Browsing by Subject "Pattern Recognition"

Now showing 1 - 3 of 3
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    Analysis of Latent Space Representations for Object Detection
    (2024-08) Dale, Ashley Susan; Christopher, Lauren; King, Brian; Salama, Paul; Rizkalla, Maher
    Deep Neural Networks (DNNs) successfully perform object detection tasks, and the Con- volutional Neural Network (CNN) backbone is a commonly used feature extractor before secondary tasks such as detection, classification, or segmentation. In a DNN model, the relationship between the features learned by the model from the training data and the features leveraged by the model during test and deployment has motivated the area of feature interpretability studies. The work presented here applies equally to white-box and black-box models and to any DNN architecture. The metrics developed do not require any information beyond the feature vector generated by the feature extraction backbone. These methods are therefore the first methods capable of estimating black-box model robustness in terms of latent space complexity and the first methods capable of examining feature representations in the latent space of black box models. This work contributes the following four novel methodologies and results. First, a method for quantifying the invariance and/or equivariance of a model using the training data shows that the representation of a feature in the model impacts model performance. Second, a method for quantifying an observed domain gap in a dataset using the latent feature vectors of an object detection model is paired with pixel-level augmentation techniques to close the gap between real and synthetic data. This results in an improvement in the model’s F1 score on a test set of outliers from 0.5 to 0.9. Third, a method for visualizing and quantifying similarities of the latent manifolds of two black-box models is used to correlate similar feature representation with increase success in the transferability of gradient-based attacks. Finally, a method for examining the global complexity of decision boundaries in black-box models is presented, where more complex decision boundaries are shown to correlate with increased model robustness to gradient-based and random attacks.
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    Applying Different Wide-Area Response-Based Controls to Different Contingencies in Power Systems
    (2019-08) Iranmanesh, Shahrzad; Steven, Rovnyak; King, Brian; dos Santos, Euzeli Cipriano
    The electrical disturbances in the power system have threatened the stability of the system. In the first step, it is necessary to detect these electrical disturbances or events. In the next step, a proper control should apply to the system to decrease the consequences of the disturbances. One-shot control is one of the effective methods for stabilizing the events. In this method, a proper amount of loads are increased or decreased to the electrical system. Determining the amounts of loads, and the location for shedding is crucial. Moreover, some control combinations are more effective for some events and less effective for some others. Therefore, this project is completed in two different sections. First, finding the effective control combinations, second, finding an algorithm for applying different control combinations to different contingencies in real-time. To find effective control combinations, sensitivity analysis is employed to locate the most effective loads in the system. Then to find the control combination commands, gradient descent, and PSO algorithm are used in this project. In the next step, a pattern recognition method is used to apply the appropriate control combination for every event. The decision tree is selected as the pattern recognition method. The three most effective control combinations found by sensitivity analysis and the PSO method are used in the remainder of this study. A decision tree is trained for each of the three control combinations, and their outputs are combined into an algorithm for selecting the best control in real-time. Finally, the algorithm is evaluated using a test set of contingencies. The final results reveal a 30\% improvement in comparison to the previous studies.
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    Detection of histological features in liver biopsy images to help identify Non-Alcoholic Fatty Liver Disease
    (2018-04-26) Sethunath, Deepak
    This thesis explores a minimally invasive approach of diagnosing Non-Alcoholic Fatty Liver disease (NAFLD) on mice and humans which can be useful for pathologists while performing their diagnosis. NAFLD is a spectrum of diseases going from least severe to most severe – steatosis, steatohepatitis, fibrosis and finally cirrhosis. This disease primarily results from fat deposition in the liver which is unrelated to alcohol or viral causes. In general, it affects individuals having a combination of at least three of the five metabolic syndromes namely, obesity, hypertension, diabetes, hypertriglyceridemia, and hyperlipidemia. Given how common these metabolic syndromes have become, the rate of NAFLD has increased dramatically over the years affecting about three-quarters of all obese individuals including many children, making it one of the most common diseases in United States. Our study focuses on building various computational models which help identify different histological features in a liver biopsy image, thereby analyzing if a person is affected by NAFLD or not. Here, we develop and validate the performance of automated classifiers built using image processing and machine learning methods for detection of macro- and microsteatosis, lobular and portal inflammation and also categorize different types fibrosis in murine and human fatty liver disease and study the correlation of automated quantification of macrosteatosis, lobular and portal inflammation, and fibrosis (amount of collagen) with expert pathologist’s semi-quantitative grades. Our research for macrosteatosis and microsteatosis prediction shows the model’s precision and sensitivity as 94.2%, 95% for macrosteatosis and 79.2%, 77% for microsteatosis. Our models detect lobular and portal inflammation(s) with a precision, sensitivity of 79.6%, 77.1% for lobular inflammation and 86%, 90.4% for portal inflammation. We also present the first study on identification of the six different types of fibrosis having a precision of 85.6% for normal fibrosis and >70% for portal fibrosis, periportal fibrosis, pericellular fibrosis, bridging fibrosis and cirrhosis. We have also quantified the amount of collagen in a liver biopsy and compared it to the pathologist semi-quantitative fibrosis grade.