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Topography Of The 410 And 660 Km Discontinuities Beneath The Tibetan Plateau And Adjacent Areas, Zexin Miao Jan 2024

Topography Of The 410 And 660 Km Discontinuities Beneath The Tibetan Plateau And Adjacent Areas, Zexin Miao

Doctoral Dissertations

"The Tibetan Plateau represents the ongoing geotectonic aftermath of a continental collision initiated approximately 65 million years ago. Despite numerous seismological studies, controversies remain regarding the structure, evolution, and dynamics of the crust and mantle beneath Tibetan Plateau. In this study, we used teleseismic broadband data to investigate the topography of the mantle transition zone (MTZ) discontinuities. A total of ~655,000 receiver functions from 735 stations was utilized to image the 410 and 660 km discontinuities bordering the MTZ. Combining the observed and velocity corrected results, several areas in the central Tibet exhibit a typical d410 and an anomalously deep …

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Critical Behavior And Dynamics Of The Superfluid-Mott Glass Transition, Jack Russell Crewse Jan 2024

Critical Behavior And Dynamics Of The Superfluid-Mott Glass Transition, Jack Russell Crewse

Doctoral Dissertations

This work studies the effects of disorder on the thermodynamic critical behavior and dynamical properties of the superfluid-Mott glass quantum phase transition. After a brief introduction covering relevant fundamentals, we present the dissertation in the form of four separate but related publications. In the first two publications, we calculate the thermodynamic critical exponents of the superfluid-Mott glass quantum phase transition in both two and three spatial dimensions. The undiluted transition exhibits critical exponents that violate the Harris criterion, and thus the critical behavior is expected to change upon introducing disorder. We confirm this behavior via Monte Carlo simulation of a …

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High-Resolution Spectroscopy Of Interstellar Lines And Comets, Chemeda Tadese Ejeta Jan 2024

High-Resolution Spectroscopy Of Interstellar Lines And Comets, Chemeda Tadese Ejeta

Doctoral Dissertations

"The study of interstellar molecules such as CO is crucial because interstellar ices in the core of a pre-solar molecular cloud provide the starting point for volatile evolution in the protoplanetary disk. A record of the initial volatile composition of the protoplanetary disk can be obtained from the study of the chemical composition of cometary nuclei. Because of their long residence in the Oort cloud and infrequent passage through the inner solar system, long-period comets are one of the most primitive bodies in our solar system that can tell us about the composition of the early solar system. High-resolution infrared …

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Adversarial Transferability And Generalization In Robust Deep Learning, Tao Wu Jan 2024

Adversarial Transferability And Generalization In Robust Deep Learning, Tao Wu

Doctoral Dissertations

Despite its remarkable achievements across a multitude of benchmark tasks, deep learning (DL) models exhibit significant fragility to adversarial examples, i.e., subtle modifications applied to inputs during testing yet effective in misleading DL models. These meticulously crafted perturbations possess the remarkable property of transferability: an adversarial example that effectively fools one model often retains its effectiveness against another model, even if the two models were trained independently. This research delves into the characteristics influencing the transferability of adversarial examples from three distinct and complementary perspectives: data, model, and optimization. Firstly, from the data perspective, we propose a new method of …

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Deep Reinforcement Learning Based Strategies For Inverter Dominated Microgrids, Oroghene Oboreh-Snapps Jan 2024

Deep Reinforcement Learning Based Strategies For Inverter Dominated Microgrids, Oroghene Oboreh-Snapps

Doctoral Dissertations

The integration of inverter-based distributed generators (IBDGs) in modern power systems has ushered in a new era of renewable energy utilization, prompting the rise of Microgrid (MG) architectures. Nevertheless, as IBDG penetration increases, a host of challenges surface. Foremost among these challenges are issues related to frequency stability stemming from the inherent lack of inertia in IBDGs, and inaccurate sharing of reactive and active power, particularly evident in parallel IBDG networks with mismatched feeder impedance. This research presents novel solutions to these challenges by adopting deep reinforcement learning (DRL), specifically, the twin delayed deep deterministic policy gradient (TD3) algorithm, to …

