HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Distributed Chemically-Inspired Runtimes for Large Scale Adaptive Computing Platforms Cédric Tedeschi

https://tel.archives-ouvertes.fr/tel-01665776/document

Distributed Chemically-Inspired Runtimes for Large Scale Adaptive Computing Platforms

In order to improve the safety of wind turbine, this paper takes the high-speed spur gear on output shaft in the transmission system of the wind turbine as the study object. The original signals of the gear in different fault states which obtained from the experimental platform decompose by the empirical mode decomposition method (EMD) and the variational mode decomposition method (VMD), respectively. Then the different gear fault types’ eigenvectors were built. Fuzzy neural network (FNN) is adopted to learn the fault types and eigenvector samples of gears, and then the fault types of gears are identified. It is found that the fault features decomposed by VMD method have reached a high accurate recognition rate point to the fact that VMD has good applicability in the fault recognition of gear in wind turbine. 

Development of gear fault identification of wind turbine’s transmission system based on VMD and FNN

While the complexity of the digital infrastructures surrounding us seems to increase continuously, autonomic computing appears as a promising paradigm to be explored. The quest for adequate high-level abstractions to program autonomic systems led to the reemergence of rule-based programming as a promising paradigm. Also, nature appears to be a great source of inspiration. The chemical analogy was at the origin of the so-called chemical programming model, a rule-based programming model enhanced with a chemically-inspired execution model, making it a promising candidate to develop autonomic systems. The metaphor envisions a computation as a set of concurrent reactions between molecules of data arising non-deterministically, until no more reactions can take place, in which case, the solution contains the final outcome of the computation. More formally, such models strongly rely on concurrent multiset rewriting: the data are a multiset of molecules, and reactions are the application of a set of conditioned rewrite rules. At run time, these rewritings are applied concurrently, until no rule can be applied anymore (the elements they need do not exist anymore in the multiset). One of the main barriers towards the actual adoption of such models come from their complexity at run time: each computation step may require a complexity in O(n^k) where n denotes the number of elements in the multiset, and k the size of the subset of elements needed to trigger one rule. In this paper, we explore the possibility of improving the complexity of searching elements (or "reactants") through a static analysis of the reaction condition. In particular, through the modeling of the problem with graphs, we give a characterisation of this complexity, based on the results of the well-known subgraph isomorphism problem. This allows us then to give an algorithm for searching reactants, that can be easily parallelised, and whose complexity is lower than the basic case, most of the time.

/pdf/on-the-complexity-of-concurrent-multiset-rewriting-mt2gj3p2jl.pdf

On the Complexity of Concurrent Multiset Rewriting

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On the Complexity of Concurrent Multiset Rewriting