Vaihtosuuntaajan reaaliaikainen lämpömalli

Vaihtosuuntaajan IGBT-moduulin liitosten lämpötiloja ei voida suoraan mitata, joten niiden arviointiin tarvitaan reaaliaikainen lämpömalli. Tässä työssä on tavoitteena kehittää tähän tarkoitukseen C-kielellä implementoitu ratkaisu, joka on riittävän tarkka ja samalla mahdollisimman laskennallisesti tehokas. Ohjelmallisen toteutuksen täytyy myös sopia erilaisille moduulityypeille ja sen on tarvittaessa otettava huomioon saman moduulin muiden sirujen lämmittävä vaikutus toisiinsa. Kirjallisuuskatsauksen perusteella valitaan olemassa olevista lämpömalleista käytännön toteutuksen pohjaksi lämpöimpedanssimatriisiin perustuva malli. Lämpöimpedanssimatriisista tehdään Simulink-ohjelmalla s-tason simulointimalli, jota käytetään referenssinä muun muassa implementoinnin tarkkuuden verifiointiin. Lämpömalli tarvitsee tiedon vaihtosuuntaajan häviöistä, joten työssä on selvitetty eri vaihtoehtoja häviölaskentaan. Lämpömallin kehittäminen s-tason mallista valmiiksi C-kieliseksi koodiksi on kuvattu tarkasti. Ensin s-tason malli diskretoidaan z-tasoon. Z-tason siirtofunktiot muutetaan puolestaan ensimmäisen kertaluvun differenssiyhtälöiksi. Työssä kehitetty monen aikatason lämpömalli saadaan jakamalla ensimmäisen kertaluvun differenssiyhtälöt eri aikatasoille suoritettavaksi sen mukaan, mikä niiden kuvaileman termin vaatima päivitysnopeus on. Tällainen toteutus voi parhaimmillaan kuluttaa alle viidesosan kellojaksoja verrattuna suoraviivaiseen yhden aikatason toteutukseen. Implementoinnin tarkkuus on hyvä. Implementoinnin vaatimia suoritusaikoja testattiin Texas Instrumentsin TMS320C6727- prosessorilla (300 MHz). Esimerkkimallin laskemisen määritettiin kuluttavan vaihtosuuntaajan toimiessa 5 kHz kytkentätaajuudella vain 0,4 % prosessorin kellojaksoista. Toteutuksen tarkkuus ja laskentakapasiteetin vähäinen vaatimus mahdollistavat lämpömallin käyttämisen lämpösuojaukseen ja lisäämisen osaksi muuta jo prosessorilla olemassa olevaa systeemiä.

Net working capital estimation model in project business

The objective of this Master’s thesis is to develop a model which estimates net working capital (NWC) monthly in a year period. The study is conducted by a constructive research which uses a case study. The estimation model is designed in the need of one case company which operates in project business. Net working capital components should be linked together by an automatic model and estimated individually, including advanced components of NWC for example POC receivables. Net working capital estimation model of this study contains three parts: output template, input template and calculation model. The output template gets estimate values automatically from the input template and the calculation model. Into the input template estimate values of more stable NWC components are inputted manually. The calculate model gets estimate values for major affecting components automatically from the systems of a company by using a historical data and made plans. As a precondition for the functionality of the estimation calculation is that sales are estimated in one year period because the sales are linked to all NWC components.

Rôle des ressources humaines dans la performance du système de référence-évacuation de Kayes au Mali

