Creating flexible and automated production facilities is a complex process that requires high levels of cooperation involving all mechatronics disciplines, where software tools being utilised have to work as closely as their users. Some of these tools are well-integrated but others can hardly exchange any data. This research aims to integrate the software systems applied by the mechatronic engineering disciplines to enable an enhanced design process characterised by a more parallel and iterative work flow. This thesis approaches systems integration from a data modelling point of view because it sees information transfer between heterogeneous data models as a key element of systems integration. A new approach has been developed which is called middle-in data modelling strategy since it is a combination of currently applied top-down and bottom-up approaches. It includes the separation of data into core design data which is modelled top-down and detailed design data modules which are modelled bottom-up. The effectiveness of the integration approach has been demonstrated in a case study undertaken for the mechatronic engineering design process of body shop production lines in the automotive industry. However, the application of the middle-in data modelling strategy is not limited to this use case: it can be used to enhance a variety of system integration tasks. The middle-in data modelling strategy is tested and evaluated in comparison with present top-down and bottom-up data modelling strategies on the basis of three test cases. These test cases simulated how the systems integration solutions based on the different data modelling strategies react to certain disturbances in the data exchange process as they would likely occur during industrial engineering design work. The result is that the top-down data modelling strategy is best in maintaining data integrity and consistency while the bottom-up strategy is most flexibly adaptable to further developments of systems integration solutions. The middle-in strategy combines the advantages of top-down and bottom-up approaches while their weaknesses and disadvantages are kept at a minimum. Hence, it enables the maintenance of data modelling consistency while being responsive to multidisciplinary requirements and adaptive during its step-by-step introduction into an industrial engineering process.

This thesis presents a data-driven approach to constrained control in the form of a subspace-based state-space system identification algorithm integrated into a model predictive controller. Generally this approach has been termed model-free predictive control in the literature. Previous research into this area focused on the system identification aspects resulting in an omission of many of the features that would make such a control strategy attractive to industry. These features include constraint handling, zero-offset setpoint tracking and non-stationary disturbance rejection. The link between non-stationary disturbance rejection in subspace-based state-space system identification and integral action in state-space based model predictive control was shown. Parameterization with Laguerre orthonormal functions was proposed for the reduction in computational load of the controller. Simulation studies were performed using three real-world systems demonstrating: identification capabilities in the presence of white noise and non-stationary disturbances; unconstrained and constrained control; and the benefits and costs of parameterization with Laguerre polynomials.

No / In this paper we discuss techniques for 3D data modelling and processing where the data are usually provided as point clouds which arise from 3D scanning devices. The particular approaches we adopt in modelling 3D data involves the use of Partial Differential Equations (PDEs). In particular we show how the continuous and discrete versions of elliptic PDEs can be used for data modelling. We show that using PDEs it is intuitively possible to model data corresponding to complex scenes. Furthermore, we show that data can be stored in compact format in the form of PDE boundary conditions. In order to demonstrate the methodology we utlise several examples of practical nature.

Diabetes has become one of the most severe deceases due to an increasing number of diabetes patients globally. A large amount of digital data on diabetes has been collected through various channels. How to utilize these data sets to help doctors to make a decision on diagnosis, treatment and prediction of diabetic patients poses many challenges to the research community. The thesis investigates mathematical models with a focus on neural networks for large scale diabetes data modelling and analysis by utilizing modern computing technologies such as grid computing and cloud computing. These computing technologies provide users with an inexpensive way to have access to extensive computing resources over the Internet for solving data and computationally intensive problems. This thesis evaluates the performance of seven representative machine learning techniques in classification of diabetes data and the results show that neural network produces the best accuracy in classification but incurs high overhead in data training. As a result, the thesis develops MRNN, a parallel neural network model based on the MapReduce programming model which has become an enabling technology in support of data intensive applications in the clouds. By partitioning the diabetic data set into a number of equally sized data blocks, the workload in training is distributed among a number of computing nodes for speedup in data training. MRNN is first evaluated in small scale experimental environments using 12 mappers and subsequently is evaluated in large scale simulated environments using up to 1000 mappers. Both the experimental and simulations results have shown the effectiveness of MRNN in classification, and its high scalability in data training. MapReduce does not have a sophisticated job scheduling scheme for heterogonous computing environments in which the computing nodes may have varied computing capabilities. For this purpose, this thesis develops a load balancing scheme based on genetic algorithms with an aim to balance the training workload among heterogeneous computing nodes. The nodes with more computing capacities will receive more MapReduce jobs for execution. Divisible load theory is employed to guide the evolutionary process of the genetic algorithm with an aim to achieve fast convergence. The proposed load balancing scheme is evaluated in large scale simulated MapReduce environments with varied levels of heterogeneity using different sizes of data sets. All the results show that the genetic algorithm based load balancing scheme significantly reduce the makespan in job execution in comparison with the time consumed without load balancing.

