293 research outputs found

    A Routine and Post-disaster Road Corridor Monitoring Framework for the Increased Resilience of Road Infrastructures

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    Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

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    Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language processing and computer vision, largely attributed to deep learning, a special class of machine learning models. Deep learning arguably surpasses traditional approaches by learning the relevant features from raw data through a series of computational layers. This thesis explores the theoretical foundations of deep learning by studying the relationship between the architecture of these models and the inherent structures found within the data they process. In particular, we ask What drives the efficacy of deep learning algorithms and allows them to beat the so-called curse of dimensionality-i.e. the difficulty of generally learning functions in high dimensions due to the exponentially increasing need for data points with increased dimensionality? Is it their ability to learn relevant representations of the data by exploiting their structure? How do different architectures exploit different data structures? In order to address these questions, we push forward the idea that the structure of the data can be effectively characterized by its invariances-i.e. aspects that are irrelevant for the task at hand. Our methodology takes an empirical approach to deep learning, combining experimental studies with physics-inspired toy models. These simplified models allow us to investigate and interpret the complex behaviors we observe in deep learning systems, offering insights into their inner workings, with the far-reaching goal of bridging the gap between theory and practice.Comment: PhD Thesis @ EPF

    Improved Deep Neural Networks for Generative Robotic Grasping

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    This thesis provides a thorough evaluation of current state-of-the-art robotic grasping methods and contributes to a subset of data-driven grasp estimation approaches, termed generative models. These models aim to directly generate grasp region proposals from a given image without the need for a separate analysis and ranking step, which can be computationally expensive. This approach allows for fully end-to-end training of a model and quick closed-loop operation of a robot arm. A number of limitations are identified within these generative models, which are identified and addressed. Contributions are proposed that directly target each stage of the training pipeline that help to form accurate grasp proposals and generalise better to unseen objects. Firstly, inspired by theories of object manipulation within the mammalian visual system, the use of multi-task learning in existing generative architectures is evaluated. This aims to improve the performance of grasping algorithms when presented with impoverished colour (RGB) data by training models to perform simultaneous tasks such as object categorisation, saliency detection, and depth reconstruction. Secondly, a novel loss function is introduced which improves overall performance by rewarding the network to focus only on learning grasps at suitable positions. This reduces overall training times and results in better performance on fewer training examples. The last contribution analyses the problems with the most common metric used for evaluating and comparing offline performance between different grasping models and algorithms. To this end, a Gaussian method of representing ground-truth labelled grasps is put forward, which optimal grasp locations tested in a simulated grasping environment. The combination of these novel additions to generative models results in improved grasp success, accuracy, and performance on common benchmark datasets compared to previous approaches. Furthermore, the efficacy of these contributions is also tested when transferred to a physical robotic arm, demonstrating the ability to effectively grasp previously unseen 3D printed objects of varying complexity and difficulty without the need for domain adaptation. Finally, the future directions are discussed for generative convolutional models within the overall field of robotic grasping

    Data Rescue : defining a comprehensive workflow that includes the roles and responsibilities of the research library.

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    Thesis (PhD (Research))--University of Pretoria, 2023.This study, comprising a case study at a selected South African research institute, focused on the creation of a workflow model for data rescue indicating the roles and responsibilities of the research library. Additional outcomes of the study include a series of recommendations addressing the troublesome findings that revealed data at risk to be a prevalent reality at the selected institute, showing the presence of a multitude of factors putting data at risk, disclosing the profusion of data rescue obstacles faced by researchers, and uncovering that data rescue at the institute is rarely implemented. The study consists of four main parts: (i) a literature review, (ii) content analysis of literature resulting in the creation of a data rescue workflow model, (iii) empirical data collection methods , and (iv) the adaptation and revision of the initial data rescue model to present a recommended version of the model. A literature review was conducted and addressed data at risk and data rescue terminology, factors putting data at risk, the nature, diversity and prevalence of data rescue projects, and the rationale for data rescue. The second part of the study entailed the application of content analysis to selected documented data rescue workflows, guidelines and models. Findings of the analysis led to the identification of crucial components of data rescue and brought about the creation of an initial Data Rescue Workflow Model. As a first draft of the model, it was crucial that the model be reviewed by institutional research experts during the next main stage of the study. The section containing the study methodology culminates in the implementation of four different empirical data collection methods. Data collected via a web-based questionnaire distributed to a sample of research group leaders (RGLs), one-on-one virtual interviews with a sample of the aforementioned RGLs, feedback supplied by RGLs after reviewing the initial Data Rescue Workflow Model, and a focus group session held with institutional research library experts resulted in findings producing insight into the institute’s data at risk and the state of data rescue. Feedback supplied by RGLs after examining the initial Data Rescue Workflow Model produced a list of concerns linked to the model and contained suggestions for changes to the model. RGL feedback was at times unrelated to the model or to data and necessitated the implementation of a mini focus group session involving institutional research library experts. The mini focus group session comprised discussions around requirements for a data rescue workflow model. The consolidation of RGL feedback and feedback supplied by research library experts enabled the creation of a recommended Data Rescue Workflow Model, with the model also indicating the various roles and responsibilities of the research library. The contribution of this research lies primarily in the increase in theoretical knowledge regarding data at risk and data rescue, and culminates in the presentation of a recommended Data Rescue Workflow Model. The model not only portrays crucial data rescue activities and outputs, but also indicates the roles and responsibilities of a sector that can enhance and influence the prevalence and execution of data rescue projects. In addition, participation in data rescue and an understanding of the activities and steps portrayed via the model can contribute towards an increase in the skills base of the library and information services sector and enhance collaboration projects with relevant research sectors. It is also anticipated that the study recommendations and exposure to the model may influence the viewing and handling of data by researchers and accompanying research procedures.Information SciencePhD (Research)Unrestricte

