Assessment of meshing techniques for fatigue analysis of load carrying welded joints using the effective notch approach

The effective notch stress approach for the fatigue strength assessment of welded structures as included in the Fatigue Design Recommendation of the IIW requires the numerical analysis of the elastic notch stress in the weld toe and weld root which is fictitiously rounded with a radius of 1mm. The goal of this thesis work was to consider alternate meshing strategies when using the effective notch stress approach to assess the fatigue strength of load carrying partial penetration fillet-welded cruciform joints. In order to establish guidelines for modeling the joint and evaluating the results, various two-dimensional (2D) finite element analyses were carried out by systematically varying the thickness of the plates, the weld throat thickness, the degree of bending, and the shape and location of the modeled effective notch. To extend the scope of this work, studies were also carried out on the influence of

Bayesian Estimation of Noise

In mathematical modeling the estimation of the model parameters is one of the most common problems. The goal is to seek parameters that fit to the measurements as well as possible. There is always error in the measurements which implies uncertainty to the model estimates. In Bayesian statistics all the unknown quantities are presented as probability distributions. If there is knowledge about parameters beforehand, it can be formulated as a prior distribution. The Bays’ rule combines the prior and the measurements to posterior distribution. Mathematical models are typically nonlinear, to produce statistics for them requires efficient sampling algorithms. In this thesis both Metropolis-Hastings (MH), Adaptive Metropolis (AM) algorithms and Gibbs sampling are introduced. In the thesis different ways to present prior distributions are introduced. The main issue is in the measurement error estimation and how to obtain prior knowledge for variance or covariance. Variance and covariance sampling is combined with the algorithms above. The examples of the hyperprior models are applied to estimation of model parameters and error in an outlier case.

Overvoltage Mitigation Techniques in Wind Power Generators

Advancements in power electronic semiconductor switching devices have lead to significantly faster switching times. In motor and generator applications, the fast switching times of pulse width modulated (PWM) inverters lead to overvoltages caused by voltage reflections with shorter and shorter cables. These excessive overvoltages may lead to a failure of the electrical machine in a matter of months. In this thesis, the causes behind the overvoltage phenomenon as well as its different mitigation techniques are studied. The most suitable techniques for mitigating the overvoltage phenomenon in wind power generator applications are chosen based on both simulations and actual measurements performed on a prototype. An RC filter at the terminals of the electrical machine and an inverter output filter designed to reduce the rise and fall times of voltage pulses are presented as a solution to the overvoltage problem. The performance and losses of both filter types are analysed.

Computational Techniques for Spectral Image Analysis

Multispectral images are becoming more common in the field of remote sensing, computer vision, and industrial applications. Due to the high accuracy of the multispectral information, it can be used as an important quality factor in the inspection of industrial products. Recently, the development on multispectral imaging systems and the computational analysis on the multispectral images have been the focus of a growing interest. In this thesis, three areas of multispectral image analysis are considered. First, a method for analyzing multispectral textured images was developed. The method is based on a spectral cooccurrence matrix, which contains information of the joint distribution of spectral classes in a spectral domain. Next, a procedure for estimating the illumination spectrum of the color images was developed. Proposed method can be used, for example, in color constancy, color correction, and in the content based search from color image databases. Finally, color filters for the optical pattern recognition were designed, and a prototype of a spectral vision system was constructed. The spectral vision system can be used to acquire a low dimensional component image set for the two dimensional spectral image reconstruction. The data obtained by the spectral vision system is small and therefore convenient for storing and transmitting a spectral image.