Modelos de simulação têm sido utilizados em estudos agronômicos e ecológicos como ferramenta de pesquisa, possibilitando organizar o entendimento das respostas das plantas a diferentes condições ambientais e, consequentemente predizer a produtividade das culturas. O sucesso na aplicação de modelos depende de sua calibração e das avaliações efetuadas por meio da estimação dos índices morfofisiológicos e dos parâmetros tecnológicos de rendimento da cultura. No presente estudo os objetivos foram: utilizar o conjunto composto por um modelo agrohidrológico (SWAP, Soil Water Atmosphere and Plant) com um modelo genérico de crescimento de cultura (WOFOST, WOrld FOod STudies) adaptando-o para a cultura da cana-deaçúcar; usar o programa PEST (Parameter ESTimation) para calibrar e realizar a análise de sensibilidade dos parâmetros desse modelo; avaliar a eficácia do modelo calibrado e validado para a estimação de massa fresca de colmos industrializáveis por hectare (TCH) e avaliar a eficácia do modelo estimando as propriedades hidráulicas do solo por funções de pedotransferência (PTFs). Dados experimentais da cultivar SP 81- 3250 foram utilizadas para a calibração (4 ciclos de produção: cana planta e 3 socas conduzidas na usina Santa Adélia), na análise de sensibilidade (cana planta conduzida na usina Santa Adélia) e validação do modelo (8 ciclos de produção: cana planta e 3 socas conduzidas em 2 usinas). A produtividade média de 25 cultivares em 10 locais da região Centro-Sul foi utilizada para avaliar a eficiência do modelo validado na previsão de colheita. A avaliação desses procedimentos foi realizada por meio dos índices estatísticos: raiz quadrada dos quadrados dos desvios (RMSE), coeficiente de regressão linear (R2), índice de concordância (d) e índice de eficiência (E) em dados de matéria seca simuladas pelo modelo versus dados observados a campo. Bons resultados foram obtidos na calibração e validação do modelo, portanto conclui-se o SWAP/WOFOST apto na utilização da simulação da massa seca da parte aérea. De modo geral, o modelo mostrou alta sensibilidade relativa aos parâmetros do grupo de assimilação de CO2 para a massa seca final da parte aérea. Ainda, o programa PEST foi eficaz na otimização dos parâmetros hidráulicos confrontando o conteúdo de água no solo medido a campo pelo método gravimétrico versus simulado pelo modelo. A previsão de safra realizada pelo SWAP/WOFOST apresentou valores de RMSE semelhantes aos obtidos com o APSIM-Sugarcane. Contudo, por meio de outros índices estatísticos o desempenho do SWAP/WOFOST foi intermediário. A eficiência do modelo em simular a massa seca da parte aérea em ambos os ciclos estudados, cana planta e socas, não foi afetada quando os parâmetros hidráulicos do solo foram obtidos por meios de PTFs. / Simulation models have been used in agricultural and ecological studies as a research tool allowing to organize and comprehend crop response under different environmental conditions, and to predict crop yield. The successful application of models depends on its calibration and assessment performed through morphophysiological index estimation and technological parameters of crop yield. The aims of this study were: to use the combination of a hydrological model (SWAP, Soil Water Atmosphere and Plant) with a generic crop growth model (WOFOST, WOrld FOod STudies) adapted to the sugarcane crop; to use PEST software (Parameter ESTimation) to calibrate and perform the sensitivity analysis of the model parameters; to appraise the model efficiency in estimating fresh weight of cane stems per hectare (TCH) and also to evaluate the effectiveness of the model when estimating soil hydraulic properties generated by pedotransfer functions (PTFs). Experimental data from SP 81-3250 cultivar were used for calibration (4 production cycles: first year cane and 3 ratoons at Santa Adélia sugarcane mill planting area), sensitivity analysis (first year cane at Santa Adélia sugarcane mill planting area) and validation (8 production cycles: 2 first year cane and 6 ratoons at two other sugarcane mill planting areas). Average yield from 25 cultivars grown among 10 different sites in Brazilian South-Central region were used to evaluate the model efficiency on yield prediction. The root mean square error (RMSE), coefficient of determination (R2), index of agreement (d) and coefficient of efficiency (E) were used to evaluate these procedures comparing above ground dry weight simulated by the model versus dry weight measured on the field. Good results were obtained during calibration and validation procedure; hence we may conclude that the SWAP/WOFOST model was able to predict sugarcane above ground dry weight. Model sensitivity analysis showed the group of CO2 assimilation parameters to be the most relevant determining final biomass. PEST software was effective in optimizing the hydraulic parameters by comparing the soil water content simulated by the model against water content measured on the field by the gravimetric method. TCH forecasts made by SWAP/WOFOST showed a similar RMSE as those obtained with the APSIMSugarcane model. However, evaluated by other statistical indexes, the SWAP/WOFOST model performance was intermediate. The model efficiency to simulate the above ground dry weight in crop cycles, first year cane and ratoon, was not affected when the hydraulic input parameters were generated by PTFs.

