Performance testing methods of boilers in transient operating conditions (start, stop and combustion power modulation sequences) need the combustion rate quantified to allow for the emissions to be quantified. One way of quantifying the combustion rate of a boiler during transient operating conditions is by measuring the flue gas flow rate. The flow conditions in chimneys of single family house boilers pose a challenge however, mainly because of the low flow velocity. The main objectives of the work were to characterize the flow conditions in residential chimneys, to evaluate the use of the Pitot-static method and the averaging Pitot method, and to develop and test a calibration method for averaging Pitot probes for low 𝑅𝑅𝑅𝑅.A literature survey and a theoretical study were performed to characterize the flow conditions in in single family house boiler chimneys. The flow velocities under normal boiler operating conditions are often below the requirements for the assumptions of non-viscous fluid justifying the use of the quadratic Bernoulli equation. A non-linear calibration coefficient is required to correct for these viscous effects in order to avoid significant measurement errors. The flow type in the studied conditions changes from laminar, across the transition regime, to fully turbulent flow, resulting in significant changes of the velocity profile during transient boiler operation. Due to geometrical settings occurring in practice measurements are often done in the hydrodynamic entrance region, where the velocity profiles are neither fully developed nor symmetrical. The predicted changes in velocity profiles are also confirmed experimentally in two chimneys.Several requirements set in ISO 10780 and ISO 3966 for Pitot-static probes are either met questionably or not met at all, meaning that the methods cannot be used as such. The main issues are the low flow velocity, viscous effects, and velocity profiles that change significantly during normal boiler operation. The Pitot-static probe can be calibrated for low 𝑅𝑅𝑅𝑅, but is not reliable because of the changing velocity profiles.The pressure averaging probe is a simple remedy to overcome the problems with asymmetric and changing velocity profiles, but still keeping low the irrecoverable pressure drop caused by the probe. However, commercial averaging probes are not calibrated for the characterized chimney conditions and the information available on the performance of averaging probes at low 𝑅𝑅𝑅𝑅 is scarce. A literature survey and a theoretical study were done to develop a method for calibrating pressure averaging probes for low 𝑅𝑅𝑒 flue gas flows in residential chimneys.The experimental part consists of constructing a calibration rig, testing the performance of differential pressure transducers, and testing a prototype pressure averaging probe. The results show good correlation over a wide operation range, but the low 𝑅𝑅𝑅𝑅 characteristics of the probe could not be identified due to instability in the chosen pressure transducer, and temperature correlation for one of the probes while not for the other. The differential pressures produced are close to the performance limitations of readily available transducers and it should be possible to improve the method by focusing on finding or building a suitable pressure transducer. The performance of the averaging method can be improved further by optimizing the geometry of the probe. Another way of reducing the uncertainty would be to increase the probe size relative to the conduit diameter to produce a higher differential pressure, at the expense of increasing the irrecoverable pressure drop.

Limited research has been conducted on inconsistencies relating to whole-body vibration (WBV) field assessments. Therefore, this study aimed to investigate a certain possible contributor to inconsistencies in vibration assessment work, namely averaging intervals. To our knowledge, this was the first study investigating the effect of multiple averaging approaches on WBV results. WBV parameters were measured for a driver operating a vehicle on a preselected test route utilising ISO 2631-1:1997. This was achieved utilizing a Quest HavPro vibration monitor with a fitted tri-axial Integrated Circuit Piezoelectric (ICP) accelerometer pad mounted on the driver’s seat. Furthermore, in an attempt to decrease differences between observed WBV results, an outlier detection method, part of the STATA software package was utilised to clean the data. Statistical analyses included hypothesis testing in the form of one-way ANOVA and Kruskal-Wallis one-way analysis of variance by ranks to determine significant differences between integration intervals. Logged data time-series durations showed a W0 = 0.04, therefore indicating unequal variance. Omission of 60s from statistical analyses showed a W0 = 0.28. The observed difference occurs when data is averaged over longer intervals, resulting in portions of data not being reflected in the final dataset. In addition, frequency-weighted root mean squared acceleration results reflected significant differences between 1s, 10s, 30s, 60s and SLOW averaging approaches, while non-significant differences were observed for crest factors and instantaneous peak accelerations. Vibration Dose Value results reflected non-significant differences after omission of 60 second averaging interval data. Cleaned data showed significant differences between various averaging approaches as well as significant differences when compared with raw vibration data. The study therefore outlined certain inconsistencies pertaining to the selection of multiple integration intervals during the assessment of WBV exposure. Data filtering could not provide a conclusion on a suitable averaging period and as such, further research is required to determine the correct averaging interval to be used for WBV assessment. / Dissertation (MPH)--University of Pretoria, 2014. / tm2015 / School of Health Systems and Public Health (SHSPH) / MPH / Unrestricted

The rapid development of Machine Learning algorithms and a growing range of its applications, as well as an increasing number of Edge Computing devices, created a need for a new paradigm that would benefit from both fields. Federated Learning, which emerged as an answer to this need, is a technique that also solves privacy-related issues arising when large amounts of information are collected on many individual devices and being used for a Machine Learning model by sending only the local updates and keeping the data. At the same time, Federated Learning heavily relies on the computational and communicational capabilities of the devices that calculate the updates and send them to the main server to be integrated into a global model using one or the other Aggregation Scheme, which is one of the most important aspects of the Federated Learning. Carefully choosing how to aggregate local updates can diminish the impacts present from a huge variety of devices. Therefore, this thesis work presents a thorough investigation of the Aggregation Schemes and analyzes their behaviors in heterogeneous Federated Learning scenarios. It provides an extensive description of the main features of schemes studied, defines the evaluation criteria, presents the resource costs associated with computational and communicational resources of the devices, and shows a fair assessment.

