Le concept d’habitat intelligent s’est largement développé ces dernières années afin de proposer des solutions face à deux préoccupations majeures : la gestion optimisée de l’énergie dans le bâtiment et l’aide au maintien à domicile de personnes âgées. C’est dans ce contexte que le projet CAPTHOM, dans lequel s’inscrit cette thèse, a été développé. Pour répondre à ces problématiques, de nombreux capteurs, de natures différentes, sont utilisés pour la détection de la présence humaine, la détermination de la localisation et de la posture de la personne. En effet, aucun capteur, ne peut, seul, répondre à l’ensemble de ces informations justifiant le développement d’un dispositif multi-capteurs et d’une politique de fusion de données. Dans ce projet, les capteurs retenus sont les détecteurs infrarouges passifs, les thermopiles et la caméra. Aucun capteur n’est porté par la personne (non invasivité du dispositif). Nous proposons une architecture globale du capteur intelligent composée de quatre modules de fusion permettant respectivement de détecter la présence humaine, de localiser en 3D la personne, de déterminer la posture et d’aider à la prise de décision finale selon l’application visée. Le module de détection de présence fusionne les informations des trois capteurs : les détecteurs IRP pour la détection du mouvement, les thermopiles pour la présence en cas d’immobilité de la personne et la caméra pour identifier l’entité détectée. La localisation 3D de la personne est réalisée grâce à l’estimation de position sur horizon glissant. Cette méthode, nommée Visual Receding Horizon Estimation (VRHE), formule le problème d’estimation de position en un problème d’optimisation non linéaire sous contraintes dans le plan image. Le module de fusion pour la détermination de posture s’appuie sur la théorie des ensembles flous. Il assure la détermination de la posture indépendamment de la personne et de sa distance vis à vis de la caméra. Enfin, un module d’aide à la décision fusionne les sorties des différents modules et permet de déclencher des alarmes dans le cas de la surveillance de personnes âgées ou de déclencher des applications domotiques (chauffage, éclairage) pour la gestion énergétique de bâtiments. / The smart home concept has been widely developed in the last years in order to propose solutions for twomain concerns : optimized energy management in building and help for in-home support for elderly people.In this context, the CAPTHOM project, in which this thesis is in line with, has been developed. To respondto these problems, many sensors, of different natures, are used to detect the human presence, to determinethe position and the posture of the person. In fact, no sensor can , alone, answers to all information justifyingthe development of a multi-sensor system and a data fusion method. In this project, the selected sensorsare passive infrared sensors (PIR), thermopiles and a video camera. No sensor is carried by the person(non invasive system). We propose a global architecture of intelligent sensor made of four fusion modulesallowing respectively to detect the human presence, to locate in 3D the person, to determine the posture andto help to make a decision according to the application. The human presence module fuses information ofthe three sensors : PIR sensors for the movement, thermopiles for the presence in case of immobility and thecamera to identify the detected entity. The 3D localisation of the person is realized thanks to position recedinghorizon estimation. This method, called Visual Receding Horizon Estimation (VRHE), formulates the positionestimation problem into an nonlinear optimisation problem under constraints in the image plane. The fusionmodule for the posture determination is based on fuzzy logic. It insures the posture determination regardlessof the person and the distance from the camera. Finally, the module to make a decision fuses the outputs of the preceding modules and gives the opportunity to launch alarms (elderly people monitoring) or to commandhome automation devices (lightning, heating) for the energy management of buildings.