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Highly Dipole-Parallel Aligned Nonlinear Optical Organic Molecular Crystalline Materials: Rational Design, Experimental And Theoretical Studies Of Supramolecular Structures And Non-Covalent Interactions, Harmeet Singh Bhoday Jan 2024

Highly Dipole-Parallel Aligned Nonlinear Optical Organic Molecular Crystalline Materials: Rational Design, Experimental And Theoretical Studies Of Supramolecular Structures And Non-Covalent Interactions, Harmeet Singh Bhoday

Doctoral Dissertations

Highly dipole-parallel aligned donor-acceptor substituted organic molecules are attractive for a wide array of applications, including nonlinear optics. However, only a limited number of crystals are known to adopt polar non-centrosymmetric space groups. The concepts guiding the fabrication of these polar materials have been described. The study focused on unsymmetrical donor-acceptor substituted azines and butadiene's. An improved design over (MeO, Y)-azines led to the realization of three crystals perfectly aligned in dipole parallel orientation for (PhO, Y)-azines with Y = Cl, Br, I, and one nearly perfectly aligned crystal for Y = F (Paper I). The unique bonding properties associated …

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Crystal Structure Prediction Of Metal Chalcogenides, Qi Zhang Jan 2024

Crystal Structure Prediction Of Metal Chalcogenides, Qi Zhang

Doctoral Dissertations

A novel crystal structure prediction (CSP) method has been developed to predict energetically favorable (stable) structures based on targeted chemical compositions. It leverages the structural characteristics of recurring motifs featured in many crystals and symmetry restrictions from space groups to effectively lower the degrees of freedom of a system when conducting CSP simulations. The proposed method is applied to predicting low-energy structures of two metal chalcogenide systems: Li3PS4 and Na6Ge2Se6. Both systems feature rigid bodies in their structures as building blocks, making them particularly suited to the proposed method. The validity …

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Adaptive Critic Optimal Control Of An Uncertain Robot Manipulator With Applications, Ravi Prakash, Laxmidhar Behera, Sarangapani Jagannathan Jan 2024

Adaptive Critic Optimal Control Of An Uncertain Robot Manipulator With Applications, Ravi Prakash, Laxmidhar Behera, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Realistic manipulation tasks involve a prolonged sequence of motor skills in varying control environments consisting of uncertain robot dynamic models and end-effector payloads. To address these challenges, this article proposes an adaptive critic (AC)-based basis function neural network (BFNN) optimal controller. Using a single neural network (NN) with a basis function, the proposed optimal controller simultaneously learns task-related optimal cost function, robot internal dynamics, and optimal control law. This is achieved through the development of a novel BFNN tuning law using closed-loop system stability. Therefore, the proposed optimal controller provides real-time, implementable, cost-effective control solutions for practical robotic tasks. The …

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Towards Explainability Of Dimension Reduction Plots Of Unsupervised Learning Model Outcomes, Tony E.Astuhuaman Davila, Daniel B. Hier, Tayo Obafemi-Ajayi Jan 2024

Towards Explainability Of Dimension Reduction Plots Of Unsupervised Learning Model Outcomes, Tony E.Astuhuaman Davila, Daniel B. Hier, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

Dimension reduction methods are used to visualize the output of unsupervised learning models when applied to complex data. These techniques improve interpretability by transforming a high-dimension space to a lower-dimension space (usually 2D or 3D). The results are typically viewed as 2D scatter plots, and class centroids may be added to increase interpretability. Although useful, the relationship of these class centroids to the underlying feature space remains opaque. The innovative aspect of this work is to create a strong link between the dimension-reduced space and the underlying high-dimension feature space by adding selected feature centroids to the 2D scatter plots. …

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Evaluation Of Transfer Learning Models On Traumatic Brain Injury Severity Classification, Ngoc Do, Daniel B. Hier, Tayo Obafemi-Ajayi Jan 2024

Evaluation Of Transfer Learning Models On Traumatic Brain Injury Severity Classification, Ngoc Do, Daniel B. Hier, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

