Covalidation of Integral Transforms and Method of Lines in Nonlinear Convection-Diffusion with Mathematica

The Mathematica system (version 4.0) is employed in the solution of nonlinear difusion and convection-difusion problems, formulated as transient one-dimensional partial diferential equations with potential dependent equation coefficients. The Generalized Integral Transform Technique (GITT) is first implemented for the hybrid numerical-analytical solution of such classes of problems, through the symbolic integral transformation and elimination of the space variable, followed by the utilization of the built-in Mathematica function NDSolve for handling the resulting transformed ODE system. This approach ofers an error-controlled final numerical solution, through the simultaneous control of local errors in this reliable ODE's solver and of the proposed eigenfunction expansion truncation order. For covalidation purposes, the same built-in function NDSolve is employed in the direct solution of these partial diferential equations, as made possible by the algorithms implemented in Mathematica (versions 3.0 and up), based on application of the method of lines. Various numerical experiments are performed and relative merits of each approach are critically pointed out.

Estimating Attractor Dimension on the Nonlinear Pendulum Time Series

Chaotic dynamical systems exhibit trajectories in their phase space that converges to a strange attractor. The strangeness of the chaotic attractor is associated with its dimension in which instance it is described by a noninteger dimension. This contribution presents an overview of the main definitions of dimension discussing their evaluation from time series employing the correlation and the generalized dimension. The investigation is applied to the nonlinear pendulum where signals are generated by numerical integration of the mathematical model, selecting a single variable of the system as a time series. In order to simulate experimental data sets, a random noise is introduced in the time series. State space reconstruction and the determination of attractor dimensions are carried out regarding periodic and chaotic signals. Results obtained from time series analyses are compared with a reference value obtained from the analysis of mathematical model, estimating noise sensitivity. This procedure allows one to identify the best techniques to be applied in the analysis of experimental data.

Simulation of Steady-State Nonlinear Heat Transfer Problems Through the Minimization of Quadratic Functionals

In this work it is presented a systematic procedure for constructing the solution of a large class of nonlinear conduction heat transfer problems through the minimization of quadratic functionals like the ones usually employed for linear descriptions. The proposed procedure gives rise to an efficient and easy way for carrying out numerical simulations of nonlinear heat transfer problems by means of finite elements. To illustrate the procedure a particular problem is simulated by means of a finite element approximation.

An Investigation into Mechanisms of Loss of Safe Basins in a 2 D.O.F. Nonlinear Oscillator

In this work we consider the transient stability of coupled motions of a 2 D.O.F. nonlinear oscillator that can represent, for example, the motions of a sea vessel under the action of trains of regular lateral waves. Instability is studied as the escape of the system from a safe potential well. The set of initial conditions in phase space that lead to acceptable motions constitutes its safe basin. We investigate the evolution of these safe basins under variation of parameters such as frequency and amplitude of waves, and an internal tuning parameter. Complex nonlinear phenomena are known to play an important role in determining the loss of safe basins as, say, wave amplitude is increased. We therefore investigate those processes, and attempt to classify them in terms of their speed relative to changes in parameter values. "Mechanism basins" are produced depicting regions of parameter space in which rapid or slow losses of safe basin are observed. We propose that a comprehensive understanding of mechanisms of loss of safe basins can be a valuable tool in assessing stability properties of these systems, and we give a conceptual view of how such information could be used.

Experimental Vibration Characteristics of a Beam with Nonlinear Support Using Receptance Coupling Analysis

This paper applies the Multi-Harmonic Nonlinear Receptance Coupling Approach (MUHANORCA) (Ferreira 1998) to evaluate the frequency response characteristics of a beam which is clamped at one end and supported at the other end by a nonlinear cubic stiffness joint. In order to apply the substructure coupling technique, the problem was characterised by coupling a clamped linear beam with a nonlinear cubic stiffness joint. The experimental results were obtained by a sinusoidal excitation with a special force control algorithm where the level of the fundamental force is kept constant and the level of the harmonics is kept zero for all the frequencies measured.

Application of the center manifold theory to the study of slewing flexible Non-ideal structures with nonlinear curvature: a case study

In this paper is Analyzed the local dynamical behavior of a slewing flexible structure considering nonlinear curvature. The dynamics of the original (nonlinear) governing equations of motion are reduced to the center manifold in the neighborhood of an equilibrium solution with the purpose of locally study the stability of the system. In this critical point, a Hopf bifurcation occurs. In this region, one can find values for the control parameter (structural damping coefficient) where the system is unstable and values where the system stability is assured (periodic motion). This local analysis of the system reduced to the center manifold assures the stable / unstable behavior of the original system around a known solution.

