Moottorimalliton skalaarisäätö

Työssä tutkitaan nopeusanturittoman vaihtovirtakäytön skalaarisia ohjaus- ja säätömenetelmiä. Työn alussa esitetään perusteoriat taajuusmuuttajista ja oikosulkumoottoreista. Tämän jälkeen esitellään yleisimmin kirjallisuudessa esiintyneet skalaariohjaukset ja –säädöt. Vektorisäätöä ja erityisesti moottoriparametrien vaikutusta säädön toimivuuteen esitellään lyhyesti. Työn tavoitteena on ACS800 taajuusmuuttajan skalaarisäädön tutkiminen. ACS800:n nykyinen skalaarisäätö on liian sidoksissa vektorisäätöön, joten simulointien ja kirjallisuustutkimuksen tarkoituksena on täysin vektorisäädöstä eriytetyn skalaarisäädön kehitysmahdollisuuksien tutkiminen. Kirjallisuudessa esiintyneiden säätöjen avulla muodostetaan diskreettiaikainen toteutus skalaarisäädölle vaihtovirtakäytössä, jossa on käytössä virran ja välipiirijännitteen takaisinkytkentä. Säädettävää moottoria mallinnetaan jatkuvaaikaisella L-sijaiskytkennällä. Välipiirin mallinnus toimii myös jatkuva-aikaisena lukuun ottamatta välipiirin tasavirtakomponenttia, joka muodostetaan virran takaisinkytkennän ja PWM-modulaattorin kytkinasentojen avulla. Simuloinnin tarkoituksena on mallintaa skalaarisäädön suurimpia ongelmia, kuten virta- ja välijännitesäätöä. Tuloksista voidaan päätellä, että perussäädöt toimivat moitteettomasti, mutta erityisesti virtasäätöä tulisi kehittää.

Spectral Analysis of Buffers in Communication Systems

Muokatun matriisi-geometrian tekniikan kehitys yleimmäksi jonoksi on esitelty tässä työssä. Jonotus systeemi koostuu useista jonoista joilla on rajatut kapasiteetit. Tässä työssä on myös tutkittu PH-tyypin jakautumista kun ne jaetaan. Rakenne joka vastaa lopullista Markovin ketjua jossa on itsenäisiä matriiseja joilla on QBD rakenne. Myös eräitä rajallisia olotiloja on käsitelty tässä työssä. Sen esitteleminen matriisi-geometrisessä muodossa, muokkaamalla matriisi-geometristä ratkaisua on tämän opinnäytetyön tulos.

Tavaratalokaupan strategiset menestystekijät

Tutkimuksen tavoitteena oli analysoida tavaratalokaupan strategisia menestystekijöitä, erityisesti tavarataloliiketoiminnan johtamisen merkitystä strategisena menestystekijänä. Empiirinen sovellus suunnattiin Sokos-tavarataloketjuun. Tutkimuksen osatavoitteena oli kuvata ja analysoida Sokos-tavarataloketjun liiketoiminnan kehittymistä 1970 - 1990 välisellä ajanjaksolla sekä pohtia syitä, miksi Sokos-liiketoiminta ajautui kriisiin 1990-luvun aikana. Vertailuksi otettiin Stockmann-tavarataloketjun menestyminen vastaavalla ajanjaksolla Tarkastelun kohteena oli johtamisen muuttuminen, liikeideamuutokset, ketjutoiminnan sekä hankinnan roolin muutokset Sokos-ketjussa. Lopuksi tavoitteena oli arvioida strategisen johtamisen onnistumista Sokos-ketjussa peilaten strategisten menesty stekij öiden viitekehykseen. Tutkimus on luonteeltaan toiminta-analyyttinenja sen aineistonkeruumenetelmänä käytettiin puolistrukturoitua haastattelua. Haastatteluja suoritettiin yhteensä kahdeksan.Empiirinen osa koostuu S-ryhmääja erityisesti Sokos-tavaratalokauppaa käsittelevästä materiaalista, kilpailustrategioiden kuvauksista, vuosikertomuksista ja kokousmuistioista sekä kahdeksan Sokos-ketjussa 90-luvulla johtavassa asemassa toimineen henkilön haastatteluista. Empiiristä aineistoa on kerätty myös yleisistä vähittäiskauppaa koskevista alan lehdistä ja artikkeleista sekä Stockmann- tavarataloketjun vuosikertomuksista. Tutkimuksessa todettiin kohdeyrityksen vaikeuksiin ajautumisen taustalta löytyvän voimakkaan talouslaman lisäksi kilpailutilanteen voimakas muuttuminen, johon ei kyetty riittävästi vastaamaan. Suunnanmuutoksia kilpailustrategiaan tehtiin useaan otteeseen, mutta kaikissa vaiheissa käytännön toteutus jäi puolinaiseksi. Ylivoimaisten kilpailuetujen rakentaminen onnistui heikosti.

