Digital Commons Network^™

Electrochemical-Thermal Model Of A Lithium-Ion Battery, Paul Kalungi

Browse all Theses and Dissertations

Lithium-ion batteries are an integral component of energy storage systems for renewable energy applications owing to their high energy density. Extensive research has therefore been carried out, utilizing both experimental and computational methods, to aid in a deeper understanding of lithium-ion batteries. Challenges related to efficiency, safety and thermal management persist, particularly during high current draw, extreme temperature conditions and extreme dynamic current operation such as in electric vehicles. This thesis work presents an electrochemical-thermal model of a lithium-ion battery that simulates and analyzes the variation of electrical behavior, chemical behavior and thermal behavior. The electrochemical model is developed by …

Empirical Investigation Of Calibration Targets In Thz In The Near Field From 550 To 700 Ghz, Anais Kypris L. Rawson

Browse all Theses and Dissertations

The uncertainty of the standard calibration procedure for radar cross-section (RCS) measurement is studied for different targets measured in the near-field from 550 to 700 GHz. Using common calibration spheres and squat cylinders mounted on a styrofoam pedestal at waterline (zero-degrees elevation), the calibration difference measure is determined for each target. Similarly, the difference metric is determined for square trihedral and tophat targets placed on a ground plane and measured at different elevation angles. The mean calibration measure is calculated using the dual calibration target method and repeated measurements in an anechoic chamber. The specific THz system is described and …

Optimizing Energy-Efficient Grid Performance: Integrating Electric Vehicles, Dstatcom, And Renewable Sources Using The Hippopotamus Optimization Algorithm, Mohamed Abdelaziz, A.A. Ali, R.A. Swief, Rasha Elazab

Renewable Electrical Energy Engineering

The rapid increase in renewable energy integration and electric vehicle (EV) adoption creates significant challenges for the stability and efficiency of power distribution networks. This study addresses the need for optimized placement and sizing of Electric Vehicle Charging Stations (EVCSs), photovoltaic (PV) systems, and Distribution Static Compensators (DSTATCOMs) to enhance grid performance. The motivation for this work arises from the fluctuating nature of renewable energy generation and the unpredictable demands of EV charging, which strain existing infrastructure. To address these challenges, we propose a novel optimization framework that introduces the Renewable Distributed Generation Hosting Factor (RDG-HF) and Electric Vehicle Hosting …

Machine-Learning-Assisted Design Of Deep Eutectic Solvents Based On Uncovered Hydrogen Bond Patterns, Usman Lame Abbas, Yuxuan Zhang, Joseph Tapia, Md Selim, Jin Chen, Jian Shi, Qing Shao

Markey Cancer Center Faculty Publications

Non-ionic deep eutectic solvents (DESs) are non-ionic designer solvents with various applications in catalysis, extraction, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation. The search for DES relies heavily on intuition or trial-and-error processes, leading to low success rates or missed opportuni- ties. Recognizing that hydrogen bonds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning (ML) models to discover new DES systems. We first analyze the …

Investigation Of Delta-Focused Ictal Electrical Source Imaging In Refractory Focal Epilepsy, Jared A. Rybarczyk

Theses and Dissertations--Electrical and Computer Engineering

Refractory focal epilepsy is characterized by the presence of seizures that cannot be controlled via anti-seizure medications. For patients suffering from this form of epilepsy, accurate identification of the seizure onset zone is a crucial step for many modalities of treatment. Electrical source imaging (ESI) allows for estimation of the seizure onset zone from electroencephalography. EEG feature extraction is an important step that can impact the final accuracy of source estimates. This work provides a review of 23 ictal ESI studies and proposes a delta-focused ictal ESI methodology. Our proposed delta-focused ictal ESI is implemented across 33 refractory focal epilepsy …