La mortalité maternelle et périnatale est un problème majeur de santé publique dans les pays en développement. Elle illustre l’écart important entre les pays développés et les pays en développement. Les interventions techniques pour améliorer la santé maternelle et périnatale sont connues dans les pays en développement, mais ce sont la faiblesse des systèmes de santé et les défis liés aux ressources qui freinent leur généralisation. L’objectif principal de ce travail était de mieux comprendre le rôle des ressources humaines en particulier ceux de la première ligne dans la performance d’un système de référence maternelle. Au Mali, la mise en place d’un système de référence maternelle, système de référence-évacuation « SRE », fait partie des mesures nationales de lutte contre la mortalité maternelle et périnatale. Les trois composantes du SRE, soit les caisses de solidarité, le transport et la communication et la mise à niveau des soins obstétricaux, permettent une action simultanée du côté de la demande et de l’offre de soins maternels et périnatals. Néanmoins, la pénurie de personnel qualifié a conduit à des compromis sur la qualification du personnel dans l’implantation de ce système. La région de Kayes, première région administrative du Mali, est une région de forte émigration. Elle dispose d’une offre de soins plus diversifiée qu’ailleurs au Mali, grâce à l’appui des Maliens de l’extérieur. Son SRE offre ainsi un terrain d’études adéquat pour l’analyse du rôle des professionnels de première ligne. De façon plus spécifique, ce travail avait pour objectifs 1) d’identifier les caractéristiques des équipes de soins de première ligne qui sont associées à une meilleure performance du SRE en termes de survie simultanée de la mère et du nouveau-né et 2) d’approfondir la compréhension des pratiques de gestion des ressources humaines, susceptibles d’expliquer les variations de la performance du SRE de Kayes. Pour atteindre ces objectifs, nous avons, à partir du cadre de référence de Michie et West modélisé les facteurs liés aux ressources humaines qui ont une influence potentielle sur la performance du SRE de Kayes. L’exploration des variations du processus motivationnel a été faite à partir de la théorie de l’attente de Vroom. Nous avons ensuite combiné une revue de la littérature et un devis de recherche mixte (quantitative et qualitative). Les données pour les analyses quantitatives proviennent d’un système d’enregistrement continu de toutes les urgences obstétricales (GESYRE : Gestion du Système de Référence Évacuation mis en place depuis 2004 dans le cadre du suivi et de l’évaluation du SRE de Kayes) et des enquêtes à passages répétés sur les données administratives et du personnel des centres de santé. Un modèle de régression biprobit a permis d’évaluer les effets du niveau d’entrée dans le SRE et des équipes de soins sur la survie jointe de la mère et du nouveau-né. A l’aide d’entrevues semi-structurées et d’observations, nous avons exploré les pratiques de gestion des personnes dans des centres de santé communautaires « CScom » sélectionnés par un échantillonnage raisonné. Les résultats de ce travail ont confirmé que la main d’œuvre humaine demeure cruciale pour la performance du SRE. Les professionnels de première ligne ont influencé la survie des femmes et des nouveau-nés, à morbidités égales, et lorsque la distance parcourue est prise en compte. La meilleure survie de la mère et du nouveau-né est retrouvée dans les cas d’accès direct à l’hôpital régional. Les femmes qui sont évacuées des centres de première ligne où il y a plus de professionnels ou un personnel plus qualifié avaient un meilleur pronostic materno-fœtal que celles qui ont consulté dans des centres qui disposent de personnel peu qualifié. Dans les centres de première ligne dirigés par un médecin, des variations favorables à la performance comme une implication directe des médecins dans les soins, un environnement de soins concurrentiel ont été retrouvés. Concernant les pratiques de gestion dans les centres de première ligne, les chefs de poste ont mis en place des incitatifs pour motiver le personnel à plus de performance. Le processus motivationnel demeure toutefois très complexe et variable. La désirabilité de bons résultats des soins (valence) est élevée pour tous les professionnels ; cependant les motifs étaient différents entre les catégories de personnel. Par ailleurs, le faible niveau d’équipements et la multiplicité des acteurs ont empêché l’établissement d’un lien entre l’effort fourni par les professionnels et les résultats de soins. Cette compréhension du rôle des professionnels de première ligne pourra aider le personnel administratif à mieux cibler le monitorage de la performance du SRE. Le personnel de soins pourra s’en servir pour reconnaitre et appliquer les pratiques associées à une bonne performance. Dans le domaine de la recherche, les défis de recherche ultérieurs sur les facteurs humains de la performance du SRE seront mieux identifiés.