Statistical Analysis of an SEIR Epidemic Model

This thesis was focussed on statistical analysis methods and proposes the use of Bayesian inference to extract information contained in experimental data by estimating Ebola model parameters. The model is a system of differential equations expressing the behavior and dynamics of Ebola. Two sets of data (onset and death data) were both used to estimate parameters, which has not been done by previous researchers in (Chowell, 2004). To be able to use both data, a new version of the model has been built. Model parameters have been estimated and then used to calculate the basic reproduction number and to study the disease-free equilibrium. Estimates of the parameters were useful to determine how well the model fits the data and how good estimates were, in terms of the information they provided about the possible relationship between variables. The solution showed that Ebola model fits the observed onset data at 98.95% and the observed death data at 93.6%. Since Bayesian inference can not be performed analytically, the Markov chain Monte Carlo approach has been used to generate samples from the posterior distribution over parameters. Samples have been used to check the accuracy of the model and other characteristics of the target posteriors.

Demographic Modelling of Human Population Growth

This work presents models and methods that have been used in producing forecasts of population growth. The work is intended to emphasize the reliability bounds of the model forecasts. Leslie model and various versions of logistic population models are presented. References to literature and several studies are given. A lot of relevant methodology has been developed in biological sciences. The Leslie modelling approach involves the use of current trends in mortality,fertility, migration and emigration. The model treats population divided in age groups and the model is given as a recursive system. Other group of models is based on straightforward extrapolation of census data. Trajectories of simple exponential growth function and logistic models are used to produce the forecast. The work presents the basics of Leslie type modelling and the logistic models, including multi- parameter logistic functions. The latter model is also analysed from model reliability point of view. Bayesian approach and MCMC method are used to create error bounds of the model predictions.

Kinetic modeling of mechanisms of industrially important organic reactions in gas and liquid phase

This dissertation is based on 5 articles which deal with reaction mechanisms of the following selected industrially important organic reactions: 1. dehydrocyclization of n-butylbenzene to produce naphthalene 2. dehydrocyclization of 1-(p-tolyl)-2-methylbutane (MB) to produce 2,6-dimethylnaphthalene 3. esterification of neopentyl glycol (NPG) with different carboxylic acids to produce monoesters 4. skeletal isomerization of 1-pentene to produce 2-methyl-1-butene and 2-methyl-2-butene The results of initial- and integral-rate experiments of n-butylbenzene dehydrocyclization over selfmade chromia/alumina catalyst were applied when investigating reaction 2. Reaction 2 was performed using commercial chromia/alumina of different acidity, platina on silica and vanadium/calcium/alumina as catalysts. On all catalysts used for the dehydrocyclization, major reactions were fragmentation of MB and 1-(p-tolyl)-2-methylbutenes (MBes), dehydrogenation of MB, double bond transfer, hydrogenation and 1,6-cyclization of MBes. Minor reactions were 1,5-cyclization of MBes and methyl group fragmentation of 1,6- cyclization products. Esterification reactions of NPG were performed using three different carboxylic acids: propionic, isobutyric and 2-ethylhexanoic acid. Commercial heterogeneous gellular (Dowex 50WX2), macroreticular (Amberlyst 15) type resins and homogeneous para-toluene sulfonic acid were used as catalysts. At first NPG reacted with carboxylic acids to form corresponding monoester and water. Then monoester esterified with carboxylic acid to form corresponding diester. In disproportionation reaction two monoester molecules formed NPG and corresponding diester. All these three reactions can attain equilibrium. Concerning esterification, water was removed from the reactor in order to prevent backward reaction. Skeletal isomerization experiments of 1-pentene were performed over HZSM-22 catalyst. Isomerization reactions of three different kind were detected: double bond, cis-trans and skeletal isomerization. Minor side reaction were dimerization and fragmentation. Monomolecular and bimolecular reaction mechanisms for skeletal isomerization explained experimental results almost equally well. Pseudohomogeneous kinetic parameters of reactions 1 and 2 were estimated by usual least squares fitting. Concerning reactions 3 and 4 kinetic parameters were estimated by the leastsquares method, but also the possible cross-correlation and identifiability of parameters were determined using Markov chain Monte Carlo (MCMC) method. Finally using MCMC method, the estimation of model parameters and predictions were performed according to the Bayesian paradigm. According to the fitting results suggested reaction mechanisms explained experimental results rather well. When the possible cross-correlation and identifiability of parameters (Reactions 3 and 4) were determined using MCMC method, the parameters identified well, and no pathological cross-correlation could be seen between any parameter pair.