Nonuniform Sampling-Based Breast Cancer Classification, Santiago Posso

Theses and Dissertations--Electrical and Computer Engineering

The emergence of deep learning models and their success in visual object recognition have fueled the medical imaging community's interest in integrating these algorithms to improve medical diagnosis. However, natural images, which have been the main focus of deep learning models and mammograms, exhibit fundamental differences. First, breast tissue abnormalities are often smaller than salient objects in natural images. Second, breast images have significantly higher resolutions but are generally heavily downsampled to fit these images to deep learning models. Models that handle high-resolution mammograms require many exams and complex architectures. Additionally, spatially resizing mammograms leads to losing discriminative details essential …

A Novel Processor Architecture Implementing The Stacked Error Diffusion Algorithm And Its Zynq-Based Realization, Qishi Hu

Theses and Dissertations--Electrical and Computer Engineering

Digital halftoning reproduces continuous-tone images using patterns of black and white dots, while multitoning extends this concept by incorporating inks with intermediate intensities. These techniques are extensively utilized in the printing industry to accommodate the limited range of inks available in printers. Stacked error diffusion is a high-quality multitoning algorithm that adheres to the blue-noise dithering standard. This thesis research studies the potential parallelism inherent in the algorithm and introduces the design of a novel processor architecture optimized for efficient execution. The architecture is realized on an FPGA development board featuring a Zynq SoC. Additionally, the hardware prototype can also …

Information-Theoretic Learning Framework Based On Covariance Operators On Reproducing Kernel Hilbert Spaces, Jhoan Keider Hoyos Osorio

Theses and Dissertations--Electrical and Computer Engineering

Information theory provides tools to quantify uncertainty, dependence, and similarity between probability distributions, which are crucial for addressing various machine-learning problems. However, estimating these quantities is challenging because data distributions are usually unknown, and only observations are available for analysis. In this dissertation, we advance the field of information-theoretic learning by developing a comprehensive framework using kernel methods for analyzing probability distributions using reproducing kernel Hilbert spaces (RKHS). By leveraging covariance operators in this representation space, we propose approaches to estimate a set of fundamental information-theoretic quantities, that, because of their resemblance with conventional quantities in information theory, we call …

Optimal Control And Evaluation Of Shipboard Power Systems, Musharrat Sabah

Theses and Dissertations--Electrical and Computer Engineering

Electric warships are involved in complex missions consisting of multiple simultaneous operations. In order to ensure mission success, future ships must be designed in a way that optimizes their performance in the presence of complex mission loads. The focus of this research is an effort to optimally control the performance of shipboard systems during missions involving multiple scenarios or vignettes. The evaluation of the performance of mission-oriented power systems is based on the degree to which these systems deliver power to the loads required to perform the mission at hand. The performance of such systems involves a dynamic interplay between …

An Analysis Of Precision: Occlusion And Perspective Geometry’S Role In 6d Pose Estimation, Jeffrey Choate, Derek Worth, Scott Nykl, Clark N. Taylor, Brett J. Borghetti, Christine M. Schubert Kabban

Faculty Publications

Achieving precise 6 degrees of freedom (6D) pose estimation of rigid objects from color images is a critical challenge with wide-ranging applications in robotics and close-contact aircraft operations. This study investigates key techniques in the application of YOLOv5 object detection convolutional neural network (CNN) for 6D pose localization of aircraft using only color imagery. Traditional object detection labeling methods suffer from inaccuracies due to perspective geometry and being limited to visible key points. This research demonstrates that with precise labeling, a CNN can predict object features with near-pixel accuracy, effectively learning the distinct appearance of the object due to perspective …

Detecting Bearing Race Defects With Inductive Magnetic Reluctance Sensors And Artificial Neural Networks, Collin Daly

College of Graduate Studies: Theses & Dissertations

This work proposes a method of detecting physical damage to bearing races in a rotational assembly by means of magnetic reluctance sensors generating a signal from a rotating gear-tooth wheel. A nominally sinusoidal signal is generated based on the rotation of a gearwheel with regularly spaced voids and lands. Detection is based on the time variance of the signal periodically in relation to the gearwheel and the bearing damage. The purpose of this work is to propose a process to detect and classify bearing race defects using existing sensors and neural networks for hazardous area equipment applications.

