Thesis (Ph. D.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains x, 97 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 81-83).

Multiscale modeling has become an important tool in material mechanics because material behavior can exhibit varied properties across different length scales. The use of multiscale modeling is essential for accurately capturing these characteristics and predicting material behavior. Mesh-free methods have also been gaining attention in recent years due to their innate ability to handle complex geometries and large deformations. These methods provide greater flexibility and efficiency in modeling complex material behavior, especially for problems involving discontinuities, such as fractures and cracks. Moreover, mesh-free methods can be easily extended to multiple lengths and time scales, making them particularly suitable for multiscale modeling. The thesis focuses on two specific problems of multiscale modeling with mesh-free methods. The first problem is the atomistically informed constitutive model for the study of high-pressure induced densification of silica glass. Molecular Dynamics (MD) simulations are carried out to study the atomistic level responses of fused silica under different pressure and strain-rate levels, Based on the data obtained from the MD simulations, a novel continuum-based multiplicative hyper-elasto-plasticity model that accounts for the anomalous densification behavior is developed and then parameterized using polynomial regression and deep learning techniques. To incorporate dynamic damage evolution, a plasticity-damage variable that controls the shrinkage of the yield surface is introduced and integrated into the elasto-plasticity model. The resulting coupled elasto-plasticity-damage model is reformulated to a non-ordinary state-based peridynamics (NOSB-PD) model for the computational efficiency of impact simulations. The developed peridynamics (PD) model reproduces coarse-scale quantities of interest found in MD simulations and can simulate at a component level. Finally, the proposed atomistically-informed multiplicative hyper-elasto-plasticity-damage model has been validated against limited available experimental results for the simulation of hyper-velocity impact simulation of projectiles on silica glass targets. The second problem addressed in the thesis involves the upscaling approach for multi-porosity media, analyzed using the so-called MultiSPH method, which is a sequential SPH (Smoothed Particle Hydrodynamics) solver across multiple scales. Multi-porosity media is commonly found in natural and industrial materials, and their behavior is not easily captured with traditional numerical methods. The upscaling approach presented in the thesis is demonstrated on a porous medium consisting of three scales, it involves using SPH methods to characterize the behavior of individual pores at the microscopic scale and then using a homogenization technique to upscale to the meso and macroscopic level. The accuracy of the MultiSPH approach is confirmed by comparing the results with analytical solutions for simple microstructures, as well as detailed single-scale SPH simulations and experimental data for more complex microstructures.

As self-awareness is important to human higher level cognition so too is the ability to self-model important to performing complex behaviors. The power of these self-models is one that I demonstrate grows with the complexity of problems being solved, and thus provides the framework for higher level cognition. I demonstrate that self-models can be used to effectively control and improve on existing control algorithms to allow agents to perform complex tasks. I further investigate new ways in which these self-models can be learned and applied to increase their efficacy and improve the ability of these models to generalize across tasks and bodies. Finally, I demonstrate the overall power of these self-models to allow for complex tasks to be completed with little data across a variety of bodies and using a number of algorithms.