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.

Stochastic variability of output levels in a pulp mill

Quite often, in the construction of a pulp mill involves establishing the size of tanks which will accommodate the material from the various processes in which case estimating the right tank size a priori would be vital. Hence, simulation of the whole production process would be worthwhile. Therefore, there is need to develop mathematical models that would mimic the behavior of the output from the various production units of the pulp mill to work as simulators. Markov chain models, Autoregressive moving average (ARMA) model, Mean reversion models with ensemble interaction together with Markov regime switching models are proposed for that purpose.

Novel Methods for Error Modeling and Parameter Identification of a Redundant Serial-Parallel Hybrid Robot

To obtain the desirable accuracy of a robot, there are two techniques available. The first option would be to make the robot match the nominal mathematic model. In other words, the manufacturing and assembling tolerances of every part would be extremely tight so that all of the various parameters would match the “design” or “nominal” values as closely as possible. This method can satisfy most of the accuracy requirements， but the cost would increase dramatically as the accuracy requirement increases. Alternatively, a more cost-effective solution is to build a manipulator with relaxed manufacturing and assembling tolerances. By modifying the mathematical model in the controller, the actual errors of the robot can be compensated. This is the essence of robot calibration. Simply put, robot calibration is the process of defining an appropriate error model and then identifying the various parameter errors that make the error model match the robot as closely as possible. This work focuses on kinematic calibration of a 10 degree-of-freedom (DOF) redundant serial-parallel hybrid robot. The robot consists of a 4-DOF serial mechanism and a 6-DOF hexapod parallel manipulator. The redundant 4-DOF serial structure is used to enlarge workspace and the 6-DOF hexapod manipulator is used to provide high load capabilities and stiffness for the whole structure. The main objective of the study is to develop a suitable calibration method to improve the accuracy of the redundant serial-parallel hybrid robot. To this end, a Denavit–Hartenberg (DH) hybrid error model and a Product-of-Exponential (POE) error model are developed for error modeling of the proposed robot. Furthermore, two kinds of global optimization methods, i.e. the differential-evolution (DE) algorithm and the Markov Chain Monte Carlo (MCMC) algorithm, are employed to identify the parameter errors of the derived error model. A measurement method based on a 3-2-1 wire-based pose estimation system is proposed and implemented in a Solidworks environment to simulate the real experimental validations. Numerical simulations and Solidworks prototype-model validations are carried out on the hybrid robot to verify the effectiveness, accuracy and robustness of the calibration algorithms.

