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Browsing by Author "Chen, Haonan, committee member"

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    Advanced processing of dual polarization weather radar signal
    (Colorado State University. Libraries, 2022) Haran, Shweta, author; Chandrasekar, V., advisor; Chen, Haonan, committee member; Siller, Thomas, committee member
    This research focuses on processing of radar data in spectral domain and analysis of micro-physical properties of hail and rain in severe convective and stratiform storms. This research also discusses the optimization of a parametric time domain method to separate cloud and drizzle data. The microphysical and kinematic properties of hydrometeors present in a precipitation event can be studied using spectral domain processing and analysis of the radar moments. This study along with polarimetric information is called spectral polarimetry. For this study, the observations made by CSU-CHIVO (Colorado State University - C-band Hydrometeorological Instrument for Volumetric Observation) radar during the RELAMPAGO (Remote sensing of Electrification, Lightning, And Mesoscale/Microscale Processes with Adaptive Ground Observations) campaign is utilized. Features such as the slope in differential reflectivity, spectrum width, and spectral copolar correlation are studied which gives a better understanding of the storm microphysics. In this thesis, microphysical properties of different types of hydrometeors such as hail, rain, and large drops are studied using convective and stratiform storm observations. A parametric time-domain method (PTDM) is utilized for the separation of cloud and drizzle data. To reduce the time latency present in processing the data, the processing code is optimized by deploying on a high-performance computer (HPC). The processing code is tested on an HPC and automated to handle errors in processing. The run time is reduced by approximately 50%, hence increasing the data processing efficiency. This study shows that optimization of the run time using an HPC is an efficient method. Data processing using an HPC can be used to deploy similar time-consuming algorithms, hence increasing the efficiency and performance.
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    Application of statistical and deep learning methods to power grids
    (Colorado State University. Libraries, 2023) Rimkus, Mantautas, author; Kokoszka, Piotr, advisor; Wang, Haonan, advisor; Nielsen, Aaron, committee member; Cooley, Dan, committee member; Chen, Haonan, committee member
    The structure of power flows in transmission grids is evolving and is likely to change significantly in the coming years due to the rapid growth of renewable energy generation that introduces randomness and bidirectional power flows. Another transformative aspect is the increasing penetration of various smart-meter technologies. Inexpensive measurement devices can be placed at practically any component of the grid. As a result, traditional fault detection methods may no longer be sufficient. Consequently, there is a growing interest in developing new methods to detect power grid faults. Using model data, we first propose a two-stage procedure for detecting a fault in a regional power grid. In the first stage, a fault is detected in real time. In the second stage, the faulted line is identified with a negligible delay. The approach uses only the voltage modulus measured at buses (nodes of the grid) as the input. Our method does not require prior knowledge of the fault type. We further explore fault detection based on high-frequency data streams that are becoming available in modern power grids. Our approach can be treated as an online (sequential) change point monitoring methodology. However, due to the mostly unexplored and very nonstandard structure of high-frequency power grid streaming data, substantial new statistical development is required to make this methodology practically applicable. The work includes development of scalar detectors based on multichannel data streams, determination of data-driven alarm thresholds and investigation of the performance and robustness of the new tools. Due to a reasonably large database of faults, we can calculate frequencies of false and correct fault signals, and recommend implementations that optimize these empirical success rates. Next, we extend our proposed method for fault localization in a regional grid for scenarios where partial observability limits the available data. While classification methods have been proposed for fault localization, their effectiveness depends on the availability of labeled data, which is often impractical in real-life situations. Our approach bridges the gap between partial and full observability of the power grid. We develop efficient fault localization methods that can operate effectively even when only a subset of power grid bus data is available. This work contributes to the research area of fault diagnosis in scenarios where the number of available phasor measurement unit devices is smaller than the number of buses in the grid. We propose using Graph Neural Networks in combination with statistical fault localization methods to localize faults in a regional power grid with minimal available data. Our contribution to the field of fault localization aims to enable the adoption of effective fault localization methods for future power grids.