Lagrangian approximations and weak solutions of the Navier-Stokes equations

The motion of a viscous incompressible fluid flow in bounded domains with a smooth boundary can be described by the nonlinear Navier-Stokes equations. This description corresponds to the so-called Eulerian approach. We develop a new approximation method for the Navier-Stokes equations in both the stationary and the non-stationary case by a suitable coupling of the Eulerian and the Lagrangian representation of the flow, where the latter is defined by the trajectories of the particles of the fluid. The method leads to a sequence of uniquely determined approximate solutions with a high degree of regularity containing a convergent subsequence with limit function v such that v is a weak solution of the Navier-Stokes equations.

Model-Based Matching of Line Drawings by Linear Combinations of Prototypes

We describe a technique for finding pixelwise correspondences between two images by using models of objects of the same class to guide the search. The object models are 'learned' from example images (also called prototypes) of an object class. The models consist of a linear combination ofsprototypes. The flow fields giving pixelwise correspondences between a base prototype and each of the other prototypes must be given. A novel image of an object of the same class is matched to a model by minimizing an error between the novel image and the current guess for the closest modelsimage. Currently, the algorithm applies to line drawings of objects. An extension to real grey level images is discussed.

Development and assessment of a coupled crop-climate model

It is well established that crop production is inherently vulnerable to variations in the weather and climate. More recently the influence of vegetation on the state of the atmosphere has been recognized. The seasonal growth of crops can influence the atmosphere and have local impacts on the weather, which in turn affects the rate of seasonal crop growth and development. Considering the coupled nature of the crop-climate system, and the fact that a significant proportion of land is devoted to the cultivation of crops, important interactions may be missed when studying crops and the climate system in isolation, particularly in the context of land use and climate change. To represent the two-way interactions between seasonal crop growth and atmospheric variability, we integrate a crop model developed specifically to operate at large spatial scales (General Large Area Model for annual crops) into the land surface component of a global climate model (GCM; HadAM3). In the new coupled crop-climate model, the simulated environment (atmosphere and soil states) influences growth and development of the crop, while simultaneously the temporal variations in crop leaf area and height across its growing season alter the characteristics of the land surface that are important determinants of surface fluxes of heat and moisture, as well as other aspects of the land-surface hydrological cycle. The coupled model realistically simulates the seasonal growth of a summer annual crop in response to the GCM's simulated weather and climate. The model also reproduces the observed relationship between seasonal rainfall and crop yield. The integration of a large-scale single crop model into a GCM, as described here, represents a first step towards the development of fully coupled crop and climate models. Future development priorities and challenges related to coupling crop and climate models are discussed.

Optimal piecewise locally linear modeling

Associative memory networks such as Radial Basis Functions, Neurofuzzy and Fuzzy Logic used for modelling nonlinear processes suffer from the curse of dimensionality (COD), in that as the input dimension increases the parameterization, computation cost, training data requirements, etc. increase exponentially. Here a new algorithm is introduced for the construction of a Delaunay input space partitioned optimal piecewise locally linear models to overcome the COD as well as generate locally linear models directly amenable to linear control and estimation algorithms. The training of the model is configured as a new mixture of experts network with a new fast decision rule derived using convex set theory. A very fast simulated reannealing (VFSR) algorithm is utilized to search a global optimal solution of the Delaunay input space partition. A benchmark non-linear time series is used to demonstrate the new approach.

Evaluation of water and energy budgets in regional climate models applied over Europe

This study presents a model intercomparison of four regional climate models (RCMs) and one variable resolution atmospheric general circulation model (AGCM) applied over Europe with special focus on the hydrological cycle and the surface energy budget. The models simulated the 15 years from 1979 to 1993 by using quasi-observed boundary conditions derived from ECMWF re-analyses (ERA). The model intercomparison focuses on two large atchments representing two different climate conditions covering two areas of major research interest within Europe. The first is the Danube catchment which represents a continental climate dominated by advection from the surrounding land areas. It is used to analyse the common model error of a too dry and too warm simulation of the summertime climate of southeastern Europe. This summer warming and drying problem is seen in many RCMs, and to a less extent in GCMs. The second area is the Baltic Sea catchment which represents maritime climate dominated by advection from the ocean and from the Baltic Sea. This catchment is a research area of many studies within Europe and also covered by the BALTEX program. The observed data used are monthly mean surface air temperature, precipitation and river discharge. For all models, these are used to estimate mean monthly biases of all components of the hydrological cycle over land. In addition, the mean monthly deviations of the surface energy fluxes from ERA data are computed. Atmospheric moisture fluxes from ERA are compared with those of one model to provide an independent estimate of the convergence bias derived from the observed data. These help to add weight to some of the inferred estimates and explain some of the discrepancies between them. An evaluation of these biases and deviations suggests possible sources of error in each of the models. For the Danube catchment, systematic errors in the dynamics cause the prominent summer drying problem for three of the RCMs, while for the fourth RCM this is related to deficiencies in the land surface parametrization. The AGCM does not show this drying problem. For the Baltic Sea catchment, all models similarily overestimate the precipitation throughout the year except during the summer. This model deficit is probably caused by the internal model parametrizations, such as the large-scale condensation and the convection schemes.

