Matching Items (100)
Description
This research report investigates the feasibility of using RFID in Traffic Sign Recognition (TSR) Systems for autonomous vehicles, specifically driver-less cars. Driver-less cars are becoming more prominent in society but must be designed to integrate with the current transportation infrastructure. Current research in TSR systems use image processing as well as LIDAR to identify traffic signs, yet these are highly dependent on lighting conditions, camera quality and sign visibility. The read rates of current TSR systems in literature are approximately 96 percent. The usage of RFID in TSR systems can improve the performance of traditional TSR systems. An RFID TSR was designed for the Autonomous Pheeno Test-bed at the Arizona State University (ASU) Autonomous Collective Systems (ACS) Laboratory. The system was tested with varying parameters to see the effect of the parameters on the read rate. It was found that high reader strength and low tag distance had a maximum read rate of 96.3 percent, which is comparable to existing literature. It was proven that an RFID TSR can perform as well as traditional TSR systems, and has the capacity to improve accuracy when used alongside RGB cameras and LIDAR.
ContributorsMendoza, Madilyn Kido (Author) / Berman, Spring (Thesis director) / Yu, Hongbin (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Patterning technologies for micro/nano-structures have been essentially used in a variety of discipline research areas, including electronics, optics, material science, and biotechnology. Therefore their importance has dramatically increased over the past decades. This dissertation presents various advanced patterning processes utilizing cross-discipline technologies, e.g., photochemical deposition, transfer printing (TP), and nanoimprint lithography (NIL), to demonstrate inexpensive, high throughput, and scalable manufacturing for advanced optical applications.
The polymer-assisted photochemical deposition (PPD) method is employed in the form of additive manufacturing (AM) to print ultra-thin (< 5 nm) and continuous film in micro-scaled (> 6.5 μm) resolution. The PPD film acts as a lossy material in the Fabry-Pérot cavity structures and generates vivid colored images with a micro-scaled resolution by inducing large modulation of reflectance. This PPD-based structural color printing performs without photolithography and vacuum deposition in ambient and room-temperature conditions, which enables an accessible and inexpensive process (Chapter 1).
In the TP process, germanium (Ge) is used as the nucleation layer of noble metallic thin films to prevent structural distortion and improve surface morphology. The developed Ge-assisted transfer printing (GTP) demonstrates its feasibility transferring sub-100 nm features with up to 50 nm thickness in a centimeter scale. The GTP is also capable of transferring arbitrary metallic nano-apertures with minimal pattern distortion, providing relatively less expensive, simpler, and scalable manufacturing (Chapter 2).
NIL is employed to fabricate the double-layered chiral metasurface for polarimetric imaging applications. The developed NIL process provides multi-functionalities with a single NIL, i.e., spacing layer, planarized surface, and formation of dielectric gratings, respectively, which significantly reduces fabrication processing time and potential cost by eliminating several steps in the conventional fabrication process. During the integration of two metasurfaces, the Moiré fringe based alignment method is employed to accomplish the alignment accuracy of less than 200 nm in both x- and y-directions, which is superior to conventional photolithography. The dramatically improved optical performance, e.g., highly improved circular polarization extinction ratio (CPER), is also achieved with the developed NIL process (Chapter 3).
ContributorsChoi, Shinhyuk (Author) / Wang, Chao (Thesis advisor) / Yu, Hongbin (Committee member) / Holman, Zachary (Committee member) / Hwa, Yoon (Committee member) / Arizona State University (Publisher)
Created2023
Description
The objective of this dissertation is to study the optical and radiative properties of inhomogeneous metallic structures. In the ongoing search for new materials with tunable optical characteristics, porous metals and nanowires provides an extensive design space to engineer its optical response based on the morphology-dependent phenomena.This dissertation firstly discusses the use of aluminum nanopillar array on a quartz substrate as spectrally selective optical filter with narrowband transmission for thermophotovoltaic systems. The narrow-band transmission enhancement is attributed to the magnetic polariton resonance between neighboring aluminum nanopillars. Tuning of the resonance wavelengths for selective filters was achieved by changing the nanopillar geometry. It concludes by showing improved efficiency of Gallium-Antimonide thermophotovoltaic system by coupling the designed filter with the cell.
