5,609 research outputs found

    Performance evaluation and control of an MMC active rectifier with half-bridge and full-bridge submodules for HVDC applications

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    Dissertation (MEng (Electrical Engineering))--University of Pretoria, 2021.The modular multilevel active rectifier was designed and evaluated, whereby the half bridge and the full bridge DC-DC converters as its submodules for the high voltage direct current transmission were compared. It was found that, by taking advantage of the unipolar modulation scheme in the full bridge converter, the switching losses in the two converters are equal when they are both operated in the linear modulation region. Furthermore, operating the full bridge converter in the overmodulation region does not give it a pronounced advantage over the half bridge converter. The conduction losses in the full bridge converter are two times higher than those in the half bridge converter, due to double the number of semiconductor devices. However, using the half bridge converter in the high voltage direct current modular multilevel converter requires an expensive DC-side breaker, while use of the full bridge converter eliminates the need for such a breaker due to the intrinsic DC-side fault current blocking capability. The clear choice between the two requires industry cost data. A design methodology for the submodule capacitor average voltage loop controllers for phase-shifted carrier modulated modular multilevel converters was carried out from first principles. The methodology enables design of such controllers to be carried out in a step by step and straightforward manner without resorting to simulation or guesswork. A simple but effective submodule capacitor sizing method was proposed. The resulting submodule capacitor size was shown to be smaller than those resulting from other sizing methods proposed in the literature while achieving the submodule capacitor voltage ripple specifications. A robust DC bus voltage controller design for modular multilevel rectifiers was presented, whereby a design method for multilevel voltage source converters with DC link capacitors was adopted for modular multilevel rectifiers. Since the modular multilevel converters for HVDC application are designed without the DC-link capacitor to mitigate the effects of a possible DC-side fault current, the submodule capacitors in the modular multilevel converter acted as an equivalent DC link capacitor to accomplish the design.Electrical, Electronic and Computer EngineeringMEng (Electrical Engineering)Unrestricte

    Converging organoids and extracellular matrix::New insights into liver cancer biology

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    Interpretable and explainable machine learning for ultrasonic defect sizing

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    Despite its popularity in literature, there are few examples of machine learning (ML) being used for industrial nondestructive evaluation (NDE) applications. A significant barrier is the ‘black box’ nature of most ML algorithms. This paper aims to improve the interpretability and explainability of ML for ultrasonic NDE by presenting a novel dimensionality reduction method: Gaussian feature approximation (GFA). GFA involves fitting a 2D elliptical Gaussian function an ultrasonic image and storing the seven parameters that describe each Gaussian. These seven parameters can then be used as inputs to data analysis methods such as the defect sizing neural network presented in this paper. GFA is applied to ultrasonic defect sizing for inline pipe inspection as an example application. This approach is compared to sizing with the same neural network, and two other dimensionality reduction methods (the parameters of 6 dB drop boxes and principal component analysis), as well as a convolutional neural network applied to raw ultrasonic images. Of the dimensionality reduction methods tested, GFA features produce the closest sizing accuracy to sizing from the raw images, with only a 23% increase in RMSE, despite a 96.5% reduction in the dimensionality of the input data. Implementing ML with GFA is implicitly more interpretable than doing so with principal component analysis or raw images as inputs, and gives significantly more sizing accuracy than 6 dB drop boxes. Shapley additive explanations (SHAP) are used to calculate how each feature contributes to the prediction of an individual defect’s length. Analysis of SHAP values demonstrates that the GFA-based neural network proposed displays many of the same relationships between defect indications and their predicted size as occur in traditional NDE sizing methods

    Advances in Micro- and Nanomechanics

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    This book focuses on recent advances in both theoretical and experimental studies of material behaviour at the micro- and nano-scales. Special attention is given to experimental studies of nanofilms, nanoparticles and nanocomposites as well as tooth defects. Various experimental techniques were used. Magneto- and thermoelastic coupling were considered, as were nonlocal models of thin structures

    A Systematic Review of Data Quality in CPS and IoT for Industry 4.0

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    The Internet of Things (IoT) and Cyber-Physical Systems (CPS) are the backbones of Industry 4.0, where data quality is crucial for decision support. Data quality in these systems can deteriorate due to sensor failures or uncertain operating environments. Our objective is to summarize and assess the research efforts that address data quality in data-centric CPS/IoT industrial applications. We systematically review the state-of-the-art data quality techniques for CPS and IoT in Industry 4.0 through a systematic literature review (SLR) study. We pose three research questions, define selection and exclusion criteria for primary studies, and extract and synthesize data from these studies to answer our research questions. Our most significant results are (i) the list of data quality issues, their sources, and application domains, (ii) the best practices and metrics for managing data quality, (iii) the software engineering solutions employed to manage data quality, and (iv) the state of the data quality techniques (data repair, cleaning, and monitoring) in the application domains. The results of our SLR can help researchers obtain an overview of existing data quality issues, techniques, metrics, and best practices. We suggest research directions that require attention from the research community for follow-up work.acceptedVersio

    Exploring Cloud Adoption Possibilities for the Manufacturing Sector: A Role of Third-Party Service Providers