After traumatic brain injury (TBI), clinicians use the Glasgow Coma Scale (GCS) to classify patients by severity and radiologists use the Rotterdam score and the Marshall score to classify CT scans by severity. This work investigates a viable efficient low-cost transfer learning model to use MRI images to predict GCS, Rotterdam, and Marshall severity class after TBI. The enhanced transfer learning model architecture integrates multiple fine-tuning steps in which a few layers of the pre-trained model is unfrozen in each iteration so that the neural network is able to learn layer by layer further aspects of the image for better …

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Evaluating Electromagnetic Interference Effects On Gnss Receivers, Giorgi Tsintsadze, Haran Manoharan, Arushi Sahai, Daryl G. Beetner, Brian Booth Jan 2024

Evaluating Electromagnetic Interference Effects On Gnss Receivers, Giorgi Tsintsadze, Haran Manoharan, Arushi Sahai, Daryl G. Beetner, Brian Booth

Electrical and Computer Engineering Faculty Research & Creative Works

Electromagnetic interference can be highly disruptive to global navigation satellite system (GNSS) receivers. Interference can be intentional but can also occur from electronics modules placed within the same system, where these modules may create sufficient unintended radiated emissions to disrupt GNSS operation. In this paper, GNSS receiver performance is evaluated in the presence of multi-tone interference. An expression for the GNSS correlator output in the presence of continuous wave interference (CWI) is derived and is extended to predict the carrier to noise density ratio, C/N0, of the receiver in the presence of multi-tone interference. C/N0 is widely used for characterizing …

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A Method For Measuring The Transfer Function Inside A Compact Metallic Enclosure Using A Slot Antenna, Xiangrui Su, Wenchang Huang, Junghee Cho, Joonki Paek, Chulsoon Hwang Jan 2024

A Method For Measuring The Transfer Function Inside A Compact Metallic Enclosure Using A Slot Antenna, Xiangrui Su, Wenchang Huang, Junghee Cho, Joonki Paek, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

Radio frequency desensitization issues comprise two components: noise radiation sources and the transfer function from noise sources to the victim antenna. For modern electronic products, noise sources are often located inside a compact metal enclosure, making it difficult to measure the transfer function using conventional methods. Moreover, opening the metallic enclosure would dramatically change the transfer function, and inserting a near-field probe into the enclosure is challenging and may not even be possible because of the limited space inside. In this article, a novel practical method for measuring the transfer function inside a compact metallic enclosure is proposed and experimentally …

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Low Noise Wireless Sensing In Termite Detection, Wei Zhang, Xiangshu Qi, Yunlong Luo, Alex Qi, Yihong Qi, Gang Feng Jan 2024

Low Noise Wireless Sensing In Termite Detection, Wei Zhang, Xiangshu Qi, Yunlong Luo, Alex Qi, Yihong Qi, Gang Feng

Electrical and Computer Engineering Faculty Research & Creative Works

Termites cause enormous financial damages all around the world every year. The majority of research on wireless sensing is always concerned with the various human-centric applications, however, the vast potential of wireless sensing for termite detection is often disregarded. In this article, a Wireless Intelligent Sensing (WISe) millimeter-wave (mmWave) radar system for termite detection is proposed. Here, we design a three-layer architecture with some intelligent resources to monitor the movement signs of the termites. In addition, we present a low noise sensing architecture that minimizes the noise in hardware and digital signal processing, which can reduce the impact of environment …

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Virtual Inertia Scheduling (Vis) For Microgrids With Static And Dynamic Security Constraints, Buxin She, Fangxing Li, Jinning Wang, Hantao Cui, Xiaofei Wang, Rui Bo Jan 2024

Virtual Inertia Scheduling (Vis) For Microgrids With Static And Dynamic Security Constraints, Buxin She, Fangxing Li, Jinning Wang, Hantao Cui, Xiaofei Wang, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Microgrids feature a high penetration of inverter-interfaced distributed energy resources (DERs). The low inertia characteristic and fast dynamics of DERs pose challenges to conventional decoupled static economic operation and dynamic control design within microgrids. Hence, this paper proposed virtual inertia scheduling (VIS) for microgrids, aiming to ensure both economy and security. First, a unified framework for device-level control and grid-level operation is introduced, with VIS serving as a key application to address low inertia issues. VIS actively harnesses the controllability and flexibility of DERs to effectively manage microgrid inertia. It updates the conventional economic operation framework by incorporating the virtual …

