Accompanying the advances in computer technology is an increase collection of high dimensional data in many scientific and social studies. Sufficient dimension reduction (SDR) is a statistical method that enable us to reduce the dimension ofpredictors without loss of regression information. In this dissertation, we introduce principal asymmetric least squares (PALS) as a unified framework for linear and nonlinear sufficient dimension reduction. Classical methods such as sliced inverse regression (Li, 1991) and principal support vector machines (Li, Artemiou and Li, 2011) often do not perform well in the presence of heteroscedastic error, while our proposal addresses this limitation by synthesizing different expectile levels. Through extensive numerical studies, we demonstrate the superior performance of PALS in terms of both computation time and estimation accuracy. For the asymptotic analysis of PALS for linear sufficient dimension reduction, we develop new tools to compute the derivative of an expectation of a non-Lipschitz function. PALS is not designed to handle symmetric link function between the response and the predictors. As a remedy, we develop expectile-assisted inverse regression estimation (EA-IRE) as a unified framework for moment-based inverse regression. We propose to first estimate the expectiles through kernel expectile regression, and then carry out dimension reduction based on random projections of the regression expectiles. Several popular inverse regression methods in the literature including slice inverse regression, slice average variance estimation, and directional regression are extended under this general framework. The proposed expectile-assisted methods outperform existing moment-based dimension reduction methods in both numerical studies and an analysis of the Big Mac data. / Statistics

Distribution load flow and state estimation are two important functions in distribution energy management systems (DEMS) and advanced distribution automation (ADA) systems. Distribution load flow analysis is a tool which helps to analyze the status of a power distribution system under steady-state operating conditions. In this research, an effective and comprehensive load flow algorithm is developed to extensively incorporate the distribution system components. Distribution system state estimation is a mathematical procedure which aims to estimate the operating states of a power distribution system by utilizing the information collected from available measurement devices in real-time. An efficient and computationally effective state estimation algorithm adapting the weighted-least-squares (WLS) method has been developed in this research. Both the developed algorithms are tested on different I testeeders and the results obtained are justified.

Hyperspectral imagery is often associated with high storage and transmission costs. Dimensionality reduction aims to reduce the time and space complexity of hyperspectral imagery by projecting data into a low-dimensional space such that all the important information in the data is preserved. Dimensionality-reduction methods based on transforms are widely used and give a data-dependent representation that is unfortunately costly to compute. Recently, there has been a growing interest in data-independent representations for dimensionality reduction; of particular prominence are random projections which are attractive due to their computational efficiency and simplicity of implementation. This dissertation concentrates on exploring the realm of computationally fast and efficient random projections by considering projections based on a random Hadamard matrix. These Hadamard-based projections are offered as an alternative to more widely used random projections based on dense Gaussian matrices. Such Hadamard matrices are then coupled with a fast singular value decomposition in order to implement a two-stage dimensionality reduction that marries the computational benefits of the data-independent random projection to the structure-capturing capability of the data-dependent singular value transform. Finally, random projections are applied in conjunction with nonnegative least squares to provide a computationally lightweight methodology for the well-known spectral-unmixing problem. Overall, it is seen that random projections offer a computationally efficient framework for dimensionality reduction that permits hyperspectral-analysis tasks such as unmixing and classification to be conducted in a lower-dimensional space without sacrificing analysis performance while reducing computational costs significantly.

DNA methylation plays a key role in disease analysis, especially for studies that compare known large scale differences in CpG sites, such as cancer/normal studies or between-tissues studies. However, before any analysis can be done, data normalization and preprocessing of methylation data are required. A useful data preprocessing pipeline for large scale comparisons is Functional Normalization (FunNorm), (Fortin et al., 2014) implemented in the minfi package in R. In FunNorm, the univariate quantiles of the methylated and unmethylated signal values in the raw data are used to preprocess the data. However, although FunNorm has been shown to outperform other preprocessing and data normalization processes for these types of studies, it does not account for the correlation between the methylated and unmethylated signals into account; the focus of this paper is to improve upon FunNorm by taking this correlation into account. The concept of a bivariate quantile is used in this study as an attempt to take the correlation between the methylated and unmethylated signals into consideration. From the bivariate quantiles found, the partial least squares method is then used on these quantiles in this preprocessing. The raw datasets used for this research were collected from the European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI) website. The results from this preprocessing algorithm were then compared and contrasted to the results from FunNorm. Drawbacks, limitations and future research are then discussed. / Thesis / Master of Science (MSc)

Directors designed for observation and fire control in naval environments consist of a mechanical pedestal moved by two electrical motors. To meet the high demands on director precision, a servo solution based on feedback control is used. The digital servo controller has to be tuned to meet demands on performance and stability. This report presents methods for automatic tuning, intended to replace today’s manual tuning procedures. System identification based on relay feedback and recursive least-squares approximations are combined with the Ziegler-Nichols and AMIGO tuning procedures for PI controllers are evaluated. Evaluations are performed in simulations, for which a SIMULINK model is constructed. Results indicate that the automatic tuning may perform well compared to the manual tuning used today, and that it could bring considerable reduction in the time required for tuning.