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    As the manufacturing sector strides towards digitalization under the influence of Industry 4.0, cloud services have emerged as the new norm, driving change and innovation in this rapidly transforming landscape. This study investigates the possibilities of cloud adoption in the manufacturing sector by developing a conceptual model to identify suitable cloud-based solutions and explores the role of third-party service providers in aiding manufacturers throughout their cloud adoption journey. The research methods consist of a comprehensive literature review of the manufacturing industry, digital transformation, cloud computing, etc., followed by qualitative analyses of industrial benchmarks case studies and an investigation into an application of the developed model to a hypothetical food manufacturing company as an example. This study indicates that cloud adoption can yield substantial benefits in the manufacturing sector, including operational efficiency, cost reduction, and innovation, etc. The study concludes that the developed conceptual model provides a practical framework to identify the most suitable cloud-based solutions during the cloud adoption process in the manufacturing context. In addition, third-party service providers like Capgemini are capable of not only filling the technical gaps but also consulting strategic directions and innovations for their client organizations, hence playing a vital role in driving the industrial digital transformation process. With an extensive mapping of their capabilities, a set of recommendations intended to assist Capgemini in enhancing capabilities and improving competitive performance in the market has been offered

    Short-term forecast techniques for energy management systems in microgrid applications

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    A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Sustainable Energy Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyIn the 2015 Paris Agreement, 195 countries adopted a global climate agreement to limit the global average temperature rise to less than 2°C. Achieving the set targets involves increasing energy efficiency and embracing cleaner energy solutions. Although advances in computing and Internet of Things (IoT) technologies have been made, there is limited scientific research work in this arena that tackles the challenges of implementing low-cost IoT-based Energy Management System (EMS) with energy forecast and user engagement for adoption by a layman both in off-grid or microgrid tied to a weak grid. This study proposes an EMS approach for short-term forecast and monitoring for hybrid microgrids in emerging countries. This is done by addressing typical submodules of EMS namely: load forecast, blackout forecast, and energy monitoring module. A short-term load forecast model framework consisting of a hybrid feature selection and prediction model was developed. Prediction error performance evaluation of the developed model was done by varying input predictors and using the principal subset features to perform supervised training of 20 different conventional prediction models and their hybrid variants. The proposed principal k-features subset union approach registered low error performance values than standard feature selection methods when it was used with the ‘linear Support Vector Machine (SVM)’ prediction model for load forecast. The hybrid regression model formed from a fusion of the best 2 models (‘linearSVM’ and ‘cubicSVM’) showed improved prediction performance than the individual regression models with a reduction in Mean Absolute Error (MAE) by 5.4%. In the case of the EMS blackout prediction aspect, a hybrid Adaptive Similar Day (ASD) and Random Forest (RF) model for short-term power outage prediction was proposed that predicted accurately almost half of the blackouts (49.16%), thereby performing slightly better than the stand-alone RF (32.23%), and ASD (46.57%) models. Additionally, a low-cost EMS smart meter was developed to realize the implemented energy forecast and offer user engagement through monitoring and control of the microgrid towards the goal of increasing energy efficiency

    IMPACTS OF WASTEWATER EFFLUENTS AND SEASONAL TRENDS ON LEVELS OF EMERGING CONTAMINANTS IN TWO COLD-REGION RIVERS

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    Emerging contaminants such as pharmaceutical drugs have been detected in waters across the globe and are of concern for human and aquatic ecosystems health. Most pharmaceuticals are found at trace concentrations, but the continuous use and potential accumulation of some of these compounds can potentially lead to effects in aquatic organisms. The principal aim of this research was to enhance our understanding of the environmental risks associated with pharmaceuticals as one group of emerging contaminants. Many pharmaceuticals are ionizable organic chemicals (IOCs), which makes their environmental and toxicological behavior particularly challenging to predict due to their partitioning mechanism which is useful to estimate the distribution of the chemical. Therefore, the objective of this thesis was to evaluate the hypothesis that uptake and effects of IOCs in aquatic organisms are influenced by the interaction between environmental, physicochemical, and biological factors. To this end, first, field studies were conducted during spring, summer, and fall of 2021 on water (diffusive gradient in thin film and conventional grab) and sediments at four locations including upstream and downstream of the wastewater treatment plants (WWTPs) of the cities of Saskatoon and Regina in the South Saskatchewan River and Wascana Creek, Saskatchewan, Canada, respectively. Second, seven representative antipsychotic pharmaceuticals were measured in water, sediment, and fish samples up- and downstream of the City of Regina WWTP. Data collected from this research effort indicate contamination with antipsychotic pharmaceuticals, with the potential to adversely impact exposed organisms. Third, non-target chemical analysis was conducted in water, sediments, and fish samples, at the two locations in Wascana Creek and throughout the three seasons. Data collected from non-target analysis indicated that pharmaceuticals, rubber components and personal care products were the priority pollutants in all the matrices and their transcriptomics changes were also supported by the qPCR analysis. Finally, transcripts of several genes of interest were determined in brain and liver samples from in fathead minnow (Pimephales promelas) exposed to the wastewater effluents in Wascana Creek during summer and fall in 2021, using a qPCR gene expression array (the EcoToxChips). The integrative approach used in this study, strongly supports the need to combine chemical analysis with transcriptomics-based approaches as useful tools for assessing of complex mixtures of contaminants in wastewater discharges and their effects in aquatic organisms. This research provides a better understanding of the risks that pharmaceuticals may pose to aquatic organisms under varying environmental conditions and thereby aid in better protecting aquatic ecosystems in the future

    Department for Education : Consolidated annual report and accounts

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