Efficient Optimization Algorithms for Nonlinear Data Analysis

Identification of low-dimensional structures and main sources of variation from multivariate data are fundamental tasks in data analysis. Many methods aimed at these tasks involve solution of an optimization problem. Thus, the objective of this thesis is to develop computationally efficient and theoretically justified methods for solving such problems. Most of the thesis is based on a statistical model, where ridges of the density estimated from the data are considered as relevant features. Finding ridges, that are generalized maxima, necessitates development of advanced optimization methods. An efficient and convergent trust region Newton method for projecting a point onto a ridge of the underlying density is developed for this purpose. The method is utilized in a differential equation-based approach for tracing ridges and computing projection coordinates along them. The density estimation is done nonparametrically by using Gaussian kernels. This allows application of ridge-based methods with only mild assumptions on the underlying structure of the data. The statistical model and the ridge finding methods are adapted to two different applications. The first one is extraction of curvilinear structures from noisy data mixed with background clutter. The second one is a novel nonlinear generalization of principal component analysis (PCA) and its extension to time series data. The methods have a wide range of potential applications, where most of the earlier approaches are inadequate. Examples include identification of faults from seismic data and identification of filaments from cosmological data. Applicability of the nonlinear PCA to climate analysis and reconstruction of periodic patterns from noisy time series data are also demonstrated. Other contributions of the thesis include development of an efficient semidefinite optimization method for embedding graphs into the Euclidean space. The method produces structure-preserving embeddings that maximize interpoint distances. It is primarily developed for dimensionality reduction, but has also potential applications in graph theory and various areas of physics, chemistry and engineering. Asymptotic behaviour of ridges and maxima of Gaussian kernel densities is also investigated when the kernel bandwidth approaches infinity. The results are applied to the nonlinear PCA and to finding significant maxima of such densities, which is a typical problem in visual object tracking.

Distribution network design and optimization

The objective of this thesis is to examine distribution network designs and modeling practices and create a framework to identify best possible distribution network structure for the case company. The main research question therefore is: How to optimize case company’s distribution network in terms of customer needs and costs? Theory chapters introduce the basic building blocks of the distribution network design and needed calculation methods and models. Framework for the distribution network projects was created based on the theory and the case study was carried out by following the defined framework. Distribution network calculations were based on the company’s sales plan for the years 2014 - 2020. Main conclusions and recommendations were that the new Asian business strategy requires high investments in logistics and the first step is to open new satellite DC in China as soon as possible to support sales and second possible step is to open regional DC in Asia within 2 - 4 years.

Natal : nach: map of the Colony of the Cape of Good Hope and neighbouring territories 1895

Kartta kuuluu A. E. Nordenskiöldin kokoelmaan

Stability Analysis in Multicriteria Discrete Portfolio Optimization.

Almost every problem of design, planning and management in the technical and organizational systems has several conflicting goals or interests. Nowadays, multicriteria decision models represent a rapidly developing area of operation research. While solving practical optimization problems, it is necessary to take into account various kinds of uncertainty due to lack of data, inadequacy of mathematical models to real-time processes, calculation errors, etc. In practice, this uncertainty usually leads to undesirable outcomes where the solutions are very sensitive to any changes in the input parameters. An example is the investment managing. Stability analysis of multicriteria discrete optimization problems investigates how the found solutions behave in response to changes in the initial data (input parameters). This thesis is devoted to the stability analysis in the problem of selecting investment project portfolios, which are optimized by considering different types of risk and efficiency of the investment projects. The stability analysis is carried out in two approaches: qualitative and quantitative. The qualitative approach describes the behavior of solutions in conditions with small perturbations in the initial data. The stability of solutions is defined in terms of existence a neighborhood in the initial data space. Any perturbed problem from this neighborhood has stability with respect to the set of efficient solutions of the initial problem. The other approach in the stability analysis studies quantitative measures such as stability radius. This approach gives information about the limits of perturbations in the input parameters, which do not lead to changes in the set of efficient solutions. In present thesis several results were obtained including attainable bounds for the stability radii of Pareto optimal and lexicographically optimal portfolios of the investment problem with Savage's, Wald's criteria and criteria of extreme optimism. In addition, special classes of the problem when the stability radii are expressed by the formulae were indicated. Investigations were completed using different combinations of Chebyshev's, Manhattan and Hölder's metrics, which allowed monitoring input parameters perturbations differently.