The ability to extract figures and tables from scientific documents can solve key use-cases such as their semantic parsing, summarization, or indexing. Although a few methods have been developed to extract figures and tables from scientific documents, their performance on scanned counterparts is considerably lower than on born-digital ones. To facilitate this, we propose methods to effectively extract figures and tables from Electronic Theses and Dissertations (ETDs), that out-perform existing methods by a considerable margin. Our contribution towards this goal is three-fold. (a) We propose a system/model for improving the performance of existing methods on scanned scientific documents for figure and table extraction. (b) We release a new dataset containing 10,182 labelled page-images spanning across 70 scanned ETDs with 3.3k manually annotated bounding boxes for figures and tables. (c) Lastly, we release our entire code and the trained model weights to enable further research (https://github.com/SampannaKahu/deepfigures-open). / Master of Science / Portable Document Format (PDF) is one of the most popular document formats. However, parsing PDF files is not a trivial task. One use-case of parsing PDF files is the search functionality on websites hosting scholarly documents (i.e., IEEE Xplore, etc.). Having the ability to extract figures and tables from a scholarly document helps this use-case, among others. Methods using deep learning exist which extract figures from scholarly documents. However, a large number of scholarly documents, especially the ones published before the advent of computers, have been scanned from hard paper copies into PDF. In particular, we focus on scanned PDF versions of long documents, such as Electronic Theses and Dissertations (ETDs). No experiments have been done yet that evaluate the efficacy of the above-mentioned methods on this scanned corpus. This work explores and attempts to improve the performance of these existing methods on scanned ETDs. A new gold standard dataset is created and released as a part of this work for figure extraction from scanned ETDs. Finally, the entire source code and trained model weights are made open-source to aid further research in this field.

Most medical treatments have been developed aiming at the best-on-average efficacy for large populations, resulting in treatments successful for some patients but not for others. It necessitates the need for precision medicine that tailors medical treatment to individual patients. Omics data holds comprehensive genetic information on individual variability at the molecular level and hence the potential to be translated into personalized therapy. However, the attempts to transform omics data-driven insights into clinically actionable models for individual patients have been limited. Meanwhile, advances in deep learning, one of the most promising branches of artificial intelligence, have produced unprecedented performance in various fields. Although several deep learning-based methods have been proposed to predict individual phenotypes, they have not established the state of the practice, due to instability of selected or learned features derived from extremely high dimensional data with low sample sizes, which often results in overfitted models with high variance. To overcome the limitation of omics data, recent advances in deep learning models, including representation learning models, generative models, and interpretable models, can be considered. The goal of the proposed work is to develop deep learning models that can overcome the limitation of omics data to enhance the prediction of personalized medical decisions. To achieve this, three key challenges should be addressed: 1) effectively reducing dimensions of omics data, 2) systematically augmenting omics data, and 3) improving the interpretability of omics data. / Doctor of Philosophy / Most medical treatments have been developed aiming at the best-on-average efficacy for large populations, resulting in treatments successful for some patients but not for others. It necessitates the need for precision medicine that tailors medical treatment to individual patients. Biological data such as DNA sequences and snapshots of genetic activities hold comprehensive information on individual variability and hence the potential to accelerate personalized therapy. However, the attempts to transform data-driven insights into clinical models for individual patients have been limited. Meanwhile, advances in deep learning, one of the most promising branches of artificial intelligence, have produced unprecedented performance in various fields. Although several deep learning-based methods have been proposed to predict individual treatment or outcome, they have not established the state of the practice, due to the complexity of biological data and limited availability, which often result in overfitted models that may work on training data but not on test data or unseen data. To overcome the limitation of biological data, recent advances in deep learning models, including representation learning models, generative models, and interpretable models, can be considered. The goal of the proposed work is to develop deep learning models that can overcome the limitation of omics data to enhance the prediction of personalized medical decisions. To achieve this, three key challenges should be addressed: 1) effectively reducing the complexity of biological data, 2) generating realistic biological data, and 3) improving the interpretability of biological data.