Searchig the links between "knowledge governance mechanisms", "the individual conditions of knowledge sharing", and "individual's knowledge sharing behaviour".

The objective of this study was to understand how organizational knowledge governance mechanisms affect individual motivation, opportunity, and the ability to share knowledge (MOA framework), and further, how individual knowledge-sharing conditions affect actual knowledge sharing behaviour. The study followed the knowledge governance approach and a micro-foundations perspective to develop a theoretical model and hypotheses, which could explain the casual relationships between knowledge governance mechanisms, individual knowledge sharing conditions, and individual knowledge sharing behaviour. The quantitative research strategy and multivariate data analysis techniques (SEM) were used in the hypotheses testing with a survey dataset of 256 employees from eleven military schools of Finnish Defence Forces (FDF). The results showed that “performance-based feedback and rewards” affects employee’s “intrinsic motivation towards knowledge sharing”, that “lateral coordination” affects employee’s “knowledge self-efficacy”, and that ”training and development” is positively related to “time availability” for knowledge sharing but affects negatively employee’s knowledge self-efficacy. Individual motivation and knowledge self-efficacy towards knowledge sharing affected knowledge sharing behaviour when work-related knowledge was shared 1) between employees in a department and 2) between employees in different departments, however these factors did not play a crucial role in subordinate–superior knowledge sharing. The findings suggest that individual motivation, opportunity, and the ability towards knowledge sharing affects individual knowledge sharing behaviour differently in different knowledge sharing situations. Furthermore, knowledge governance mechanisms can be used to manage individual-level knowledge sharing conditions and individual knowledge sharing behaviour but their affect also vary in different knowledge sharing situations.

Economical potential of controlling charging of electric vehicles by using price signal

The number of electric vehicles grows continuously and the implementation of charging electric vehicles is an important issue for the future. Increasing amount of electric vehicles can cause problems to distribution grid by increasing peak load. Currently charging of electric vehicles is uncontrolled, but as the amount of electric vehicles grows, smart charg-ing (controlled charging) will be one possible solution to handle this situation. In this thesis smart charging of electric vehicles is examined from electricity retailers` point of view. The purpose is to find out plausible saving potentials of smart charging, when it´s controlled by price signal. Saving potential is calculated by comparing costs of price signal controlled charging and uncontrolled charging.

Process modeling method for electric accessories manufacturing enterprise

The theoretical research of the study focused to business process management and business process modeling, the goal was to found a new business process modeling method for electrical accessories manufacturing enterprise. The focus was to find few options for business process modeling methods where company could have chosen the best one for its needs The study was carried out as a qualitative research with an action study and a case study as the most important ways collect data. In the empirical part of the study examples of company’s processes modeled with the new modeling method and process modeling process are presented. The new way of modeling processes improves especially visual presentation of the processes and improves the understanding how employees should work in the organizational interfaces of the process and in the interfaces between different processes. The results of the study is a new unified way to model company’s processes, which makes it easier to understand and create the process models. This improved readability makes it possible to reduce the costs that were created from the unclear old process models.

Individual learner, peer group and teacher roles in fostering autonomous language-learning behaviour

Julkaisumaa: Bulgaria

Enhancing the Usability of Drug Materials by Electrostatic Atomisation

Electrospraying or electrostatic atomisation is a process of liquid disruption by electrostatic forces. When liquid is brought into an electric field, charge is induced to its surface. Once the repulsive electrostatic force exceeds the liquid surface tension, the liquid disrupts into small highly charged droplets. The size of the electrosprayed droplets can range from hundreds of micrometers down to a few tens of nanometers. Electrospraying can be used not only to produce droplets, but also solid particles. The research presented in this thesis concentrates on producing drug particles by this method. In the experiments, a drug powder was dissolved in a convenient solvent and the solution was atomised. The solvent was then evaporated from the formed droplets in a drying medium and inside each droplet, a dense cluster of the dissolved drug remained. From the pharmaceutical point of view, the most important characteristics of the produced particles are size distribution, porosity, crystal form and degree of crystallinity. These properties affect the dissolution behaviour and ultimately the drug bioavailability in the body. The effects of electrostatic atomization on the aforementioned characteristics are generally not well understood. The research focused on studying these particle properties and finding possible correlations with the spraying parameters. The produced droplets were dried either under atmospheric or reduced pressure, the latter in order to improve the drying process. Special emphasis was put on implementing the spraying under reduced pressure, and the effects of the drying pressure on particle properties. Based on the results, the possibilities to enhance the dissolution of poorly soluble drugs by this method were estimated. In the course of experiments, it was also discovered that electrospraying may have a profound effect on the polymorphic form of the produced drug particles. In the light of the obtained results, it was concluded that electrospraying may offer a valuable tool to overcome some of the challenges met in modern drug development and formulation.

