Inverse convection problem of simultaneous estimation of two boundary heat fluxes in parallel plate channels

This paper deals with the use of the conjugate gradient method of function estimation for the simultaneous identification of two unknown boundary heat fluxes in parallel plate channels. The fluid flow is assumed to be laminar and hydrodynamically developed. Temperature measurements taken inside the channel are used in the inverse analysis. The accuracy of the present solution approach is examined by using simulated measurements containing random errors, for strict cases involving functional forms with discontinuities and sharp-corners for the unknown functions. Three different types of inverse problems are addressed in the paper, involving the estimation of: (i) Spatially dependent heat fluxes; (ii) Time-dependent heat fluxes; and (iii) Time and spatially dependent heat fluxes.

Heat Source Estimation with the Conjugate Gradient Method in Inverse Linear Diffusive Problems

In this work, we present the solution of a class of linear inverse heat conduction problems for the estimation of unknown heat source terms, with no prior information of the functional forms of timewise and spatial dependence of the source strength, using the conjugate gradient method with an adjoint problem. After describing the mathematical formulation of a general direct problem and the procedure for the solution of the inverse problem, we show applications to three transient heat transfer problems: a one-dimensional cylindrical problem; a two-dimensional cylindrical problem; and a one-dimensional problem with two plates.

On state and parameter estimation in chaotic systems

State-of-the-art predictions of atmospheric states rely on large-scale numerical models of chaotic systems. This dissertation studies numerical methods for state and parameter estimation in such systems. The motivation comes from weather and climate models and a methodological perspective is adopted. The dissertation comprises three sections: state estimation, parameter estimation and chemical data assimilation with real atmospheric satellite data. In the state estimation part of this dissertation, a new filtering technique based on a combination of ensemble and variational Kalman filtering approaches, is presented, experimented and discussed. This new filter is developed for large-scale Kalman filtering applications. In the parameter estimation part, three different techniques for parameter estimation in chaotic systems are considered. The methods are studied using the parameterized Lorenz 95 system, which is a benchmark model for data assimilation. In addition, a dilemma related to the uniqueness of weather and climate model closure parameters is discussed. In the data-oriented part of this dissertation, data from the Global Ozone Monitoring by Occultation of Stars (GOMOS) satellite instrument are considered and an alternative algorithm to retrieve atmospheric parameters from the measurements is presented. The validation study presents first global comparisons between two unique satellite-borne datasets of vertical profiles of nitrogen trioxide (NO3), retrieved using GOMOS and Stratospheric Aerosol and Gas Experiment III (SAGE III) satellite instruments. The GOMOS NO3 observations are also considered in a chemical state estimation study in order to retrieve stratospheric temperature profiles. The main result of this dissertation is the consideration of likelihood calculations via Kalman filtering outputs. The concept has previously been used together with stochastic differential equations and in time series analysis. In this work, the concept is applied to chaotic dynamical systems and used together with Markov chain Monte Carlo (MCMC) methods for statistical analysis. In particular, this methodology is advocated for use in numerical weather prediction (NWP) and climate model applications. In addition, the concept is shown to be useful in estimating the filter-specific parameters related, e.g., to model error covariance matrix parameters.

Probabilistic position and orientation estimation of rigid objects using an RGB-D sensor

The recent emergence of low-cost RGB-D sensors has brought new opportunities for robotics by providing affordable devices that can provide synchronized images with both color and depth information. In this thesis, recent work on pose estimation utilizing RGBD sensors is reviewed. Also, a pose recognition system for rigid objects using RGB-D data is implemented. The implementation uses half-edge primitives extracted from the RGB-D images for pose estimation. The system is based on the probabilistic object representation framework by Detry et al., which utilizes Nonparametric Belief Propagation for pose inference. Experiments are performed on household objects to evaluate the performance and robustness of the system.

Från plåtpolis till livräddare : Behovsanalys för automatisk trafiksäkerhetskontroll 2011 – 2015

