Les récentes recherches en robotique ont étendu l’utilisation des robots au-delà des environnements industriels traditionnels. Les robots humanoïdes sont bien adaptés pour effectuer des tâches accomplies par l’homme, en raison de leurs formes et leurs capacités de mouvements «humaines». En général, les robots humanoïdes ont un torse, une tête, deux bras et deux jambes. Ils ont été développés pour effectuer des tâches humaines telles que l’assistance personnelle, où ils devraient être en mesure d’aider les personnes âgées ou malades, ou effectuer des missions dangereuses, etc. C’est pourquoi les chercheurs ont besoin d’extraire des connaissances sur le mouvement humain grâce à l’observation continue du comportement humain. Ceci aidera les robots à être capables d’effectuer et d’accomplir des tâches en interagissant avec un humain. Le mouvement humain peut être analysé grâce à des systèmes de capture de mouvement, par exemple le système Vicon. L’objectif principal du projet de recherche est de développer des algorithmes afin qu’un robot soit capable d’interagir avec un partenaire humain en temps réel. Dans le présent travail, nous proposons et validons un algorithme probabiliste complet pour la prédiction du mouvement humain, nous montrerons que le modèle d’inférence peut anticiper vigoureusement les actions de l’être humain. Notre approche est basée sur un algorithme intégrant les paramètres des GMMs (Gaussian Mixture Models) dans un filtre de Kalman.

<p>Feedback control of Chest Acceleration or Chest Deflection is believed to be a good way of minimizing the risk of injury. In order to implement such a controller in a car, an observer estimating these responses is needed. The objective of the study was to develop a model of the dummy’s chest capable of estimating the Chest Acceleration and the Chest Deflection during frontal crashes in real time. The used sensor data come from car accelerometer and spindle rotation sensor of the belt, the data has been collected from dummies during crash tests. This study has accomplished the aims using a simple linear model of the chest using masses, springs and dampers. The parameters of the model have been estimated through system identification. Two types of black-box models have also been studied, one ARX model and one state-space model. The models have been tested and validated against data coming from different crash setups. The results show that all of the studied models can be used to estimate the dummy responses, the physical grey-box model and the black-box state-space model in particular.</p> / <p>Genom att använda återkoppling av storheterna bröstacceleration och bröstintryck antas man kunna minska risken för skador vid krockar i personbilar. För att kunna implementera detta behövs en observatör för dessa storheter. Målet med denna studie är att ta fram en modell för att kunna skatta accelerationen i bröstkorgen samt bröstintrycket i realtid i frontala krockar. Sensordata som använts kom från en accelerometer och en givare för att mäta rotationen i bältessnurran. Detta har gjorts genom att modellera bröstkorgen med linjära fjädrar och dämpare. Dess parametrar har skattats från data från krocktester från krockdockor. Två s.k. black-box-modeller har också tagits fram, en ARX-modell och en på tillståndsform. Modellerna har testats och validerats mha data från olika sorters krocktester. Resultaten visar att alla studerade modeller kan användas för att skatta de ovan nämnda storheterna, den fysikaliska modellen och black-box-modellen på tillståndsform fungerade bäst.</p>