Mathematical modeling and optimal control of malaria

Malaria continues to infect millions and kill hundreds of thousands of people worldwide each year, despite over a century of research and attempts to control and eliminate this infectious disease. Challenges such as the development and spread of drug resistant malaria parasites, insecticide resistance to mosquitoes, climate change, the presence of individuals with subpatent malaria infections which normally are asymptomatic and behavioral plasticity in the mosquito hinder the prospects of malaria control and elimination. In this thesis, mathematical models of malaria transmission and control that address the role of drug resistance, immunity, iron supplementation and anemia, immigration and visitation, and the presence of asymptomatic carriers in malaria transmission are developed. A within-host mathematical model of severe Plasmodium falciparum malaria is also developed. First, a deterministic mathematical model for transmission of antimalarial drug resistance parasites with superinfection is developed and analyzed. The possibility of increase in the risk of superinfection due to iron supplementation and fortification in malaria endemic areas is discussed. The model results calls upon stakeholders to weigh the pros and cons of iron supplementation to individuals living in malaria endemic regions. Second, a deterministic model of transmission of drug resistant malaria parasites, including the inflow of infective immigrants, is presented and analyzed. The optimal control theory is applied to this model to study the impact of various malaria and vector control strategies, such as screening of immigrants, treatment of drug-sensitive infections, treatment of drug-resistant infections, and the use of insecticide-treated bed nets and indoor spraying of mosquitoes. The results of the model emphasize the importance of using a combination of all four controls tools for effective malaria intervention. Next, a two-age-class mathematical model for malaria transmission with asymptomatic carriers is developed and analyzed. In development of this model, four possible control measures are analyzed: the use of long-lasting treated mosquito nets, indoor residual spraying, screening and treatment of symptomatic, and screening and treatment of asymptomatic individuals. The numerical results show that a disease-free equilibrium can be attained if all four control measures are used. A common pitfall for most epidemiological models is the absence of real data; model-based conclusions have to be drawn based on uncertain parameter values. In this thesis, an approach to study the robustness of optimal control solutions under such parameter uncertainty is presented. Numerical analysis of the optimal control problem in the presence of parameter uncertainty demonstrate the robustness of the optimal control approach that: when a comprehensive control strategy is used the main conclusions of the optimal control remain unchanged, even if inevitable variability remains in the control profiles. The results provide a promising framework for the design of cost-effective strategies for disease control with multiple interventions, even under considerable uncertainty of model parameters. Finally, a separate work modeling the within-host Plasmodium falciparum infection in humans is presented. The developed model allows re-infection of already-infected red blood cells. The model hypothesizes that in severe malaria due to parasite quest for survival and rapid multiplication, the Plasmodium falciparum can be absorbed in the already-infected red blood cells which accelerates the rupture rate and consequently cause anemia. Analysis of the model and parameter identifiability using Markov chain Monte Carlo methods is presented.

Inverting ionospheric D-region electron density pro-file from riometer measurement

The aim of this work is to invert the ionospheric electron density profile from Riometer (Relative Ionospheric opacity meter) measurement. The newly Riometer instrument KAIRA (Kilpisjärvi Atmospheric Imaging Receiver Array) is used to measure the cosmic HF radio noise absorption that taking place in the D-region ionosphere between 50 to 90 km. In order to invert the electron density profile synthetic data is used to feed the unknown parameter Neq using spline height method, which works by taking electron density profile at different altitude. Moreover, smoothing prior method also used to sample from the posterior distribution by truncating the prior covariance matrix. The smoothing profile approach makes the problem easier to find the posterior using MCMC (Markov Chain Monte Carlo) method.

Bayesian analysis of SEIR epidemic models

This thesis concerns the analysis of epidemic models. We adopt the Bayesian paradigm and develop suitable Markov Chain Monte Carlo (MCMC) algorithms. This is done by considering an Ebola outbreak in the Democratic Republic of Congo, former Zaïre, 1995 as a case of SEIR epidemic models. We model the Ebola epidemic deterministically using ODEs and stochastically through SDEs to take into account a possible bias in each compartment. Since the model has unknown parameters, we use different methods to estimate them such as least squares, maximum likelihood and MCMC. The motivation behind choosing MCMC over other existing methods in this thesis is that it has the ability to tackle complicated nonlinear problems with large number of parameters. First, in a deterministic Ebola model, we compute the likelihood function by sum of square of residuals method and estimate parameters using the LSQ and MCMC methods. We sample parameters and then use them to calculate the basic reproduction number and to study the disease-free equilibrium. From the sampled chain from the posterior, we test the convergence diagnostic and confirm the viability of the model. The results show that the Ebola model fits the observed onset data with high precision, and all the unknown model parameters are well identified. Second, we convert the ODE model into a SDE Ebola model. We compute the likelihood function using extended Kalman filter (EKF) and estimate parameters again. The motivation of using the SDE formulation here is to consider the impact of modelling errors. Moreover, the EKF approach allows us to formulate a filtered likelihood for the parameters of such a stochastic model. We use the MCMC procedure to attain the posterior distributions of the parameters of the SDE Ebola model drift and diffusion parts. In this thesis, we analyse two cases: (1) the model error covariance matrix of the dynamic noise is close to zero , i.e. only small stochasticity added into the model. The results are then similar to the ones got from deterministic Ebola model, even if methods of computing the likelihood function are different (2) the model error covariance matrix is different from zero, i.e. a considerable stochasticity is introduced into the Ebola model. This accounts for the situation where we would know that the model is not exact. As a results, we obtain parameter posteriors with larger variances. Consequently, the model predictions then show larger uncertainties, in accordance with the assumption of an incomplete model.

