Non-Linear Unsteady Aerodynamic Response Approximation Using Multi-Layer Functionals

Non-linear functional representation of the aerodynamic response provides a convenient mathematical model for motion-induced unsteady transonic aerodynamic loads response, that accounts for both complex non-linearities and time-history effects. A recent development, based on functional approximation theory, has established a novel functional form; namely, the multi-layer functional. For a large class of non-linear dynamic systems, such multi-layer functional representations can be realised via finite impulse response (FIR) neural networks. Identification of an appropriate FIR neural network model is facilitated by means of a supervised training process in which a limited sample of system input-output data sets is presented to the temporal neural network. The present work describes a procedure for the systematic identification of parameterised neural network models of motion-induced unsteady transonic aerodynamic loads response. The training process is based on a conventional genetic algorithm to optimise the network architecture, combined with a simplified random search algorithm to update weight and bias values. Application of the scheme to representative transonic aerodynamic loads response data for a bidimensional airfoil executing finite-amplitude motion in transonic flow is used to demonstrate the feasibility of the approach. The approach is shown to furnish a satisfactory generalisation property to different motion histories over a range of Mach numbers in the transonic regime.

Building organizational creativity – a multitheory and multilevel approach for understanding and stimulating organizational creativity

Organizational creativity is increasingly important for organizations aiming to survive and thrive in complex and unexpectedly changing environments. It is precondition of innovation and a driver of an organization’s performance success. Whereas innovation research increasingly promotes high-involvement and participatory innovation, the models of organizational creativity are still mainly based on an individual-creativity view. Likewise, the definitions of organizational creativity and innovation are somewhat equal, and they are used as interchangeable constructs, while on the other hand they are seen as different constructs. Creativity is seen as generation of novel and useful ideas, whereas innovation is seen as the implementation of these ideas. The research streams of innovation and organizational creativity seem to be advancing somewhat separately, although together they could provide many synergy advantages. Thereby, this study addresses three main research gaps. First, as the knowledge and knowing is being increasingly expertized and distributed in organizations, the conceptualization of organizational creativity needs to face that perspective, rather than relying on the individual-creativity view. Thus, the conceptualization of organizational creativity needs clarification, especially as an organizational-level phenomenon (i.e., creativity by an organization). Second, approaches to consciously build organizational creativity to increase the capacity of an organization to demonstrate novelty in its knowledgeable actions are rare. The current creativity techniques are mainly based on individual-creativity views, and they mainly focus on the occasional problem-solving cases among a limited number of individuals, whereas, the development of collective creativity and creativity by the organization lacks approaches. Third, in terms of organizational creativity as a collective phenomenon, the engagement, contributions, and participation of organizational members into activities of common meaning creation are more important than the individualcreativity skills. Therefore, the development approaches to foster creativity as social, emerging, embodied, and collective creativity are needed to complement the current creativity techniques. To address these gaps, the study takes a multiparadigm perspective to face the following three objectives. The first objective of this study is to clarify and extend the conceptualization of organizational creativity. The second is to study the development of organizational creativity. The third is to explore how an improvisational theater based approach fosters organizational creativity. The study consists of two parts comprising the introductory part (part I) and six publications (part II). Each publication addresses the research questions of the thesis through detailed subquestions. The study makes three main contributions to the research of organizational creativity. First, it contributes toward the conceptualization of organizational creativity by extending the current view of organizational creativity. This study views organizational creativity as a multilevel construct constituting both of individual and collective (group and organizational) creativity. In contrast to current views of organizational creativity, this study bases on organizational (collective) knowledge that is based on and demonstrated through the knowledgeable actions of an organization as a whole. The study defines organizational creativity as an overall ability of an organization to demonstrate novelty in its knowledgeable actions (through what it does and how it does what it does).Second, this study contributes toward the development of organizational creativity as multi-level phenomena, introducing developmental approaches that face two or more of these levels simultaneously. More specifically, the study presents the cross-level approaches to building organizational creativity, by using an approach based in improvisational theater and considering assessment of organizational renewal capability. Third, the study contributes on development of organizational creativity using an improvisational theater based approach as twofold meaning. First, it fosters individual and collective creativity simultaneously and builds space for creativity to occur. Second, it models collective and distributed creativity processes, thereby, contributing to the conceptualization of organizational creativity.