    Gulf Cooperation Council Countries’ Electricity Sector Forecasting : Consumption Growth Issue and Renewable Energy Penetration Progress Challenges

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    The Gulf Cooperation Council (GCC) countries depend on substantial fossil fuel consumption to generate electricity which has resulted in significant environmental harm. Fossil fuels also represent the principal source of economic income in the region. Climate change is closely associated with the use of fossil fuels and has thus become the main motivation to search for alternative solutions, including solar and wind energy technologies, to eliminate their reliance on fossil fuels and the associated impacts upon climate. This research provides a comprehensive investigation of the consumption growth issue, together with an exploration of the potential of solar and wind energy resources, a strict follow-up to shed light on the renewable energy projects, as currently implemented in the GCC region, and a critical discussion of their prospects. The projects foreshadow the GCC countries’ ability to comply with future requirements and spearhead the renewable energy transition toward a more sustainable and equitable future. In addition, four forecasting models were developed to analyse the future performance of GCC power sectors, including solar and wind energy resources along with the ambient temperatures, based on 40 years of historical data. These were Monte Carlo Simulation (MCS), Brownian Motion (BM), and a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model model-based time series, and bidirectional long short-term memory (BI-LSTM) and gated recurrent unit (GRU) model-based neural networks. The MCS and BM prediction models apply a regression analysis (which describes the behaviour of an instrument) to a large set of random trials so as to construct a credible set of probable future outcomes. The MCS and BM prediction models have proven to be an exceptional investigative solution for long-term prediction for different types of historical data, including: (i) four types of fossil fuel data; (ii) three types of solar irradiance data, (iii) wind speed data; and, (iv) temperature data. In addition, the prediction model is able to cope with large volumes of historical data and different intervals, including yearly, quarterly, and daily. The simplicity of implementation is a strength of MCS and BM techniques. The SARIMAX technique applies a time series approach with seasonal and exogenous influencing factors, an approach that helps to reduce the error values and improve the overall model accuracy, even in the case of close input and output dataset lengths. This iii research proposes a forecasting framework that applies the SARIMAX model to forecast the long-term performance of the electricity sector (including electricity consumption, generation, peak load, and installed capacity). The SARIMAX model was used to forecast the aforementioned factors in the GCC region for a forecasted period of 30 years from 2021 to 2050. The experimental findings indicate that the SARIMAX model has potential performance in terms of categorisation and consideration, as it has significantly improved forecasting accuracy when compared with simpler, autoregressive, integrated, moving average-based techniques.The BI-LSTM model has the advantage of manipulating information in two opposing directions and providing feedback to the same outputs via two different hidden layers. A BI-LSTM’s output layer concurrently receives information from both the backward and forward layers. The BI-LSTM prediction model was designed to predict solar irradiance which includes global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI) for the next 169 hours. The findings demonstrate that the BI-LSTM model has an encouraging performance in terms of evaluation, with considerable accuracy for all three types of solar irradiance data from the six GCC countries. The model can handle different sizes of sequential data and generates low error metrics. The GRU prediction model automatically learned the features, used fewer training parameters, and required a shorter time to train as compared to other types of RNNs. The GRU model was designed to forecast 169 hours ahead in terms of forecasted wind speeds and temperature values based on 36 years of hourly interval historical data (1st January 1985 to 26th June 2021) collected from the GCC region. The findings notably indicate that the GRU model offers a promising performance, with significant prediction accuracies in terms of overfitting, reliability, resolution, efficiency, and generalisable processes. The GRU model is characterised by its superior performance and influential evaluation error metrics for wind speed and temperature fluctuations. Finally, the models aim to help address the issue of a lack of future planning and accurate analyses of the energy sector's forecasted performance and intermittency, providing a reliable forecasting technique which is a prerequisite for modern energy systems

    Operational research:methods and applications

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    Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

    Operational Research: Methods and Applications

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    Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

    The experiences of social workers working in multi-disciplinary teams in state hospitals in the Waterberg District, Limpopo Province

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    Following South Africa’s independence in 1994, the number of hospital social work posts in state hospitals were substantially increased. Subsequently, unprecedented contextual changes have affected hospital services, including hospital social workers. This study focused on the experiences, challenges and coping strategies of social workers working in multi- disciplinary teams in state hospitals in the Waterberg District, Limpopo Province, amidst these changes. A qualitative approach using a phenomenological research design, augmented by exploratory, descriptive and contextual research designs was used. The purposive sample of ten social workers based in eight state hospitals in the Waterberg District were interviewed using semi-structured interviews facilitated by an interview guide. The analysis of the data was achieved using Tesch’s eight steps in coding (1992:117). The bioecological systems approach (Bronfenbrenner 2005) and the Life Model theory (Gitterman & Germain 2008) were combined to frame the study. The data collected were supported by a virtual online discussion forum. Guba and Lincoln’s (1981) concept of trustworthiness: principles of credibility, transferability, dependability and neutrality were used to verify the data. Ethical principles of informed consent, confidentiality and anonymity, beneficence and careful management of data upheld the ethical integrity of study and the safety of research participants.Social WorkM.A. (Social Work
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