Thesis (Ph.D.)--University of Nebraska-Lincoln, 2006. / Title from title screen (site viewed on Sept. 12, 2006). PDF text of dissertation: xiii, 305 p. : ill., maps (some col.) ; 17.47Mb. UMI publication number: AAT 3208113. Includes bibliographical references. Also available in microfilm, microfiche and paper format.

Thesis (M. S.)--Psychology, Georgia Institute of Technology, 2007. / Bruce N. Walker, Committee Chair ; Wendy Rogers, Committee Member ; Gregory Corso, Committee Member.

The dissertation argues that depictions of cultural trauma in literature are a natural progression from depictions of individual trauma by tracing the development of trauma studies from its roots in Freudian psychoanalysis to its current position as an interdisciplinary field of study. It accomplishes this by focusing on one symptom of Posttraumatic Stress Disorder, a tendency to perceive illusory patterns - patterns that do not really exist, such as conspiracy theories - in response to feelings of helplessness that stem from a traumatic event. This study contends that depictions of illusory pattern perceptions, while they may initially suggest a simple and definitive answer to healing from the traumatic event if the individual can fully grasp the pattern and get others to see it, actually demonstrate an extension of the trauma by forcing the individual to continuously relive it. Through the use of poetry, fiction, film, and graphic novels from three lingering national crises - a chapter each for the Kennedy assassination, the Vietnam War, and 9/11 - this study demonstrates that the perception of an illusory pattern is a simplistic attempt to deal with the ramifications of a traumatic event which must be dismissed in favor of embracing the complexities of the trauma in order to move beyond it. Finally, in the conclusion this study argues that depictions of memorials in literature can serve as a positive alternative to the destructive force of illusory pattern perception.

M.Tech. (Radiography (Diagnostic)) / In order to meet the needs of the country South African radiographers need to become multi skilled radiographers. Empowering diagnostic radiographers with pattern recognition skills to enable them to comment on images could address the problem in many South African departments where a shortage of radiologists’ results in delayed reports or no reports to referring doctors. The research assessed the ability of qualified diagnostic radiographers in two Gauteng Government Hospital’s to apply pattern recognition and provide a comment on a radiograph after training in musculoskeletal pattern recognition. The study employed a pre- and post-test model with an intervention which comprised training of radiographers in musculoskeletal pattern recognition. The post- test results showed a significant improvement in the accuracy of identifying abnormal images and the comments provided were more complete than before training. Thus the intervention was successful in improving the ability of the radiographers to recognize normal and abnormal images, however, the training would need to be more extensive for an accurate comment to be provided on musculoskeletal images.

Four studies were undertaken to investigate the advance planning and perception of simple rhythmic patterns. Subjects listened to patterns of identical, computer-generated tones and then reproduced them as accurately as possible by tapping on a single response key. Section One focussed on the advance planning of isochronous rhythmic patterns in which subjects performed the additional task of initiating pattern reproduction as quickly as possible. In Experiment 1, subjects listened to patterns of one to six tones with interstimulus intervals (ISIs) of 300 ms. The reproduction phase involved no stimulus uncertainty. Reaction time (RT) was found to increase linearly with number of response events. Advance planning thus occurs for patterns reproduced as slow as 300 ms per response event. Stimulus uncertainty is not a necessary condition for RT to increase with response complexity. In Experiment 2, subjects reproduced patterns of one to eight tones with ISIs of 200, 400, 600, and 800 ms. A linear RT trend was found only at the 200-ms rate. Patterns slower than this rate did not display "response coherence". Patterns at the 200-ms and 400-ms rates showed evidence of grouping through the accenting of first and last intervals. These patterns' displayed "perceptual coherence". Section Two focussed on the perceptual organization of patterns in which pattern structures could suggest the grouping of events as two equal-duration intervals. In Experiment 3, subjects reproduced two series of patterns, one series in which the suggested grouping-intervals were initiated by external-world events, and one in which they were not. Pattern structures in the latter series were not suggestive enough to induce grouping of events as two equal-duration intervals. Patterns were instead grouped as two intervals of unequal duration showing that the relative temporal positions of external-world events dominates in simple perceptual grouping. Experiment 4 investigated the upper temporal limit of perceptual grouping intervals and the influence of number of group constituents. Results showed that perceptual grouping of events that span more than 1800 ms is seldom accomplished and that grouping occurs when intervals contain up to seven constituents. / Graduate and Postdoctoral Studies / Graduate