Yes / The Poisson's equation is an essential entity of applied mathematics for modelling many phenomena of importance. They include the theory of gravitation, electromagnetism, fluid flows and geometric design. In this regard, finding efficient solution methods for the Poisson's equation is a significant problem that requires addressing. In this paper, we show how it is possible to generate approximate solutions of the Poisson's equation subject to various boundary conditions. We make use of the discrete finite difference operator, which, in many ways, is similar to the standard finite difference method for numerically solving partial differential equations. Our approach is based upon the Laplacian averaging operator which, as we show, can be elegantly applied over many folds in a computationally efficient manner to obtain a close approximation to the solution of the equation at hand. We compare our method by way of examples with the solutions arising from the analytic variants as well as the numerical variants of the Poisson's equation subject to a given set of boundary conditions. Thus, we show that our method, though simple to implement yet computationally very efficient, is powerful enough to generate approximate solutions of the Poisson's equation. / Supported by the European Union’s Horizon 2020 Programme H2020-MSCA-RISE-2017, under the project PDE-GIR with grant number 778035.

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Bayesian Model Averaging (BMA) is a new area in modern applied statistics that provides data analysts with an efficient tool for discovering promising models and obtaining esti-mates of their posterior probabilities via Markov chain Monte Carlo (MCMC). These probabilities can be further used as weights for model averaged predictions and estimates of the parameters of interest. As a result, variance components due to model selection are estimated and accounted for, contrary to the practice of conventional data analysis (such as, for example, stepwise model selection). In addition, variable activation probabilities can be obtained for each variable of interest. This dissertation is aimed at connecting BMA and various ramifications of the multivari-ate technique called Reduced-Rank Regression (RRR). In particular, we are concerned with Canonical Correspondence Analysis (CCA) in ecological applications where the data are represented by a site by species abundance matrix with site-specific covariates. Our goal is to incorporate the multivariate techniques, such as Redundancy Analysis and Ca-nonical Correspondence Analysis into the general machinery of BMA, taking into account such complicating phenomena as outliers and clustering of observations within a single data-analysis strategy. Traditional implementations of model averaging are concerned with selection of variables. We extend the methodology of BMA to selection of subgroups of observations and im-plement several approaches to cluster and outlier analysis in the context of the multivari-ate regression model. The proposed algorithm of cluster analysis can accommodate re-strictions on the resulting partition of observations when some of them form sub-clusters that have to be preserved when larger clusters are formed. / Ph. D.

Ecological data is often fraught with many problems such as Missing Data and Spatial Correlation. In this dissertation we use a data set collected by the Ohio EPA as motivation for studying techniques to address these problems. The data set is concerned with the benthic health of Ohio's waterways. A new method for incorporating covariate structure and missing data mechanisms into missing data analysis is considered. This method allows us to detect relationships other popular methods do not allow. We then further extend this method into model selection. In the special case where the unobserved covariates are assumed normally distributed we use the Bayesian Model Averaging method to average the models, select the highest probability model and do variable assessment. Accuracy in calculating the posterior model probabilities using the Laplace approximation and an approximation based on the Bayesian Information Criterion (BIC) are explored. It is shown that the Laplace approximation is superior to the BIC based approximation using simulation. Finally, Hierarchical Spatial Linear Models are considered for the data and we show how to combine analysis which have spatial correlation within and between clusters. / Ph. D.

The sideband phenomenon is a common but obscure characteristic of the Rotary Dynamics field. In the automotive industry these sidebands have been found to produce a poor sound quality, resulting in customer dissatisfaction and warranty returns. In the interest of continued product improvement, research and development must resolve uncertainties in the current design methods. Qualitative relationships between the sideband characteristics and design parameters have developed in recent years, but the quantitative goal hasn't been achieved. A Synchronous Sampling (SS) data acquisition system is applied to a helical planetary gear set to improve the understanding of the sideband characteristics and enhance the design process. An optical encoder, a component of the SS system, mechanically locked to the rotating system controls the A/D converter to sample at constant shaft angle increments. The phase-locked nature of SS allows the use of vector averaging to significantly lower the noise floor and improve the representation of the order domain. In this work, the advantages of using a SS system with vector averaging capabilities are compared to the disadvantages of using a fixed sampling (FS) system. Utilizing the SS tool, this work also illustrates the influences of loading styles and values and speed on a gearmesh and its dominant sideband orders. Inspection of these influences suggests an opportunity for future work. / Master of Science

Um dos mais investigados problemas na teoria qualitativa dos sistemas dinâmicos no plano é o XVI problema de Hilbert que trata dos ciclos limites. Mais precisamente, a segunda parte do referido problema questiona sobre o número máximo de ciclos limites de um sistema diferencial polinomial plano de grau n. Por ciclo limite entendemos uma órbita fechada isolada no conjunto de todas as órbitas periódicas de um sistema diferencial plano.Uma maneira clássica de obter um ciclo limite é perturbando um sistema com uma singularidade do tipo centro. Nesta dissertação apresentamos dois métodos utilizados para a análise do número de ciclos limites que bifurcam de um centro, a saber o método das integrais abelianas e o método do averaging / One of the most investigated problems in the qualitative theory of dynamical systems in the plane is the XVI Hilberts problem which deals with limit cycles. More precisely, the second part of the problem asks about the maximum number of limit cycles of a polynomial differential system of degree n. A limit cycle is a single closed orbit on the set of all periodic orbits of a differential planar system. A classic way to obtain a limit cycle is perturbing a system with a singularity of center type.In this work we discuss about two methods used to investigate the number of limit cycles which bifurcate from a center; they are known as Abelian integrals and averaging theory