Linguistic models for decision support

Linguistic modelling is a rather new branch of mathematics that is still undergoing rapid development. It is closely related to fuzzy set theory and fuzzy logic, but knowledge and experience from other fields of mathematics, as well as other fields of science including linguistics and behavioral sciences, is also necessary to build appropriate mathematical models. This topic has received considerable attention as it provides tools for mathematical representation of the most common means of human communication - natural language. Adding a natural language level to mathematical models can provide an interface between the mathematical representation of the modelled system and the user of the model - one that is sufficiently easy to use and understand, but yet conveys all the information necessary to avoid misinterpretations. It is, however, not a trivial task and the link between the linguistic and computational level of such models has to be established and maintained properly during the whole modelling process. In this thesis, we focus on the relationship between the linguistic and the mathematical level of decision support models. We discuss several important issues concerning the mathematical representation of meaning of linguistic expressions, their transformation into the language of mathematics and the retranslation of mathematical outputs back into natural language. In the first part of the thesis, our view of the linguistic modelling for decision support is presented and the main guidelines for building linguistic models for real-life decision support that are the basis of our modeling methodology are outlined. From the theoretical point of view, the issues of representation of meaning of linguistic terms, computations with these representations and the retranslation process back into the linguistic level (linguistic approximation) are studied in this part of the thesis. We focus on the reasonability of operations with the meanings of linguistic terms, the correspondence of the linguistic and mathematical level of the models and on proper presentation of appropriate outputs. We also discuss several issues concerning the ethical aspects of decision support - particularly the loss of meaning due to the transformation of mathematical outputs into natural language and the issue or responsibility for the final decisions. In the second part several case studies of real-life problems are presented. These provide background and necessary context and motivation for the mathematical results and models presented in this part. A linguistic decision support model for disaster management is presented here – formulated as a fuzzy linear programming problem and a heuristic solution to it is proposed. Uncertainty of outputs, expert knowledge concerning disaster response practice and the necessity of obtaining outputs that are easy to interpret (and available in very short time) are reflected in the design of the model. Saaty’s analytic hierarchy process (AHP) is considered in two case studies - first in the context of the evaluation of works of art, where a weak consistency condition is introduced and an adaptation of AHP for large matrices of preference intensities is presented. The second AHP case-study deals with the fuzzified version of AHP and its use for evaluation purposes – particularly the integration of peer-review into the evaluation of R&D outputs is considered. In the context of HR management, we present a fuzzy rule based evaluation model (academic faculty evaluation is considered) constructed to provide outputs that do not require linguistic approximation and are easily transformed into graphical information. This is achieved by designing a specific form of fuzzy inference. Finally the last case study is from the area of humanities - psychological diagnostics is considered and a linguistic fuzzy model for the interpretation of outputs of multidimensional questionnaires is suggested. The issue of the quality of data in mathematical classification models is also studied here. A modification of the receiver operating characteristics (ROC) method is presented to reflect variable quality of data instances in the validation set during classifier performance assessment. Twelve publications on which the author participated are appended as a third part of this thesis. These summarize the mathematical results and provide a closer insight into the issues of the practicalapplications that are considered in the second part of the thesis.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Limited-sampling strategy models for estimating the pharmacokinetic parameters of 4-methylaminoantipyrine, an active metabolite of dipyrone

Bioanalytical data from a bioequivalence study were used to develop limited-sampling strategy (LSS) models for estimating the area under the plasma concentration versus time curve (AUC) and the peak plasma concentration (Cmax) of 4-methylaminoantipyrine (MAA), an active metabolite of dipyrone. Twelve healthy adult male volunteers received single 600 mg oral doses of dipyrone in two formulations at a 7-day interval in a randomized, crossover protocol. Plasma concentrations of MAA (N = 336), measured by HPLC, were used to develop LSS models. Linear regression analysis and a "jack-knife" validation procedure revealed that the AUC0-¥ and the Cmax of MAA can be accurately predicted (R²>0.95, bias <1.5%, precision between 3.1 and 8.3%) by LSS models based on two sampling times. Validation tests indicate that the most informative 2-point LSS models developed for one formulation provide good estimates (R²>0.85) of the AUC0-¥ or Cmax for the other formulation. LSS models based on three sampling points (1.5, 4 and 24 h), but using different coefficients for AUC0-¥ and Cmax, predicted the individual values of both parameters for the enrolled volunteers (R²>0.88, bias = -0.65 and -0.37%, precision = 4.3 and 7.4%) as well as for plasma concentration data sets generated by simulation (R²>0.88, bias = -1.9 and 8.5%, precision = 5.2 and 8.7%). Bioequivalence assessment of the dipyrone formulations based on the 90% confidence interval of log-transformed AUC0-¥ and Cmax provided similar results when either the best-estimated or the LSS-derived metrics were used.

