An Interactive EA for Multifractal Bayesian Denoising

International audienceWe present in this paper a multifractal bayesian denoising technique based on an interactive EA. The multifractal denoising algorithm that serves as a basis for this technique is adapted to complex images and signals, and depends on a set of parameters. As the tuning of these parameters is a difficult task, highly dependent on psychovisual and subjective factors, we propose to use an interactive EA to drive this process. Comparative denoising results are presented with automatic and interactive EA optimisation. The proposed technique yield efficient denoising in many cases, comparable to classical denoising techniques. The versatility of the interactive implementation is however a major advantage to handle difficult images like IR or medical images

Interactivité efficace pour les algorithmes évolutionnaires interactifs. Application au débruitage multifractal d'images

Les algorithmes évolutionnaires interactifs pâtissent souvent de ce que l'on peut appeler le « goulot d'étranglement de l'utilisateur », autrement dit une perte d'efficacité de l'algorithme due à la fatigue de l'utilisateur (qui n'apprécie évidemment pas des interactions répétitives et/ou inintéressantes). Nous proposons et analysons dans ce papier une méthode de gestion de l'interaction-utilisateur permettant à la fois de glaner les informations utiles au bon déroulement de l'algorithme d'optimisation évolutionnaire, tout en limitant le nombre d'interactions. Cette méthode a été développée dans le cadre d'une application en débruitage multifractal d'images : le débruitage multifractal est adapté aux images complexes, mais dépend d'un jeu de paramètres délicats à ajuster. Une approche évolutionnaire interactive simple a été élaborée dans des travaux précédents pour l'ajustement de ces paramètres. Nous étudions ici l'emploi d'un module d'approximation du jugement de l'utilisateur, par le biais d'une « carte de fitness, » afin de réduire le nombre d'interactions avec l'utilisateur. La méthode peut être étendue facilement à d'autres applications évolutionnaires, interactives ou calculatoirement coûteuses

Evolutionary multifractal signal/image denoising

This chapter investigates the use of Evolutionary techniques for multifractal signal/image denoising. Two strategies are considered: using evolution as a pure stochastic optimiser, or using interactive evolution for a meta-optimisation task. Both strategies are complementary as they allow to address dierent aspects of signal/image denoising

Débruitage multifractal par évolution interactive

Nous présentons dans ce papier une méthode interactive de débruitage fondée sur une technique de débruitage multifractal bayésien adaptée aux signaux complexes. Cette technique nécessite le réglage d'un jeu de paramètres, et le résultat dépend fortement de facteurs psychovisuels et subjectifs. L'originalité de l'approche réside dans l'emploi d'un algorithme évolutionnaire interactif pour gérer l'ajustement des paramètres. Nous présentons des résultats comparatifs de débruitage, qui prouvent l'efficacité et la flexibilité de la méthode

Innovation Of Petrophysical And Geomechanical Experiment Methodologies: The Application Of 3D Printing Technology

The petrophysical and geomechanical properties of rocks link the geology origin with engineering practice, which serves as the fundamental of various disciplinaries associated with subsurface porous media, including civil engineering, underground water, geological exploration, and petroleum engineering. The research methodologies can be mainly divided into three aspects: theoretical modelling, numerical simulation, and experiments, in which the last approach plays a critical role that can support, validate, calibrate, or even refute a hypothesis. Only replying on repeatable trials and consolidate analysis of precise results can the experiments be successful and convincing, though uncertainties, due to multiple factors, need to be scrutinized and controlled. The challenges also existed in the characterization and measurements of rock properties as a result of heterogeneity and anisotropy as well as the inevitable impact of experimental operation. 3D printing, a cutting-edge technology, was introduced and utilized in the study that is supposed to be capable of controlling the mineralogy, microstructure, physical properties of physical rock replicas and further benefit the petrophysical and geomechanical experimental methodologies. My PhD research project attempted to answer the questions from the standpoint of petrophysicisits and geomechanics scientist: Can 3D printed rocks replicate natural rocks in terms of microstructure, petrophysical and geomechanical properties? If not, by any means can we improve the quality of replicas to mimic the common rock types? Which 3D printing method is best suitable for our research purposes? How could it be applied in the conventional experiments and integrated with theoretical calculation or numerical simulation? Three main types of printing materials and techniques (gypsum, silica sand, resin) were characterized first individually, which demonstrated varying microstructure, anisotropy, petrophysical and geomechanical properties. Post-processing effect was examined on the 3D printed gypsum rocks that show impact differences on nanoscale and microscale pore structures. Through comparison, resin, the material used in stereolithography technology, best suits the reconstruction of intricate pore network that aims to complement digital rock physics and ultimately be applied in petrophysical research. Gypsum material, however, has been proved as the best candidate for geomechanical research spanning from reference samples to upscaling methods validation. Currently, a practical approach of utilizing 3D printing in petroleum geoscience is taking advantages of the characteristics we focus on the research while disregarding the other properties, by which a suitable 3D printing material and technique can emerge

Mathematical Modeling and Simulation in Mechanics and Dynamic Systems

The present book contains the 16 papers accepted and published in the Special Issue “Mathematical Modeling and Simulation in Mechanics and Dynamic Systems” of the MDPI “Mathematics” journal, which cover a wide range of topics connected to the theory and applications of Modeling and Simulation of Dynamic Systems in different field. These topics include, among others, methods to model and simulate mechanical system in real engineering. It is hopped that the book will find interest and be useful for those working in the area of Modeling and Simulation of the Dynamic Systems, as well as for those with the proper mathematical background and willing to become familiar with recent advances in Dynamic Systems, which has nowadays entered almost all sectors of human life and activity

Persistence in complex systems

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems’ persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)

Persistence in complex systems

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)

Training Data Generation Framework For Machine-Learning Based Classifiers

In this thesis, we propose a new framework for the generation of training data for machine learning techniques used for classification in communications applications. Machine learning-based signal classifiers do not generalize well when training data does not describe the underlying probability distribution of real signals. The simplest way to accomplish statistical similarity between training and testing data is to synthesize training data passed through a permutation of plausible forms of noise. To accomplish this, a framework is proposed that implements arbitrary channel conditions and baseband signals. A dataset generated using the framework is considered, and is shown to be appropriately sized by having $11\%$ lower entropy than state-of-the-art datasets. Furthermore, unsupervised domain adaptation can allow for powerful generalized training via deep feature transforms on unlabeled evaluation-time signals. A novel Deep Reconstruction-Classification Network (DRCN) application is introduced, which attempts to maintain near-peak signal classification accuracy despite dataset bias, or perturbations on testing data unforeseen in training. Together, feature transforms and diverse training data generated from the proposed framework, teaching a range of plausible noise, can train a deep neural net to classify signals well in many real-world scenarios despite unforeseen perturbations