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    Integrating geometric deep learning with a set-based design approach for the exploration of graph-based engineering systems
    (Colorado State University. Libraries, 2024) Sirico, Anthony, Jr., author; Herber, Daniel R., advisor; Chen, Haonan, committee member; Simske, Steven, committee member; Conrad, Steven, committee member
    Many complex engineering systems can be represented in a topological form, such as graphs. This dissertation introduces a framework of Graph-Set-Based Design (GSBD) that integrates graph-based techniques with Geometric Deep Learning (GDL) within a Set-Based Design (SBD) approach to address graph-centric design problems. We also introduce Iterative Classification (IC), a method for narrowing down large datasets to a subset of more promising and feasible solutions. When we combine the two, we have IC-GSBD, a methodological framework where the primary goal is to effectively and efficiently seek the best-performing solutions with lower computational costs. IC-GSBD is a method that employs an iterative approach to efficiently narrow down a graph-based dataset containing diverse design solutions to identify the most useful options. This approach is particularly valuable as the dataset would be computationally expensive to process using other conventional methods. The implementation involves analyzing a small subset of the dataset to train a machine-learning model. This model is then utilized to predict the remaining dataset iteratively, progressively refining the top solutions with each iteration. In this work, we present two case studies demonstrating this method. In the first case study utilizing IC-GSBD, the goal is the analysis of analog electrical circuits, aiming to match a specific frequency response within a particular range. Previous studies generated 43,249 unique undirected graphs representing valid potential circuits through enumeration techniques. However, determining the sizing and performance of these circuits proved computationally expensive. By using a fraction of the circuit graphs and their performance as input data for a classification-focused GDL model, we can predict the performance of the remaining graphs with favorable accuracy. The results show that incorporating additional graph-based features enhances model performance, achieving a classification accuracy of 80% using only 10% of the graphs and further subdividing the graphs into targeted groups with medians significantly closer to the best and containing 88.2 of the top 100 best-performing graphs on average using 25% of the graphs.
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    Visible & thermal imaging and deep learning based approach for automated robust detection of potholes to prioritize highway maintenance
    (Colorado State University. Libraries, 2023) Chen, Wei-Hsiang, author; Jia, Gaofeng, advisor; Guo, Yanlin, committee member; Chen, Haonan, committee member
    Potholes are a primary pavement distress that can compromise safety and cause expensive damage claims. Potholes are results of deterioration of pavements due to aging, weather and traffic overloads and are common problems across the U.S. Potholes are even more common in the Mountain Plains region due to the snow and freeze/thaw effect. Identifying and repairing potholes is one critical aspect of highway maintenance. Accurate, robust and fast detection of potholes is critical to enabling timely and cost-effective pavement maintenance. Recently, there has been growing interest and research in using machine learning techniques for pothole detection using different views of visible images. However, quality of potholes detection using only visible images may be significantly compromised due to poor lighting, weather conditions, low contract to surrounding pavement. On the other hand, thermal images are more robust to lighting and weather conditions. Although thermal images may lack the texture details of visible images, they can offer additional unique features compared to visible images, e.g., temperature difference between pothole and surrounding pavement, which can be potentially used for pothole detection. However, so far, the great potential and effectiveness of integrating both visible and thermal images as well as using fused images to enable accurate and robust pothole detection have not been investigated. This research aims to develop an automated deep learning based pothole detection and mapping tool for highway maintenance using the fusion of visible and thermal images. First, a unique and valuable database of geotagged and labeled trios of visible, thermal and fused images is established for training pothole detection algorithms. This is done through collecting pothole images using a low-cost FLIR ONE thermal camera connected to a smart phone. These data are used to train the machine learning algorithms for pothole detection. To establish an accurate pothole detection algorithm, we proposed and compared the performances of three machine learning algorithms, i.e., Anisotropic Diffusion Fusion (ADF) + Mask R-CNN, RTFNet, and RTFNet with Enhancement Parameters (EPs). These algorithms differ in how the visible and thermal images are fused and used for pothole detection. We achieved the best F1-score of 93.7% in the daytime scenario by the RTFNet method and 90.9% in the nighttime scenario by the RTFNet with EPs method. To best leverage the information from the thermal images, in the end we developed a Bright-Dark detector to determine the lighting conditions of candidate testing images, and then feed the images to the respective algorithms for pothole detection. For images with potholes detected, we also developed a mapping tool to map the location of the pothole using GPS information of the images. In the end, the trained overall algorithm is packaged as a tool with graphical user interface (GUI) to facilitate its adoption by highway maintenance team. As more images are collected, the overall algorithm can be continuously improved to further increase the pothole detection accuracy.