Designs and model effects definitions in the initial stage of a plant breeding program

The objective of this work was to compare the relative efficiency of initial selection and genetic parameter estimation, using augmented blocks design (ABD), augmented blocks twice replicated design (DABD) and group of randomised block design experiments with common treatments (ERBCT), by simulations, considering fixed effect model and mixed model with regular treatment effects as random. For the simulations, eight different conditions (scenarios) were considered. From the 600 simulations in each scenario, the mean percentage selection coincidence, the Pearsons´s correlation estimates between adjusted means for the fixed effects model, and the heritability estimates for the mixed model were evaluated. DABD and ERBCT were very similar in their comparisons and slightly superior to ABD. Considering the initial stages of selection in a plant breeding program, ABD is a good alternative for selecting superior genotypes, although none of the designs had been effective to estimate heritability in all the different scenarios evaluated.

System identification using a linear combination of cumulant slices

In this paper we develop a new linear approach to identify the parameters of a moving average (MA) model from the statistics of the output. First, we show that, under some constraints, the impulse response of the system can be expressed as a linear combination of cumulant slices. Then, thisresult is used to obtain a new well-conditioned linear methodto estimate the MA parameters of a non-Gaussian process. Theproposed method presents several important differences withexisting linear approaches. The linear combination of slices usedto compute the MA parameters can be constructed from dif-ferent sets of cumulants of different orders, providing a generalframework where all the statistics can be combined. Further-more, it is not necessary to use second-order statistics (the autocorrelation slice), and therefore the proposed algorithm stillprovides consistent estimates in the presence of colored Gaussian noise. Another advantage of the method is that while mostlinear methods developed so far give totally erroneous estimates if the order is overestimated, the proposed approach doesnot require a previous estimation of the filter order. The simulation results confirm the good numerical conditioning of thealgorithm and the improvement in performance with respect to existing methods.

Kinetics of selective oxidation of lactose to lactobionic acid over Pd-active carbon catalyst

Laktoosi eli maitosokeri on tärkein ainesosa useimpien nisäkkäiden tuottamassa maidossa. Sitä erotetaan herasta, juustosta ja maidosta. Laktoosia käytetään elintarvike- ja lääketeollisuuden raaka-aineena monissaeri tuotteissa. Lääketeollisuudessa laktoosia käytetään esimerkiksi tablettien täyteaineena. Hapettamalla laktoosia voidaan valmistaa laktobionihappoa, 2-keto-laktobionihappoa ja laktuloosia. Laktobionihappoa käytetään biohajoavien pintojen ja kosmetiikkatuotteiden valmistuksessa, sekä sisäelinten säilöntäliuoksissa, joissa laktobionihappo estää happiradikaalien aiheuttamien kudosvaurioiden syntymistä. Tässä työssä laktoosia hapetettiin laktobionihapoksi sekoittimella varustetussa laboratoriomittakaavaisessa panosreaktorissa käyttäenkatalyyttinä palladiumia aktiivihiilellä. Muutamissa kokeissa katalyytin promoottorina käytettiin vismuttia, joka hidastaa katalyytin deaktivoitumista. Työn tarkoituksena oli saada lisää tietoa laktoosin hapettamisen kinetiikasta. Laktoosin hapettumisessa laktobionihapoksi havaittiin selektiivisyyteen vaikuttavan muunmuassa reaktiolämpötila, paine, pH ja käytetyn katalyytin määrä. Katalyyttiä kierrättämällä eri kokeiden välillä saatiin paremmat konversiot, selektiivisyydet ja saannot. Parhaat koetulokset saatiin hapetettaessa synteettisellä ilmalla 60 oC lämpötilassa ja 1 bar paineessa. Tehdyissä kokeissa pH:n säätö tehtiin manuaalisesti, joten pH ei pysynyt koko ajan haluttuna. Laktoosin konversio oli parhaimmillaan 95 %. Laktobionihapon suhteellinen selektiivisyys oli 100% ja suhteellinen saanto 100 %. Kinetiikan matemaattinen mallinnus tehtiin Modest-ohjelmalla käyttäen kokeista saatuja mittaustuloksia.Ohjelman avulla estimoitiin parametreja ja saatiin matemaattinen malli reaktorille. Tässä työssä tehtiin kineettinen mallinnus myös ravistelureaktorissa tehdyille laktoosin hapetuskokeille, missä pH pysyi koko ajan haluttuna 'in-situ' titrauksen avulla. Työn yhteydessä selvitettiin myös mahdollisuutta käyttää monoliittikatalyyttejä laktoosin hapetusreaktiossa.

