Auswahl der wissenschaftlichen Literatur zum Thema „Orchestration automatisée“
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Zeitschriftenartikel zum Thema "Orchestration automatisée"
Pisarić, Milan, Vladimir Dimitrieski, Marko Vještica, Goran Krajoski und Mirna Kapetina. „Towards a Flexible Smart Factory with a Dynamic Resource Orchestration“. Applied Sciences 11, Nr. 17 (28.08.2021): 7956. http://dx.doi.org/10.3390/app11177956.
Der volle Inhalt der QuelleGrüner, Sten, und Mario Hoernicke. „Orchestrierung von Hybridanlagen“. atp magazin 63, Nr. 11-12 (06.12.2022): 68–74. http://dx.doi.org/10.17560/atp.v63i11-12.2631.
Der volle Inhalt der QuelleDissertationen zum Thema "Orchestration automatisée"
Teiller, Alexandre. „Aspects algorithmiques de l'optimisation « multistage »“. Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS471.
Der volle Inhalt der QuelleN a classical combinatorial optimization setting, given an instance of a problem one needs to find a good feasible solution. However, in many situations, the data may evolve over time and one has to solve a sequence of instances. Gupta et al. (2014) and Eisenstat et al. (2014) proposed a multistage model where given a time horizon the input is a sequence of instances (one for each time step), and the goal is to find a sequence of solutions (one for each time step) reaching a trade-off between the quality of the solutions in each time step and the stability/similarity of the solutions in consecutive time steps. In Chapter 1 of the thesis, we will present an overview of optimization problems tackling evolving data. Then, in Chapter 2, the multistage knapsack problem is addressed in the offline setting. The main contribution is a polynomial time approximation scheme (PTAS) for the problem in the offline setting. In Chapter 3, the multistage framework is studied for multistage problems in the online setting. The main contribution of this chapter was the introduction of a structure for these problems and almost tight upper and lower bounds on the best-possible competitive ratio for these models. Finally in chapter 4 is presented a direct application of the multistage framework in a musical context i.e. the target-based computed-assisted orchestration problem. Is presented a theoretical analysis of the problem, with NP-hardness and approximation results as well as some experimentations
Crestel, Léopold. „Neural networks for automatic musical projective orchestration“. Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS625.
Der volle Inhalt der QuelleOrchestration is the art of composing a musical discourse over a combinatorial set of instrumental possibilities. For centuries, musical orchestration has only been addressed in an empirical way, as a scientific theory of orchestration appears elusive. In this work, we attempt to build the first system for automatic projective orchestration, and to rely on machine learning. Hence, we start by formalizing this novel task. We focus our effort on projecting a piano piece onto a full symphonic orchestra, in the style of notable classic composers such as Mozart or Beethoven. Hence, the first objective is to design a system of live orchestration, which takes as input the sequence of chords played by a pianist and generate in real-time its orchestration. Afterwards, we relax the real-time constraints in order to use slower but more powerful models and to generate scores in a non-causal way, which is closer to the writing process of a human composer. By observing a large dataset of orchestral music written by composers and their reduction for piano, we hope to be able to capture through statistical learning methods the mechanisms involved in the orchestration of a piano piece. Deep neural networks seem to be a promising lead for their ability to model complex behaviour from a large dataset and in an unsupervised way. More specifically, in the challenging context of symbolic music which is characterized by a high-dimensional target space and few examples, we investigate autoregressive models. At the price of a slower generation process, auto-regressive models allow to account for more complex dependencies between the different elements of the score, which we believe to be of the foremost importance in the case of orchestration
Vu, Thi My Hang. „Aider les enseignants pendant la phase d'élaboration du scénario pédagogique par une approche ontologique de la modélisation des connaissances tâches-techniques du domaine considéré“. Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM060.
Der volle Inhalt der QuelleThis thesis is in the field of the Learning Sciences with a focus on scripting, i.e., the elaboration of learning scenarios. The objective is to support teachers in reflecting on the different tasks (exercises) of a considered domain, the different techniques and their interrelationships. We propose (1) a process for the semi-automatic elaboration of a task-technique knowledge model as an ontology and (2) interfaces providing teachers with easy access to the knowledge represented in the ontology while preparing a learning scenario. The tests show that these interfaces are useful and usable
Doucy, Jérémie. „Méthodologie pour l’orchestration sémantique de services, application au traitement de documents multimédia“. Thesis, Rouen, INSA, 2011. http://www.theses.fr/2011ISAM0014.
Der volle Inhalt der QuelleAfter a complete state of the art we detailed our semantic services approach which uses an innovative method for services composition: processing chains patterns. Our approach is composed on an hybrid semantic servicers registry which propose different levels of matching between services, some composition rules when the matching phase failde and an execution engine which is able to dynamically resolve and com^pose services. In order to solve the service regitry population issue, we have designed an upper ontology, which enables links between a service taxonomy class with a semantically annotated abstract service. Finally, we have evaluated our prototype using real processing chains used by Cassidian platforms
Pérennou, Loïc. „Virtual machine experience design : a predictive resource allocation approach for cloud infrastructures“. Thesis, Paris, CNAM, 2019. http://www.theses.fr/2019CNAM1246/document.
Der volle Inhalt der QuelleOne of the main challenges for cloud computing providers remains to offer trustable performance for all users, while maintaining an efficient use of hardware and energy resources. In the context of this CIFRE thesis lead with Outscale, apublic cloud provider, we perform an in-depth study aimed at making management algorithms use new sources of information. We characterize Outscale’s workload to understand the resulting stress for the orchestrator, and the contention for hardware resources. We propose models to predict the runtime of VMs based on features which are available when they start. We evaluate the sensitivity with respect to prediction error of a VM placement algorithm from the literature that requires such predictions. We do not find any advantage in coupling our prediction model and the selected algorithm, but we propose alternative ways to use predictions to optimize the placement of VMs
Pérennou, Loïc. „Virtual machine experience design : a predictive resource allocation approach for cloud infrastructures“. Electronic Thesis or Diss., Paris, CNAM, 2019. http://www.theses.fr/2019CNAM1246.
Der volle Inhalt der QuelleOne of the main challenges for cloud computing providers remains to offer trustable performance for all users, while maintaining an efficient use of hardware and energy resources. In the context of this CIFRE thesis lead with Outscale, apublic cloud provider, we perform an in-depth study aimed at making management algorithms use new sources of information. We characterize Outscale’s workload to understand the resulting stress for the orchestrator, and the contention for hardware resources. We propose models to predict the runtime of VMs based on features which are available when they start. We evaluate the sensitivity with respect to prediction error of a VM placement algorithm from the literature that requires such predictions. We do not find any advantage in coupling our prediction model and the selected algorithm, but we propose alternative ways to use predictions to optimize the placement of VMs