Evaluation of water and energy budgets in regional climate models applied over Europe

Abstract This study presents a model intercomparison of four regional climate models (RCMs) and one variable resolution atmospheric general circulation model (AGCM) applied over Europe with special focus on the hydrological cycle and the surface energy budget. The models simulated the 15 years from 1979 to 1993 by using quasi-observed boundary conditions derived from ECMWF re-analyses (ERA). The model intercomparison focuses on two large atchments representing two different climate conditions covering two areas of major research interest within Europe. The first is the Danube catchment which represents a continental climate dominated by advection from the surrounding land areas. It is used to analyse the common model error of a too dry and too warm simulation of the summertime climate of southeastern Europe. This summer warming and drying problem is seen in many RCMs, and to a less extent in GCMs. The second area is the Baltic Sea catchment which represents maritime climate dominated by advection from the ocean and from the Baltic Sea. This catchment is a research area of many studies within Europe and also covered by the BALTEX program. The observed data used are monthly mean surface air temperature, precipitation and river discharge. For all models, these are used to estimate mean monthly biases of all components of the hydrological cycle over land. In addition, the mean monthly deviations of the surface energy fluxes from ERA data are computed. Atmospheric moisture fluxes from ERA are compared with those of one model to provide an independent estimate of the convergence bias derived from the observed data. These help to add weight to some of the inferred estimates and explain some of the discrepancies between them. An evaluation of these biases and deviations suggests possible sources of error in each of the models. For the Danube catchment, systematic errors in the dynamics cause the prominent summer drying problem for three of the RCMs, while for the fourth RCM this is related to deficiencies in the land surface parametrization. The AGCM does not show this drying problem. For the Baltic Sea catchment, all models similarily overestimate the precipitation throughout the year except during the summer. This model deficit is probably caused by the internal model parametrizations, such as the large-scale condensation and the convection schemes.

Simulated variations in atmospheric CO2 over a Wisconsin forest using a coupled ecosystem-atmosphere model

Ecosystem fluxes of energy, water, and CO2 result in spatial and temporal variations in atmospheric properties. In principle, these variations can be used to quantify the fluxes through inverse modelling of atmospheric transport, and can improve the understanding of processes and falsifiability of models. We investigated the influence of ecosystem fluxes on atmospheric CO2 in the vicinity of the WLEF-TV tower in Wisconsin using an ecophysiological model (Simple Biosphere, SiB2) coupled to an atmospheric model (Regional Atmospheric Modelling System). Model parameters were specified from satellite imagery and soil texture data. In a companion paper, simulated fluxes in the immediate tower vicinity have been compared to eddy covariance fluxes measured at the tower, with meteorology specified from tower sensors. Results were encouraging with respect to the ability of the model to capture observed diurnal cycles of fluxes. Here, the effects of fluxes in the tower footprint were also investigated by coupling SiB2 to a high-resolution atmospheric simulation, so that the model physiology could affect the meteorological environment. These experiments were successful in reproducing observed fluxes and concentration gradients during the day and at night, but revealed problems during transitions at sunrise and sunset that appear to be related to the canopy radiation parameterization in SiB2.