Functional Monitoring For Run-Time Assurance Of A Real-Time Cyber Physical System, Matthew W. Gelber

Theses and Dissertations

As cyber-physical systems (CPS) become more integrated into everyday life, the security of these systems must also be considered during their development due to their ever-increasing importance. With the growth of physical components in the system, more autonomous control requirements, and increased dependence on proper functionality, verifying system safety and correct operation becomes increasingly difficult. CPS have become more complex through the combination of additional hardware and the resulting interconnected software in many layers, each requiring unique security solutions. One example of such a safety-critical CPS embedded system is the Flight Control System (FCS) of an Unmanned Aerial System (UAS). …

Machine Learning Assisted Optimization For Calculation And Automated Tuning Of Antennas, Lauren Linkous

Theses and Dissertations

The Antenna Calculation and Autotuning Tool (AntennaCAT) software suite represents a significant advancement in the field of antenna design by automating the entire design, CAD, simulation, and optimization process compatible with several EM simulation software suites. It is the first comprehensive implementation of machine learning in this context. In particular, this work includes the capability to create and export structured datasets from the aforementioned EM software for iterative improvement and includes an expandable selection of optimizers.

Experimental Investigation Of Self-Mixing Interferometric Technique For Displacement Assessment, Dulanjana Anuhas Egodagamage

Chulalongkorn University Theses and Dissertations (Chula ETD)

The self-mixing interferometric (SMI) technique has proven effective for achieving precise, contactless measurements of submicron displacements. This study aims to further investigate and experimentally assess the capabilities of the SMI technique in displacement sensing. While extensive research has been conducted on the SMI technique for displacement sensing, its performance can still be further explored in scenarios where the target exhibits varying displacement amplitudes and oscillation frequencies. Understanding the limitations and ranges of the SMI technique is crucial for advancing laboratory-based setups into practical applications. An experimental optoelectronic setup was used in this work, employing a semiconductor laser diode module integrated …

การพัฒนาแพลตฟอร์มเมตาเวิร์สแบบเว็บที่มีระบบสนับสนุนปัญญาประดิษฐ์, สืบพงศ์ น้อยศรี

Chulalongkorn University Theses and Dissertations (Chula ETD)

วิทยานิพนธ์ฉบับนี้นำเสนอการพัฒนาต้นแบบแพลตฟอร์มเมตาเวิร์สที่มีขีดความสามารถขั้นพื้นฐานครอบคลุมระบบอวาทาร์ ระบบผู้ใช้หลายราย ระบบสื่อสารตามเวลา ระบบการเชื่อมต่อแชตบอต และฉากโลกเสมือนจริงสามมิติที่เหมือนจริง แพลตฟอร์มที่พัฒนาขึ้นนี้ถูกนำไปใช้ประโยชน์กับหน่วยงานภาคการศึกษาและภาคธุรกิจ ทั้งหน่วยงานภายในจุฬาลงกรณ์มหาวิทยาลัยและสถาบันการศึกษาทั้งในประเทศและระดับนานาชาติ ผลการทดสอบพบว่าระบบทำงานได้ให้ผลสัมฤทธิ์ที่ดีและมีคุณภาพตามความต้องการใช้งานในทางปฏิบัติ สามารถประยุกต์ใช้กับการสร้างห้องเรียนเสมือนออนไลน์ในโลกเสมือนจริง รองรับระบบการสื่อสารระหว่างผู้ใช้ทั้งภาพ เสียง และข้อความ อีกทั้งยังสามารถใช้งานร่วมกับระบบแชตบอตจากภายนอกได้ผ่านระบบเอพีไอที่พัฒนาขึ้น ผลการทดสอบบนระบบคลาวด์ของบริษัท โทรคมนาคมแห่งชาติ จำกัด แสดงให้เห็นว่าระบบสามารถรองรับปริมาณผู้ใช้งานได้ไม่น้อยกว่า 30 รายพร้อมกัน ผลการทดสอบในเชิงประสบการณ์การใช้งานมีระดับความพึงพอใจในเชิงบวก งานวิจัยและพัฒนาจึงให้ประโยชน์และเป็นจุดเริ่มต้นของการประยุกต์ใช้เทคโนโลยีเมตาเวิร์สในวงกว้าง

