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Дисертації з теми "Apprentissage automatique – Prévision – Utilisation"
Loisel, Julie. "Détection des ruptures de la chaîne du froid par une approche d'apprentissage automatique." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASB014.
Повний текст джерелаThe cold chain is essential to ensure food safety and avoid food waste. Wireless sensors are increasingly used to monitor the air temperature through the cold chain, however, the exploitation of these measurements is still limited. This thesis explores how machine learning can be used to predict the temperature of different food products types from the measured air temperature in a pallet and detect cold chain breaks. We introduced, firstly, a definition of a cold chain break based on two main product categories: products obligatorily preserved at a regulated temperature such as meat and fish, and products for which a temperature is recommended such as fruits and vegetables. The cold chain break leads to food poisoning for the first product category and organoleptic quality degradation for the second one.For temperature-regulated products, it is crucial to predict the product temperature to ensure that it does not exceed the regulatory temperature. Although several studies demonstrated the effectiveness of neural networks for the prediction, none has compared the synthetic and experimental data to train them. In this thesis, we proposed to compare these two types of data in order to provide guidelines for the development of neural networks. In practice, the products and packaging are diverse; experiments for each application are impossible due to the complexity of implementation. By comparing synthetic and experimental data, we were able to determine best practices for developing neural networks to predict product temperature and maintain cold chain. For temperature-regulated products, once the cold chain break is detected, they are no more consumable and must be eliminated. For temperature-recommended products, we compared three different approaches to detect cold chain breaks and implement corrective actions: a) method based on a temperature threshold, b) method based on a classifier which determines whether the products will be delivered with the expected qualities, and c) method also based on a classifier but which integrates the cost of the corrective measure in the decision-making process. The performances of the three methods are discussed and prospects for improvement are proposed
De, Carvalho Gomes Fernando. "Utilisation d'algorithmes stochastiques en apprentissage." Montpellier 2, 1992. http://www.theses.fr/1992MON20254.
Повний текст джерелаToqué, Florian. "Prévision et visualisation de l'affluence dans les transports en commun à l'aide de méthodes d'apprentissage automatique." Thesis, Paris Est, 2019. http://www.theses.fr/2019PESC2029.
Повний текст джерелаAs part of the fight against global warming, several countries around the world, including Canada and some European countries, including France, have established measures to reduce greenhouse gas emissions. One of the major areas addressed by the states concerns the transport sector and more particularly the development of public transport to reduce the use of private cars. To this end, the local authorities concerned aim to establish more accessible, clean and sustainable urban transport systems. In this context, this thesis, co-directed by the University of Paris-Est, the french institute of science and technology for transport, development and network (IFSTTAR) and Polytechnique Montréal in Canada, focuses on the analysis of urban mobility through research conducted on the forecasting and visualization of public transport ridership using machine learning methods. The main motivations concern the improvement of transport services offered to passengers such as: better planning of transport supply, improvement of passenger information (e.g., proposed itinerary in the case of an event/incident, information about the crowd in the train at a chosen time, etc.). In order to improve transport operators' knowledge of user travel in urban areas, we are taking advantage of the development of data science (e.g., data collection, development of machine learning methods). This thesis thus focuses on three main parts: (i) long-term forecasting of passenger demand using event databases, (ii) short-term forecasting of passenger demand and (iii) visualization of passenger demand on public transport. The research is mainly based on the use of ticketing data provided by transport operators and was carried out on three real case study, the metro and bus network of the city of Rennes, the rail and tramway network of "La Défense" business district in Paris, France, and the metro network of Montreal, Quebec in Canada
Kashnikov, Yuriy. "Une approche holistique pour la prédiction des optimisations du compilateur par apprentissage automatique." Versailles-St Quentin en Yvelines, 2013. http://www.theses.fr/2013VERS0047.