Next, isotropic nanoporous gold films are investigated for applications in energy conversion and three-dimensional laser printing. The fabricated nanoporous gold samples are characterized by scanning electron microscopy, and the spectral hemispherical reflectance is measured with an integrating sphere. The effective isotropic optical constants of nanoporous gold with varying pore volume fraction are modeled using the Bruggeman effective medium theory.
Nanoporous gold are metastable and to understand its temperature dependent optical properties, a lab-scale fiber-based optical spectrometer setup is developed to characterize the in-situ specular reflectance of nanoporous gold thin films at temperatures ranging from 25 to 500 oC. The in-situ and the ex-situ measurements suggest that the
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specular, diffuse, and hemispherical reflectance varies as a function of temperature due to the morphology (ligament diameter) change observed.
The dissertation continues with modeling and measurements of the radiative properties of porous powders. The study shows the enhanced absorption by mixing porous copper to copper powder. This is important from the viewpoint of scalability to get end products such as sheets and tubes with the requirement of high absorptance that can be produced through three-dimensional printing.
Finally, the dissertation concludes with recommendations on the methods to fabricate the suggested optical filters to improve thermophotovoltaic system efficiencies. The results presented in this dissertation will facilitate not only the manufacturing of materials but also the promising applications in solar thermal energy and optical systems.
ContributorsRamesh, Rajagopalan (Author) / Wang, Liping (Thesis advisor) / Azeredo, Bruno (Thesis advisor) / Phelan, Patrick (Committee member) / Yu, Hongbin (Committee member) / Rykaczewski, Konrad (Committee member) / Arizona State University (Publisher)
Created2022
Description
The integration of Distributed Energy Resources (DER), including wind energy and photovoltaic (PV) panels, into power systems, increases the potential for events that could lead to outages and cascading failures. This risk is heightened by the limited dynamic information in energy grid datasets, primarily due to sparse Phasor Measurement Units (PMUs) placement. This data quality issue underscores the need for effective methodologies to manage these challenges. One significant challenge is the data gaps in low-resolution (LR) data from RTU and smart meters, hindering robust machine learning (ML) applications. To address this, a systematic approach involves preparing data effectively and designing efficient event detection methods, utilizing both intrinsic physics and extrinsic correlations from power systems. The process begins by interpolating LR data using high-resolution (HR) data, aiming to create virtual PMUs for improved grid management. Current interpolation methods often overlook extrinsic spatial-temporal correlations and intrinsic governing equations like Ordinary Differential Equations (ODEs) or Differential Algebraic Equations (DAEs). Physics-Informed Neural Networks (PINNs) are used for this purpose, though they face challenges with limited LR samples. The solution involves exploring the embedding space governed by ODEs/DAEs, generating extrinsic correlations for initial LR data imputation, and enforcing intrinsic physical constraints for refinement. After data preparation, event data dimensions such as spatial, temporal, and measurement categories are recovered in a tensor. To prevent overfitting, common in traditional ML methods, tensor decomposition is used. This technique merges intrinsic and physical information across dimensions, yielding informative and compact feature vectors for efficient feature extraction and learning in event detection. Lastly, in grids with insufficient data, knowledge transfer from grids with similar event patterns is a viable solution. This involves optimizing projected and transferred vectors from tensor decomposition to maximize common knowledge utilization across grids. This strategy identifies common features, enhancing the robustness and efficiency of ML event detection models, even in scenarios with limited event data.
ContributorsMa, Zhihao (Author) / Weng, Yang (Thesis advisor) / Wu, Meng (Committee member) / Yu, Hongbin (Committee member) / Matavalam, Amarsagar Reddy Ramapuram (Committee member) / Arizona State University (Publisher)
Created2023
Description
Although the increasing penetration of electric vehicles (EVs) has reduced the emissionof the greenhouse gas caused by vehicles, it would lead to serious congestion
on-road and in charging stations. Strategic coordination of EV charging would benefit
the transportation system. However, it is difficult to model a congestion game,
which includes choosing charging routes and stations. Furthermore, conventional algorithms
cannot balance System Optimization and User Equilibrium, which can cause
a huge waste to the whole society. To solve these problems, this paper shows (1) a
congestion game setup to optimize and reveal the relationship between EV users,
(2) using ε – Nash Equilibrium to reduce the inefficient impact from the self-minded
the behavior of the EV users, and (3) finding the relatively optimal solution to approach
Pareto-Optimal solution. The proposed method can reduce more total EVs charging
time and most EV users’ charging time than existing methods. Numerical simulations
demonstrate the advantages of the new method compared to the current methods.