Pulsed electric field assisted sol-gel preparation of TiO2 particles and their photocatalytic properties

The objective of this thesis was to study the effect of pulsed electric field on the preparation of TiO2 nanoparticles via sol-gel method. The literature part deals with properties of different TiO2 crystal forms, principles of photocatalysis, sol-gel method and pulsed electric field processing. It was expected that the pulsed electric field would have an influence on crystallite size, specific surface area, polymorphism and photocatalytic activity of produced particles. TiO2 samples were prepared by using different frequencies and treatment times of pulsed electric field. The properties of produced TiO2 particles were examined X-ray diffraction (XRD), Raman spectroscopy and BET surface area analysis. The photocatalytic activities of produced TiO2 particles were determined by using them as photocatalysts for the degradation of formic acid under UVA-light. The photocatalytic activities of samples produced with sol-gel method were also compared with the commercial TiO2 powder Aeroxide® (Evonic Degussa GmbH). Pulsed electric field did not have an effect on the morphology of particles. Results from XRD and Raman analysis showed that all produced TiO2 samples were pure anatase. However, pulsed electric field did have an effect on crystallite size, specific surface area and photocatalytic activity of TiO2 particles. Generally, the crystallite sizes were smaller, specific surface areas larger and initial formic acid degradation rates higher for samples that were produced by applying the pulsed electric field. The higher photocatalytic activities were attributed to larger surface areas and smaller crystallite sizes. Though, with all of the TiO2 samples produced by the sol-gel method the initial formic acid degradation rates were significantly slower than with the commercial TiO2 powder.

Defining the keyhole modes – the effects on the weld geometry and the molten pool behaviour in high power laser welding of stainless steels

Keyhole welding, meaning that the laser beam forms a vapour cavity inside the steel, is one of the two types of laser welding processes and currently it is used in few industrial applications. Modern high power solid state lasers are becoming more used generally, but not all process fundamentals and phenomena of the process are well known and understanding of these helps to improve quality of final products. This study concentrates on the process fundamentals and the behaviour of the keyhole welding process by the means of real time high speed x-ray videography. One of the problem areas in laser welding has been mixing of the filler wire into the weld; the phenomena are explained and also one possible solution for this problem is presented in this study. The argument of this thesis is that the keyhole laser welding process has three keyhole modes that behave differently. These modes are trap, cylinder and kaleidoscope. Two of these have sub-modes, in which the keyhole behaves similarly but the molten pool changes behaviour and geometry of the resulting weld is different. X-ray videography was used to visualize the actual keyhole side view profile during the welding process. Several methods were applied to analyse and compile high speed x-ray video data to achieve a clearer image of the keyhole side view. Averaging was used to measure the keyhole side view outline, which was used to reconstruct a 3D-model of the actual keyhole. This 3D-model was taken as basis for calculation of the vapour volume inside of the keyhole for each laser parameter combination and joint geometry. Four different joint geometries were tested, partial penetration bead on plate and I-butt joint and full penetration bead on plate and I-butt joint. The comparison was performed with selected pairs and also compared all combinations together.

Influence of Pulsed-Electric Field Irradiation in Crystallization

The growing pharmaceutical interest, among others, in the polymorphic composition of the emerging solid end-products from production processes has been traced to the need for attainment of high product purity. This is more so as the presence of different polymorphs may constitute physical impurity of the product. Hence, the need for optimization of the yield of desired product component(s) through controlled crystallization kinetics for instance. This study was carried out to investigate the impact of pulsed electric field (PEF) irradiation on the crystal morphology of glycine obtained by cooling crystallization (without seeding) from commercial glycine sample in distilled deionized water solution. In doing so, three different pulse frequencies (294, 950 and 145 Hz) and a case without PEF were studied at three cooling rates (5, 10 and 20 ºC/h). The crystal products obtained were analyzed for polymorphic composition by powder x-ray diffraction (PXRD) and Fourier transform infrared (FTIR) spectroscopy while the particles characterization was done on Morphologi G3. The results obtained from this study showed that pulsed electric field irradiation had significant impact on metastability of the aqueous solution as well as on the polymorphic composition of the end product. With increasing PEF frequency applied, nucleation started earlier and the γ-glycine polymorph content of the product crystals increased. These were found to have been aided by cooling rate, as the most significant effect was observed at 5 ºC/h. It was also discovered that PEF application had no measurable impact on the pH of the aqueous solution as well as the size distribution of the particles. Cooling on the contrary was believed to be responsible for the broadening of the particle size distribution with a downward shift of the lower limit of the raw material from about 100 μm to between 10 and 50 μm.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.