I det här arbetet utformades en utvecklings- och investeringsplan för automatisk trafiksäkerhetsövervakning för åren 2011-2015 för landsvägsnätet i Egentliga Finland och Satakunta. Planen innehåller analys av den nuvarande verksamhetsmodellen och förslag hur verksamhetsmodellen kunde förbättras samt förslag om vilka vägavsnitt som ska få automatisk övervakning och vilka projekt som ska prioriteras under åren 2011-2015. Vidare beskrivs i planen den automatiska övervakningsteknik och verksamhetsmodell som används i Sverige samt åtgärdsförslag för att öka den automatiska trafikövervakningens acceptans. Den nuvarande verksamhetsmodellen och behovet av att utveckla den automatiska trafikövervakningen har utvärderats på basis av intervjuer med experter. Experterna ansåg att den nuvarande verksamhetsmodellen i huvudsak är funktionell och effektiv, eftersom man med relativt små kostnader har uppnått effektiva resultat. Den automatiska trafikövervakningen måste dock fortfarande utvecklas och Sveriges verksamhetsmodell ansågs vara en bra förebild och ett gott exempel. De viktigaste utvecklingsområdena ansågs vara utveckling av tekniken, snabbare informationsbehandling och automatisering, vilket skulle öka polisens resurser att verkställa övervakningen. Vägavsnitten som ska få automatisk övervakning under åren 2011-2015 prioriterades främst på basis av olycksfrekvens, antal olyckor, Tarva-beräkningsresultat, hastighetsdata och trafikmängder. Av olycksstatistiken har man främst uppmärksammat personskadeolyckor. Tarva-beräkningarna användes också för att utvärdera den automatiska trafikövervakningens inverkan och effektivitet. Enligt de intervjuade experterna är öppenhet kring övervakningen, synlig övervakningsapparatur och kontinuerlig informationsförmedling bland de viktigaste sätten att öka acceptansen. Genom att informera för man fram systemets fördelar och dess betydelse för trafiksäkerheten. Att förändra övervakningskamerans varumärke till säkerhetskamera enligt Sveriges modell ansågs även vara bra. I detta arbete presenteras en plan för att utveckla acceptansen för automatisk trafiksäkerhetsövervakning.

Scenario-based traffic forecasts for the routes between the PENTA ports in 2020

Maritime transport is the foundation for trade in the Baltic Sea area. It represents over 15% of the world’s cargo traffic and it is predicted to increase by over 100% in the future. There are currently over 2,000 ships sailing on the Baltic Sea and both the number and the size of ships have been growing in recent years. Due to the importance of maritime traffic in the Baltic Sea Region, ports have to be ready to face future challenges and adapt to the changing operational environment. The companies within the transportation industry – in this context ports, shipowners and logistics companies – compete continuously and although the number of companies in the business is not particularly substantial because the products offered are very similar, other motives for managing the supply chain arise. The factors creating competitive advantage are often financial and related to cost efficiency, but geographical location, road infrastructure in the hinterland and vessel connections are among the most important factors. The PENTA project focuses on adding openness, transparency and sharing knowledge and information, so that the challenges of the future can be better addressed with regard to cooperation. This report presents three scenario-based traffic forecasts for routes between the PENTA ports in 2020. The chosen methodology is PESTE, in which the focus in on economic factors affecting future traffic flows. The report further analyses the findings and results of the first PENTA WP2 report “Drivers of demand in cargo and passenger traffic between PENTA ports” and utilises the same material, which was obtained through interviews and mail surveys.

Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat — Estimation of various production chains by using LCA methodology

More discussion is required on how and which types of biomass should be used to achieve a significant reduction in the carbon load released into the atmosphere in the short term. The energy sector is one of the largest greenhouse gas (GHG) emitters and thus its role in climate change mitigation is important. Replacing fossil fuels with biomass has been a simple way to reduce carbon emissions because the carbon bonded to biomass is considered as carbon neutral. With this in mind, this thesis has the following objectives: (1) to study the significance of the different GHG emission sources related to energy production from peat and biomass, (2) to explore opportunities to develop more climate friendly biomass energy options and (3) to discuss the importance of biogenic emissions of biomass systems. The discussion on biogenic carbon and other GHG emissions comprises four case studies of which two consider peat utilization, one forest biomass and one cultivated biomasses. Various different biomass types (peat, pine logs and forest residues, palm oil, rapeseed oil and jatropha oil) are used as examples to demonstrate the importance of biogenic carbon to life cycle GHG emissions. The biogenic carbon emissions of biomass are defined as the difference in the carbon stock between the utilization and the non-utilization scenarios of biomass. Forestry-drained peatlands were studied by using the high emission values of the peatland types in question to discuss the emission reduction potential of the peatlands. The results are presented in terms of global warming potential (GWP) values. Based on the results, the climate impact of the peat production can be reduced by selecting high-emission-level peatlands for peat production. The comparison of the two different types of forest biomass in integrated ethanol production in pulp mill shows that the type of forest biomass impacts the biogenic carbon emissions of biofuel production. The assessment of cultivated biomasses demonstrates that several selections made in the production chain significantly affect the GHG emissions of biofuels. The emissions caused by biofuel can exceed the emissions from fossil-based fuels in the short term if biomass is in part consumed in the process itself and does not end up in the final product. Including biogenic carbon and other land use carbon emissions into the carbon footprint calculations of biofuel reveals the importance of the time frame and of the efficiency of biomass carbon content utilization. As regards the climate impact of biomass energy use, the net impact on carbon stocks (in organic matter of soils and biomass), compared to the impact of the replaced energy source, is the key issue. Promoting renewable biomass regardless of biogenic GHG emissions can increase GHG emissions in the short term and also possibly in the long term.

