The aim of this thesis is to analyze the properties of robust estimates and to compare these estimates with regard to the properties between them. The analysis of properties depends on the type of a variable (continuous or discrete), its probability distribution, the range of random sample and the proportion of outliers in random sample. Comparing the properties in different situations will give "guidance" to determine which of the estimates is preferable in specific situations, and which of them should be rather avoided. An adjusted bootstrap method is used to obtain the estimates of properties estimates. The thesis is devided into two parts. In the first part, the parameter estimates, type and design of robust estimators and the bootstrap method are monitored. In the second practical part we determine the suitability of bootstrap to obtain estimates of the properties of robust estimators, followed by obtaining estimates of the properties of the estimates and compare them. At the conclusion of the practical part we observe and compare the values of bootstrap confidence intervals on real data on household income. The results of this thesis shows us, that the bootstrap method does not provide good estimates of the properties of robust estimators in all cases. The results also bring us to the conclusion that from a certain extent of random sample regardless of the number of outliers, you can choose from a robust estimate only on the basis of its value, properties of robust estimates are very similar. Contemplated robust estimates of variability are not suitable estimates in most cases.

Optimisation is one of the most important and challenging part of any engineering design. In real world design problems one faces multiobjective optimisation under constraints. The optimal solution in these cases is not unique because the objectives can contradict each other. In such cases, a set of optimal solutions which forms a Pareto frontier in the objective space is considered. There are many algorithms to generate the Pareto frontier. However, only a few of them are potentially capable of providing an evenly distributed set of the solutions. Such a property is especially important in real-life design because a decision maker is usually able to analyse only a very limited quantity of solutions. This thesis consists of two main parts. At first, it develops and gives the detailed description of two different algorithms that are able to generate an evenly distributed Pareto set in a general formulation. One is a classical approach and called Directed Search Domain (DSD) and the other, the cylindrical constraint evolutionary algorithm (CCEA), is a hybrid population based method. The efficiency of the algorithms are demonstrated by a number of challenging test cases and the comparisons with the results of the other existing methods. It is shown that the proposed methods are successful in generating the Pareto solutions even when some existing methods fail. In real world design problems, deterministic approaches cannot provide a reliable solution as in the event of uncertainty, deterministic optimal solution would be infeasible in many instances. Therefore a solution less sensitive to problem perturbation is desirable. This leads to the robust solution which is the focus of the second part of the thesis. In the literature, there are some techniques tailored for robust optimisation. However, most of them are either computationally expensive or do not systematically articulate the designer preferences into a robust solution. In this thesis, by introducing a measure for robustness in multiobjective context, a tunable robust function (TRF) is presented. Including the TRF in the problem formulation, it is demonstrated that the desirable robust solution based on designer preferences can be obtained. This not only provides the robust solution but also gives a control over the robustness level. The method is efficient as it only increases the dimension of the problem by one irrespective of the dimension of the original problem.

This dissertation examines the robust regression methods. The primary purpose of this work is to propose an extension, derivation and summary (including computational algorithm) for Theil-Sen's regression estimates (or in some literature also referred to as Passing-Bablok's regression method) for multi-dimensional space and compare this method to other robust regression methods. The combination of these two objectives is the primary and the original contribution of the dissertation. Based on the available literature it is unknown if anyone has discussed this problem in greater depth and solved it in total. Therefore this work provides a summary overview of the issue and offers a new alternative of this multidimensional, nonparametric, robust regression method. Secondary goals include a clear summary of other robust methods, a summary of findings related to these robust regression methods, robust methods compared with each other placing emphasis on the comparison with the proposed Theil-Sen's regression estimates method and with the least squares method. The summary also includes individual mathematical context and interchangeability of the proposed methods. These secondary objectives are also another benefit of this dissertation in the field of robust regression problems; this is especially important to gain a unified view of the problems of robust regression methods and estimates in general.

The multimedia revolution has made a strong impact on our society. The explosive growth of the Internet, the access to this digital information generates new opportunities and challenges. The ease of editing and duplication in digital domain created the concern of copyright protection for content providers. Various schemes to embed secondary data in the digital media are investigated to preserve copyright and to discourage unauthorized duplication: where digital watermarking is a viable solution. This thesis proposes a novel invisible watermarking scheme: a discrete cosine transform (DCT) domain based watermark embedding and blind extraction algorithm for copyright protection of the color images. Testing of the proposed watermarking scheme's robustness and security via different benchmarks proves its resilience to digital attacks. The detectors response, PSNR and RMSE results show that our algorithm has a better security performance than most of the existing algorithms.