It has been a while that computers have replaced our record books, and medical records are no exception. Electronic Health Records (EHR) are digital version of a patient's medical records. EHRs are available to authorized users, and they contain the medical records of the patient, which should help doctors understand a patient's condition quickly. In recent years, Deep Learning models have proved their value and have become state-of-the-art in computer vision, natural language processing, speech and other areas. The private nature of EHR data has prevented public access to EHR datasets. There are many obstacles to create a deep learning model with EHR data. Because EHR data are primarily consisting of huge sparse matrices, these challenges are mostly unique to this field. Due to this, research in this area is limited, and we can improve existing research substantially. In this study, we focus on high-performance synthetic data generation in EHR datasets. Artificial data generation can help reduce privacy leakage for dataset owners as it is proven that de-identification methods are prone to re-identification attacks. We propose a novel approach we call Improved Correlation Capturing Wasserstein Generative Adversarial Network (SCorGAN) to create EHR data. This work, leverages Deep Convolutional Neural Networks to extract and understand spatial dependencies in EHR data. To improve our model's performance, we focus on our Deep Convolutional AutoEncoder to better map our real EHR data to our latent space where we train the Generator. To assess our model's performance, we demonstrate that our generative model can create excellent data that are statistically close to the input dataset. Additionally, we evaluate our synthetic dataset against the original data using our previous work that focused on GAN Performance Evaluation. This work is publicly available at https://github.com/mohibeyki/SCorGAN / Master of Science / Artificial Intelligence (AI) systems have improved greatly in recent years. They are being used to understand all kinds of data. A practical use case for AI systems is to leverage their power to identify illnesses and find correlations between different conditions. To train AI and Machine Learning systems, we need to feed them huge datasets, and in the training process, we need to guide them so that they learn different features in our data. The more data an intelligent system has seen, the better it performs. However, health records are private, and we cannot share real people's health records with the public, whether they are a researcher or not. This study provides a novel approach to synthetic data generation that others can use with intelligent systems. Then these systems can work with actual health records can give us accurate feedback on people's health conditions. We then show that our synthetic dataset is a good substitute for real datasets to train intelligent systems. Lastly, we present an intelligent system that we have trained using synthetic datasets to identify illnesses in a real dataset with high accuracy and precision.

This work addresses the problem of automatically detecting human skin in images without reliance on color information. Unlike previous methods, we present a new approach that performs well in the absence of such information. A key aspect of the work is that color-space augmentation is applied strategically during the training, with the goal of reducing the influence of features that are based entirely on color and increasing more semantic understanding. The resulting system exhibits a dramatic improvement in performance for images in which color details are diminished. We have demonstrated the concept using the U-Net architecture, and experimental results show improvements in evaluations for all Fitzpatrick skin tones in the ECU dataset. We further tested the system with RFW dataset to show that the proposed method is consistent across different ethnicities and reduces bias to any skin tones. Therefore, this work has strong potential to aid in mitigating bias in automated systems that can be applied to many applications including surveillance and biometrics. / Master of Science / Skin segmentation deals with the classification of skin and non-skin pixels and regions in a image containing these information. Although most previous skin-detection methods have used color cues almost exclusively, they are vulnerable to external factors (e.g., poor or unnatural illumination and skin tones). In this work, we present a new approach based on U-Net that performs well in the absence of color information. To be specific, we apply a new color space augmentation into the training stage to improve the performance of skin segmentation system over the illumination and skin tone diverse. The system was trained and tested with both original and color changed ECU dataset. We also test our system with RFW dataset, a larger dataset with four human races with different skin tones. The experimental results show improvements in evaluations for skin tones and complex illuminations.