Blockchain Based Framework For Secure And Decentralized Energy Trading - Transforming Local Energy Markets For Sustainable Communities, Saurabh Sachdeva

Dissertations and Theses

The emergence of prosumers provides an opportunity for the setup of a local energy market (LEM) where individual households with distributed energy resources (DERs) can produce, store, and trade energy. Peer-to-peer(P2P) and decentralized energy trading (ET) can be implemented between the participants within or across microgrids. Several solutions based on the existing technologies have been proposed worldwide for the integration of prosumers in the existing energy setup and to enable and support ET, but these solutions present the issues of centralization, data integrity, and confidentiality, user and anonymity, and transparency. The applications of blockchain technology have recently become fascinating for …

Ring Resonators Intergrating With Dichoric Materials And In Spin-Valley Controlled Photonic Topological System, Yuma Kawaguchi

Dissertations and Theses

Photonic technology plays an important role in the development of diverse applications, ranging from optical telecommunications to sensing and imaging. The utilization of photonic circuits emerges as a promising platform for the establishment of advanced communication systems characterized by high speed and large capacity. This advancement is crucial to facilitate fast and efficient data transfer while concurrently managing multiple devices. It is imperative that essential components such as lasers, modulators, and isolators exhibit compactness without incurring significant losses.

Non-reciprocal devices represent a valuable addition to photonic circuits, enabling the creation of unidirectional waveguides that function as optical isolators, preventing signals …

Hyperspectral And Polarimetric Imaging Of The Ocean For The Characterization Of The Surface Effects And Measurement Uncertainties, Mateusz Malinowski

Dissertations and Theses

Ocean and coastal waters are monitored by Ocean Color satellite sensors to determine concentrations of chlorophyll and water properties and identify areas of algal blooms and other events. The light radiance from the ocean is weak in comparison with the sky radiance, which requires very accurate atmospheric correction of the radiance measured at the top of the atmosphere (TOA) on the satellite and heavy validation of the derived water leaving radiance by field measurements from the ships and ocean platforms. For TOA and especially above water radiance the skylight reflected from the ocean surface represents one of the main sources …

Re-Envisioning Electric Railway Power Systems In Dense Urban Regions, Rohama Ahmad

Dissertations and Theses

Grid decarbonization and modernization are key requisites to combat global warming. Decarbonization entails the deployment of renewable energy sources, improving the energy efficiency of major consumers, and the electrification of the transportation and building sectors. Various US states and cities have pledged to reduce greenhouse gas (GHG) emissions (e.g., the Climate Leadership and Community Protection Act (CLCPA) in New York aims to reduce emissions to 40% below 1990 levels by 2030 and then to 85% below 1990 levels by 2050) via a set of targets in terms of renewable energy, energy storage system (ESS) capacity, and the number of electric …

Sparse Representation Learning For Temporal Networks, Maxwell Mcneil

Electronic Theses & Dissertations (2024 - present)

Temporal networks arise in many domains including activity of social network users, sensor network readings over time, and time course gene expression within the interaction network of a model organism. Data of this type contains a wealth of prior information such as the connectivity among nodes (e.g., a friendship graph), and prior knowledge of expected temporal patterns (e.g., periodicity). Modeling these temporal and network patterns jointly is essential for state-of-the-art performance in temporal network data analysis and mining. Sparse dictionary encoding is one modeling approach for such underlying patterns. However, most classical approaches consider only one dimension of the data …

Effect Of Annealing On Photoluminescence From Defects In Gan, Oleksandr Andrieiev