Bayesian classification of forest ecological type from satellite data

The main objective of this study was todo a statistical analysis of ecological type from optical satellite data, using Tipping's sparse Bayesian algorithm. This thesis uses "the Relevence Vector Machine" algorithm in ecological classification betweenforestland and wetland. Further this bi-classification technique was used to do classification of many other different species of trees and produces hierarchical classification of entire subclasses given as a target class. Also, we carried out an attempt to use airborne image of same forest area. Combining it with image analysis, using different image processing operation, we tried to extract good features and later used them to perform classification of forestland and wetland.

Direct torque control of permanent magnet synchronous machines - analysis and implementation

Thedirect torque control (DTC) has become an accepted vector control method besidethe current vector control. The DTC was first applied to asynchronous machines,and has later been applied also to synchronous machines. This thesis analyses the application of the DTC to permanent magnet synchronous machines (PMSM). In order to take the full advantage of the DTC, the PMSM has to be properly dimensioned. Therefore the effect of the motor parameters is analysed taking the control principle into account. Based on the analysis, a parameter selection procedure is presented. The analysis and the selection procedure utilize nonlinear optimization methods. The key element of a direct torque controlled drive is the estimation of the stator flux linkage. Different estimation methods - a combination of current and voltage models and improved integration methods - are analysed. The effect of an incorrect measured rotor angle in the current model is analysed andan error detection and compensation method is presented. The dynamic performance of an earlier presented sensorless flux estimation method is made better by improving the dynamic performance of the low-pass filter used and by adapting the correction of the flux linkage to torque changes. A method for the estimation ofthe initial angle of the rotor is presented. The method is based on measuring the inductance of the machine in several directions and fitting the measurements into a model. The model is nonlinear with respect to the rotor angle and therefore a nonlinear least squares optimization method is needed in the procedure. A commonly used current vector control scheme is the minimum current control. In the DTC the stator flux linkage reference is usually kept constant. Achieving the minimum current requires the control of the reference. An on-line method to perform the minimization of the current by controlling the stator flux linkage reference is presented. Also, the control of the reference above the base speed is considered. A new estimation flux linkage is introduced for the estimation of the parameters of the machine model. In order to utilize the flux linkage estimates in off-line parameter estimation, the integration methods are improved. An adaptive correction is used in the same way as in the estimation of the controller stator flux linkage. The presented parameter estimation methods are then used in aself-commissioning scheme. The proposed methods are tested with a laboratory drive, which consists of a commercial inverter hardware with a modified software and several prototype PMSMs.

Oikosulkumoottorin roottorivaurioiden tunnistaminen staattorivirran avulla

Diplomityössä esitellään menetelmiä sauvarikon toteamiseksi. Työn tarkoituksena on tutkia roottorivaurioita staattorivirran avulla. Työ jaetaan karkeasti kolmeen osa-alueeseen: oikosulkumoottorin vikoihin, roottorivaurioiden tunnistamiseen ja signaalinkäsittelymenetelmiin, jonka avulla havaitaan sauvarikko. Oikosulkumoottorin vikoja ovat staattorikäämien vauriot ja roottorivauriot. Roottorikäämien vaurioita ovat roottori sauvojen murtuminen sekä roottorisauvan irtoaminen oikosulkujenkaan päästä. Roottorivaurioiden tunnistamismenetelmiä ovat parametrin arviointi ja virtaspektrianalyysi. Työn alkuosassa esitellään oikosulkumoottorien rakenne ja toiminta. Esitellään moottoriin kohdistuvia vikoja ja etsitään ratkaisumenetelmiä roottorivaurioiden tunnistamisessa. Lopuksi tutkitaan, kuinka staattorimittaustietojen perusteella saadut tulokset voidaan käsitellä FFT -algoritmilla ja kuinka FFT -algoritmi voidaan toteuttaa sulautettuna Sharc -prosessorin avulla. Työssä käytetään ADSP 21062 EZ -LAB kehitysympäristöä, jonka avulla voidaan ajaa ohjelmia RAM-sirusta, joka on vuorovaikutuksessa SHARC -laudassa oleviin laitteisiin.