ContributorsYu, Hao (Author) / Weng, Yang (Thesis advisor) / Yu, Hongbin (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2022
Description
The strong demand for the advancing of Moore’s law on device size scaling down has accelerated the miniaturization of passive devices. Among these important electronic components, inductors are facing challenges because the inductance value, which is strongly dependent on the coil number for the air core inductor case, will be sacrificed when the size is shrinking. Adding magnetic core is one of the solutions due to its enhancement of inductance density but it will also add complexity to the fabrication process, and the core loss induced by the eddy current at high frequency is another drawback. In this report, the output of this research will be presented, which has three parts. In the first part, the CoZrTaB thin films are sputtered on different substrates and characterized comprehensively. The laminated CoZrTaB thin films have been also investigated, showing low coercivity and anisotropy field on both Si and polyimide substrates. Also, the different process conditions that could affect the magnetic properties are investigated.
In the second part, Ansys Maxwell software is used to optimize the lamination profile and the magnetic core inductor structure. The measured M-H loop is imported to improve the simulation accuracy.
In the third part, a novel method to fabricate the magnetic core inductors on flexible substrates is proposed. The sandwich magnetic core inductor is fabricated and assembled with flipchip bonder. The measurement result shows that this single-turn magnetic core inductor can achieve up to 24% inductance enhancement and quality factor of 7.42. The super low DC resistance (< 60 mΩ) proves that it is a good candidate to act as the passive component in the power delivery module and the use of polyimide-based substrate extends its compatibility to more packaging form factors.
ContributorsWu, Yanze (Author) / Yu, Hongbin (Thesis advisor) / Chickamenahalli, Shamala (Committee member) / Rizzo, Nicholas (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2022
Description
Voltage Source Inverter (VSI) is an integral component that converts DC voltage to AC voltage suitable for driving the electric motor in Electric Vehicles/Hybrid Electric Vehicles (EVs/HEVs) and integration with electric grid in grid-connected photovoltaic (PV) converter. Performance of VSI is significantly impacted by the type of Pulse Width Modulation (PWM) method used.In this work, a new PWM method called 240° Clamped Space Vector PWM (240CPWM) is studied extensively. 240CPWM method has the major advantages of clamping a phase to the positive or negative rail for 240° in a fundamental period, clamping of two phases simultaneously at any given instant, and use of only active states, completely eliminating the zero states. These characteristics lead to a significant reduction in switching losses of the inverter and lower DC link capacitor current stress as compared to Conventional Space Vector PWM. A unique six pulse dynamically varying DC link voltage is required for 240CPWM instead of constant DC link voltage to maintain sinusoidal output voltage. Voltage mode control of DC-DC stage with Smith predictor is developed for shaping the dynamic DC link voltage that meets the requirements for fast control.
Experimental results from a 10 kW hardware prototype with 10 kHz switching frequency validate the superior performance of 240CPWM in EV/HEV traction inverters focusing on loss reduction and DC link capacitor currents. Full load efficiency with the proposed 240CPWM for the DC-AC stage even with conventional Silicon devices exceeds 99%.
Performance of 240CPWM is evaluated in three phase grid-connected PV converter. It is verified experimentally that 240CPWM performs well under adverse grid conditions like sag/swell and unbalance in grid voltages, and under a wide range of power factor. Undesired low frequency harmonics in inverter currents are minimized using the Harmonic Compensator that results in Total Harmonic Distortion (THD) of 3.5% with 240CPWM in compliance with grid interconnection standards. A new, combined performance index is proposed to compare the performance of different PWM schemes in terms of switching loss, THD, DC link current stress, Common Mode Voltage and leakage current. 240CPWM achieves the best value for this index among the PWM methods studied.