Feedback control of Chest Acceleration or Chest Deflection is believed to be a good way of minimizing the risk of injury. In order to implement such a controller in a car, an observer estimating these responses is needed. The objective of the study was to develop a model of the dummy’s chest capable of estimating the Chest Acceleration and the Chest Deflection during frontal crashes in real time. The used sensor data come from car accelerometer and spindle rotation sensor of the belt, the data has been collected from dummies during crash tests. This study has accomplished the aims using a simple linear model of the chest using masses, springs and dampers. The parameters of the model have been estimated through system identification. Two types of black-box models have also been studied, one ARX model and one state-space model. The models have been tested and validated against data coming from different crash setups. The results show that all of the studied models can be used to estimate the dummy responses, the physical grey-box model and the black-box state-space model in particular. / Genom att använda återkoppling av storheterna bröstacceleration och bröstintryck antas man kunna minska risken för skador vid krockar i personbilar. För att kunna implementera detta behövs en observatör för dessa storheter. Målet med denna studie är att ta fram en modell för att kunna skatta accelerationen i bröstkorgen samt bröstintrycket i realtid i frontala krockar. Sensordata som använts kom från en accelerometer och en givare för att mäta rotationen i bältessnurran. Detta har gjorts genom att modellera bröstkorgen med linjära fjädrar och dämpare. Dess parametrar har skattats från data från krocktester från krockdockor. Två s.k. black-box-modeller har också tagits fram, en ARX-modell och en på tillståndsform. Modellerna har testats och validerats mha data från olika sorters krocktester. Resultaten visar att alla studerade modeller kan användas för att skatta de ovan nämnda storheterna, den fysikaliska modellen och black-box-modellen på tillståndsform fungerade bäst.

The demand of space applications has been increasing over the years. This has derivedin new satellites structures that required from precise and robust control management.The satellite design is evolving towards the development of lighter structures. The combinationof lighter structures with precise and robust control has arisen the problem ofstructure vibration control. The control design of structures with large appendages likeantennas, booms or solar panels has become a challenge. The flexible dynamics of theappendages needs to be considered when performing the attitude analysis of the satellite,since these parts can be easily excited by the environment perturbations such us gravity,gravity gradient or solar wind. The objective of these research project is to develop ahigh-fidelity model plant of a satellite with flexible panels and review different systemidentification techniques used to observe the states of the system. The equations of themodel are reviewed and the model is verified against a multi-body software, Adams. Thesensors and actuators are selected and modelled for the control of the rigid body and theobservation of the rigid and flexible body. For the implementation of the flexible structureobservations a technique based in Genetic Algorithm is applied for optimal sensor location.Finally, different system identification techniques are reviewed for the identificationof modal parameters and rigid body parameters. The results illustrate the performanceof the model and how the different system identification techniques are performed whenobserving the model states.

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Background: Respiratory illnesses can have a substantial impact on population health and burden hospitals in terms of patient load. Advance warnings of the spread of such illness could inform public health interventions and help hospitals manage patient services. Previous research showed that calls for respiratory complaints to Telehealth Ontario are correlated up to two weeks in advance with emergency department visits for respiratory illness at the provincial level. Objectives: This thesis examined whether Telehealth Ontario calls for respiratory complaints could be used to accurately forecast the daily and weekly number of emergency department visits for respiratory illness at the health unit level for each of the 36 health units in Ontario up to 14 days in advance in the context of a real-time syndromic surveillance system. The forecasting abilities of three different time series modeling techniques were compared. Methods: The thesis used hospital emergency department visit data from the National Ambulatory Care Reporting System database and Telehealth Ontario call data and from June 1, 2004 to March 31, 2006. Parallel Cascade Identification (PCI), Fast Orthogonal Search (FOS), and Numerical Methods for Subspace State Space System Identification (N4SID) algorithms were used to create prediction models for the daily number of emergency department visits using Telehealth call counts and holiday/weekends as predictors. Prediction models were constructed using the first year of the study data and their accuracy was measured over the second year of data. Factors associated with prediction accuracy were examined. Results: Forecast error varied widely across health units. Prediction error increased with lead time and lower call-to-visits ratio. Compared with N4SID, PCI and FOS had significantly lower forecast error. Forecasts of the weekly aggregate number of visits showed little evidence of ability to accurately flag corresponding actual increases. However, when visits were aggregated over a four day period, increases could be flagged more accurately than chance in six of the 36 health units accounting for approximately half of the Ontario population. Conclusions: This thesis suggests that Telehealth Ontario data collected by a real-time syndromic surveillance system could play a role in forecasting health services demand for respiratory illness. / Thesis (Master, Community Health & Epidemiology) -- Queen's University, 2009-08-11 16:20:44.553