Feature extraction and tensor recovery problems are important yet challenging, particularly for multi-dimensional data with missing values and/or noise. Low-rank tensor decomposition approaches are widely used for solving these problems. This thesis focuses on three common tensor decompositions (CP, Tucker and t-SVD) and develops a set of decomposition-based approaches. The proposed methods aim to extract low-dimensional features from complete/incomplete data and recover tensors given partial and/or grossly corrupted observations.;Based on CP decomposition, semi-orthogonal multilinear principal component analysis (SO-MPCA) seeks a tensor-to-vector projection that maximizes the captured variance with the orthogonality constraint imposed in only one mode, and it further integrates the relaxed start strategy (SO-MPCA-RS) to achieve better feature extraction performance. To directly obtain the features from incomplete data, low-rank CP and Tucker decomposition with feature variance maximization (TDVM-CP and TDVM-Tucker) are proposed. TDVM methods explore the relationship among tensor samples via feature variance maximization, while estimating the missing entries via low-rank CP and Tucker approximation, leading to informative features extracted directly from partial observations. TDVM-CP extracts low-dimensional vector features viewing the weight vectors as features and TDVM-Tucker learns low-dimensional tensor features viewing the core tensors as features. TDVM methods can be generalized to other variants based on other tensor decompositions. On the other hand, this thesis solves the missing data problem by introducing low-rank matrix/tensor completion methods, and also contributes to automatic rank estimation. Rank-one matrix decomposition coupled with L1-norm regularization (L1MC) addresses the matrix rank estimation problem. With the correct estimated rank, L1MC refines its model without L1-norm regularization (L1MC-RF) and achieve optimal recovery results given enough observations. In addition, CP-based nuclear norm regularized orthogonal CP decomposition (TREL1) solves the challenging CP- and Tucker-rank estimation problems. The estimated rank can improve the tensor completion accuracy of existing decomposition-based methods. Furthermore, tensor singular value decomposition (t-SVD) combined with tensor nuclear norm (TNN) regularization (ARE_TNN) provides automatic tubal-rank estimation. With the accurate tubal-rank determination, ARE_TNN relaxes its model without the TNN constraint (TC-ARE) and results in optimal tensor completion under mild conditions. In addition, ARE_TNN refines its model by explicitly utilizing its determined tubal-rank a priori and then successfully recovers low-rank tensors based on incomplete and/or grossly corrupted observations (RTC-ARE: robust tensor completion/RTPCA-ARE: robust tensor principal component analysis).;Experiments and evaluations are presented and analyzed using synthetic data and real-world images/videos in machine learning, computer vision, and data mining applications. For feature extraction, the experimental results of face and gait recognition show that SO-MPCA-RS achieves the best overall performance compared with competing algorithms, and its relaxed start strategy is also effective for other CP-based PCA methods. In the applications of face recognition, object/action classification, and face/gait clustering, TDVM methods not only stably yield similar good results under various multi-block missing settings and different parameters in general, but also outperform the competing methods with significant improvements. For matrix/tensor rank estimation and recovery, L1MC-RF efficiently estimates the true rank and exactly recovers the incomplete images/videos under mild conditions, and outperforms the state-of-the-art algorithms on the whole. Furthermore, the empirical evaluations show that TREL1 correctly determines the CP-/Tucker- ranks well, given sufficient observed entries, which consistently improves the recovery performance of existing decomposition-based tensor completion. The t-SVD recovery methods TC-ARE, RTPCA-ARE, and RTC-ARE not only inherit the ability of ARE_TNN to achieve accurate rank estimation, but also achieve good performance in the tasks of (robust) image/video completion, video denoising, and background modeling. This outperforms the state-of-the-art methods in all cases we have tried so far with significant improvements.

No description available.

A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science, Johannesburg, 30 March 2018. / Accuracy of the load forecasts is very critical in the power system industry, which is the lifeblood of the global economy to such an extent that its art-of-the-state management is the focus of the Short-Term Load Forecasting (STLF) models. In the past few years, South Africa faced an unprecedented energy management crisis that could be addressed in advance, inter alia, by carefully forecasting the expected load demand. Moreover, inaccurate or erroneous forecasts may result in either costly over-scheduling or adventurous under-scheduling of energy that may induce heavy economic forfeits to power companies. Therefore, accurate and reliable models are critically needed. Traditional statistical methods have been used in STLF but they have limited capacity to address nonlinearity and non-stationarity of electric loads. Also, such traditional methods cannot adapt to abrupt weather changes, thus they failed to produce reliable load forecasts in many situations. In this research report, we built a STLF model using Artificial Neural Networks (ANNs) to address the accuracy problem in this field so as to assist energy management decisions makers to run efficiently and economically their daily operations. ANNs are a mathematical tool that imitate the biological neural network and produces very accurate outputs. The built model is based on the Multilayer Perceptron (MLP), which is a class of feedforward ANNs using the backpropagation (BP) algorithm as its training algorithm, to produce accurate hourly load forecasts. We compared the MLP built model to a benchmark Seasonal Autoregressive Integrated Moving Average with Exogenous variables (SARIMAX) model using data from Eskom, a South African public utility. Results showed that the MLP model, with percentage error of 0.50%, in terms of the MAPE, outperformed the SARIMAX with 1.90% error performance. / LG2018

No description available.

No description available.