Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Spectral processing for algae monitoring and mapping (SPAMM): remote sensing methodologies for river ecology(Montana State University - Bozeman, College of Engineering, 2024) Logan, Riley Donovan; Chairperson, Graduate Committee: Joseph A. Shaw; This is a manuscript style paper that includes co-authored chapters.Inland water quality is a growing concern to public health, riparian ecosystems, and recreational uses of our waterways. Many modern water quality programs include measures of the presence and abundance of harmful and nuisance algae. In southwestern Montana, large blooms of the nuisance algae, Cladophora glomerata, have become common in the Upper Clark Fork River due to a combination of warming water temperatures, naturally high phosphorus levels, and an influx of contaminants through wastewater and anthropogenic activity along its banks. To improve understanding of bloom dynamics, such as algal biomass and percent algae cover, and their effects on water quality, a UAV-based hyperspectral imaging system was used to monitor several locations along the Upper Clark Fork River. Image data were collected across the spectral range of 400 - 1000 nm with 2.1 nm spectral resolution during field sampling campaigns across the entirety of the project, beginning in 2019 and ending in 2023. In this dissertation, methodologies for monitoring water quality were developed. These methods include estimating benthic algal pigment abundance using spectral band ratios achieving R 2 values of up to 0.62 for chlorophyll alpha and 0.96 for phycocyanin; creating spatial algae distribution maps and estimating percent algae cover using machine learning classification algorithms with accuracies greater than 99%; combining spatial algae distribution maps and improved pigment estimation using machine learning regression algorithms for creating chlorophyll alpha abundance maps, achieving an R 2 of 0.873, while also comparing abundance values to Montana water quality thresholds; and identifying salient wavelengths for monitoring and mapping algae to inform the design of a low-cost and compact multispectral imager. Throughout all field campaigns, significant spatial variations in algal growth within each river reach and frequent violations of current water quality standards were observed, demonstrating the need for high-spatial resolution monitoring techniques to be incorporated in current water quality monitoring programs.Item Data-driven approaches for distribution grid modernization: exploring state estimaion, pseudo-measurement generation and false data detection(Montana State University - Bozeman, College of Engineering, 2023) Radhoush, Sepideh; Chairperson, Graduate Committee: Brad WhitakerDistribution networks must be regularly updated to enhance their performance and meet customer electricity requirements. Advanced technologies and infrastructure--including two- way communication, smart measuring devices, distributed generations in various forms, electric vehicles, variable loads, etc.--have been added to improve the overall efficiency of distribution networks. Corresponding to these new features and structures, the continuous control and monitoring of distribution networks should be intensified to keep track of any modifications to the distribution network performance. Distribution system state estimation has been introduced for real-time monitoring of distribution networks. State estimation calculations are highly dependent on measurement data which are collected from measurement devices in distribution networks. However, the installation of measurement devices is not possible at all buses to ensure the distribution network is fully observable. To address the lack of real measurements, pseudo- measurements are produced from historical load and generation data. Available measurements, along with physical distribution network topology, are fed into a state estimation algorithm to determine system state variables. Then, state estimation results are sent to a control center for further processing to enhance distribution network operation. However, the accuracy of state estimation results could be degraded by false data injection attacks on measurement data. If these attacks are not detected, distribution network operation could be significantly influenced. Different methods have been developed to enhance a distribution network operation and management. Machine learning approaches have also been identified to be beneficial in solving different types of problems in a power grid. In this dissertation, machine learning is applied to three areas of distribution systems: generating pseudo-measurements, performing distribution system state estimation calculations, and detecting false data injection attacks on measurement data. In addition to addressing these areas individually, machine learning is used to simultaneously perform distribution system state estimation calculation and false data injection attack detection. This is done by taking advantage of conventional and smart measurement data at different time scales. The results reveal that the operation and performance of a distribution network are improved using machine learning algorithms, leading to more effective power grid modernization.Item Automatic 2D material detection and quantum