Fokuset i denna studie är e-tjänster för riskgruppen äldre personer i samband med framförallt pandemier. Undersökningen handlar om att kartlägga målgruppens bild av, och användning av, de offentliga och kommunala e-tjänster som erbjuds. Tidigare forskning visar att äldre personer i större utsträckning upplever ett utanförskap när det gäller tillgänglighet och digitala tjänster men däremot att det finns väldigt lite forskning kring riskgrupper och tillgänglighet kopplat till krislägen, som pandemier. Användarvänlighet, användbarhet och tillgänglighet används i studien för att förklara UX (User experience). Det finns fundamentala riktlinjer för att implementera god UX design vilket vi vidare förklarar i stycket “2.6 User Experience (UX) och Informationsarkitektur” (Chesnut & Nichols 2015, s.9). Studien undersöker den upplevda tillgängligheten hos e-tjänster samt vilka typer av e-tjänster som den äldre riskgruppen upplever har behov av, och som därmed är en hjälp, vid en pandemi. Studien undersöker tillgänglighet och dess ramverk (WCAG 2.1) d.v.s. synlig, manövrerbar, begriplig och robust (W3C, 2018). Vår studie baseras på en metodkombination där en enkät och intervjuer utförts för att undersöka hur riskgruppen, den äldre målgruppen upplever e-tjänster vid en pågående pandemi. De e-tjänster som intervjupersonerna använder såsom exempelvis skatteverkets e-tjänst för skatteåterbetalning uppfyller fortfarande inte tillgänglighetens krav och kan därmed förbättras. Både enkätsvar och intervjuer visade att de viktigaste e-tjänsterna för äldre under pandemin är de som berör vården och dess olika e-tjänster på exempelvis 1177 eller olika vårdcentraler.

Nous nous intéressons à estimer la moyenne d'une variable aléatoire de loi à queue lourde. Nous adoptons une approche plus robuste que la moyenne empirique classique communément utilisée. L'objectif est de développer des inégalités de concentration de type sous-gaussien sur l'erreur d'estimation. En d'autres termes, nous cherchons à garantir une forte concentration sous une hypothèse plus faible que la bornitude : une variance finie. Deux estimateurs de la moyenne pour une loi à support réel sont invoqués et leurs résultats de concentration sont rappelés. Plusieurs adaptations en dimension supérieure sont envisagées. L'utilisation appropriée de ces estimateurs nous permet d'introduire une nouvelle technique de minimisation du risque empirique pour des variables aléatoires à queue lourde. Quelques applications de cette technique sont développées. Nous appuyons ces résultats sur des simulations sur des jeux de données simulées. Dans un troisième temps, nous étudions un problème d'estimation multivarié dans le cadre des U-statistiques où les estimateurs précédents offrent, là aussi, une généralisation naturelle d'estimateurs présents dans la littérature. / In this thesis, we are interested in estimating the mean of heavy-tailed random variables. We focus on a robust estimation of the mean approach as an alternative to the classical empirical mean estimation. The goal is to develop sub-Gaussian concentration inequalities for the estimating error. In other words, we seek strong concentration results usually obtained for bounded random variables, in the context where the bounded condition is replaced by a finite variance condition. Two existing estimators of the mean of a real-valued random variable are invoked and their concentration results are recalled. Several new higher dimension adaptations are discussed. Using those estimators, we introduce a new version of empirical risk minimization for heavy-tailed random variables. Some applications are developed. These results are illustrated by simulations on artificial data samples. Lastly, we study the multivariate case in the U-statistics context. A natural generalization of existing estimators is offered, once again, by previous estimators.