This dissertation contributes to IS research by explaining user intentions while using AR features in mobile social networking and retail app contexts. It consists of three essays, which use partial least squares modeling to analyze different consumer behavior models. The first essay examines the influence of quality, human, and environmental factors on AR reuse intention in a mobile social networking context. The second essay introduces position relevance, a new construct essential to AR research in e-commerce, and it looks at the influence of this construct and app involvement on user purchase intention, while using view-in-room features on mobile retail apps. The third essay examines the influence of service quality and visual quality on recommendation intention of mobile retail apps while using view-in-room features compared to shopping without using these AR features.

Hybrit development AB är ett bolag som strävar mot att kunna producera fossilfritt stål genom att reducera järnmalmspellets med hjälp av vätgas. Därför har Hybrit utfört experimentella kampanjer där genomförbarheten av att reducera järnmalmspellets med hjälp av vätgas undersökts och studerats. Vid produktion av järn och stål måste produktkvalitén tas i beaktan. Reduktionsprocessen karaktäriseras av en mängd olika process- och kvalitetsparametrar, där kvalitetsparametrarna beskriver produktkvalitén. Det är av intresse att studera hur processparametrarna påverkar produktkvalitén. Processparametrarna kan mätas vid vilket tidpunkt som helst genom olika sensorer. Produktkvalitén kan bestämmas först efter att järnmalmspelletsen är färdigreducerad. Därför präglas processen av en tidsfördröjning mellan mätningen av processparametrarna och labanalysemätningarna av kvalitetsparametrarna. På grund av tidsfördröjningen är det av intresse att kunna prediktera produktkvalitén utifrån processparametrarna. Om det går att prediktera produktkvalitén, är det av vikt att kunna avgöra prediktionens giltighet.  Examensarbetets syfte är att identifiera hur reduktionsprocessparametrarna påverkar reducerade järnets kvalitetsparametrar. En processövervakningsmetod som passar för processövervakning ska testas och undersökas utifrån hur metoden kan användas för att avgöra prediktionens giltighet. Processövervakningen ska användas för att avgöra om processen befinner sig i ett processläge som bidrar till en någorlunda korrekt och lämplig prediktion av produktkvalitén.  För analys av data användes 65 processparametrar och 6 kvalitetsparametrar. Den multivariata analysmetoden Partial Least Squares (PLS) användes för att nå syftet med examensarbetet. Via PLS skapades en modell som kunde beskriva vilka processparametrar som påverkade kvalitetsparametrarna samt hur processparametrarna påverkade kvalitetsparametrarna. PLS-modellen kunde prediktera kvalitetsparametrarna någorlunda korrekt och lämpligt, givet att processen befinner sig inom ramen för modellen och att det är en hög förklaringsgrad för kvalitetsparametern som predikteras. Kvalitetsparametern Y6-1 predikterades sämre eftersom förklaringsgraden för Y6-1 var låg. Processövervakningsmetoden som testades och undersöktes var PLS-övervakning. För att undersöka hur PLS-övervakning kan användas för att avgöra prediktions giltighet, användes tre processövervakningsverktyg. Dessa var X-scores processövervakning, Hotelling T2 och SPE. Resultatet var att PLS-övervakning kunde angiva hur processen förhåller sig till modellen. Observationerna som avvek i PLS-övervakningen predikterades sämre. Därmed kunde information om prediktionens giltighet genom PLS-övervakning erhållas. Att tillämpa PLS-övervakning för att avgöra prediktionens giltighet är en större framgång. Detta på grund av att information om produktkvalitén innan reduktionsprocessen är genomförd kan användas för att säkerställa produktion med tillfredställande kvalitet. Att tillämpa multivariata processövervakningsmetoder för att övervaka de predikterade kvalitetsparametrarna kan vara av intresse för framtida studier. Detta då processövervakningen kan användas för att minimera den interna variationen hos kvalitetsparametrarna. / Hybrit development AB strives to produce fossil-free steel by using hydrogen for the direct reduction process of iron ore pellets. To achieve that goal, Hybrit has carried out experimental campaigns where the feasibility of direct reduction using hydrogen gas has been investigated and studied. The quality of the reduced iron must be considered when producing iron and steel. The reduction process is characterized by a variety of process- and quality parameters. Because the quality parameters describe the quality of the product, it is of interest to study how the process parameters affect the quality parameters. The process parameters can be measured at any time through various sensors around the reactor in which the iron ore pellets are reduced. While the quality of the product can only be determined after the iron ore pellets have been completely reduced. Therefore, the process is characterized by a time delay between the measurement of the process parameters and the measurement of the quality parameters, where the reduced iron must be analyzed in a laboratory before the quality parameters can be measured. Because of the time delay, it is of interest to be able to predict the quality of the product based on the process parameters. If it is possible to predict the quality, then it is of importance to be able to determine the validity of the prediction.  The aim of this master thesis is to identify how the reduction process parameters affect the quality parameters of the reduced iron. A process monitoring method suitable for monitoring the process need be tested and investigated based on how the method can be used to determine the validity of the prediction. The process monitoring will be used to determine whether the process is in a process state that contributes to a reasonably accurate and appropriate prediction of the quality of the product.  65 process parameters and 6 quality parameters were used for the analysis of how the reduction process parameters affect the quality parameters of the reduced iron. The multivariate analysis method Partial Least Squares (PLS) was used to achieve the aim of the thesis. A multivariate model which could describe how the process parameters affect the quality parameters was created through PLS. The PLS-model was able to predict the quality parameters reasonably correctly and appropriately, given that the process is within the scope of the model and that the explanatory power is high for the quality parameter that is predicted. The quality parameter Y6-1 could not be predicted reasonably correct as the explanatory power for Y6-1 was low. The process monitoring method tested and investigated was PLS monitoring. Three process monitoring tools were used when PLS monitoring was investigated based on how they can be used to determine the validity of the prediction. These tools were X-scores process monitoring, Hotelling T2 and SPE. The result was that PLS monitoring could indicate how the process relates to the model. Observations that deviated in the PLS monitoring could not be predicted correctly. Thus, information about the validity of the prediction through PLS monitoring could be obtained. Applying PLS monitoring to determine the validity of the prediction is a greater success. This is because information about the quality of the product before the reduction process is completed can be used to ensure production with a satisfactory product quality. Applying multivariate process monitoring methods to monitor the predicted quality parameters may be of interest for future studies. This is because the process monitoring can be used to minimize the internal variation of the quality parameters.