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Coupling-Informed Data-Driven Scheme For Joint Angle And Frequency Estimation In Uniform Linear Array With Mutual Coupling Present, Yanming Zhang, Wenchao Xu, A. Long Jin, Min Li, Peifeng Ma, Lijun Jiang, Steven Gao Jan 2024

Coupling-Informed Data-Driven Scheme For Joint Angle And Frequency Estimation In Uniform Linear Array With Mutual Coupling Present, Yanming Zhang, Wenchao Xu, A. Long Jin, Min Li, Peifeng Ma, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a novel coupling-informed data-driven algorithm tailored for the concurrent estimation of frequency and angle within a uniform linear array (ULA), while addressing the complicating influence of mutual coupling. Leveraging the hybrid dynamic mode decomposition (DMD) methodology, termed as averaged DMD, we incorporate moving average techniques to achieve effective denoising. The averaged DMD further decomposes the received signal into eigenvalues and corresponding eigenvectors. The frequency information is derived from the eigenvalues and the corresponding eigenvectors represent the steering vectors of sources. Subsequently, mutual coupling is informed into the calibration of the steering vector for each source. Specifically, the …

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A Tensor-Based Data-Driven Approach For Multidimensional Harmonic Retrieval And Its Application For Mimo Channel Sounding., Yanming Zhang, Wenchao Xu, A. Long Jin, Min Li, Ping Yuan, Lijun Jiang, Steven Gao Jan 2024

A Tensor-Based Data-Driven Approach For Multidimensional Harmonic Retrieval And Its Application For Mimo Channel Sounding., Yanming Zhang, Wenchao Xu, A. Long Jin, Min Li, Ping Yuan, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

In wireless channel sounding, accurately estimating multiple parameters within a multipath signal, such as azimuth, elevation, Doppler shift, and delay, necessitates addressing the challenges posed by the multidimensional harmonic retrieval (MHR) problem. To overcome these complexities, we propose a framework based on high-order dynamic mode decomposition (HODMD) that designed for robustly estimating frequencies of interest from high-dimensional sinusoidal signals, particularly in additive white Gaussian noise conditions. The HODMD approach, a hybrid algorithm amalgamating high-order singular value decomposition (HOSVD) and dynamic mode decomposition (DMD), operates by initially decomposing observed tensorial data into a core tensor and R mode matrices through HOSVD. …

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Analytical Modeling Of Partially-Filled Tm010-Mode Dielectric Resonator For Accurate Dk And Df Extraction, Mehdi Mousavi, Chaofeng Li, Reza Asadi, Seyedmostafa Mousavi, Reza Vahdani, Xiaoning Ye, Mina Esmaeelpour, Donghyun Kim Jan 2024

Analytical Modeling Of Partially-Filled Tm010-Mode Dielectric Resonator For Accurate Dk And Df Extraction, Mehdi Mousavi, Chaofeng Li, Reza Asadi, Seyedmostafa Mousavi, Reza Vahdani, Xiaoning Ye, Mina Esmaeelpour, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

In high-speed printed circuit board (PCB) modeling, the accurate determination of the dielectric constant (DK) and dissipation factor (DF) is crucial for signal integrity analysis at design stage. This paper introduces an analytical approach using a partially filled TM010-mode dielectric resonator, an effective tool for DK and DF extraction of PCB material. We begin by discussing the theoretical underpinnings of TM010-mode dielectric resonators and their applicability in measuring the DK and DF of the material under test. A mathematical model is then presented, linking the resonant characteristics directly to the DK and DF of the dielectric material. The …

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Modeling Of Power Distrubition Network (Pdn) Noise Coupling Induced Clock Phase Noise, Zhekun Peng, Junyong Park, Chaofeng Li, Joey Stecher, Srinivas Venkataraman, Xu Wang, Granthana Rangaswamy, Donghyun Kim Jan 2024

