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Статті в журналах з теми "Apprentissage automatique – Applications industrielles"
Postadjian, Tristan, Arnaud Le Bris, Hichem Sahbi, and Clément Mallet. "Classification à très large échelle d'images satellites à très haute résolution spatiale par réseaux de neurones convolutifs." Revue Française de Photogrammétrie et de Télédétection, no. 217-218 (September 21, 2018): 73–86. http://dx.doi.org/10.52638/rfpt.2018.418.
Повний текст джерелаBeaudoin, Laurent, and Loïca Avanthey. "Stratégies pour adapter une chaîne de reconstruction 3D au milieu sous-marin : des idées à la pratique." Revue Française de Photogrammétrie et de Télédétection, no. 217-218 (September 21, 2018): 51–61. http://dx.doi.org/10.52638/rfpt.2018.416.
Повний текст джерелаДисертації з теми "Apprentissage automatique – Applications industrielles"
Langlois, Julien. "Vision industrielle et réseaux de neurones profonds : application au dévracage de pièces plastiques industrielles." Thesis, Nantes, 2019. http://www.theses.fr/2019NANT4010/document.
Повний текст джерелаThis work presents a pose estimation method from a RGB image of industrial parts placed in a bin. In a first time, neural networks are used to segment a certain number of parts in the scene. After applying an object mask to the original image, a second network is inferring the local depth of the part. Both the local pixel coordinates of the part and the local depth are used in two networks estimating the orientation of the object as a quaternion and its translation on the Z axis. Finally, a registration module working on the back-projected local depth and the 3D model of the part is refining the pose inferred from the previous networks. To deal with the lack of annotated real images in an industrial context, an data generation process is proposed. By using various light parameters, the dataset versatility allows to anticipate multiple challenging exploitation scenarios within an industrial environment
Le, Nguyen Minh Huong. "Online machine learning-based predictive maintenance for the railway industry." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT027.
Повний текст джерелаBeing an effective long-distance mass transit, the railway will continue to flourish for its limited carbon footprint in the environment. Ensuring the equipment's reliability and passenger safety brings forth the need for efficient maintenance. Apart from the prevalence of corrective and periodic maintenance, predictive maintenance has come into prominence lately. Recent advances in machine learning and the abundance of data drive practitioners to data-driven predictive maintenance. The common practice is to collect data to train a machine learning model, then deploy the model for production and keep it unchanged afterward. We argue that such practice is suboptimal on a data stream. The unboundedness of the stream makes the model prone to incomplete learning. Dynamic changes on the stream introduce novel concepts unseen by the model and decrease its accuracy. The velocity of the stream makes manual labeling infeasible and disables supervised learning algorithms. Therefore, switching from a static, offline learning paradigm to an adaptive, online one is necessary, especially when new generations of connected trains continuously generating sensor data have already been a reality. We investigate the applicability of online machine learning for predictive maintenance on typical complex systems in the railway. First, we develop InterCE as an active learning-based framework that extracts cycles from an unlabeled stream by interacting with a human expert. Then, we implement a long short-term memory autoencoder to transform the extracted cycles into feature vectors that are more compact yet remain representative. Finally, we design CheMoc as a framework that continuously monitors the condition of the systems using online adaptive clustering. Our methods are evaluated on the passenger access systems on two fleets of passenger trains managed by the national railway company SNCF of France
Teytaud, Olivier. "Apprentissage, réseaux de neurones et applications." Lyon 2, 2001. http://theses.univ-lyon2.fr/documents/lyon2/2001/teytaud_o.
Повний текст джерелаTeytaud, Olivier Paugam-Moisy Hélène. "Apprentissage, réseaux de neurones et applications." [S.l.] : [s.n.], 2001. http://demeter.univ-lyon2.fr:8080/sdx/theses/lyon2/2001/teytaud_o.