Falsification and corroboration of conceptual hydrological models using geophysical data

Geophysical data may provide crucial information about hydrological properties, states, and processes that are difficult to obtain by other means. Large data sets can be acquired over widely different scales in a minimally invasive manner and at comparatively low costs, but their effective use in hydrology makes it necessary to understand the fidelity of geophysical models, the assumptions made in their construction, and the links between geophysical and hydrological properties. Geophysics has been applied for groundwater prospecting for almost a century, but it is only in the last 20 years that it is regularly used together with classical hydrological data to build predictive hydrological models. A largely unexplored venue for future work is to use geophysical data to falsify or rank competing conceptual hydrological models. A promising cornerstone for such a model selection strategy is the Bayes factor, but it can only be calculated reliably when considering the main sources of uncertainty throughout the hydrogeophysical parameter estimation process. Most classical geophysical imaging tools tend to favor models with smoothly varying property fields that are at odds with most conceptual hydrological models of interest. It is thus necessary to account for this bias or use alternative approaches in which proposed conceptual models are honored at all steps in the model building process.

Implisiittisen kalibroinnin soveltamismahdollisuudet kemiantekniikassa

Työn taustalla oli tavoite parantaa erään teollisuusprosessin toimintaa ja sen tuottoa mallintamalla reaktiovaiheen alussa tapahtuvan välituotteen muodostumisen reaktiokinetiikka sekä perinteisellä tavalla että implisiittisellä kalibroinnilla. Toisena tavoitteena oli selvittää, kuinka implisiittistä kalibrointia voidaan yleisemmin hyödyntää kemiantekniikassa. Implisiittinen kalibrointi on menetelmä, jolla voidaan ratkaista jonkin teoreettisen mallin parametrit suoraan epäsuorasta mittausdatasta (esimerkiksi spektreistä) lähes kokonaan ilman off-line analyysejä. Tämän työn kirjallisuusosassa on esitetty implisiittisen kalibroinnin toimintaperiaate sekä lyhyesti FTIR-spektrometrian perusteita. Työn kokeellisessa osassa on estimoitu tutkitun välituotteen muodostumisen kineettiset parametrit sekä tavanomaisella parametriestimoinnilla että implisiittisellä kalibroinnilla. Lisäksi kokeellisessa osassa on selvitetty lyhyesti tutkitun prosessin FTIR-spektrien lämpötilariippuvuuksia ja esitetty neljä mahdollista uutta sovelluskohdetta implisiittiselle kalibroinnille. Tavanomaisella parametriestimoinnilla saatiin estimoitua varsin yksiselitteiset arvot kineettisille parametreille. Myös mallin sovitus koedataan on hyvä kolmessa kokeessa viidestä. Parametriestimointi implisiittisellä kalibroinnilla onnistui lupaavasti vaikka tulokset eivät ole aivan niin hyviä kuin tavanomaisessa parametriestimoinnissa. Parhaat tulokset implisiittisessä kalibroinnissa saavutettiin suoralla kalibrointitavalla GRR (Generalized Ridge Regression)-kalibrointimenetelmää käyttämällä.

Is the matching function Cobb-Douglas?

[cat] Estudiem les propietats teòriques que una funció d.emparellament ha de satisfer per tal de representar un mercat laboral amb friccions dins d'un model d'equilibri general amb emparellament aleatori. Analitzem el cas Cobb-Douglas, CES i altres formes funcionals per a la funció d.emparellament. Els nostres resultats estableixen restriccions sobre els paràmetres d'aquests formes funcionals per assegurar que l.equilibri és interior. Aquestes restriccions aporten raons teòriques per escollir entre diverses formes funcionals i permeten dissenyar tests d'error d'especificació de model en els treballs empírics.