Modeling Of Power Distrubition Network (Pdn) Noise Coupling Induced Clock Phase Noise, Zhekun Peng, Junyong Park, Chaofeng Li, Joey Stecher, Srinivas Venkataraman, Xu Wang, Granthana Rangaswamy, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

Phase noise analysis is important to clock design. Noise sources of spurs shown on the phase noise result are challenging to find out due to the unknown source locations and coupling mechanisms. Noise from power distribution network (PDN) is one of the most troublesome sources. A behavioral modeling methodology is proposed to simulate the clock phase noise induced by PDN noise coupling for two different mechanisms: PDN-to-clock additive coupling and PDN-to-PDN up-conversion modulation. The thermal noise and 1/f flicker noise are simplified to provide a straightforward view of spur level. The model can be applied to both single-ended clock and …

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Lifelong Safe Optimal Adaptive Tracking Control Of Nonlinear Strict-Feedback Discrete-Time Systems, Behzad Farzanegan, S. Jagannathan Jan 2024

Lifelong Safe Optimal Adaptive Tracking Control Of Nonlinear Strict-Feedback Discrete-Time Systems, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a comprehensive approach for achieving multi-task safe optimal adaptive tracking (MSOAT) for a class of nonlinear discrete-time systems, particularly those in strict-feedback form, utilizing a multi-layer neural network (MNN)-based framework. To begin, a cost function with a novel Barrier function (BF) term is introduced for each subsystem to address the weak safely reachable problem, serving as a crucial tool for guiding the system's trajectory toward the safe set while avoiding unwanted sets. To deal with the tracking problem, the Hamilton-Jacobi-Bellman (HJB) framework is used through the actor-critic MNN-based backstepping technique to estimate the solution of the value …

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Relative Altitude Estimation Of Infrared Thermal Uav Images Using Sift Features, Shirin Nasr Esfahani, Jagannathan Sarangapani Jan 2024

Relative Altitude Estimation Of Infrared Thermal Uav Images Using Sift Features, Shirin Nasr Esfahani, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Unmanned Aerial Vehicles (UAVs) have become indispensable in various applications, including surveillance, urban scene analysis, and agricultural monitoring. Accurate altitude estimation is critical for UAV operations, especially in environments where traditional sensors like GPS, pressure altimeters, and radar may fail. This paper explores the use of infrared and thermal imaging for relative altitude estimation of UAVs, highlighting their significant advantages over traditional RGB images. Infrared and thermal imaging offer superior performance in low-light and adverse weather conditions, providing clearer visibility and more reliable feature detection. By leveraging the Scale-Invariant Feature Transform (SIFT) features, this approach utilizes the inherent benefits of …

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Lifelong Direct Error-Driven Learning For Uav Altitude Estimation In Different Weather Conditions, Shirin Nasr-Esfahani, Jagannathan Sarangapani Jan 2024

Lifelong Direct Error-Driven Learning For Uav Altitude Estimation In Different Weather Conditions, Shirin Nasr-Esfahani, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

While deep neural networks achieve remarkable visual perception capabilities for UAV position and orientation estimation, their resilience to different weather conditions still needs improvement. These models often suffer from catastrophic forgetting when adapted to new environments, losing previously acquired knowledge. Lifelong learning methods aim to balance learning flexibility and memory stability. In this paper, we present an image-based approach to estimate the relative altitude of a UAV using 2D images under varying weather conditions, including sunny, sunset, and foggy scenarios. Our experiments demonstrate significant performance degradation when the model is trained sequentially on different weather datasets, especially when new images …

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In-Plane And Out-Of-Plane 2-D Microdisplacement Sensor Based On A Single Microwave Resonator With Machine Learning, Shiyu Li, Osamah Alsalman, Jie Huang, Chen Zhu Jan 2024

In-Plane And Out-Of-Plane 2-D Microdisplacement Sensor Based On A Single Microwave Resonator With Machine Learning, Shiyu Li, Osamah Alsalman, Jie Huang, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