ContributorsQamar, Haleema (Author) / Ayyanar, Raja (Thesis advisor) / Yu, Hongbin (Committee member) / Lei, Qin (Committee member) / Weng, Yang (Committee member) / Arizona State University (Publisher)
Created2022
Description
The performance of voltage source inverter (VSI) in terms of output waveform quality, conversion efficiency and common mode noise depends greatly on the pulse width modulation (PWM) method. In this work, a low-loss space vector PWM i.e., 240°-clamped space vector PWM (240CPWM) is proposed to improve the performance of VSIs in electric/hybrid electric vehicles (EV/HEVs) and grid connected photovoltaic (PV) systems. The salient features of 240CPWM include 240° clamping of each phase pole to positive or negative DC bus in a fundamental cycle ensuring that switching losses are reduced by a factor of seven as compared to conventional space vector PWM (CSVPWM) at unity power factor. Zero states are completely eliminated and only two nearest active states are used ensuring that there is no penalty in terms of total harmonic distortion (THD) in line current. The THD of the line current is analyzed using the notion of stator flux ripple and compared with conventional and discontinuous PWM method. Discontinuous PWM methods achieve switching loss reduction at the expense of higher THD while 240CPWM achieves a much greater loss reduction without impacting the THD. The analysis and performance of 240CPWM are validated on a 10 kW two-stage experimental prototype.
Common mode voltage (CMV) and leakage current characteristics of 240CPWM are analyzed in detail. It is shown analytically that 240CPWM reduces the CMV and leakage current as compared to other PWM methods while simultaneously reducing the switching loss and THD. Experimental results from a 10-kW hardware prototype conform to the analytical discussions and validate the superior performance of 240CPWM.
240CPWM requires a six-pulse dynamic DC link voltage that introduces low frequency harmonics in DC input current and/or AC line currents that can affect maximum power point tracking, battery life or THD in line current. Four topologies have been proposed to minimize the low frequency harmonics in input and line currents in grid-connected PV system with 240CPWM.
In order to achieve further benefits in terms of THD and device stress reduction, 240CPWM is extended to three-level inverters. The performance metrics such as THD and switching loss for 240CPWM are analyzed in three-level inverter.
ContributorsQamar, Hafsa (Author) / Ayyanar, Raja (Thesis advisor) / Yu, Hongbin (Committee member) / Lei, Qin (Committee member) / Weng, Yang (Committee member) / Arizona State University (Publisher)
Created2022
Description
A remarkable phenomenon in contemporary physics is quantum scarring in classically chaoticsystems, where the wave functions tend to concentrate on classical periodic orbits. Quantum
scarring has been studied for more than four decades, but the problem of efficiently detecting
quantum scars has remained to be challenging, relying mostly on human visualization of
wave function patterns. This paper develops a machine learning approach to detecting
quantum scars in an automated and highly efficient manner. In particular, this paper exploits Meta
learning. The first step is to construct a few-shot classification algorithm, under the
requirement that the one-shot classification accuracy be larger than 90%. Then propose a
scheme based on a combination of neural networks to improve the accuracy. This paper shows that
the machine learning scheme can find the correct quantum scars from thousands images of
wave functions, without any human intervention, regardless of the symmetry of the underlying
classical system. This will be the first application of Meta learning to quantum systems. Interacting spin networks are fundamental to quantum computing. Data-based tomography oftime-independent spin networks has been achieved, but an open challenge is to ascertain the
structures of time-dependent spin networks using time series measurements taken locally
from a small subset of the spins. Physically, the dynamical evolution of a spin network under
time-dependent driving or perturbation is described by the Heisenberg equation of motion.
Motivated by this basic fact, this paper articulates a physics-enhanced machine learning framework
whose core is Heisenberg neural networks. This paper demonstrates that, from local measurements, not only the local Hamiltonian can be recovered but the Hamiltonian reflecting the interacting structure of the whole system can
also be faithfully reconstructed. Using Heisenberg neural machine on spin networks of a
variety of structures. In the extreme case where measurements are taken from only one spin,
the achieved tomography fidelity values can reach about 90%. The developed machine
learning framework is applicable to any time-dependent systems whose quantum dynamical
evolution is governed by the Heisenberg equation of motion.
ContributorsHan, Chendi (Author) / Lai, Ying-Cheng (Thesis advisor) / Yu, Hongbin (Committee member) / Dasarathy, Gautam (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2022
Description
Visual impairment is a significant challenge that affects millions of people worldwide. Access to written text, such as books, documents, and other printed materials, can be particularly difficult for individuals with visual impairments. In order to address this issue, our project aims to develop a text-to-Braille and speech translating device that will help people with visual impairments to access written text more easily and independently.
ContributorsNguyen, Vu (Author) / Yu, Hongbin (Thesis director) / Dasarathy, Gautam (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2023-05