Is the matching function Cobb-Douglas?

[cat] Estudiem les propietats teòriques que una funció d.emparellament ha de satisfer per tal de representar un mercat laboral amb friccions dins d'un model d'equilibri general amb emparellament aleatori. Analitzem el cas Cobb-Douglas, CES i altres formes funcionals per a la funció d.emparellament. Els nostres resultats estableixen restriccions sobre els paràmetres d'aquests formes funcionals per assegurar que l.equilibri és interior. Aquestes restriccions aporten raons teòriques per escollir entre diverses formes funcionals i permeten dissenyar tests d'error d'especificació de model en els treballs empírics.

Economic and social convergence in Colombia

GDP has usually been used as a proxy for human well-being. Nevertheless, other social aspects should also be considered, such as life expectancy, infant mortality, educational enrolment and crime issues. With this paper we investigate not only economic convergence but also social convergence between regions in a developing country, Colombia, in the period 1975-2005. We consider several techniques in our analysis: sigma convergence, stochastic kernel estimations, and also several empirical models to find out the beta convergence parameter (cross section and panel estimates, with and without spatial dependence). The main results confirm that we can talk about convergence in Colombia in key social variables, although not in the classic economic variable, GDP per capita. We have also found that spatial autocorrelation reinforces convergence processes through deepening market and social factors, while isolation condemns regions to nonconvergence.

A nonparametric method for the measurement of size diversity with emphasis on data standardization

The most suitable method for estimation of size diversity is investigated. Size diversity is computed on the basis of the Shannon diversity expression adapted for continuous variables, such as size. It takes the form of an integral involving the probability density function (pdf) of the size of the individuals. Different approaches for the estimation of pdf are compared: parametric methods, assuming that data come from a determinate family of pdfs, and nonparametric methods, where pdf is estimated using some kind of local evaluation. Exponential, generalized Pareto, normal, and log-normal distributions have been used to generate simulated samples using estimated parameters from real samples. Nonparametric methods include discrete computation of data histograms based on size intervals and continuous kernel estimation of pdf. Kernel approach gives accurate estimation of size diversity, whilst parametric methods are only useful when the reference distribution have similar shape to the real one. Special attention is given for data standardization. The division of data by the sample geometric mean is proposedas the most suitable standardization method, which shows additional advantages: the same size diversity value is obtained when using original size or log-transformed data, and size measurements with different dimensionality (longitudes, areas, volumes or biomasses) may be immediately compared with the simple addition of ln k where kis the dimensionality (1, 2, or 3, respectively). Thus, the kernel estimation, after data standardization by division of sample geometric mean, arises as the most reliable and generalizable method of size diversity evaluation

Framework for Traffic Pattern Identification: Required Step for Short-term Forecasting

In the world of transport management, the term ‘anticipation’ is gradually replacing ‘reaction’. Indeed, the ability to forecast traffic evolution in a network should ideally form the basis for many traffic management strategies and multiple ITS applications. Real-time prediction capabilities are therefore becoming a concrete need for the management of networks, both for urban and interurban environments, and today’s road operator has increasingly complex and exacting requirements. Recognising temporal patterns in traffic or the manner in which sequential traffic events evolve over time have been important considerations in short-term traffic forecasting. However, little work has been conducted in the area of identifying or associating traffic pattern occurrence with prevailing traffic conditions. This paper presents a framework for detection pattern identification based on finite mixture models using the EM algorithm for parameter estimation. The computation results have been conducted taking into account the traffic data available in an urban network.