Microwave displacement sensors have garnered significant research interest in recent years and have found successful applications in industrial automation and aerospace engineering. However, most microwave sensors are limited to measuring in-plane displacement, with the assumption that out-of-plane displacement remains constant during operation. In this work, we propose and experimentally demonstrate a novel concept for 2-D micro displacement sensing that simultaneously measures both in-plane and out-of-plane displacement. We developed a proof-of-concept sensor system based on a custom-made coaxial cable resonator (CCR) serving as the stator and a rubber-metal heterogeneous plate as the movable part. The in-plane and out-of-plane micromovements of the …

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Electromagnetic Near-Field Scanning With A Spatially Sparse Sampling Strategy Utilizing Kriging-Dmd, Yanming Zhang, Steven Gao, Lijun Jiang Jan 2024

Electromagnetic Near-Field Scanning With A Spatially Sparse Sampling Strategy Utilizing Kriging-Dmd, Yanming Zhang, Steven Gao, Lijun Jiang

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This paper proposes a hybrid method for time-resolved electromagnetic near-field scanning, merging model-based (Gaussian processes regression model, a.k.a. Kriging method) and data-driven (dynamic mode decomposition) techniques. Specifically, Latin hypercube sampling enables spatially sparse measurements, followed by dynamic mode decomposition to analyze resulting sparse spatial-temporal data, extracting frequency information and sparse dynamic modes. The Kriging method is then employed for full-state reconstruction. The proposed approach is evaluated using crossed dipole antennas. Results indicate that, even with a spatial subsampling factor of 130, achieving a fully reconstructed field distribution suitable for engineering applications with frequency information extraction is feasible. This hybrid framework …

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A Hybrid Algorithm To Dual Sparse Sampling Measurement In Time-Resolved Electromagnetic Near-Field Scanning, Yanming Zhang, Steven Gao, Lijun Jiang Jan 2024

A Hybrid Algorithm To Dual Sparse Sampling Measurement In Time-Resolved Electromagnetic Near-Field Scanning, Yanming Zhang, Steven Gao, Lijun Jiang

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Time-resolved electromagnetic near-field scanning is vital for antenna measurement and addressing complex electromagnetic interference and compatibility issues. However, the swift acquisition of high-resolution spatiotemporal data remains challenging due to physical constraints, such as moving the probe position and allowing sufficient time for sampling. This paper introduces a novel hybrid approach that combines Kriging for sparse spatial measurement, compressed sensing (CS) for sparse temporal sampling, and dynamic mode decomposition (DMD) for a comprehensive analysis of dual-sparse sampling electromagnetic near-field data. CS optimizes sparse sampling in the time domain, capitalizing on the inherent sparsity within electromagnetic radiated signals, resulting in reliable representation …

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A Data-Driven Approach To Time-Domain Electromagnetic Modeling Based On Dynamic Mode Decomposition, Yanming Zhang, Steven Gao, Lijun Jiang Jan 2024

A Data-Driven Approach To Time-Domain Electromagnetic Modeling Based On Dynamic Mode Decomposition, Yanming Zhang, Steven Gao, Lijun Jiang

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This paper presents a data-driven methodology that utilizes Dynamic Mode Decomposition (DMD) for the time-domain (TD) electromagnetic (EM) modeling of microwave devices. As an unsupervised machine learning technique, DMD leverages a limited set of unlabeled spatio-temporal electromagnetic (EM) data to determine DMD eigenvalues and eigenmodes. Then, the obtained DMD model reconstructs the dynamics as a series of exponential terms based on linear assumptions. The effectiveness of this approach is demonstrated through the TD EM modeling of photonic crystal waveguides. Comparative analysis with the finite-difference time-domain (FDTD) method shows that the DMD model not only achieves precise modeling but also facilitates …

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An Unsupervised Learning Framework For Determining The Excitation Coefficients Using Near-Field Antenna Measurements, Yanming Zhang, Peifeng Ma, Steven Gao, Lijun Jiang Jan 2024

An Unsupervised Learning Framework For Determining The Excitation Coefficients Using Near-Field Antenna Measurements, Yanming Zhang, Peifeng Ma, Steven Gao, Lijun Jiang