emission prediction using deep learning-based models(Montana State University - Bozeman, College of Engineering, 2023) Ramezani, Fereshteh; Chairperson, Graduate Committee: Brad WhitakerThe realm of quantum engineering holds immense promise for revolutionizing technological landscapes, particularly with the advent of 2D materials in quantum device applications. The fundamental properties of these materials make them pivotal in various quantum applications. However, the progress in quantum engineering faces significant roadblocks, primarily centered around two challenges: accurate 2D material detection and understanding the random nature of quantum fluctuations. In response to the first challenge, I have successfully implemented a new deep learning pipeline to identify 2D materials in microscopic images. I have used a state-of-the-art two-stage object detector and trained it on images containing flakes of varying thickness of hexagonal boron nitride (hBN, a 2D material). The trained model achieved a high detection accuracy for the rare category of thin flakes (< or = 50 atomic layers thick). My further analysis shows that this proposed pipeline is robust against changes in color or substrate background, and could be generalized to various microscope settings. As an achievement, I have integrated my proposed method to the 2D quantum material pipeline (2D-QMaP), that has been under development by the MonArk Quantum Foundry, to provide automated capabilities that unite and accelerate the primary stages of sample preparation and device fabrication for 2D quantum materials research. My proposed algorithm has given the 2D-QMaP fully automatic real-time 2D flake detection capabilities, which has never been done effectively before. To address the second challenge, I assessed the random nature of quantum fluctuations, and I developed time series forecasting deep learning models to analyze and predict quantum emission fluctuations for the first time. My trained models can roughly follow the actual trend of the data and, under certain data processing conditions, can predict peaks and dips of the fluctuations. The ability to anticipate these fluctuations will allow physicists to harness quantum fluctuation characteristics to develop novel scientific advances in quantum computing that will greatly benefit quantum technologies. The automated 2D material identification, addressing the laborious process of flake detection, and the introduction of innovative quantum fluctuations analysis with predictive capabilities not only streamline research processes but also hold the promise of creating more stable and dependable quantum emission devices, thus significantly advancing the broader field of quantum engineering.Item Opto-mechanical design and analysis for coherent active imaging(Montana State University - Bozeman, College of Engineering, 2022) Neeley, Jaime Branson; Co-chairs, Graduate Committee: Wm. Randall Babbitt and Joseph A. ShawThe objective of this thesis project was to design a monostatic lidar transmit (Tx) and receive (Rx) opto-mechanical apparatus for remote sensing at a variable range of 50 m - 500 m. The scope of this project begins from the fiber output of a pre-designed Frequency-Modulated Continuous Wave (FMCW) lidar system. After design criteria for the lidar module are given, the optical and mechanical design is presented, opto-mechanical tolerancing is presented, and assembly, alignment, and testing procedures are covered as well. This thesis shows that the required design criteria of diffraction-limited optical performance was achieved while accounting for predictable manufacturing and assembly errors modeled using a Monte Carlo tolerance analysis. Furthermore, this thesis shows that the modeled and measured optical performance results were in good agreement and recommendations are given for improvements for the next-generation revision of the lidar Tx/Rx module.Item RadPC@Scale: an approach to mitigate single event upsets in the memory of space computers(Montana State University - Bozeman, College of Engineering, 2022) Williams, Justin Patrick; Chairperson, Graduate Committee: Brock LaMeresThis thesis presents the flight test results of a single event upset (SEU) mitigation strategy for computer data memory. This memory fault mitigation strategy is part of a larger effort to build a radiation tolerant computing system using commercial-off-the-shelf (COTS) field programmable gate arrays (FPGAs) called RadPC. While previous iterations of RadPC used FPGA block RAM (BRAM) for its data memory, the specific component of RadPC that is presented in this paper is a novel external memory scheme with accompanying systems that can detect, and correct faults that occur in the proposed data memory of the computer while allowing the computer to continue foreground operation. A prototype implementation of this memory protection scheme was flown on a Raven Aerostar Thunderhead high-altitude balloon system in July of 2021. This flight carried the experiment to an altitude of 75,000 feet for 50 hours allowing the memory in the experiment to be bombarded with ionizing radiation without being attenuated by the majority of Earth's atmosphere. This thesis discusses the details of the fault mitigation strategy, the design-of-experiments for the flight demonstration, and the results