Modeling reaction kinetics and mass transfer in ozonation in water solutions

This dissertation is based on four articles dealing with modeling of ozonation. The literature part of this considers some models for hydrodynamics in bubble column simulation. A literature review of methods for obtaining mass transfer coefficients is presented. The methods presented to obtain mass transfer are general models and can be applied to any gas-liquid system. Ozonation reaction models and methods for obtaining stoichiometric coefficients and reaction rate coefficients for ozonation reactions are discussed in the final section of the literature part. In the first article, ozone gas-liquid mass transfer into water in a bubble column was investigated for different pH values. A more general method for estimation of mass transfer and Henry’s coefficient was developed from the Beltrán method. The ozone volumetric mass transfer coefficient and the Henry’s coefficient were determined simultaneously by parameter estimation using a nonlinear optimization method. A minor dependence of the Henry’s law constant on pH was detected at the pH range 4 - 9. In the second article, a new method using the axial dispersion model for estimation of ozone self-decomposition kinetics in a semi-batch bubble column reactor was developed. The reaction rate coefficients for literature equations of ozone decomposition and the gas phase dispersion coefficient were estimated and compared with the literature data. The reaction order in the pH range 7-10 with respect to ozone 1.12 and 0.51 the hydroxyl ion were obtained, which is in good agreement with literature. The model parameters were determined by parameter estimation using a nonlinear optimization method. Sensitivity analysis was conducted using object function method to obtain information about the reliability and identifiability of the estimated parameters. In the third article, the reaction rate coefficients and the stoichiometric coefficients in the reaction of ozone with the model component p-nitrophenol were estimated at low pH of water using nonlinear optimization. A novel method for estimation of multireaction model parameters in ozonation was developed. In this method the concentration of unknown intermediate compounds is presented as a residual COD (chemical oxygen demand) calculated from the measured COD and the theoretical COD for the known species. The decomposition rate of p-nitrophenol on the pathway producing hydroquinone was found to be about two times faster than the p-nitrophenol decomposition rate on the pathway producing 4- nitrocatechol. In the fourth article, the reaction kinetics of p-nitrophenol ozonation was studied in a bubble column at pH 2. Using the new reaction kinetic model presented in the previous article, the reaction kinetic parameters, rate coefficients, and stoichiometric coefficients as well as the mass transfer coefficient were estimated with nonlinear estimation. The decomposition rate of pnitrophenol was found to be equal both on the pathway producing hydroquinone and on the path way producing 4-nitrocathecol. Comparison of the rate coefficients with the case at initial pH 5 indicates that the p-nitrophenol degradation producing 4- nitrocathecol is more selective towards molecular ozone than the reaction producing hydroquinone. The identifiability and reliability of the estimated parameters were analyzed with the Marcov chain Monte Carlo (MCMC) method. @All rights reserved. No part of the publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of the author.

Minimization of the experimental workload for the prediction of pollutants propagation in rivers. Mathematical modelling and knowledge re-use

The present thesis in focused on the minimization of experimental efforts for the prediction of pollutant propagation in rivers by mathematical modelling and knowledge re-use. Mathematical modelling is based on the well known advection-dispersion equation, while the knowledge re-use approach employs the methods of case based reasoning, graphical analysis and text mining. The thesis contribution to the pollutant transport research field consists of: (1) analytical and numerical models for pollutant transport prediction; (2) two novel techniques which enable the use of variable parameters along rivers in analytical models; (3) models for the estimation of pollutant transport characteristic parameters (velocity, dispersion coefficient and nutrient transformation rates) as functions of water flow, channel characteristics and/or seasonality; (4) the graphical analysis method to be used for the identification of pollution sources along rivers; (5) a case based reasoning tool for the identification of crucial information related to the pollutant transport modelling; (6) and the application of a software tool for the reuse of information during pollutants transport modelling research. These support tools are applicable in the water quality research field and in practice as well, as they can be involved in multiple activities. The models are capable of predicting pollutant propagation along rivers in case of both ordinary pollution and accidents. They can also be applied for other similar rivers in modelling of pollutant transport in rivers with low availability of experimental data concerning concentration. This is because models for parameter estimation developed in the present thesis enable the calculation of transport characteristic parameters as functions of river hydraulic parameters and/or seasonality. The similarity between rivers is assessed using case based reasoning tools, and additional necessary information can be identified by using the software for the information reuse. Such systems represent support for users and open up possibilities for new modelling methods, monitoring facilities and for better river water quality management tools. They are useful also for the estimation of environmental impact of possible technological changes and can be applied in the pre-design stage or/and in the practical use of processes as well.