Comparison of Different Concentrated Solar Power Collector Designs and Development of a Linear Fresnel Solar Collector Model

Concentrated solar power (CSP) is a renewable energy technology, which could contribute to overcoming global problems related to pollution emissions and increasing energy demand. CSP utilizes solar irradiation, which is a variable source of energy. In order to utilize CSP technology in energy production and reliably operate a solar field including thermal energy storage system, dynamic simulation tools are needed in order to study the dynamics of the solar field, to optimize production and develop control systems. The object of this Master’s Thesis is to compare different concentrated solar power technologies and configure a dynamic solar field model of one selected CSP field design in the dynamic simulation program Apros, owned by VTT and Fortum. The configured model is based on German Novatec Solar’s linear Fresnel reflector design. Solar collector components including dimensions and performance calculation were developed, as well as a simple solar field control system. The preliminary simulation results of two simulation cases under clear sky conditions were good; the desired and stable superheated steam conditions were maintained in both cases, while, as expected, the amount of steam produced was reduced in the case having lower irradiation conditions. As a result of the model development process, it can be concluded, that the configured model is working successfully and that Apros is a very capable and flexible tool for configuring new solar field models and control systems and simulating solar field dynamic behaviour.

Parameter identification in time series models

Time series analysis can be categorized into three different approaches: classical, Box-Jenkins, and State space. Classical approach makes a basement for the analysis and Box-Jenkins approach is an improvement of the classical approach and deals with stationary time series. State space approach allows time variant factors and covers up a broader area of time series analysis. This thesis focuses on parameter identifiablity of different parameter estimation methods such as LSQ, Yule-Walker, MLE which are used in the above time series analysis approaches. Also the Kalman filter method and smoothing techniques are integrated with the state space approach and MLE method to estimate parameters allowing them to change over time. Parameter estimation is carried out by repeating estimation and integrating with MCMC and inspect how well different estimation methods can identify the optimal model parameters. Identification is performed in probabilistic and general senses and compare the results in order to study and represent identifiability more informative way.

Differential effects of aging on spatial contrast sensitivity to linear and polar sine-wave gratings

Changes in visual function beyond high-contrast acuity are known to take place during normal aging. We determined whether sensitivity to linear sine-wave gratings and to an elementary stimulus preferentially processed in extrastriate areas could be distinctively affected by aging. We measured spatial contrast sensitivity twice for concentric polar (Bessel) and vertical linear gratings of 0.6, 2.5, 5, and 20 cycles per degree (cpd) in two age groups (20-30 and 60-70 years). All participants were free of identifiable ocular disease and had normal or corrected-to-normal visual acuity. Participants were more sensitive to Cartesian than to polar gratings in all frequencies tested, and the younger adult group was more sensitive to all stimuli tested. Significant differences between sensitivities of the two groups were found for linear (only 20 cpd; P<0.01) and polar gratings (all frequencies tested; P<0.01). The young adult group was significantly more sensitive to linear than to circular gratings in the 20 cpd frequency. The older adult group was significantly more sensitive to linear than to circular gratings in all spatial frequencies, except in the 20 cpd frequency. The results suggest that sensitivity to the two kinds of stimuli is affected differently by aging. We suggest that neural changes in the aging brain are important determinants of this difference and discuss the results according to current models of human aging.

Calculation of viscoelastic properties of edible films: application of three models

The viscoelastic properties of edible films can provide information at the structural level of the biopolymers used. The objective of this work was to test three simple models of linear viscoelastic theory (Maxwell, Generalized Maxwell with two units in parallel, and Burgers) using the results of stress relaxation tests in edible films of myofibrillar proteins of Nile Tilapia. The films were elaborated according to a casting technique and pre-conditioned at 58% relative humidity and 22ºC for 4 days. The testing sample (15mm x 118mm) was submitted to tests of stress relaxation in an equipment of physical measurements, TA.XT2i. The deformation, imposed to the sample, was 1%, guaranteeing the permanency in the domain of the linear viscoelasticity. The models were fitted to experimental data (stress x time) by nonlinear regression. The Generalized Maxwell model with two units in parallel and the Burgers model represented the relaxation curves of stress satisfactorily. The viscoelastic properties varied in a way that they were less dependent on the thickness of the films.