Purpose: With the growing importance of performance measurement and management, this exploratory study intends to examine the practices of maintenance managers with regards to maintenance measures, as used in their organizations. In this process, the study attempts to uncover the relevant maintenance performance dimensions from the perspectives of the surveyed managers. In addition, the mediating effect of information availability on the main performance measures utilization is studied. Design/methodology/approach: The research at hand is survey-based. It utilizes the responses of a sample of ninety-five (95) experienced maintenance managers to identify the most relevant maintenance performance measures. Factor analysis is then utilized to uncover the important dimensions of performance, as seen by the respondents. Additionally, using the Partial Least Squares method, several models were studied. Findings: The findings of this exploratory research appear to suggest that maintenance managers are beginning to broaden their perspective with regard to performance management. While machine and plant-related performance measures are still emphasized, maintenance managers are slowly moving toward a wider organizational orientation. While the manufacturing organizations are becoming more and more customer-oriented open systems, the maintenance function of these organizations is still, for the most part, operating under the semi-open system orientation. Overall, it appears that an emerging maintenance strategy is slowly taking shape. Research limitations/implications: For the most part, performance measures and measurement related to maintenance have not received enough attention from researchers. Therefore, the literature dealing with the different facets of performance in maintenance has not been forthcoming. The study attempts to fill this apparent gap in the literature. This is important, as maintenance managers are being asked to contribute to the achievement of the competitive strategies of their organizations. Therefore, they must quickly learn how to view maintenance from a coherent strategic organizational perspective. Such a perspective should help in integrating the maintenance, resources, capabilities, and technical know-how in order to serve the strategic goal of their organization. The research at hand is limited to a sample from Portugal. Therefore, the results and conclusions must be interpreted accordingly. Practical implications: As maintenance managers struggle to move from a machine-orientation to a more organizational-wide strategic orientation, they are often left with many questions and few answers. This study attempts to bring this problem to the spotlight so that it can receive more systematic empirical and practical research. In this context, the role of maintenance managers in the process of organizational strategy formulation should be examined. Originality/value: The study presented in this article has practical, as well as theoretical contributions. It deals with an area of performance measurement, which so far has been relatively ignored. It uses a system orientation (closed vs open), in addition to the strategic orientation (single vs multi-faceted strategy) in order to shed some light on the need to have consistency between the nature of the system and its strategic objective.

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