Повний текст джерелаZennir, Youcef. "Apprentissage par renforcement et systèmes distribués : application à l'apprentissage de la marche d'un robot hexapode." Lyon, INSA, 2004. http://theses.insa-lyon.fr/publication/2004ISAL0034/these.pdf.
Повний текст джерелаThe goal of this thesis is to study and to develop reinforcement learning techniques in order a hexapod robot to learn to walk. The main assumption on which this work is based is that effective gaits can be obtained as the control of the movements is distributed on each leg rather than centralised in a single decision centre. A distributed approach of the Q-learning technique is adopted in which the agents contributing to the same global objective perform their own learning process taking into account or not the other agents. The centralised and distributed approaches are compared. Different simulations and tests are carried out so as to generate stable periodic gaits. The influence of the learning parameters on the quality of the gaits are studied. The walk appears as an emerging phenomenon from the individual movements of the legs. Problems of fault tolerance and lack of state information are investigated. Finally it is verified that with the developed algorithm the simulated robot learns how to reach a desired trajectory while controlling its posture
Makiou, Abdelhamid. "Sécurité des applications Web : Analyse, modélisation et détection des attaques par apprentissage automatique." Thesis, Paris, ENST, 2016. http://www.theses.fr/2016ENST0084/document.
Повний текст джерелаWeb applications are the backbone of modern information systems. The Internet exposure of these applications continually generates new forms of threats that can jeopardize the security of the entire information system. To counter these threats, there are robust and feature-rich solutions. These solutions are based on well-proven attack detection models, with advantages and limitations for each model. Our work consists in integrating functionalities of several models into a single solution in order to increase the detection capacity. To achieve this objective, we define in a first contribution, a classification of the threats adapted to the context of the Web applications. This classification also serves to solve some problems of scheduling analysis operations during the detection phase of the attacks. In a second contribution, we propose an architecture of Web application firewall based on two analysis models. The first is a behavioral analysis module, and the second uses the signature inspection approach. The main challenge to be addressed with this architecture is to adapt the behavioral analysis model to the context of Web applications. We are responding to this challenge by using a modeling approach of malicious behavior. Thus, it is possible to construct for each attack class its own model of abnormal behavior. To construct these models, we use classifiers based on supervised machine learning. These classifiers use learning datasets to learn the deviant behaviors of each class of attacks. Thus, a second lock in terms of the availability of the learning data has been lifted. Indeed, in a final contribution, we defined and designed a platform for automatic generation of training datasets. The data generated by this platform is standardized and categorized for each class of attacks. The learning data generation model we have developed is able to learn "from its own errors" continuously in order to produce higher quality machine learning datasets
Knyazeva, Elena. "Apprendre par imitation : applications à quelques problèmes d'apprentissage structuré en traitement des langues." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS134/document.
Повний текст джерелаStructured learning has become ubiquitousin Natural Language Processing; a multitude ofapplications, such as personal assistants, machinetranslation and speech recognition, to name just afew, rely on such techniques. The structured learningproblems that must now be solved are becomingincreasingly more complex and require an increasingamount of information at different linguisticlevels (morphological, syntactic, etc.). It is thereforecrucial to find the best trade-off between the degreeof modelling detail and the exactitude of the inferencealgorithm. Imitation learning aims to perform approximatelearning and inference in order to better exploitricher dependency structures. In this thesis, we explorethe use of this specific learning setting, in particularusing the SEARN algorithm, both from a theoreticalperspective and in terms of the practical applicationsto Natural Language Processing tasks, especiallyto complex tasks such as machine translation.Concerning the theoretical aspects, we introduce aunified framework for different imitation learning algorithmfamilies, allowing us to review and simplifythe convergence properties of the algorithms. With regardsto the more practical application of our work, weuse imitation learning first to experiment with free ordersequence labelling and secondly to explore twostepdecoding strategies for machine translation
Mokhtari, Myriam. "Réseau neuronal aléatoire : applications à l'apprentissage et à la reconnaissance d'images." Paris 5, 1994. http://www.theses.fr/1994PA05S019.