This thesis focuses on assessing and improving statistical methods implemented in two areas of public health research. The first topic involves estimation of national influenza-associated mortality rates via mathematical modeling. The second topic involves the timely detection of infectious disease outbreaks using statistical process control monitoring. For over fifty years, the Centers for Disease Control and Prevention has been estimating annual rates of U.S. deaths attributable to influenza. These estimates have been used to determine costs and benefits associated with influenza prevention and control strategies. Quantifying the effect of influenza on mortality, however, can be challenging since influenza infections typically are not confirmed virologically nor specified on death certificates. Consequently, a wide range of ecologically based, mathematical modeling approaches have been applied to specify the association between influenza and mortality. To date, all influenza-associated death estimates have been based on mortality data first aggregated at the national level and then modeled. Unfortunately, there are a number of local-level seasonal factors that may confound the association between influenza and mortality - thus suggesting that data be modeled at the local level and then pooled to make national estimates of death. The first component of the thesis topic involving mortality estimation addresses this issue by introducing and implementing a two-stage hierarchical Bayesian modeling approach. In the first stage, city-level data with varying trends in mortality and weather were modeled using semi-parametric, generalized additive models. In the second stage, the log-relative risk estimates calculated for each city in stage 1 represented the “outcome” variable, and were modeled two ways: (1) assuming spatial independence across cities using a Bayesian generalized linear model, and (2) assuming correlation among cities using a Bayesian spatial correlation model. Results from these models were compared to those from a more-conventional approach. The second component of this topic examines the extent to which seasonal confounding and collinearity affect the relationship between influenza and mortality at the local (city) level. Disentangling the effects of temperature, humidity, and other seasonal confounders on the association between influenza and mortality is challenging since these covariates are often temporally collinear with influenza activity. Three modeling strategies with varying representations of background seasonality were compared. Seasonal covariates entered into the model may have been measured (e.g., ambient temperature) or unmeasured (e.g., time-based smoothing splines or Fourier terms). An advantage of modeling background seasonality via time splines is that the amount of seasonal curvature can be controlled by the number of degrees of freedom specified for the spline. A comparison of the effects of influenza activity on mortality based on these varying representations of seasonal confounding is assessed. The third component of this topic explores the relationship between mortality rates and influenza activity using a flexible, natural cubic spline function to model the influenza term. The conventional approach of fitting influenza-activity terms linearly in regression was found to be too constraining. Results show that the association is best represented nonlinearly. The second area of focus in this thesis involves infectious disease outbreak detection. A fundamental goal of public health surveillance, particularly syndromic surveillance, is the timely detection of increases in the rate of unusual events. In syndromic surveillance, a significant increase in the incidence of monitored disease outcomes would trigger an alert, possibly prompting the implementation of an intervention strategy. Public health surveillance generally monitors count data (e.g., counts of influenza-like illness, sales of over-the-counter remedies, and number of visits to outpatient clinics). Statistical process control charts, designed for quality control monitoring in industry, have been widely adapted for use in disease and syndromic surveillance. The behavior of these detection methods on discrete distributions, however, has not been explored in detail. For this component of the thesis, a simulation study was conducted to compare the CuSum and EWMA methods for detection of increases in negative binomial rates with varying amounts of dispersion. The goal of each method is to detect an increase in the mean number of cases as soon as possible after an upward rate shift has occurred. The performance of the CuSum and EWMA detection methods is evaluated using the conditional expected delay criterion, which is a measure of the detection delay, i.e., the time between the occurrence of a shift and when that shift is detected. Detection capabilities were explored under varying shift sizes and times at which the shifts occurred.

published_or_final_version / abstract / Civil Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Statistics and Actuarial Science / Master / Master of Philosophy

published_or_final_version / abstract / Urban Planning and Environmental Management / Doctoral / Doctor of Philosophy

The research examines the difficulties relating to the construction of a computer model for the automation of the design and cost estimation practices in engineering. The research has been carried out in an industrial context and the work has included both theoretical and pragmatic issues relating to the modelling of expertise for the production of commercially useful software in the field of combustion system design. The difficulties relating to the capture of ill-defined knowledge and subjective human decision-making with 'traditional' programming languages is examined and a study undertaken regarding the use of Artificial Intelligence techniques. A particular emphasis has been placed on decision-making under uncertainty. The work has resulted in the construction of a prototype expert system that can be used to produce design quotations relating to single burner gas-fired combustion systems. An important aspect of the decision-making characteristic of engineering design, and particularly for the cost estimating stages of a project, is the mannner in which engineers combine both cost and technical data in order to arrive at design solutions on the criterion 'value for money'. The formal mathematics of probability theory and confirmation theory provide tools for modelling expertise of this kind and the research has developed and examined two parallel systems. The first is based on the use of Bayes' theorem and the second makes use of ideas from both confirmation and fuzzy set theory. The general approach, developed within the research, of combining knowledge types relating to 'fitness for purpose' and 'cost' into ordinal measures of 'value' is fundamental to many areas of decision-making and has many applications. The research also addresses the use of rule-based methods for application domains where the knowledge is continually changing and the expertise of users is variable.

The primary objective of this work is to develop and demonstrate a steady-state stochastic simulation method to estimate the PV hosting capacity of a given distribution, based on the ANSI voltage regulation standard. The work discusses the key factors that determine the voltage rise due to distributed PV. Load demand analysis is done to determine statistically representative minimum daylight load demand for PV analysis. And lastly, the steady-state, stochastic simulation method is discussed and implemented to estimate the PV hosting capacity for small-scale and large-scale PV Deployments. / text

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