Modelos de simulação têm sido utilizados em estudos agronômicos e ecológicos como ferramenta de pesquisa, possibilitando organizar o entendimento das respostas das plantas a diferentes condições ambientais e, consequentemente predizer a produtividade das culturas. O sucesso na aplicação de modelos depende de sua calibração e das avaliações efetuadas por meio da estimação dos índices morfofisiológicos e dos parâmetros tecnológicos de rendimento da cultura. No presente estudo os objetivos foram: utilizar o conjunto composto por um modelo agrohidrológico (SWAP, Soil Water Atmosphere and Plant) com um modelo genérico de crescimento de cultura (WOFOST, WOrld FOod STudies) adaptando-o para a cultura da cana-deaçúcar; usar o programa PEST (Parameter ESTimation) para calibrar e realizar a análise de sensibilidade dos parâmetros desse modelo; avaliar a eficácia do modelo calibrado e validado para a estimação de massa fresca de colmos industrializáveis por hectare (TCH) e avaliar a eficácia do modelo estimando as propriedades hidráulicas do solo por funções de pedotransferência (PTFs). Dados experimentais da cultivar SP 81- 3250 foram utilizadas para a calibração (4 ciclos de produção: cana planta e 3 socas conduzidas na usina Santa Adélia), na análise de sensibilidade (cana planta conduzida na usina Santa Adélia) e validação do modelo (8 ciclos de produção: cana planta e 3 socas conduzidas em 2 usinas). A produtividade média de 25 cultivares em 10 locais da região Centro-Sul foi utilizada para avaliar a eficiência do modelo validado na previsão de colheita. A avaliação desses procedimentos foi realizada por meio dos índices estatísticos: raiz quadrada dos quadrados dos desvios (RMSE), coeficiente de regressão linear (R2), índice de concordância (d) e índice de eficiência (E) em dados de matéria seca simuladas pelo modelo versus dados observados a campo. Bons resultados foram obtidos na calibração e validação do modelo, portanto conclui-se o SWAP/WOFOST apto na utilização da simulação da massa seca da parte aérea. De modo geral, o modelo mostrou alta sensibilidade relativa aos parâmetros do grupo de assimilação de CO2 para a massa seca final da parte aérea. Ainda, o programa PEST foi eficaz na otimização dos parâmetros hidráulicos confrontando o conteúdo de água no solo medido a campo pelo método gravimétrico versus simulado pelo modelo. A previsão de safra realizada pelo SWAP/WOFOST apresentou valores de RMSE semelhantes aos obtidos com o APSIM-Sugarcane. Contudo, por meio de outros índices estatísticos o desempenho do SWAP/WOFOST foi intermediário. A eficiência do modelo em simular a massa seca da parte aérea em ambos os ciclos estudados, cana planta e socas, não foi afetada quando os parâmetros hidráulicos do solo foram obtidos por meios de PTFs. / Simulation models have been used in agricultural and ecological studies as a research tool allowing to organize and comprehend crop response under different environmental conditions, and to predict crop yield. The successful application of models depends on its calibration and assessment performed through morphophysiological index estimation and technological parameters of crop yield. The aims of this study were: to use the combination of a hydrological model (SWAP, Soil Water Atmosphere and Plant) with a generic crop growth model (WOFOST, WOrld FOod STudies) adapted to the sugarcane crop; to use PEST software (Parameter ESTimation) to calibrate and perform the sensitivity analysis of the model parameters; to appraise the model efficiency in estimating fresh weight of cane stems per hectare (TCH) and also to evaluate the effectiveness of the model when estimating soil hydraulic properties generated by pedotransfer functions (PTFs). Experimental data from SP 81-3250 cultivar were used for calibration (4 production cycles: first year cane and 3 ratoons at Santa Adélia sugarcane mill planting area), sensitivity analysis (first year cane at Santa Adélia sugarcane mill planting area) and validation (8 production cycles: 2 first year cane and 6 ratoons at two other sugarcane mill planting areas). Average yield from 25 cultivars grown among 10 different sites in Brazilian South-Central region were used to evaluate the model efficiency on yield prediction. The root mean square error (RMSE), coefficient of determination (R2), index of agreement (d) and coefficient of efficiency (E) were used to evaluate these procedures comparing above ground dry weight simulated by the model versus dry weight measured on the field. Good results were obtained during calibration and validation procedure; hence we may conclude that the SWAP/WOFOST model was able to predict sugarcane above ground dry weight. Model sensitivity analysis showed the group of CO2 assimilation parameters to be the most relevant determining final biomass. PEST software was effective in optimizing the hydraulic parameters by comparing the soil water content simulated by the model against water content measured on the field by the gravimetric method. TCH forecasts made by SWAP/WOFOST showed a similar RMSE as those obtained with the APSIMSugarcane model. However, evaluated by other statistical indexes, the SWAP/WOFOST model performance was intermediate. The model efficiency to simulate the above ground dry weight in crop cycles, first year cane and ratoon, was not affected when the hydraulic input parameters were generated by PTFs.