Optimization and Measuring Techniques for Collect-and-Place Machines in Printed Circuit Board Industry

This thesis considers optimization problems arising in printed circuit board assembly. Especially, the case in which the electronic components of a single circuit board are placed using a single placement machine is studied. Although there is a large number of different placement machines, the use of collect-and-place -type gantry machines is discussed because of their flexibility and increasing popularity in the industry. Instead of solving the entire control optimization problem of a collect-andplace machine with a single application, the problem is divided into multiple subproblems because of its hard combinatorial nature. This dividing technique is called hierarchical decomposition. All the subproblems of the one PCB - one machine -context are described, classified and reviewed. The derived subproblems are then either solved with exact methods or new heuristic algorithms are developed and applied. The exact methods include, for example, a greedy algorithm and a solution based on dynamic programming. Some of the proposed heuristics contain constructive parts while others utilize local search or are based on frequency calculations. For the heuristics, it is made sure with comprehensive experimental tests that they are applicable and feasible. A number of quality functions will be proposed for evaluation and applied to the subproblems. In the experimental tests, artificially generated data from Markov-models and data from real-world PCB production are used. The thesis consists of an introduction and of five publications where the developed and used solution methods are described in their full detail. For all the problems stated in this thesis, the methods proposed are efficient enough to be used in the PCB assembly production in practice and are readily applicable in the PCB manufacturing industry.

Optimization of a dynamic district heating power plant

The purpose of this Thesis is to find the most optimal heat recovery solution for Wärtsilä’s dynamic district heating power plant considering Germany energy markets as in Germany government pays subsidies for CHP plants in order to increase its share of domestic power production to 25 % by 2020. Different heat recovery connections have been simulated dozens to be able to determine the most efficient heat recovery connections. The purpose is also to study feasibility of different heat recovery connections in the dynamic district heating power plant in the Germany markets thus taking into consideration the day ahead electricity prices, district heating network temperatures and CHP subsidies accordingly. The auxiliary cooling, dynamical operation and cost efficiency of the power plant is also investigated.

Optimization of the RNeasy Mini Kit to obtain high-quality total RNA from sessile cells of Staphylococcus aureus

Biofilm formed by Staphylococcus aureus is considered an important virulence trait in the pathogenesis of infections associated with implantable medical devices. Gene expression analyses are important strategies for determining the mechanisms involved in production and regulation of biofilm. Obtaining intact RNA preparations is the first and most critical step for these studies. In this article, we describe an optimized protocol for obtaining total RNA from sessile cells of S. aureus using the RNeasy Mini Kit. This method essentially consists of a few steps, as follows: 1) addition of acetone-ethanol to sessile cells, 2) lysis with lysostaphin at 37°C/10 min, 3) vigorous mixing, 4) three cycles of freezing and thawing, and 5) purification of the lysate in the RNeasy column. This simple pre-kit procedure yields high-quality total RNA from planktonic and sessile cells of S. aureus.

Use of the Wasserman equation in optimization of the duration of the power ramp in a cardiopulmonary exercise test: a study of Brazilian men