Modeling and Parameter Estimation of Double-Star Permanent Magnet Synchronous Machines

The power rating of wind turbines is constantly increasing; however, keeping the voltage rating at the low-voltage level results in high kilo-ampere currents. An alternative for increasing the power levels without raising the voltage level is provided by multiphase machines. Multiphase machines are used for instance in ship propulsion systems, aerospace applications, electric vehicles, and in other high-power applications including wind energy conversion systems. A machine model in an appropriate reference frame is required in order to design an efficient control for the electric drive. Modeling of multiphase machines poses a challenge because of the mutual couplings between the phases. Mutual couplings degrade the drive performance unless they are properly considered. In certain multiphase machines there is also a problem of high current harmonics, which are easily generated because of the small current path impedance of the harmonic components. However, multiphase machines provide special characteristics compared with the three-phase counterparts: Multiphase machines have a better fault tolerance, and are thus more robust. In addition, the controlled power can be divided among more inverter legs by increasing the number of phases. Moreover, the torque pulsation can be decreased and the harmonic frequency of the torque ripple increased by an appropriate multiphase configuration. By increasing the number of phases it is also possible to obtain more torque per RMS ampere for the same volume, and thus, increase the power density. In this doctoral thesis, a decoupled d–q model of double-star permanent-magnet (PM) synchronous machines is derived based on the inductance matrix diagonalization. The double-star machine is a special type of multiphase machines. Its armature consists of two three-phase winding sets, which are commonly displaced by 30 electrical degrees. In this study, the displacement angle between the sets is considered a parameter. The diagonalization of the inductance matrix results in a simplified model structure, in which the mutual couplings between the reference frames are eliminated. Moreover, the current harmonics are mapped into a reference frame, in which they can be easily controlled. The work also presents methods to determine the machine inductances by a finite-element analysis and by voltage-source inverters on-site. The derived model is validated by experimental results obtained with an example double-star interior PM (IPM) synchronous machine having the sets displaced by 30 electrical degrees. The derived transformation, and consequently, the decoupled d–q machine model, are shown to model the behavior of an actual machine with an acceptable accuracy. Thus, the proposed model is suitable to be used for the model-based control design of electric drives consisting of double-star IPM synchronous machines.

Estimation of weed dry biomass and grain yield as a function of growth and yield traits under allelopathic weed management in maize

Growing concerns about toxicity and development of resistance against synthetic herbicides have demanded looking for alternative weed management approaches. Allelopathy has gained sufficient support and potential for sustainable weed management. Aqueous extracts of six plant species (sunflower, rice, mulberry, maize, brassica and sorghum) in different combinations alone or in mixture with 75% reduced dose of herbicides were evaluated for two consecutive years under field conditions. A weedy check and S-metolachlor with atrazine (pre emergence) and atrazine alone (post emergence) at recommended rates was included for comparison. Weed dynamics, maize growth indices and yield estimation were done by following standard procedures. All aqueous plant extract combinations suppressed weed growth and biomass. Moreover, the suppressive effect was more pronounced when aqueous plant extracts were supplemented with reduced doses of herbicides. Brassica-sunflower-sorghum combination suppressed weeds by 74-80, 78-70, 65-68% during both years of study that was similar with S-metolachlor along half dose of atrazine and full dose of atrazine alone. Crop growth rate and dry matter accumulation attained peak values of 32.68 and 1,502 g m-2 d-1 for brassica-sunflower-sorghum combination at 60 and 75 days after sowing. Curve fitting regression for growth and yield traits predicted strong positive correlation to grain yield and negative correlation to weed dry biomass under allelopathic weed management in maize crop.

Automatic traffic sign inventory- and condition analysis

This thesis researches automatic traffic sign inventory and condition analysis using machine vision and pattern recognition methods. Automatic traffic sign inventory and condition analysis can be used to more efficient road maintenance, improving the maintenance processes, and to enable intelligent driving systems. Automatic traffic sign detection and classification has been researched before from the viewpoint of self-driving vehicles, driver assistance systems, and the use of signs in mapping services. Machine vision based inventory of traffic signs consists of detection, classification, localization, and condition analysis of traffic signs. The produced machine vision system performance is estimated with three datasets, from which two of have been been collected for this thesis. Based on the experiments almost all traffic signs can be detected, classified, and located and their condition analysed. In future, the inventory system performance has to be verified in challenging conditions and the system has to be pilot tested.

A simple model for the estimation of congenital malformation frequency in racially mixed populations

A simple model is proposed, using the method of maximum likelihood to estimate malformation frequencies in racial groups based on data obtained from hospital services. This model uses the proportions of racial admixture, and the observed malformation frequency. It was applied to two defects: postaxial polydactyly and cleft lip, the frequencies of which are recognizedly heterogeneous among racial groups. The frequencies estimated in each racial group were those expected for these malformations, which proves the applicability of the method.

