulnerabilities. In particular, an attacker can inject fake high priority messages within the CAN through the contact points, while preventing legitimate messages from controlling the CAN (Denial-of-Service (DoS) attack). In this paper, we propose a Moving Target …

Distributed Decision Making For V2v Charge Sharing In Intelligent Transportation Systems, Punyasha Chatterjee, Pratham Majumder, Arpita Debnath, Sajal K. Das

Computer Science Faculty Research & Creative Works

Electric vehicles (EVs) have emerged in the intelligent transportation system (ITS) to meet the increasing environmental concerns. To facilitate on-demand requirement of EV charging, vehicle-to-vehicle (V2V) charge transfer can be employed. However, most of the existing approaches to V2V charge sharing are centralized or semi-centralized, incurring huge message overhead, long waiting time, and infrastructural cost. In this paper, we propose novel distributed heuristic algorithms for V2V charge sharing based on the multi-criteria decision-making policy. The problem is mapped to an alias classical problem (i.e., optimum matching in weighted bipartite graphs), where the goal is to maximize the matching cardinality while …

Rssafe: Personalized Driver Behavior Prediction For Safe Driving, Bhumika, Debasis Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

While the increased demand for taxi services like Uber, Lyft, Hailo, Ola, Grab, Cabify etc. provides livelihood to many drivers, the desire to raise income forces the drivers to work very hard without rest. However, continuous journeys not only affect their health, but also lead to abnormal driving behavior such as rash driving, swerving, sideslipping, sudden brakes, or weaving, leading to accidents in the worst cases. Motivated by the severity of rising accidents and health issues among drivers, this paper proposes a recommendation system, called RsSafe, for the safety of drivers. Aiming to improve the driving quality and the driver's …

An Icn-Based Secure Task Cooperation Scheme In Challenging Wireless Edge Networks, Ningchun Liu, Shuai Gao, Teng Liang, Xindi Hou, Sajal K. Das

Computer Science Faculty Research & Creative Works

Task cooperation is an effective way to execute a complex task in challenging wireless edge networks. Existing TCP/IP-based solutions encounter the problem of low network resource utilization and the heavy dependency of infrastructure connections. Information-centric networking (ICN) is a promising architecture to address these issues. In existing ICN-based task cooperation schemes, the data reuse feature of ICN improves the utilization of network resources, which also brings potential security threats to the reused data. To guarantee the security of data reuse in task cooperation without affecting the data reuse feature, we propose an ICN-based secure task cooperation scheme. In our scheme, …

Toward Feature-Preserving Vector Field Compression, Xin Liang, Sheng Di, Franck Cappello, Mukund Raj, Chunhui Liu, Kenji Ono, Zizhong Chen, Tom Peterka, Hanqi Guo

Computer Science Faculty Research & Creative Works

The objective of this work is to develop error-bounded lossy compression methods to preserve topological features in 2D and 3D vector fields. Specifically, we explore the preservation of critical points in piecewise linear and bilinear vector fields. We define the preservation of critical points as, without any false positive, false negative, or false type in the decompressed data, (1) keeping each critical point in its original cell and (2) retaining the type of each critical point (e.g., saddle and attracting node). The key to our method is to adapt a vertex-wise error bound for each grid point and to compress …

Delivery With Uavs: A Simulated Dataset Via Ats, Giulio Rigoni, Cristina M. Pinotti, Bhumika, Debasis Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

We consider a delivery food service operated by Unmanned Aerial Vehicles (UAVs). Due to the absence of a dataset on UAVs deliveries in the literature, and since it is not possible to perform real tests, we create a dataset using an open-Air Traffic Simulator (ATS). Precisely, we converted a set of food deliveries operated by wheeled vehicles, proposed in the literature [1], into a set of simulated UAVs deliveries. For each delivery, we ran a UAV flight from the source to the destination. The results showed that, as expected, the UAV's course is shorter than the vehicle trajectory on the …

Targeted Content-Sharing In A Multi-Group Dtn Application Using Attribute-Based Encryption, Xiaofei Cao, Shudip Datta, Ram Charan Bolla, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

In a battlefield, multiple groups operate with different missions, but their missions and groups can dynamically change based on the evolving situation. Due to the unavailability of network infrastructure after deployment, group members form a Delay Tolerant Network (DTN) which is prone to security attacks. Hence, based on the mission attributes, group memberships, nodes' interests, and data tags determination, targeted contents need to be distributed in a secure fashion to different users. Though existing Attributes Based Encryption (ABE) can provide security of information, revoking a member from a group is always an issue in DTN as the Attribute Authority (AA) …

Active Learning Augmented Folded Gaussian Model For Anomaly Detection In Smart Transportation, Venkata Praveen Kumar Madhavarapu, Prithwiraj Roy, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

Smart transportation networks have become instrumental in smart city applications with the potential to enhance road safety, improve the traffic management system and driving experience. A Traffic Message Channel (TMC) is an IoT device that records the data collected from the vehicles and forwards it to the Roadside Units (RSUs). This data is further processed and shared with the vehicles to inquire the fastest route and incidents that can cause significant delays. The failure of the TMC sensors can have adverse effects on the transportation network. In this paper, we propose a Gaussian distribution-based trust scoring model to identify anomalous …

Improving Age Of Information With Interference Problem In Long-Range Wide Area Networks, Preti Kumari, Hari Prabhat Gupta, Tanima Dutta, Sajal K. Das

Computer Science Faculty Research & Creative Works

Low Power Wide Area Networks (LPWAN) offer a promising wireless communications technology for Internet of Things (IoT) applications. Among various existing LPWAN technologies, Long-Range WAN (LoRaWAN) consumes minimal power and provides virtual channels for communication through spreading factors. However, LoRaWAN suffers from the interference problem among nodes connected to a gateway that uses the same spreading factor. Such interference increases data communication time, thus reducing data freshness and suitability of LoRaWAN for delay-sensitive applications. To minimize the interference problem, an optimal allocation of the spreading factor is requisite for determining the time duration of data transmission. This paper proposes a …