Повний текст джерелаEffective compiler optimizations can greatly improve applications performance. These optimizations are numerous and can be applied in any order. Compilers select these optimizations using solutions driven by heuristics which may degrade programs performance. Therefore, developers resort to the tedious manual search for the best optimizations. Combinatorial search space makes this effort intractable and one can easily fall into a local minimum and miss the best combination. This thesis develops a holistic approach to improve applications performance with compiler optimizations and machine learning. A combination of static loop analysis and statistical learning is used to analyze a large corpus of loops and reveal good potential for compiler optimizations. Milepost GCC, a machine-learning based compiler, is applied to optimize benchmarks and an industrial database application. It uses function level static features and classification algorithms to predict a good sequence of optimizations. While Milepost GCC can mispredict the best optimizations, in general it obtains considerable speedups and outperforms state-of-the-art compiler heuristics. The culmination of this thesis is the ULM meta-optimization framework. ULM characterizes applications at different levels with static code features and hardware performance counters and finds the most important combination of program features. By selecting among three classification algorithms and tuning their parameters, ULM builds a sophisticated predictor that can outperform existing solutions. As a result, the ULM framework predicted correctly the best sequence of optimizations sequence in 92% of cases
Dupont, Pierre. "Utilisation et apprentissage de modèles de langage pour la reconnaissance de la parole continue /." Paris : École nationale supérieure des télécommunications, 1996. http://catalogue.bnf.fr/ark:/12148/cb35827695q.
Повний текст джерелаMelzi, Fateh. "Fouille de données pour l'extraction de profils d'usage et la prévision dans le domaine de l'énergie." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1123/document.
Повний текст джерелаNowadays, countries are called upon to take measures aimed at a better rationalization of electricity resources with a view to sustainable development. Smart Metering solutions have been implemented and now allow a fine reading of consumption. The massive spatio-temporal data collected can thus help to better understand consumption behaviors, be able to forecast them and manage them precisely. The aim is to be able to ensure "intelligent" use of resources to consume less and consume better, for example by reducing consumption peaks or by using renewable energy sources. The thesis work takes place in this context and aims to develop data mining tools in order to better understand electricity consumption behaviors and to predict solar energy production, then enabling intelligent energy management.The first part of the thesis focuses on the classification of typical electrical consumption behaviors at the scale of a building and then a territory. In the first case, an identification of typical daily power consumption profiles was conducted based on the functional K-means algorithm and a Gaussian mixture model. On a territorial scale and in an unsupervised context, the aim is to identify typical electricity consumption profiles of residential users and to link these profiles to contextual variables and metadata collected on users. An extension of the classical Gaussian mixture model has been proposed. This allows exogenous variables such as the type of day (Saturday, Sunday and working day,...) to be taken into account in the classification, thus leading to a parsimonious model. The proposed model was compared with classical models and applied to an Irish database including both electricity consumption data and user surveys. An analysis of the results over a monthly period made it possible to extract a reduced set of homogeneous user groups in terms of their electricity consumption behaviors. We have also endeavoured to quantify the regularity of users in terms of consumption as well as the temporal evolution of their consumption behaviors during the year. These two aspects are indeed necessary to evaluate the potential for changing consumption behavior that requires a demand response policy (shift in peak consumption, for example) set up by electricity suppliers.The second part of the thesis concerns the forecast of solar irradiance over two time horizons: short and medium term. To do this, several approaches have been developed, including autoregressive statistical approaches for modelling time series and machine learning approaches based on neural networks, random forests and support vector machines. In order to take advantage of the different models, a hybrid model combining the different models was proposed. An exhaustive evaluation of the different approaches was conducted on a large database including four locations (Carpentras, Brasilia, Pamplona and Reunion Island), each characterized by a specific climate as well as weather parameters: measured and predicted using NWP models (Numerical Weather Predictions). The results obtained showed that the hybrid model improves the results of photovoltaic production forecasts for all locations
Thorey, Jean. "Prévision d’ensemble par agrégation séquentielle appliquée à la prévision de production d’énergie photovoltaïque." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066526/document.
Повний текст джерелаOur main objective is to improve the quality of photovoltaic power forecasts deriving from weather forecasts. Such forecasts are imperfect due to meteorological uncertainties and statistical modeling inaccuracies in the conversion of weather forecasts to power forecasts. First we gather several weather forecasts, secondly we generate multiple photovoltaic power forecasts, and finally we build linear combinations of the power forecasts. The minimization of the Continuous Ranked Probability Score (CRPS) allows to statistically calibrate the combination of these forecasts, and provides probabilistic forecasts under the form of a weighted empirical distribution function. We investigate the CRPS bias in this context and several properties of scoring rules which can be seen as a sum of quantile-weighted losses or a sum of threshold-weighted losses. The minimization procedure is achieved with online learning techniques. Such techniques come with theoretical guarantees of robustness on the predictive power of the combination of the forecasts. Essentially no assumptions are needed for the theoretical guarantees to hold. The proposed methods are applied to the forecast of solar radiation using satellite data, and the forecast of photovoltaic power based on high-resolution weather forecasts and standard ensembles of forecasts
Nachouki, Mirna. "L'acquisition de connaissances dans les systèmes dynamiques : production et utilisation dans le cadre de l'atelier de génie didacticiel intégré." Toulouse 3, 1995. http://www.theses.fr/1995TOU30001.