Color ad spectral image assessment using novel quality and fidelity techniques

The ongoing development of the digital media has brought a new set of challenges with it. As images containing more than three wavelength bands, often called spectral images, are becoming a more integral part of everyday life, problems in the quality of the RGB reproduction from the spectral images have turned into an important area of research. The notion of image quality is often thought to comprise two distinctive areas – image quality itself and image fidelity, both dealing with similar questions, image quality being the degree of excellence of the image, and image fidelity the measure of the match of the image under study to the original. In this thesis, both image fidelity and image quality are considered, with an emphasis on the influence of color and spectral image features on both. There are very few works dedicated to the quality and fidelity of spectral images. Several novel image fidelity measures were developed in this study, which include kernel similarity measures and 3D-SSIM (structural similarity index). The kernel measures incorporate the polynomial, Gaussian radial basis function (RBF) and sigmoid kernels. The 3D-SSIM is an extension of a traditional gray-scale SSIM measure developed to incorporate spectral data. The novel image quality model presented in this study is based on the assumption that the statistical parameters of the spectra of an image influence the overall appearance. The spectral image quality model comprises three parameters of quality: colorfulness, vividness and naturalness. The quality prediction is done by modeling the preference function expressed in JNDs (just noticeable difference). Both image fidelity measures and the image quality model have proven to be effective in the respective experiments.

VOC emissions and control techniques in softwood veneer dryers

The objective in this Master’s Thesis was to determine VOC emissions from veneer drying in softwood plywood manufacturing. Emissions from plywood industry have become an important factor because of the tightened regulations worldwide. In this Thesis is researched quality and quantity of the VOCs from softwood veneer drying. One of the main objectives was to find out suitable cleaning techniques for softwood VOC emissions. In introduction part is presented veneer drying machines, wood mechanical and chemical properties. VOC control techniques and specified VOC limits are introduced also in the introduction part. Plywood mills have not had interest to VOC emissions previously nevertheless nowadays plywood mills worldwide must consider reduction of the emissions. This Thesis includes measuring of emissions from softwood veneer dryer, analyzation of measured test results and reviewing results. Different air conditions inside of the dryer were considered during planning of the measurements. Results of the emissions measurements were compared to the established laws. Results from this Thesis were softwood veneer dryer emissions in different air conditions. Emission control techniques were also studied for softwood veneer dryer emissions for further specific research.

Salesperson's customer orientation in the personal selling process and in the use of personal selling techniques in the business-to-business setting

Selling is much maligned, often under-valued subject whose inadequate showing in business schools is in inverse proportion to the many job opportunities it offers and the importance of salespeople bringing incomes to companies. The purpose of this research is to increase the understanding of customer-oriented selling and examine the influence of customer-oriented philosophy on selling process, the applicability of selling techniques to this philosophy and the importance of them to salespeople. The empirical section of the study is two-fold. Firstly, the data of qualitative part was collected by conducting five thematic interviews among sales consultants and case company representatives. The findings of the study indicate that customer-oriented selling requires the activity of salespeople. In the customer-oriented personal selling process, salespeople invest time in the preplanning, the need analysis and the benefit demonstration stages. However, the findings propose that salespeople today must also have the basic capabilities for executing the traditional sales process, and the balance between traditional and consultative selling process will change as the duration of the relationship between the salesperson and customer increases. The study also proposes that selling techniques still belong to the customer-oriented selling process, although their roles might be modest. This thesis mapped 75 selling techniques and the quantitative part of the study explored what selling techniques are considered to be important by salespeople in direct selling industry when they make sales with new and existing customers. Response rate of the survey was 69.5%.

Evaluating multidimensional visualization techniques in data mining tasks

Visual data mining (VDM) tools employ information visualization techniques in order to represent large amounts of high-dimensional data graphically and to involve the user in exploring data at different levels of detail. The users are looking for outliers, patterns and models – in the form of clusters, classes, trends, and relationships – in different categories of data, i.e., financial, business information, etc. The focus of this thesis is the evaluation of multidimensional visualization techniques, especially from the business user’s perspective. We address three research problems. The first problem is the evaluation of projection-based visualizations with respect to their effectiveness in preserving the original distances between data points and the clustering structure of the data. In this respect, we propose the use of existing clustering validity measures. We illustrate their usefulness in evaluating five visualization techniques: Principal Components Analysis (PCA), Sammon’s Mapping, Self-Organizing Map (SOM), Radial Coordinate Visualization and Star Coordinates. The second problem is concerned with evaluating different visualization techniques as to their effectiveness in visual data mining of business data. For this purpose, we propose an inquiry evaluation technique and conduct the evaluation of nine visualization techniques. The visualizations under evaluation are Multiple Line Graphs, Permutation Matrix, Survey Plot, Scatter Plot Matrix, Parallel Coordinates, Treemap, PCA, Sammon’s Mapping and the SOM. The third problem is the evaluation of quality of use of VDM tools. We provide a conceptual framework for evaluating the quality of use of VDM tools and apply it to the evaluation of the SOM. In the evaluation, we use an inquiry technique for which we developed a questionnaire based on the proposed framework. The contributions of the thesis consist of three new evaluation techniques and the results obtained by applying these evaluation techniques. The thesis provides a systematic approach to evaluation of various visualization techniques. In this respect, first, we performed and described the evaluations in a systematic way, highlighting the evaluation activities, and their inputs and outputs. Secondly, we integrated the evaluation studies in the broad framework of usability evaluation. The results of the evaluations are intended to help developers and researchers of visualization systems to select appropriate visualization techniques in specific situations. The results of the evaluations also contribute to the understanding of the strengths and limitations of the visualization techniques evaluated and further to the improvement of these techniques.