from the flight data. This thesis may be of interest to engineers that are designing flight computer systems that will be exposed to ionizing radiation and are looking for a lower cost SEU mitigation strategy compared to existing radiation- hardened solutions.Item Widefield micro-camera integrated into the objective lens of a reflectance confocal microscope for concurrent image registration(Montana State University - Bozeman, College of Engineering, 2023) Aist, Joseph Nicholas; Chairperson, Graduate Committee: David L. DickensheetsWith millions of new skin disease cases reported annually, non-invasive imaging methods have been developed to diagnose skin disease accurately. Reflectance confocal microscopes (RCM) have led these new technologies with high sensitivity and specificity. However, current methods use multiple devices: a digital camera, a dermoscope, and an RCM, which are not co-registered. Therefore, locating small, microscopic RCM fields-of-view (0.5x0.5 mm) at specific suspicion sites within the larger dermoscopic field-of-view (10x10 mm) is extremely difficult. This 'blind' RCM imaging results in lower and more variable diagnostic accuracy, particularly sensitivity, where positive and negative predictive values can drop by up to 30%. Our team has designed a new objective lens with an integrated micro-camera to deliver a concurrent widefield image of the skin surface surrounding the location of microscopic RCM imaging. The widefield image can be used directly to provide context for RCM or can be registered to a previously stored high-resolution clinical image to show where RCM imaging is occurring. In this thesis, the micro-camera is characterized and tested in laboratory and clinical settings. In addition, this thesis investigates a co- and cross-polarized micro-camera and LED system. It compares them to the non-polarized system to explore whether the cross-polarized version enhances feature contrast and enables better dermoscopic imaging. Non-polarized, co-polarized, and cross-polarized mock-up probes of the objective lens with a micro-camera were designed and built for testing. Images of resolution targets, color charts, and skin were taken to obtain modulation transfer function (MTF) measurements, color analysis data, and representative skin images. The results showed improvement in the MTF for the cross- polarized probe when compared to the co- and non-polarized probes. It was also found that the polarization of the imaging system did not significantly affect the color quality of the images. When tested by scientists at Memorial Sloan Kettering Cancer Center, sub-surface features not seen with the co- and non-polarized probes were observed with the cross-polarized probe. The cross-polarized probe suppressed the surface reflections, allowing for sub-surface information to be captured.Item Using sparse coding as a preprocessing technique for insect detection in pulsed LIDAR data(Montana State University - Bozeman, College of Engineering, 2022) Zsidisin, Connor Reece; Chairperson, Graduate Committee: Brad WhitakerThis research proposes using sparse coding as a preprocessing technique on insect lidar based data. This preprocessing technique will be used in conjunction with the Adaptive Boosting (AdaBoost), Random UnderSampling Boosting (RUSBoost), and neural network algorithms to automatically detect insects. The project aims to increase the effectiveness of these algorithms by using new images created by sparse coding. The K-Singular Value Decomposition (KSVD) algorithm will be used to train a dictionary on images that contain the majority class (non-insects). This trained dictionary will be used along with Orthogonal Matching Pursuit (OMP) to reconstruct all lidar images. The difference between the original image and the reconstructed image will be taken and processed by the feature extraction function and then used to train and test the models. Using a complete and an overcomplete dictionary our results show that the algorithms are able to detect insects at a higher rate. Using an overcomplete dictionary we are able to classify 93.18% of insect containing images in the testing dataset. Using the complete dictionary we were able to maintain 99.70% of non-insect images while increasing the percentage of insects classified to 84.09%.Item Resilience assessment of active distribution networks(Montana State University - Bozeman, College of Engineering, 2021) Miller, Ryan Jared Alexander; Chairperson, Graduate Committee: Maryam BahramipanahPower system resilience focuses on a system's ability to prepare for and recover from events which would severely degrade its performance. With severe weather events and regional disasters such as hurricanes, polar vortex cold, and wildfires increasing in frequency and intensity in recent years, work toward simulation and quantification techniques of power system resilience is more necessary than ever. To generate a realistic model, this work produces a geographic topography to geographically lay out and test power system. Furthermore, different extreme events such as flooding, hurricanes, wildfires, and tornadoes are modeled, and the proposed technique evaluates their impacts on the power system degradation and resilience. The availability of recovery resources and several stochastic recovery dynamics that modify the