This master thesis deals with the evaluation of SAP customer quality using fuzzy logic theory and its application. The core of the work is to create models in the MATLAB and Microsoft Excel development environment. Also to use it to create and compare ratings of several customers with different parameters. Using the results, the company can determine the following procedure to solve customer’s problems.

The diploma thesis deals with the use of fuzzy logic in the evaluation of customers in the business company SPINA Trade, s.r.o. MS Excel and MATLAB are used to program two models. Based on the input attributes regarding the customer, the model evaluates the recommended approach to the customer and his priority.

This diploma thesis deals with the use of fuzzy logic principles for evaluation and selection of an optimal supplier of transport services. Based on stated requirements, two models have been created. These models serve as a tool for decision support. The suggestion of solution itself has been created using MS Excel editor and MathWorks MATLAB programming platform. Subsequently, eveluation of particular offers has taken place using the models.

The master‘s thesis is focused on the design and creation of an application for administration of lunch orders. The thesis includes a theoretical basis, analysis of the current situation and design of the application for orders administration. The application is created with regard to user requirements and its purpose is to increase work efficiency.

Railways are one of the largest transportation networks in the world that play an important role in the mass transportation of both the passengers and freight. The speed of trains and as well as the axial load carrying capacity have been increasing significantly during the past few decades to keep in pace with the population and economy growth and to compete with other modes of transportation such as the road, air and water transportation system. Billions of dollars are spent annually for maintenance of rail tracks in the world. The efficient and optimum use of these funds is a challenging task that demands innovative and cutting edge technologies in railway engineering. The railway subgrade is an important part of railway foundation and should be capable of providing a suitable base supporting the ballast and subballast to accommodate the stresses due to traffic loads without failure or excessive deformation. The progressive shear failure is a well-known and age old challenging problem for railways over the world for centuries. The subgrade of railway track which typically constitutes of fine-grained material tends to fail through the accumulation of soil movements up- and sideward developing a path for the least resistance along which progressive shear failure occurs under repeated train-induced loads and due to the effects of climate factors. To-date, limited number of studies have addressed failure mechanism associated with the progressive shear failure, especially using the mechanics of unsaturated soils. In this thesis, a novel and first of its kind, Visual Basic program developed in AutoCAD environment based on Mohr-Coulomb failure criteria and unsaturated soil mechanics theory. This program is capable of taking account of the influence of matric suction and simulate progressive shear failure in the subgrade under moving train. Simulation results suggest several parameters that include stress distribution, matric suction, cohesion, coefficient of lateral earth pressure at rest, and coefficient of residual friction as well as the angle of internal friction have a significant effect on the progressive shear failure and the shape of failure planes in the subgrade. The progressive shear failure in subgrade can be reduced by increasing matric suction, cohesion, coefficient of lateral earth pressure at rest, and coefficient of residual friction as well as the angle of internal friction, and optimizing combination of these parameters. The simulation results suggest the progressive shear failure can be well simulated with the Mohr-Coulomb failure criteria. Several suggestions are made for railway subgrade construction and maintenance based on the results of this study.

Diploma thesis is focused on the analysis of the financial health of microenterprise using statistical methods, specifically time series. The selected size of the enterprise is related to important asset, since this type of analysis is not usually automated. In theoretical part, thesis will discuss each monitored indicator that forms the basis for statistical analysis, which is also described in the theoretical level. The practical part will focus on specific statements obtained, their financial and statistical analysis, and then creating a program in the language of Excel VBA to automate these activities.

The thesis deals with the new design of Dashboard application developed for Vodafone Czech Republic company. The application serves for sales monitoring on all direct retail stores of the company. Major goal of this project is to design a brand new application and enhancing its functionality with new analytical tools and motivational indicators. These innovations will empower the performance of salesmen along with providing the new analytical possibilities for store managers.

The topic of this Thesis is fuzzy logic usage as a tool for choosing a supplier. There was made decision model based on available information which is used as decision support for supplier selection. The solution with application of evaluation is made in programs MS Excel and MATLAB.

This diploma thesis deals with the evaluation of offers with the usage of fuzzy logic. It describes methods and processes of a model building. The aim is to make a decision – making model that helps a customer to make decisions between more properties.