Electromagnetic and thermal design of a multilevel converter with high power density and reliability

Electric energy demand has been growing constantly as the global population increases. To avoid electric energy shortage, renewable energy sources and energy conservation are emphasized all over the world. The role of power electronics in energy saving and development of renewable energy systems is significant. Power electronics is applied in wind, solar, fuel cell, and micro turbine energy systems for the energy conversion and control. The use of power electronics introduces an energy saving potential in such applications as motors, lighting, home appliances, and consumer electronics. Despite the advantages of power converters, their penetration into the market requires that they have a set of characteristics such as high reliability and power density, cost effectiveness, and low weight, which are dictated by the emerging applications. In association with the increasing requirements, the design of the power converter is becoming more complicated, and thus, a multidisciplinary approach to the modelling of the converter is required. In this doctoral dissertation, methods and models are developed for the design of a multilevel power converter and the analysis of the related electromagnetic, thermal, and reliability issues. The focus is on the design of the main circuit. The electromagnetic model of the laminated busbar system and the IGBT modules is established with the aim of minimizing the stray inductance of the commutation loops that degrade the converter power capability. The circular busbar system is proposed to achieve equal current sharing among parallel-connected devices and implemented in the non-destructive test set-up. In addition to the electromagnetic model, a thermal model of the laminated busbar system is developed based on a lumped parameter thermal model. The temperature and temperature-dependent power losses of the busbars are estimated by the proposed algorithm. The Joule losses produced by non-sinusoidal currents flowing through the busbars in the converter are estimated taking into account the skin and proximity effects, which have a strong influence on the AC resistance of the busbars. The lifetime estimation algorithm was implemented to investigate the influence of the cooling solution on the reliability of the IGBT modules. As efficient cooling solutions have a low thermal inertia, they cause excessive temperature cycling of the IGBTs. Thus, a reliability analysis is required when selecting the cooling solutions for a particular application. The control of the cooling solution based on the use of a heat flux sensor is proposed to reduce the amplitude of the temperature cycles. The developed methods and models are verified experimentally by a laboratory prototype.

Pigment orientation changes detected by low temperature linear dichroism spectroscopy: cold-hardening transition in phycobilisome-containing organisms

Low temperature (77K) linear dichroism spectroscopy was used to characterize pigment orientation changes accompanying the light state transition in the cyanobacterium, Synechococcus sp. pee 6301, and cold-hardening in winter rye (Secale cereale L. cv. Puma). Samples were oriented for spectroscopy using the gel squeezing method (Abdourakhmanov et aI., 1979) and brought to 77K in liquid nitrogen. The linear dichroism (LD) spectra of Synechococcus 6301 phycobilisome/thylakoid membrane fragments cross-linked in light state 1 and light state 2 with glutaraldehyde showed differences in both chlorophyll a and phycobilin orientation. A decrease in the relative amplitude of the 681nm chlorophyll a positive LD peak was observed in membrane fragments in state 2. Reorientation of the phycobilisome (PBS) during the transition to state 2 resulted in an increase in core allophycocyanin absorption parallel to the membrane, and a decrease in rod phycocyanin parallel absorption. This result supports the "spillover" and "PBS detachment" models of the light state transition in PBS-containing organisms, but not the "mobile PBS" model. A model was proposed for PBS reorientation upon transition to state 2, consisting of a tilt in the antenna complex with respect to the membrane plane. Linear dichroism spectra of PBS/thylakoid fragments from the red alga, Porphyridium cruentum, grown in green light (containing relatively more PSI) and red light (containing relatively more PSll) were compared to identify chlorophyll a absorption bands associated with each photosystem. Spectra from red light - grown samples had a larger positive LD signal on the short wavelength side of the 686nm chlorophyll a peak than those from green light - grown fragments. These results support the identification of the difference in linear dichroism seen at 681nm in Synechococcus spectra as a reorientation of PSll chromophores. Linear dichroism spectra were taken of thylakoid membranes isolated from winter rye grown at 20°C (non-hardened) and 5°C (cold-hardened). Differences were seen in the orientation of chlorophyll b relative to chlorophyll a. An increase in parallel absorption was identified at the long-wavelength chlorophyll a absorption peak, along with a decrease in parallel absorption from chlorophyll b chromophores. The same changes in relative pigment orientation were seen in the LD of isolated hardened and non-hardened light-harvesting antenna complexes (LHCII). It was concluded that orientational differences in LHCII pigments were responsible for thylakoid LD differences. Changes in pigment orientation, along with differences observed in long-wavelength absorption and in the overall magnitude of LD in hardened and non-hardened complexes, could be explained by the higher LHCII monomer:oligomer ratio in hardened rye (Huner et ai., 1987) if differences in this ratio affect differential light scattering properties, or fluctuation of chromophore orientation in the isolated LHCII sample.