This thesis presents both a control scheme for naturalistic control of an exoskeleton glove and a glove design. Exoskeleton development has been focused primarily on design, improving soft actuator and cable-driven systems, with only limited focus on intelligent control. There is a need for control that is not limited to position or force reference signals and is user-driven. By implementing a motion amplification controller to increase weak movements of an impaired individual, a finger joint trajectory can be observed and used to predict their grasping intention. The motion amplification functions off of a virtual dynamical system that safely enforces the range of motion of the finger joints and ensures stability. Three grasp prediction algorithms are developed with improved levels of accuracy: regression, trajectory, and deep learning based. These algorithms were tested on published finger joint trajectories. The fusion of the amplification and prediction could be used to achieve naturalistic, user-guided control of an exoskeleton glove. The key to accomplishing this is series elastic actuators to move the finger joints, thereby allowing the wearer to deflect against the glove and inform the controller of their intention. These actuators are used to move the fingers in a nine degree of freedom exoskeleton that is capable of achieving all the grasps used most frequently in daily life. The controllers and exoskeleton presented here are the basis for improved exoskeleton glove control that can be used to assist or rehabilitate impaired individuals. / Master of Science / Millions of Americans report difficulty holding small or even lightweight objects. In many of these cases, their difficulty stems from a condition such as a stroke or arthritis, requiring either rehabilitation or assistance. For both treatments, exoskeleton gloves are a potential solution; however, widespread deployment of exoskeletons in the treatment of hand conditions requires significant advancement. Towards that end, the research community has devoted itself to improving the design of exoskeletons. Systems that use soft actuation or are driven by artificial tendons have merit in that they are comfortable to the wearer, but lack the rigidity required for monitoring the state of the hand and controlling it. Electromyography sensors are also a commonly explored technology for determining motion intention; however, only primitive conclusions can be drawn when using these sensors on the muscles that control the human hand. This thesis proposes a system that does not rely on soft actuation but rather a deflectable exoskeleton that can be used in rehabilitation or assistance. By using series elastic actuators to move the exoskeleton, the wearer of the glove can exert their influence over the machine. Additionally, more intelligent control is needed in the exoskeleton. The approach taken here is twofold. First, a motion amplification controller increases the finger movements of the wearer. Second, the amplified motion is processed using machine learning algorithms to predict what type of grasp the user is attempting. The controller would then be able to fuse the two, the amplification and prediction, to control the glove naturalistically.

Text detection in the wild is the problem of locating text in images of everyday scenes. It is a challenging problem due to the complexity of everyday scenes. This problem possesses a great importance for many trending applications, such as self-driving cars. Previous research in text detection has been dominated by multi-stage sequential approaches which suffer from many limitations including error propagation from one stage to the next. Another line of work is the use of deep learning techniques. Some of the deep methods used for text detection are box detection models and fully convolutional models. Box detection models suffer from the nature of the annotations, which may be too coarse to provide detailed supervision. Fully convolutional models learn to generate pixel-wise maps that represent the location of text instances in the input image. These models suffer from the inability to create accurate word level annotations without heavy post processing. To overcome these aforementioned problems we propose a novel end-to-end system based on a mix of novel deep learning techniques. The proposed system consists of an attention model, based on a new deep architecture proposed in this dissertation, followed by a deep network based on Faster-RCNN. The attention model produces a high-resolution map that indicates likely locations of text instances. A novel aspect of the system is an early fusion step that merges the attention map directly with the input image prior to word-box prediction. This approach suppresses but does not eliminate contextual information from consideration. Progressively larger models were trained in 3 separate phases. The resulting system has demonstrated an ability to detect text under difficult conditions related to illumination, resolution, and legibility. The system has exceeded the state of the art on the ICDAR 2013 and COCO-Text benchmarks with F-measure values of 0.875 and 0.533, respectively. / Ph. D.

We are witnessing a proliferation of massive visual data. Visual content is arguably the fastest growing data on the web. Photo-sharing websites like Flickr and Facebook now host more than 6 and 90 billion photos, respectively. Unfortunately, scaling existing computer vision algorithms to large datasets leaves researchers repeatedly solving the same algorithmic and infrastructural problems. Designing and implementing efficient and provably correct computer vision algorithms is extremely challenging. Researchers must repeatedly solve the same low-level problems: building and maintaining a cluster of machines, formulating each component of the computer vision pipeline, designing new deep learning layers, writing custom hardware wrappers, etc. This thesis introduces CloudCV, an ambitious system that contain algorithms for end-to-end processing of visual content. The goal of the project is to democratize computer vision; one should not have to be a computer vision, big data and deep learning expert to have access to state-of-the-art distributed computer vision algorithms. We provide researchers, students and developers access to state-of-art distributed computer vision and deep learning algorithms as a cloud service through web interface and APIs. / Master of Science