L’imagerie acoustique est une technique performante pour la localisation et la reconstruction de puissance des sources acoustiques en utilisant des mesures limitées au réseau des microphones. Elle est largement utilisée pour évaluer l’influence acoustique dans l’industrie automobile et aéronautique. Les méthodes d’imagerie acoustique impliquent souvent un modèle direct de propagation acoustique et l’inversion de ce modèle direct. Cependant, cette inversion provoque généralement un problème inverse mal-posé. Par conséquent, les méthodes classiques ne permettent d’obtenir de manière satisfaisante ni une haute résolution spatiale, ni une dynamique large de la puissance acoustique. Dans cette thèse, nous avons tout d’abord nous avons créé un modèle direct discret de la puissance acoustique qui devient alors à la fois linéaire et déterminé pour les puissances acoustiques. Et nous ajoutons les erreurs de mesures que nous décomposons en trois parties : le bruit de fond du réseau de capteurs, l’incertitude du modèle causée par les propagations à multi-trajets et les erreurs d’approximation de la modélisation. Pour la résolution du problème inverse, nous avons tout d’abord proposé une approche d’hyper-résolution en utilisant une contrainte de parcimonie, de sorte que nous pouvons obtenir une plus haute résolution spatiale robuste à aux erreurs de mesures à condition que le paramètre de parcimonie soit estimé attentivement. Ensuite, afin d’obtenir une dynamique large et une plus forte robustesse aux bruits, nous avons proposé une approche basée sur une inférence bayésienne avec un a priori parcimonieux. Toutes les variables et paramètres inconnus peuvent être estimées par l’estimation du maximum a posteriori conjoint (JMAP). Toutefois, le JMAP souffrant d’une optimisation non-quadratique d’importants coûts de calcul, nous avons cherché des solutions d’accélération algorithmique: une approximation du modèle direct en utilisant une convolution 2D avec un noyau invariant. Grâce à ce modèle, nos approches peuvent être parallélisées sur des Graphics Processing Unit (GPU) . Par ailleurs, nous avons affiné notre modèle statistique sur 2 aspects : prise en compte de la non stationarité spatiale des erreurs de mesures et la définition d’une loi a priori pour les puissances renforçant la parcimonie en loi de Students-t. Enfin, nous ont poussé à mettre en place une Approximation Variationnelle Bayésienne (VBA). Cette approche permet non seulement d’obtenir toutes les estimations des inconnues, mais aussi de fournir des intervalles de confiance grâce aux paramètres cachés utilisés par les lois de Students-t. Pour conclure, nos approches ont été comparées avec des méthodes de l’état-de-l’art sur des données simulées, réelles (provenant d’essais en soufflerie chez Renault S2A) et hybrides. / Acoustic imaging is an advanced technique for acoustic source localization and power reconstruction using limited measurements at microphone sensor array. This technique can provide meaningful insights into performances, properties and mechanisms of acoustic sources. It has been widely used for evaluating the acoustic influence in automobile and aircraft industries. Acoustic imaging methods often involve in two aspects: a forward model of acoustic signal (power) propagation, and its inverse solution. However, the inversion usually causes a very ill-posed inverse problem, whose solution is not unique and is quite sensitive to measurement errors. Therefore, classical methods cannot easily obtain high spatial resolutions between two close sources, nor achieve wide dynamic range of acoustic source powers. In this thesis, we firstly build up a discrete forward model of acoustic signal propagation. This signal model is a linear but under-determined system of equations linking the measured data and unknown source signals. Based on this signal model, we set up a discrete forward model of acoustic power propagation. This power model is both linear and determined for source powers. In the forward models, we consider the measurement errors to be mainly composed of background noises at sensor array, model uncertainty caused by multi-path propagation, as well as model approximating errors. For the inverse problem of the acoustic power model, we firstly propose a robust super-resolution approach with the sparsity constraint, so that we can obtain very high spatial resolution in strong measurement errors. But the sparsity parameter should be carefully estimated for effective performance. Then for the acoustic imaging with large dynamic range and robustness, we propose a robust Bayesian inference approach with a sparsity enforcing prior: the double exponential law. This sparse prior can better embody the sparsity characteristic of source distribution than the sparsity constraint. All the unknown variables and parameters can be alternatively estimated by the Joint Maximum A Posterior (JMAP) estimation. However, this JMAP suffers a non-quadratic optimization and causes huge computational cost. So that we improve two following aspects: In order to accelerate the JMAP estimation, we investigate an invariant 2D convolution operator to approximate acoustic power propagation model. Owing to this invariant convolution model, our approaches can be parallelly implemented by the Graphics Processing Unit (GPU). Furthermore, we consider that measurement errors are spatially variant (non-stationary) at different sensors. In this more practical case, the distribution of measurement errors can be more accurately modeled by Students-t law which can express the variant variances by hidden parameters. Moreover, the sparsity enforcing distribution can be more conveniently described by the Student's-t law which can be decomposed into multivariate Gaussian and Gamma laws. However, the JMAP estimation risks to obtain so many unknown variables and hidden parameters. Therefore, we apply the Variational Bayesian Approximation (VBA) to overcome the JMAP drawbacks. One of the fabulous advantages of VBA is that it can not only achieve the parameter estimations, but also offer the confidential interval of interested parameters thanks to hidden parameters used in Students-t priors. To conclude, proposed approaches are validated by simulations, real data from wind tunnel experiments of Renault S2A, as well as the hybrid data. Compared with some typical state-of-the-art methods, the main advantages of proposed approaches are robust to measurement errors, super spatial resolutions, wide dynamic range and no need for source number nor Signal to Noise Ration (SNR) beforehand.