system's depth of degradation and recovery profile during repair time are studied in this work. Multiple resilience metrics are proposed to aid in analyzing the system's recovery performance. The performance of this proposed technique is then evaluated for a flood of intermediate intensity which causes component failures and system outages within the grid. System recovery resources are varied by adjusting the number of crews who can simultaneously repair the system. Resilience indices are evaluated, and it is shown that with increasing availability of repair crews and recovery resources, the system resilience improves. The proposed strategy can be applied to an arbitrary test system with ease. Different strategies such as energy storage management and repair prioritization can be modified in future works to test potential improvements or optimizations for a given test system under the occurrence of a specific extreme event.Item Diode-laser-based high spectral resolution LIDAR(Montana State University - Bozeman, College of Engineering, 2021) Colberg, Luke Stewart; Chairperson, Graduate Committee: Kevin S. RepaskyThis thesis describes the design, construction, and testing of a high spectral resolution lidar (HSRL) as a part of a combined HSRL and differential absorption lidar (DIAL) system. The combined HSRL and DIAL instrument is constructed using the MicroPulse DIAL (MPD) architecture and uses distributed Bragg reflector lasers. The MPD architecture is unique because it is eye-safe and cost-effective; therefore, it is ideal for creating a network of ground-based lidars. This instrument is designed for thermodynamic profiling of the lower troposphere. A network of these instruments would be helpful for wide-scale atmospheric monitoring for weather forecasting and climate science. The purpose of the HSRL is to retrieve the optical properties of aerosols in the lower troposphere. The HSRL uses the DIAL offline laser, which has a wavelength of 770.1085 nm, and a potassium vapor cell as the spectral filter. The data retrieved from the HSRL provides the aerosol backscatter coefficient and the backscatter ratio up to an altitude of 7 km during nighttime operation and 5 km during daytime operation. The time resolution for these measurements is 5 minutes, and the range resolution is 150 m. These aerosol optical properties are valuable for aerosol studies and climate modeling; aerosols introduce the most significant degree of uncertainty in modeling the heat flux of the atmosphere. Additionally, these aerosol optical properties can be used to find the planetary boundary layer height (PBLH). The planetary boundary layer controls the exchange of heat, water vapor, aerosols, and momentum between the surface and the atmosphere. It has been demonstrated that the PBLH strongly affects turbulent mixing, convective transport, and cloud entrainment, which makes the PBLH an important parameter for weather forecasting and climate modeling. Despite its significance in atmospheric science, there is no standard method for defining the PBLH. A retrieval method for finding the daytime PBLH using HSRL data is proposed, and data comparisons to radiosonde PBLH retrievals are provided. The algorithm shows a good agreement with the radiosonde retrievals for conditions with a well-behaved boundary layer.Item Wide-area control strategies for improving transient stability in a multi-machine power systems(Montana State University - Bozeman, College of Engineering, 2020) Ojetola, Samuel Toluwanimi; Chairperson, Graduate Committee: Todd Kaiser and Josh WoldTransient stability is the ability of synchronous machines in an interconnected power system to remain in synchronism after been subjected to a large disturbance. Transient instability is one of the less probable but severe events that a power system encounter in its daily operations. Historically, it has been the dominant stability problem in power systems and has been the focus of much of the power industry's attention. Traditionally, when a generator or group of generators begin to lose synchronism with the rest of the system, they are tripped or islanded from the network to maintain transient stability and to prevent or limit cascaded outages. However, with the increase in the penetration of inverter-based generation, tripping schemes may become difficult to apply because of wide distribution of generation and loss of system inertia. This research presents control strategies that improves the transient stability of a power system without having to trip generators. This is achieved by modulating the active power absorbed or injected by distributed energy storage devices. These devices are located at the high voltage bus of several generators in a synchronous power system and are independently controlled. The strategy is based upon local and center-of-inertia frequency estimated in real time from wide-area measurements. It is shown that by absorbing or injecting real-power into a power system to remove as much kinetic energy gained during a disturbance as quickly as possible before it is converted to potential energy, synchronism can be maintained. The performance of the control strategy is evaluated on several multi-machine power system models. The result shows that this control strategy significantly improves the transient stability of power systems.