This study aimed to analyze the agreement between measurements of unloaded oxygen uptake and peak oxygen uptake based on equations proposed by Wasserman and on real measurements directly obtained with the ergospirometry system. We performed an incremental cardiopulmonary exercise test (CPET), which was applied to two groups of sedentary male subjects: one apparently healthy group (HG, n=12) and the other had stable coronary artery disease (n=16). The mean age in the HG was 47±4 years and that in the coronary artery disease group (CG) was 57±8 years. Both groups performed CPET on a cycle ergometer with a ramp-type protocol at an intensity that was calculated according to the Wasserman equation. In the HG, there was no significant difference between measurements predicted by the formula and real measurements obtained in CPET in the unloaded condition. However, at peak effort, a significant difference was observed between oxygen uptake (V˙O2)peak(predicted)and V˙O2peak(real)(nonparametric Wilcoxon test). In the CG, there was a significant difference of 116.26 mL/min between the predicted values by the formula and the real values obtained in the unloaded condition. A significant difference in peak effort was found, where V˙O2peak(real)was 40% lower than V˙O2peak(predicted)(nonparametric Wilcoxon test). There was no agreement between the real and predicted measurements as analyzed by Lin’s coefficient or the Bland and Altman model. The Wasserman formula does not appear to be appropriate for prediction of functional capacity of volunteers. Therefore, this formula cannot precisely predict the increase in power in incremental CPET on a cycle ergometer.

Optimization of random amplified polymorphic DNA protocol for molecular identification of Lophius gastrophysus

Lophius gastrophysus has important commercial value in Brazil particularly for foreign trade. In this study, we described the optimization of Random Amplified Polymorphic DNA (RAPD) protocol for identification of L. gastrophysus. Different conditions (annealing temperatures, MgCl concentrations, DNA quantity) were tested to find reproducible and adequate profiles. Amplifications performed with primers A01, ² A02 and A03 generate the best RAPD profiles when the conditions were annealing temperature of 36ºC, 25 ng of DNA quantity and 2.5 mM MgCl2. Exact identification of the species and origin of marine products is necessary and RAPD could be used as an accurate, rapid tool to expose commercial fraud.

Optimization and validation of a methodology to determine total arsenic, As(III) and As(V), in water samples, through graphite furnace atomic absorption spectrometry

The Graphite furnace atomic absorption spectrometry (GF AAS) was the technique chosen by the inorganic contamination laboratory (INCQ/ FIOCRUZ) to be validated and applied in routine analysis for arsenic detection and quantification. The selectivity, linearity, sensibility, detection, and quantification limits besides accuracy and precision parameters were studied and optimized under Stabilized Temperature Platform Furnace (STPF) conditions. The limit of detection obtained was 0.13 µg.L-1 and the limit of quantification was 1.04 µg.L-1, with an average precision, for total arsenic, less than 15% and an accuracy of 96%. To quantify the chemical species As(III) and As(V), an ion-exchange resin (Dowex 1X8, Cl- form) was used and the physical-chemical parameters were optimized resulting in a recuperation of 98% of As(III) and of 90% of As(V). The method was applied to groundwater, mineral water, and hemodialysis purified water samples. All results obtained were lower than the maximum limit values established by the legal Brazilian regulations, in effect, 50, 10, and 5 µg.L-1 para As total, As(III) e As(V), respectively. All results were statistically evaluated.

Docosahexaenoic acid ethyl esther (DHAEE) microcapsule production by spray-drying: optimization by experimental design

Docosahexaenoic acid is an essential polyunsaturated fatty acid with important metabolic activities. Its conjugated double bonds make it susceptible to decomposition. Its stability may be improved through fatty acid entrapment with a spray-drying technique; however, the many parameters involved in this technique must be considered to avoid affecting the final product quality. Therefore, this study aimed to evaluate the entrapment conditions and yields of fish oil enriched with docosahexaenoic acid ethyl ester. Microcapsules were obtained from Acacia gum using a spray-drying technique. The experimental samples were analyzed by chromatography and delineated by Statistica software, which found the following optimum entrapment conditions: an inlet temperature of 188 °C; 30% core material; an N2 flow rate of 55 mm; and a pump flow rate of 12.5 mL/minute. These conditions provided a 66% yield of docosahexaenoic acid ethyl ester in the oil, corresponding to 19.8% of entrapped docosahexaenoic acid ethyl ester (w/w). This result was considered significant since 30% corresponded to wall material.