Computation and Analysis of Multiple Structural-Change Models

In a recent paper, Bai and Perron (1998) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least-squares operations of order O(T 2) for any number of breaks. Our method can be applied to both pure and partial structural-change models. Secondly, we consider the problem of forming confidence intervals for the break dates under various hypotheses about the structure of the data and the errors across segments. Third, we address the issue of testing for structural changes under very general conditions on the data and the errors. Fourth, we address the issue of estimating the number of breaks. We present simulation results pertaining to the behavior of the estimators and tests in finite samples. Finally, a few empirical applications are presented to illustrate the usefulness of the procedures. All methods discussed are implemented in a GAUSS program available upon request for non-profit academic use.

Simulation-Based Finite-Sample Normality Tests in Linear Regressions

In the literature on tests of normality, much concern has been expressed over the problems associated with residual-based procedures. Indeed, the specialized tables of critical points which are needed to perform the tests have been derived for the location-scale model; hence reliance on available significance points in the context of regression models may cause size distortions. We propose a general solution to the problem of controlling the size normality tests for the disturbances of standard linear regression, which is based on using the technique of Monte Carlo tests.

Aggregations and Marginalization of GARCH and Stochastic Volatility Models

The GARCH and Stochastic Volatility paradigms are often brought into conflict as two competitive views of the appropriate conditional variance concept : conditional variance given past values of the same series or conditional variance given a larger past information (including possibly unobservable state variables). The main thesis of this paper is that, since in general the econometrician has no idea about something like a structural level of disaggregation, a well-written volatility model should be specified in such a way that one is always allowed to reduce the information set without invalidating the model. To this respect, the debate between observable past information (in the GARCH spirit) versus unobservable conditioning information (in the state-space spirit) is irrelevant. In this paper, we stress a square-root autoregressive stochastic volatility (SR-SARV) model which remains true to the GARCH paradigm of ARMA dynamics for squared innovations but weakens the GARCH structure in order to obtain required robustness properties with respect to various kinds of aggregation. It is shown that the lack of robustness of the usual GARCH setting is due to two very restrictive assumptions : perfect linear correlation between squared innovations and conditional variance on the one hand and linear relationship between the conditional variance of the future conditional variance and the squared conditional variance on the other hand. By relaxing these assumptions, thanks to a state-space setting, we obtain aggregation results without renouncing to the conditional variance concept (and related leverage effects), as it is the case for the recently suggested weak GARCH model which gets aggregation results by replacing conditional expectations by linear projections on symmetric past innovations. Moreover, unlike the weak GARCH literature, we are able to define multivariate models, including higher order dynamics and risk premiums (in the spirit of GARCH (p,p) and GARCH in mean) and to derive conditional moment restrictions well suited for statistical inference. Finally, we are able to characterize the exact relationships between our SR-SARV models (including higher order dynamics, leverage effect and in-mean effect), usual GARCH models and continuous time stochastic volatility models, so that previous results about aggregation of weak GARCH and continuous time GARCH modeling can be recovered in our framework.

Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-fit in Multivariate Regressions with Application to Asset Pricing Models

We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross-equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared to simulation-based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal, Student t; normal mixtures and stable error models. In the Gaussian case, finite-sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi-stage Monte Carlo test methods. For non-Gaussian distribution families involving nuisance parameters, confidence sets are derived for the the nuisance parameters and the error distribution. The procedures considered are evaluated in a small simulation experi-ment. Finally, the tests are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995.