Evaluation of energy efficiency and best available techniques in pumping processes

The potential for enhancing the energy efficiency of industrial pumping processes is estimated to be in some cases up to 50 %. One way to define further this potential is to implement techniques in accordance to definition of best available techniques in pumping applications. These techniques are divided into three main categories: Design, control method & maintenance and distribution system. In the theory part of this thesis first the definition of best available techniques (BAT) and its applicability on pumping processes is issued. Next, the theory around pumping with different pump types is handled, the main stress being in centrifugal pumps. Other components needed in a pumping process are dealt by presenting different control methods, use of an electric motor, variable speed drive and the distribution system. Last part of the theory is about industrial pumping processes from water distribution, sewage water and power plant applications, some of which are used further on in the empirical part as example cases. For the empirical part of this study four case studies on typical pumping processes from older Master’s these were selected. Firstly the original results were analyzed by studying the distribution of energy consumption between different system components and using the definition of BAT in pumping, possible ways to improve energy efficiency were evaluated. The goal in this study was that by the achieved results it would be possible to identify the characteristic energy consumption of these and similar pumping processes. Through this data it would then be easier to focus energy efficiency actions where they might be the most applicable, both technically and economically.

Bayesian methods for estimation, optimization and experimental design

Mathematical models often contain parameters that need to be calibrated from measured data. The emergence of efficient Markov Chain Monte Carlo (MCMC) methods has made the Bayesian approach a standard tool in quantifying the uncertainty in the parameters. With MCMC, the parameter estimation problem can be solved in a fully statistical manner, and the whole distribution of the parameters can be explored, instead of obtaining point estimates and using, e.g., Gaussian approximations. In this thesis, MCMC methods are applied to parameter estimation problems in chemical reaction engineering, population ecology, and climate modeling. Motivated by the climate model experiments, the methods are developed further to make them more suitable for problems where the model is computationally intensive. After the parameters are estimated, one can start to use the model for various tasks. Two such tasks are studied in this thesis: optimal design of experiments, where the task is to design the next measurements so that the parameter uncertainty is minimized, and model-based optimization, where a model-based quantity, such as the product yield in a chemical reaction model, is optimized. In this thesis, novel ways to perform these tasks are developed, based on the output of MCMC parameter estimation. A separate topic is dynamical state estimation, where the task is to estimate the dynamically changing model state, instead of static parameters. For example, in numerical weather prediction, an estimate of the state of the atmosphere must constantly be updated based on the recently obtained measurements. In this thesis, a novel hybrid state estimation method is developed, which combines elements from deterministic and random sampling methods.