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This article presents a novel unsupervised learning framework based on multiscale dynamic mode decomposition for determining the excitation coefficients of antennas using time-domain near-field measurements. The proposed framework integrates temporal multiscale analysis to extract a joint distribution of frequency, damping factors, and spatial modes, enabling precise extraction of excitation frequencies, rising/falling edges, and phases without labeled data. We validate the effectiveness of the proposed approach through two examples involving on-off keying modulation and a phase-shift dipole antenna. It is found that the proposed method performs well in handling nonstationary excitation signals and proves particularly advantageous for calibrating tunable antenna systems. …

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Descriptive Statistical Analysis Of Experimental Data For Wettability Alteration With Smart Water Flooding In Carbonate Reservoirs, Muhammad Ali Buriro, Mingzhen Wei, Baojun Bai, Ya Yao Jan 2024

Descriptive Statistical Analysis Of Experimental Data For Wettability Alteration With Smart Water Flooding In Carbonate Reservoirs, Muhammad Ali Buriro, Mingzhen Wei, Baojun Bai, Ya Yao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Smart water flooding is a promising eco-friendly method for enhancing oil recovery in carbonate reservoirs. the optimal salinity and ionic composition of the injected water play a critical role in the success of this method. This study advances the field by employing machine learning and data analytics to streamline the determination of these critical parameters, which are traditionally reliant on time-intensive laboratory work. the primary objectives are to utilize data analytics to examine how smart water flooding influences wettability modification, identify key parameter ranges that notably alter the contact angle, and formulate guidelines and screening criteria for successful lab design. …

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2023 Scholarly Productivity Report, Missouri University Of Science And Technology Dec 2023

2023 Scholarly Productivity Report, Missouri University Of Science And Technology

Civil, Architectural and Environmental Engineering Scholarly Productivity Reports

No abstract provided.

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Ai-Enabled Assessment Of Roadway Integrity: Forecasting Bitumen Deformation And Road Stability Throughout The Lifecycle Under Traffic Impact, Sabahudin Vrtagic, Milan Dordevic, Fatih Dogan, Muhammed Codur, Mario Hoxha, Edis Softic Dec 2023

Ai-Enabled Assessment Of Roadway Integrity: Forecasting Bitumen Deformation And Road Stability Throughout The Lifecycle Under Traffic Impact, Sabahudin Vrtagic, Milan Dordevic, Fatih Dogan, Muhammed Codur, Mario Hoxha, Edis Softic

Materials Science and Engineering Faculty Research & Creative Works

Asphalt-paved Road junctions frequently encounter deformation and degradation challenges due to heavy vehicular traffic and varying climatic conditions, such as temperature fluctuations and precipitation. This study employs a multifaceted approach, incorporating a Multilayer Perceptron (MLP) model, ancillary machine learning techniques, and optimization methodologies, to address these challenges effectively. The primary objectives are the prediction and analysis of pavement deformation, the optimization of maintenance strategies, and the evaluation of road effectiveness. Our findings underscore the substantial contribution of heavy vehicles to road erosion and the profound impact of vehicular retention and braking at intersections. A Multilayer Perceptron (MLP) model is utilized …

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Enhancing Diversity And Inclusion In Computer Science Undergraduate Programs: The Role Of Admissions, Ouldooz Baghban Karimi, Giulia Toti, Mirela Gutica, Rebecca Robinson, Lisa Zhang, James Paterson, Peggy Lindner, Michael O'Dea Dec 2023

Enhancing Diversity And Inclusion In Computer Science Undergraduate Programs: The Role Of Admissions, Ouldooz Baghban Karimi, Giulia Toti, Mirela Gutica, Rebecca Robinson, Lisa Zhang, James Paterson, Peggy Lindner, Michael O'Dea

Engineering Management and Systems Engineering Faculty Research & Creative Works

Despite continued efforts to further the participation of women in Computer Science (CS), progress has been limited during the past decades. Recent efforts have been focused on recruitment and retention, with a notable gap in exploring the impact of admissions processes on diversity and inclusion. Through an extensive literature review, contextual analysis of public admissions data from 40 universities across four regions around the world, and qualitative and quantitative analysis on surveys and interviews, we explored the role of admissions in enhancing diversity and inclusion in CS undergraduate programs. Our findings highlight the role of financials, the possible positive effects …

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