Use of fluorescent probes to follow membrane traffic in nerve terminals

Optical tracers in conjunction with fluorescence microscopy have become widely used to follow the movement of synaptic vesicles in nerve terminals. The present review discusses the use of these optical methods to understand the regulation of exocytosis and endocytosis of synaptic vesicles. The maintenance of neurotransmission depends on the constant recycling of synaptic vesicles and important insights have been gained by visualization of vesicles with the vital dye FM1-43. A number of questions related to the control of recycling of synaptic vesicles by prolonged stimulation and the role of calcium to control membrane internalization are now being addressed. It is expected that optical monitoring of presynaptic activity coupled to appropriate genetic models will contribute to the understanding of membrane traffic in synaptic terminals.

Designing sustainable industrial ecosystems : the case of a biogas-for-traffic solution

This thesis focuses on the development of sustainable industrial architectures for bioenergy based on the metaphors of industrial symbiosis and industrial ecosystems, which imply exchange of material and energy side-flows of various industries in order to improve sustainability of those industries on a system level. The studies on industrial symbiosis have been criticised for staying at the level of incremental changes by striving for cycling waste and by-flows of the industries ‘as is’ and leaving the underlying industry structures intact. Moreover, there has been articulated the need for interdisciplinary research on industrial ecosystems as well as the need to extend the management and business perspectives on industrial ecology. This thesis addresses this call by applying a business ecosystem and business model perspective on industrial symbiosis in order to produce knowledge on how industrial ecosystems can be developed that are sustainable environmentally and economically. A case of biogas business is explored and described in four research papers and an extended summary that form this thesis. Since the aim of the research was to produce a normative model for developing sustainable industrial ecosystems, the methodology applied in this research can be characterised as constructive and collaborative. A constructive research mode was required in order to expand the historical knowledge on industrial symbiosis development and business ecosystem development into the knowledge of what should be done, which is crucial for sustainability and the social change it requires. A collaborative research mode was employed through participating in a series of projects devoted to the development of a biogas-for-traffic industrial ecosystem. The results of the study showed that the development of material flow interconnections within industrial symbiosis is inseparable from larger business ecosystem restructuring. This included a shift in the logic of the biogas and traffic fuel industry and a subsequent development of a business ecosystem that would entail the principles of industrial symbiosis and localised energy production and consumption. Since a company perspective has been taken in this thesis, the role of an ecosystem integrator appeared as a crucial means to achieve the required industry restructuring. This, in turn, required the development of a modular and boundary-spanning business model that had a strong focus on establishing collaboration among ecosystem stakeholders and development of multiple local industrial ecosystems as part of business growth. As a result, the designed business model of the ecosystem integrator acquired the necessary flexibility in order to adjust to local conditions, which is crucial for establishing industrial symbiosis. This thesis presents a normative model for the development of a business model required for creating sustainable industrial ecosystems, which contributes to approaches at the policy-makers’ level, proposed earlier. Therefore, this study addresses the call for more research on the business level of industrial ecosystem formation and the implications for the business models of the involved actors. Moreover, the thesis increases the understanding of system innovation and innovation in business ecosystems by explicating how business model innovation can be the trigger for achieving more sustainable industry structures, such as those relying on industrial symbiosis.

A priori estimation of accuracy and of the number of wells to be employed in limiting dilution assays

The use of limiting dilution assay (LDA) for assessing the frequency of responders in a cell population is a method extensively used by immunologists. A series of studies addressing the statistical method of choice in an LDA have been published. However, none of these studies has addressed the point of how many wells should be employed in a given assay. The objective of this study was to demonstrate how a researcher can predict the number of wells that should be employed in order to obtain results with a given accuracy, and, therefore, to help in choosing a better experimental design to fulfill one's expectations. We present the rationale underlying the expected relative error computation based on simple binomial distributions. A series of simulated in machina experiments were performed to test the validity of the a priori computation of expected errors, thus confirming the predictions. The step-by-step procedure of the relative error estimation is given. We also discuss the constraints under which an LDA must be performed.

The estimation of tuberculosis transmission parameters using ABC and MCMC methods

The aim of this work is to apply approximate Bayesian computation in combination with Marcov chain Monte Carlo methods in order to estimate the parameters of tuberculosis transmission. The methods are applied to San Francisco data and the results are compared with the outcomes of previous works. Moreover, a methodological idea with the aim to reduce computational time is also described. Despite the fact that this approach is proved to work in an appropriate way, further analysis is needed to understand and test its behaviour in different cases. Some related suggestions to its further enhancement are described in the corresponding chapter.