<p>Technology of today makes it possible to change the way traditional healthcare is conducted. As the population grows older, and the elderly is becoming an increasing part of the whole population, the need for cost efficient and personalised care increases. By implementing home healthcare IT projects, it is possible for more patients to be treated at home with sustained quality of care.</p><p>This thesis documents the work of a master’s degree project carried out during the autumn of 2005. The project is part of a research project within the Department of Biomedical Engineering at Linköping University. The project aims at developing a data model for storing medical data. The model should be general and easy to expand. This model is intended to be used within a larger system allowing a patient to measure medical data from a remote location.</p>

Technology of today makes it possible to change the way traditional healthcare is conducted. As the population grows older, and the elderly is becoming an increasing part of the whole population, the need for cost efficient and personalised care increases. By implementing home healthcare IT projects, it is possible for more patients to be treated at home with sustained quality of care. This thesis documents the work of a master’s degree project carried out during the autumn of 2005. The project is part of a research project within the Department of Biomedical Engineering at Linköping University. The project aims at developing a data model for storing medical data. The model should be general and easy to expand. This model is intended to be used within a larger system allowing a patient to measure medical data from a remote location.

Software development processes such as the Waterfall process and Extreme Programming are Project Management Methods (PMMs) which are well known and widely used. However, conventional Project Management (PM) lacks the process concepts expressed in PMMs, and the connection between PMMs and PM is not much explored in the literature. Our research problem is to make this connection. We present data models for PM and PMM, in a framework that can articulate the PM-to-PMM relationship, illustrating with simple examples. Tools and visualizations created in terms of our framework can make use of the familiarity, history and context of project management tools, and the prescriptiveness and reactivity of PMMs, and we believe these may assist the management of complicated projects, such as IT projects. Project Mentor, a prototype Java/XML implementation of the framework semantics, can create and then revise a PMM-aware project, conforming to a specified PMM. The PM-to-PMM connection is persistent in project data, and we describe a visualization of the footsteps of a PMM in project data that does not rely on the state of a PMM process. The visualization can also be used by Project Mentor, to indicate the state of a PMM. We test for possible applications of our framework with a case study and survey of some existing project data, and conclude with a description of further work.

Orientador: Gilmar Barreto / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-04T17:28:20Z (GMT). No. of bitstreams: 1 Pucciarelli_AmilcarJose_M.pdf: 3241769 bytes, checksum: cfad3cee97a35c84df363b3a8578479d (MD5) Previous issue date: 2005 / Resumo: Este estudo primeiramente investiga fundamentos teóricos para análise, desenvolvimento e implementação de algoritmos para modelagem de dados de sistemas dinâmicos e de séries temporais com a finalidade de predição. As séries temporais utilizadas são baseadas em dados reais retirados da literatura. A grande vantagem de se modelar uma série temporal e de se prever um dado futuro é poder tomar ações antecipadas sobre ela o quem vem a ser muito útil, por exemplo em controle. O modelo nebuloso Takagi-Sugeno será utilizado na modelagem das séries temporais onde os conjuntos nebulosos do antecedente e os parâmetros do conseqüente são estimados via métodos de agrupamentos e identificação paramétrica, respectivamente / Abstract: This work firstly explores theoretical foundations for analisis, development and implementation of algorithms for data modelling dynamic systems and time series with a prediction goaI. The used time series are based on real data from the literature. The main advantage of time series modelling and forecasting is make antecipated decisions about it, and this becomes very useful, for example, in controI. The Takagi-Sugeno fuzzy model is used for time series modelling where the antecedent fuzzy partitions and the consequent parameters are estimated by clustering methods and parametric identification, respectively / Mestrado / Automação / Mestre em Engenharia Elétrica