Towards the design of a unified semantics model for OLTP and OLAP systems

The concepts of on-line transactional processing (OLTP) and on-line analytical processing (OLAP) are often confused with the technologies or models that are used to design transactional and analytics based information systems. This in some way has contributed to existence of gaps between the semantics in information captured during transactional processing and information stored for analytical use. In this paper, we propose the use of a unified semantics design model, as a solution to help bridge the semantic gaps between data captured by OLTP systems and the information provided by OLAP systems. The central focus of this design approach is on enabling business intelligence using not just data, but data with context.

Using the past to constrain the future: how the palaeorecord can improve estimates of global warming

Climate sensitivity is defined as the change in global mean equilibrium temperature after a doubling of atmospheric CO2 concentration and provides a simple measure of global warming. An early estimate of climate sensitivity, 1.5—4.5°C, has changed little subsequently, including the latest assessment by the Intergovernmental Panel on Climate Change. The persistence of such large uncertainties in this simple measure casts doubt on our understanding of the mechanisms of climate change and our ability to predict the response of the climate system to future perturbations. This has motivated continued attempts to constrain the range with climate data, alone or in conjunction with models. The majority of studies use data from the instrumental period (post-1850), but recent work has made use of information about the large climate changes experienced in the geological past. In this review, we first outline approaches that estimate climate sensitivity using instrumental climate observations and then summarize attempts to use the record of climate change on geological timescales. We examine the limitations of these studies and suggest ways in which the power of the palaeoclimate record could be better used to reduce uncertainties in our predictions of climate sensitivity.

Using an individual-based model to select among alternative foraging strategies of woodpigeons: data support a memory-based model with a flocking mechanism

Population modelling is increasingly recognised as a useful tool for pesticide risk assessment. For vertebrates that may ingest pesticides with their food, such as woodpigeon (Columba palumbus), population models that simulate foraging behaviour explicitly can help predicting both exposure and population-level impact. Optimal foraging theory is often assumed to explain the individual-level decisions driving distributions of individuals in the field, but it may not adequately predict spatial and temporal characteristics of woodpigeon foraging because of the woodpigeons’ excellent memory, ability to fly long distances, and distinctive flocking behaviour. Here we present an individual-based model (IBM) of the woodpigeon. We used the model to predict distributions of foraging woodpigeons that use one of six alternative foraging strategies: optimal foraging, memory-based foraging and random foraging, each with or without flocking mechanisms. We used pattern-oriented modelling to determine which of the foraging strategies is best able to reproduce observed data patterns. Data used for model evaluation were gathered during a long-term woodpigeon study conducted between 1961 and 2004 and a radiotracking study conducted in 2003 and 2004, both in the UK, and are summarised here as three complex patterns: the distributions of foraging birds between vegetation types during the year, the number of fields visited daily by individuals, and the proportion of fields revisited by them on subsequent days. The model with a memory-based foraging strategy and a flocking mechanism was the only one to reproduce these three data patterns, and the optimal foraging model produced poor matches to all of them. The random foraging strategy reproduced two of the three patterns but was not able to guarantee population persistence. We conclude that with the memory-based foraging strategy including a flocking mechanism our model is realistic enough to estimate the potential exposure of woodpigeons to pesticides. We discuss how exposure can be linked to our model, and how the model could be used for risk assessment of pesticides, for example predicting exposure and effects in heterogeneous landscapes planted seasonally with a variety of crops, while accounting for differences in land use between landscapes.

Multi‐method global sensitivity analysis (MMGSA) for modelling floodplain hydrological processes

When studying hydrological processes with a numerical model, global sensitivity analysis (GSA) is essential if one is to understand the impact of model parameters and model formulation on results. However, different definitions of sensitivity can lead to a difference in the ranking of importance of the different model factors. Here we combine a fuzzy performance function with different methods of calculating global sensitivity to perform a multi-method global sensitivity analysis (MMGSA). We use an application of a finite element subsurface flow model (ESTEL-2D) on a flood inundation event on a floodplain of the River Severn to illustrate this new methodology. We demonstrate the utility of the method for model understanding and show how the prediction of state variables, such as Darcian velocity vectors, can be affected by such a MMGSA. This paper is a first attempt to use GSA with a numerically intensive hydrological model.