Повний текст джерелаBaudin, Paul. "Prévision séquentielle par agrégation d'ensemble : application à des prévisions météorologiques assorties d'incertitudes." Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLS117/document.
Повний текст джерелаIn this thesis, we study sequential prediction problems. The goal is to devise and apply automatic strategy, learning from the past, with potential help from basis predictors. We desire these strategies to have strong mathematical guarantees and to be valid in the most general cases. This enables us to apply the algorithms deriving from the strategies to meteorological data predictions. Finally, we are interested in theoretical and practical versions of this sequential prediction framework to cumulative density function prediction. Firstly, we study online prediction of bounded stationary ergodic processes. To do so, we consider the setting of prediction of individual sequences and propose a deterministic regression tree that performs asymptotically as well as the best L-Lipschitz predictor. Then, we show why the obtained regret bound entails the asymptotical optimality with respect to the class of bounded stationary ergodic processes. Secondly, we propose a specific sequential aggregation method of meteorological simulation of mean sea level pressure. The aim is to obtain, with a ridge regression algorithm, better prediction performance than a reference prediction, belonging to the constant linear prediction of basis predictors. We begin by recalling the mathematical framework and basic notions of environmental science. Then, the used datasets and practical performance of strategies are studied, as well as the sensitivity of the algorithm to parameter tuning. We then transpose the former method to another meteorological variable: the wind speed 10 meter above ground. This study shows that the wind speed exhibits different behaviors on a macro level. In the last chapter, we present the tools used in a probabilistic prediction framework and underline their merits. First, we explain the relevancy of probabilistic prediction and expose this domain's state of the art. We carry on with an historical approach of popular probabilistic scores. The used algorithms are then thoroughly described before the descriptions of their empirical results on the mean sea level pressure and wind speed
Desrousseaux, Christophe. "Utilisation d'un critère entropique dans les systèmes de détection." Lille 1, 1998. https://pepite-depot.univ-lille.fr/LIBRE/Th_Num/1998/50376-1998-229.pdf.
Повний текст джерелаКниги з теми "Apprentissage automatique – Prévision – Utilisation"
Ranka, Sanjay, Chengliang Yang, Chris Delcher, and Elizabeth Shenkman. Data Driven Approaches for Healthcare: Machine Learning for Identifying High Utilizers. Taylor & Francis Group, 2019.
Знайти повний текст джерелаData Driven Approaches for Health Care: Machine Learning for Identifying High Utilizers. Taylor & Francis Group, 2019.
Знайти повний текст джерелаRanka, Sanjay, Chengliang Yang, Chris Delcher, and Elizabeth Shenkman. Data Driven Approaches for Healthcare: Machine Learning for Identifying High Utilizers. Taylor & Francis Group, 2019.
Знайти повний текст джерелаRanka, Sanjay, Chengliang Yang, Chris Delcher, and Elizabeth Shenkman. Data Driven Approaches for Healthcare: Machine Learning for Identifying High Utilizers. Taylor & Francis Group, 2019.
Знайти повний текст джерелаRanka, Sanjay, Chengliang Yang, Chris Delcher, and Elizabeth Shenkman. Data Driven Approaches for Healthcare: Machine Learning Approaches for Identifying High Utilizers. Taylor & Francis Group, 2019.
Знайти повний текст джерелаArslan, Hüseyin, and Ertuğrul Başar. Flexible and Cognitive Radio Access Technologies for 5G and Beyond. Institution of Engineering & Technology, 2020.
Знайти повний текст джерелаFlexible and Cognitive Radio Access Technologies for 5G and Beyond. Institution of Engineering & Technology, 2020.
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