Main steam temperatures play a significant role in large coal fired power plant operation. Ideally, main steam temperatures should be accurately controlled to protect the thick wall components against long term overheating and thermal stress while meeting the design conditions at the steam turbine inlet. Although high steam temperatures are beneficial for thermal efficiency, it accelerates creep damage in high temperature components which is detrimental to the life of components. Alternatively, low steam temperatures increase the moisture content at the last stage blades of the turbine, causing the blades to deteriorate and fail. Control of the outlet steam temperature according to design conditions at variable loads is maintained via a balance between heat input (flue gas temperature and mass flow rate), evaporator outlet steam mass flow and spray water. The present control philosophy accuracy of main steam temperatures at an Eskom coal fired power plant was evaluated and compared to the latest technology and control strategies. Improving and optimizing steam temperature controls ensures design efficiency while maintaining long term plant health. The level of spatial discretization applied in simplifying the real boiler for modelling purposes was approached at a relatively high level. The intention was to model normal operating conditions and certain transients such as variable heat input and load changes to see its effect on steam temperatures and to be able to evaluate the performance of different temperature control techniques. The main outcome of this project was to design a robust control system for a dynamic model of the boiler using sets of low order linear models to account for uncertainty. The main concepts, models and theories used in the development of this dissertation include: 1) A detailed thermo-fluid model developed using Flownex to have high fidelity models of the process under varying operating conditions. This model was used to test and evaluate the robust controller design. 2) System Identification in Matlab to construct mathematical models of dynamic systems from measured inputoutput data and identify linear continuous time transfer functions under all operating conditions [1]. 3) Quantitative Feedback Theory (QFT) to design controllers for an attemperator control system at various onload operating conditions. This design was used understand the engineering requirements and seeks to design fixed gain controllers that will give desired performance under all operating conditions. 4) The design of a valve position controller to increase the heat uptake in a convective pass, thereby improving efficiency: Excessive attemperation in the superheater passes is generally associated with high flue gas temperatures which decrease thermal efficiency. Therefore, robust control of the attemperation system leads to an increase in heat uptake between the flue gas and steam in the boiler, resulting in a reduction in the flue gas temperature leaving the boiler, thus improving efficiency. The robust QFT controllers were set up using the valve position control technique and were used to confirm the improvement of control performance. The theories mentioned above were used to understand the control performance under varying plant conditions using a standard cascaded arrangement. It incorporated robust control design and engineering requirements such as bandwidth, plant life, spray water and thermodynamic efficiency. The control effort allocated to each superheaterattemperator subsystem in the convective pass was designed as a multi-loop problem.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第22417号 / 工博第4678号 / 新制||工||1730(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 高橋 良和, 教授 清野 純史, 教授 八木 知己 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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Health care, and especially access to health care, has always been a critical metric for countries. In 2017, South Africa spent 9% of its GDP on health care. Despite the GDP health care allocation being 5% higher than recommended by the World Health Organisation for a country of its socio-economic status, South Africa's health status is poor compared to similar countries. In 1994, South Africa implemented a health care policy to make health care accessible to all South Africans. A primary health care facility within 5km of the place of residence is deemed accessible. There is still a significant gap between the actual and desired accessibility, especially for the lower-income communities. There is a need to improve access to public health care for all South Africans. Cost-effective and sustainable solutions are required to solve this problem. Therefore, an opportunity was identified to investigate the location of low-cost container clinics in lower-income communities. This report uses robust optimisation and goal programming to find robust sites for cost-effective container clinics over multiple years in an uncertain environment using multiple future city development scenarios. The study area of the report includes three metro municipalities (City of Tshwane, City of Johannesburg, and City of Ekurhuleni) in Gauteng, South Africa. Three future development scenarios were created for this study using a synthetic population and urban growth simulation model developed by the CSIR. The model provided the population distribution from 2018 to 2030 for all three of the scenarios. The simulation model provides household attribute tables as an output. Household attributes that have a causal relationship with health care demand were investigated during the literature review. Based on the literature and the available household attributes, four attributes were selected to forecast the health care demand. The four attributes are household income, the number of children in the household, the household size, and the nearest clinic's distance. Using associative forecasting, the primary health care demand was forecasted from 2018 to 2030. These forecasts were used as input into the facility location models. A p-median facility location model was developed and implemented in Python. Since facility location problems are classified as NP-hard problems, heuristics and metaheuristics were investigated to speed up the problem solving. A GA selected as the metaheuristic be used to determine a suitable configuration of facilities for each scenario. The model determined good locations of clinics from a set of candidate locations. A good year to open each clinic is also determined by the model. These decisions are made by minimising three variables: total distances travelled by the households to their nearest clinics, the total distance from the selected distribution centre to the open clinics and the total building cost. An accessibility target of 90% was added to the model to ensure that at least 90% of the households are within 5km of the nearest clinic within the first five years. In these models, operating costs were not included. Therefore all the results are skewed, with most of the clinics being opened in the first year when it is the cheapest since there is no penalty for opening a clinic before it is needed — the exclusion of operating costs is a shortcoming to address in future work. A goal programming model was developed with the variables of the individual scenarios as the goals. The goal programming model was implemented in Python and used to determine a robust configuration of where and in what year to open container clinics. A difference of 25% was set as the upper limit for the difference between the robust configuration variables and the good or acceptable variables for the individual scenarios as the scenarios investigated are very different. This ensured that the robust solution would perform well for any of the three scenarios. The model was able to find locations that provided a relatively good solution to all the scenarios. This came with a cost increase, but that is a trade-off that must be made when dealing with uncertainty. This model is a proof of concept to bridge the gap between urban planning with multiple development scenarios and facility location, more specifically robust facility location. The biggest rendement was achieved by constructing and placing the container clinics in the shortest space of time because the 90% accessibility requirement can be addressed cost-effectively without an operating cost penalty ― this is unfortunately not possible in reality due to budget constraints. An accessibility analysis was conducted to investigate the impact of the accessibility percentage on the variable values and to test the model in a scenario closer resembling the real world by adding a budget constraint. The time limit of the accessibility requirement was removed. In this case, a gradual improvement in the accessibility over the 12 years was observed due to the gradual opening of clinics over the years. Based on the analyses results, it was concluded that the model is sensitive to changes in parameters and that the model can be used for different scenarios. / Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2021. / Industrial and Systems Engineering / MEng (Industrial Engineering) / Unrestricted