Aikaerotteisesti fluoresenssia mittaavan reaaliaikaisen PCR-laitteen kehitys

Kvantitatiivinen reaaliaikainen polymeraasiketjureaktio (engl. polymerase chain reaction, PCR) on osoittautunut käyttäjäystävällisimmäksi menetelmäksi nukleiinihapposekvenssien kvantitoimisessa. Tätä menetelmää voidaan herkistää pienempien DNA-pitoisuuksien havaitsemiseen käyttämällä hyväksi aikaerotteista fluorometriaa (engl. time-resolved fluorometry, TRF) ja luminoivia lantanidileimoja, joiden fluoresenssin pitkän eliniän ansiosta emission mittaus voidaan suorittaa vasta hetki virittävän valopulssin jälkeen, jolloin lyhytikäinen taustasäteily ehtii sammua. Tuloksena saadaan korkea signaali-taustasuhde. Tämän diplomityön tarkoituksena oli rakentaa TRF:än pystyvä reaaliaikainen PCR-laite, sillä tällaista laitetta ei ole markkinoilla tarjolla. Laite rakennettiin kehittämällä lämpökierrätin ja yhdistämällä se valmiiseen TRF:än kykenevään mittapäähän. Mittapään ja lämpökierrättimen hallitsemiseksi kehitettiin myös tietokoneohjelma. Valon tuottamiseksi ja mittaamiseksi haluttiin käyttää edullisia komponentteja, joten työssä käytettiin valmiin mittapään optiikkaa, jossa viritys tapahtuu hohtodiodilla (engl. light-emitting diode, LED) ja lantanidileiman emission mittaus fotodiodilla (engl. photodiode, PD) tai valomonistinputkella (engl. photomultiplier tube, PMT). Myös mittapään suorituskykyä tutkittiin. Työtä varten kehitettiin lämpökierrätin, joka koostui Peltier-elementillä lämmitettävästä PCR-putkitelineestä ja lämpökannesta. Mittalaitteen suorituskyvyn tutkimiseen käytettiin kelaattikomplementaatioon perustuvaa PCR-tuotteen havaitsemismenetelmää. Kelaattikomplementaatio perustuu kahteen erilliseen oligonukleotidimolekyyliin, joista toiseen on sidottu lantanidi-ioni ja toiseen valoa absorboiva ligandirakenne, jotka yhdessä muodostavat fluoresoivan kokonaisuuden. Kehitetyn lämpökierrättimen todettiin olevan tarpeeksi tarkka sekä tehokas ja sen lämmitys- ja jäähdytysnopeuden maksimeiksi saatiin 2,6 °C/sekunti. Detektorina käytetyn PD:n ei todettu olevan tarpeeksi herkkä emission havainnoimiseksi ja se korvattiin laitteessa PMT:llä. Käytetyllä PCR-määrityksellä kynnyssykleiksi (engl. threshold cycle, Ct) sekä kehitetylle että referenssilaitteelle saatiin 28,4 käyttämällä samaa 100 000 kopion DNA:n aloitusmäärää. Työssä osoitettiin, että on mahdollista kehittää edullisia komponentteja käyttävä, TRF:än pystyvä, reaaliaikainen PCR-laite, joka kykenee vastaavaan Ct-arvoon kuin vertailulaite. PD:n herkkyys ei kuitenkaan riittänyt. Tulokset olivat lupaavia, sillä LED- ja PD-teknologiat kehittyvät ja markkinoille on tullut myös muita komponentteja, joiden avulla on tulevaisuudessa mahdollista kehittää vielä herkempi laite.