Theses and Dissertations

Annealing is a critical process in modern GaN technology, essential for achieving p-type conductivity by activating the Mg_Ga acceptor, as first demonstrated by Shuji Nakamura in the early 1990s. Despite the omnipresence of hydrogen as an impurity in GaN crystals, the precise mechanisms governing hydrogen diffusion and acceptor passivation remain only partially understood. The presented research investigates the effects of annealing-induced activation and hydrogen passivation on C and Be acceptors in GaN grown by MOCVD, HVPE, and MBE methods. We explored these effects by using several annealing techniques, gas compositions, and thermal regimes. A transient behavior between the C …

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 …

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 …

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 …

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. …

Generalized Conditional Feedback System With Model Uncertainty, Chengbo Dai, Zhiqiang Gao, Yangquan Chen, Donghai Li

Electrical and Computer Engineering Faculty Publications

Model uncertainty creates a largely open challenge for industrial process control, which causes a trade-off between robustness and performance optimality. In such a case, we propose a generalized conditional feedback (GCF) system to largely eliminate conflicts between robustness and performance optimality. This approach leverages a nominal model to design an optimal control in the virtual domain and defines an ancillary feedback controller to drive the physical process to track the trajectory of the virtual domain. The effectiveness of the proposed GCF scheme is demonstrated in a simulation for six typical industrial processes and three model-based control methods, and in a …

Explore Security And Machine Learning Applications In Next Generation Wireless Networks, Haolin Tang

Theses and Dissertations

Next-generation (NextG) or Beyond-Fifth-Generation (B5G) wireless networks have become a prominent focus in academic and industry circles. This is driven by the increasing demand for cutting-edge applications such as mobile health, self-driving cars, the metaverse, digital twins, virtual reality, and more. These diverse applications typically require high communication network performance, including spectrum utilization, data speed, and latency. New technologies are emerging to meet the communication requirements of various applications. Intelligent Reflecting Surface (IRS) and Artificial Intelligence (AI) are two representatives that have been demonstrated as promising and powerful technologies in NextG communications. While new technologies significantly enhance communication performance, they …

Hybrid Design Approaches For Reconfigurable Em Structures, Jonathan D. Lundquist

Theses and Dissertations

The modern world is replete with reasons for employing reconfigurable electromagnetic (EM) structures, but some of the most pressing needs require advancements in power consumption, speed, and footprint [1]. Next generation air defense (NGAD) fighters could benefit from new electronic warfare (EW) countermeasures [2], as well as adaptive stealth technology [3], while doctors could benefit from reconfigurable microwave ablation antennas that can modify radiation pattern and input impedance to match changes in tissue during ablation and account for variations in tumor shapes [4]. The future of antennas, filters, and lenses is reconfigurable as real-world matters of life and death may …

Optimal Pilot Length For Key Generation In Correlated Wireless Channels, Danqi Li, Shihao Yan, Jia Zhang, Jiande Sun, Feng Shu

Research outputs 2022 to 2026

This work examines the optimal pilot length for key generation in correlated wireless channels. We first deduce the secret key capacity as a function of the pilot length N and other system parameters. Then, derive two fixed-point equations to identify the optimal N with and without an eavesdropper. Our analysis and numerical results confirm the existence of the optimal N that maximizes the secret key capacity, and both results for optimal N are consistent. Furthermore, the optimal N generally decreases with average signal-to-noise ratio and maximum Doppler shift. Our examination can provide useful guidelines in designing practical key generation systems.

Particle Accelerator Spin-Transparent Storage Rings For Beyond State-Of-The-Art Science, R. Suleiman, Y. Derbenev, M. Grau, V. Morozov

Physics Faculty Publications

We will describe spin-transparent storage rings that exhibit spin-coherence times of several hours and store a large number of particles and their use in novel applications. For example, these rings can be used to directly measure the electric dipole moment of the electron, relevant to CP violation and matter-antimatter asymmetry in the universe, and to search for dark energy and ultra-light dark matter*. These rings can also serve as a compelling platform for quantum computing. In this presentation, we will describe how spin-transparent rings can be used in conjunction with ion traps to enhance scalability and increase quantum coherence times …