Повний текст джерелаBély, Marina. "Détection automatique et correction des carences en azote assimilable des fermentations alcooliques en conditions œnologiques : étude cinétique et approche physiologique." Montpellier 2, 1990. http://www.theses.fr/1990MON20292.
Повний текст джерелаBérard, Alexandre. "Neural machine translation architectures and applications." Thesis, Lille 1, 2018. http://www.theses.fr/2018LIL1I022/document.
Повний текст джерелаThis thesis is centered on two main objectives: adaptation of Neural Machine Translation techniques to new tasks and research replication. Our efforts towards research replication have led to the production of two resources: MultiVec, a framework that facilitates the use of several techniques related to word embeddings (Word2vec, Bivec and Paragraph Vector); and a framework for Neural Machine Translation that implements several architectures and can be used for regular MT, Automatic Post-Editing, and Speech Recognition or Translation. These two resources are publicly available and now extensively used by the research community. We extend our NMT framework to work on three related tasks: Machine Translation (MT), Automatic Speech Translation (AST) and Automatic Post-Editing (APE). For the machine translation task, we replicate pioneer neural-based work, and do a case study on TED talks where we advance the state-of-the-art. Automatic speech translation consists in translating speech from one language to text in another language. In this thesis, we focus on the unexplored problem of end-to-end speech translation, which does not use an intermediate source-language text transcription. We propose the first model for end-to-end AST and apply it on two benchmarks: translation of audiobooks and of basic travel expressions. Our final task is automatic post-editing, which consists in automatically correcting the outputs of an MT system in a black-box scenario, by training on data that was produced by human post-editors. We replicate and extend published results on the WMT 2016 and 2017 tasks, and propose new neural architectures for low-resource automatic post-editing
Книги з теми "Apprentissage automatique – Applications industrielles"
Brazdil, Pavel B. Metalearning: Applications to data mining. Berlin: Springer, 2009.
Знайти повний текст джерелаPython machine learning from scratch: Machine learning concepts and applications for beginners. Lewis, Delware: AI Sciences, 2016.
Знайти повний текст джерелаMining software specifications: Methodologies and applications. Boca Raton, FL: CRC Press, 2011.
Знайти повний текст джерелаCost-sensitive machine learning. Boca Raton, FL: CRC Press, 2012.
Знайти повний текст джерелаMachine learning: A probabilistic perspective. Cambridge, MA: MIT Press, 2012.
Знайти повний текст джерелаHow to build a person: A prolegomenon. Cambridge, Mass: MIT Press, 1989.
Знайти повний текст джерелаAgrawal, Rashmi, Abhishek Kumar, Pramod Singh Rathore, and Dac-Nhuong Le. Machine learning for healthcare: Handling and managing data. Boca Raton: Chapman & Hall/CRC, 2021.
Знайти повний текст джерелаBernhard, Schölkopf, Burges Christopher J. C, and Smola Alexander J, eds. Advances in kernel methods: Support vector learning. Cambridge, Mass: MIT Press, 1999.
Знайти повний текст джерелаLarrañaga, Pedro, David Atienza, Javier Diaz-Rozo, Alberto Ogbechie, and Carlos Esteban Puerto-Santana. Industrial Applications of Machine Learning. Taylor & Francis Group, 2018.
Знайти повний текст джерелаLarrañaga, Pedro. Industrial Applications of Machine Learning. Taylor & Francis Group, 2020.
Знайти повний текст джерелаТези доповідей конференцій з теми "Apprentissage automatique – Applications industrielles"
Fourcade, A. "Apprentissage profond : un troisième oeil pour les praticiens." In 66ème Congrès de la SFCO. Les Ulis, France: EDP Sciences, 2020. http://dx.doi.org/10.1051/sfco/20206601014.
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