First dice your dill – new methods and techniques in sample handling

This book is dedicated to celebrate the 60th birthday of Professor Rainer Huopalahti. Professor Rainer “Repe” Huopalahti has had, and in fact is still enjoying a distinguished career in the analysis of food and food related flavor compounds. One will find it hard to make any progress in this particular field without a valid and innovative sample handling technique and this is a field in which Professor Huopalahti has made great contributions. The title and the front cover of this book honors Professor Huopahti’s early steps in science. His PhD thesis which was published on 1985 is entitled “Composition and content of aroma compounds in the dill herb, Anethum graveolens L., affected by different factors”. At that time, the thesis introduced new technology being applied to sample handling and analysis of flavoring compounds of dill. Sample handling is an essential task that in just about every analysis. If one is working with minor compounds in a sample or trying to detect trace levels of the analytes, one of the aims of sample handling may be to increase the sensitivity of the analytical method. On the other hand, if one is working with a challenging matrix such as the kind found in biological samples, one of the aims is to increase the selectivity. However, quite often the aim is to increase both the selectivity and the sensitivity. This book provides good and representative examples about the necessity of valid sample handling and the role of the sample handling in the analytical method. The contributors of the book are leading Finnish scientists on the field of organic instrumental analytical chemistry. Some of them are also Repe’ s personal friends and former students from the University of Turku, Department of Biochemistry and Food Chemistry. Importantly, the authors all know Repe in one way or another and are well aware of his achievements on the field of analytical chemistry. The editorial team had a great time during the planning phase and during the “hard work editorial phase” of the book. For example, we came up with many ideas on how to publish the book. After many long discussions, we decided to have a limited edition as an “old school hard cover book” – and to acknowledge more modern ways of disseminating knowledge by publishing an internet version of the book on the webpages of the University of Turku. Downloading the book from the webpage for personal use is free of charge. We believe and hope that the book will be read with great interest by scientists working in the fascinating field of organic instrumental analytical chemistry. We decided to publish our book in English for two main reasons. First, we believe that in the near future, more and more teaching in Finnish Universities will be delivered in English. To facilitate this process and encourage students to develop good language skills, it was decided to be published the book in English. Secondly, we believe that the book will also interest scientists outside Finland – particularly in the other member states of the European Union. The editorial team thanks all the authors for their willingness to contribute to this book – and to adhere to the very strict schedule. We also want to thank the various individuals and enterprises who financially supported the book project. Without that support, it would not have been possible to publish the hardcover book.

Communicative and Anticipatory Decision-Making Supported by Bayesian Networks

The purpose of this research is to draw up a clear construction of an anticipatory communicative decision-making process and a successful implementation of a Bayesian application that can be used as an anticipatory communicative decision-making support system. This study is a decision-oriented and constructive research project, and it includes examples of simulated situations. As a basis for further methodological discussion about different approaches to management research, in this research, a decision-oriented approach is used, which is based on mathematics and logic, and it is intended to develop problem solving methods. The approach is theoretical and characteristic of normative management science research. Also, the approach of this study is constructive. An essential part of the constructive approach is to tie the problem to its solution with theoretical knowledge. Firstly, the basic definitions and behaviours of an anticipatory management and managerial communication are provided. These descriptions include discussions of the research environment and formed management processes. These issues define and explain the background to further research. Secondly, it is processed to managerial communication and anticipatory decision-making based on preparation, problem solution, and solution search, which are also related to risk management analysis. After that, a solution to the decision-making support application is formed, using four different Bayesian methods, as follows: the Bayesian network, the influence diagram, the qualitative probabilistic network, and the time critical dynamic network. The purpose of the discussion is not to discuss different theories but to explain the theories which are being implemented. Finally, an application of Bayesian networks to the research problem is presented. The usefulness of the prepared model in examining a problem and the represented results of research is shown. The theoretical contribution includes definitions and a model of anticipatory decision-making. The main theoretical contribution of this study has been to develop a process for anticipatory decision-making that includes management with communication, problem-solving, and the improvement of knowledge. The practical contribution includes a Bayesian Decision Support Model, which is based on Bayesian influenced diagrams. The main contributions of this research are two developed processes, one for anticipatory decision-making, and the other to produce a model of a Bayesian network for anticipatory decision-making. In summary, this research contributes to decision-making support by being one of the few publicly available academic descriptions of the anticipatory decision support system, by representing a Bayesian model that is grounded on firm theoretical discussion, by publishing algorithms suitable for decision-making support, and by defining the idea of anticipatory decision-making for a parallel version. Finally, according to the results of research, an analysis of anticipatory management for planned decision-making is presented, which is based on observation of environment, analysis of weak signals, and alternatives to creative problem solving and communication.