Etude expérimentale et numérique des défauts de bouclage et de glissement lors de la mise en forme de composites structuraux à base de fibres synthétiques et végétales

Les composites à renforts fibreux sont très prisés dans les industries de pointe comme l’aéronautique ou l’automobile du fait de leur rapport propriété mécanique/masse supérieur à celui des métaux. Leur mise en forme complexe présente de forts enjeux scientifiques, notamment pour les composites à renforts tissés. En effet, les renforts tissés sont sujets à l’apparition de défauts lors de leur mise en forme sur géométries complexes à double courbure. Certains de ces défauts ont déjà fait l’objet de plusieurs études alors que d’autres, comme les défauts de bouclage et de glissement des mèches, n’ont pour le moment pas encore été totalement explorés. À l’heure actuelle, les codes de simulations ne peuvent pas prédire précisément l’apparition et le développement des défauts de bouclage et de glissement des mèches lors de la mise en forme des renforts tissés. L’une des raisons est le manque de connaissances sur l’origine et la cinématique de développement de ces défauts. Ce travail de thèse propose d’apporter plus de compréhension sur ces défauts par une approche expérimentale et numérique. Concernant le défaut de bouclage, l’influence des tensions dans les réseaux de mèches, de la nature du renfort, de l’armure du renfort et des dimensions des mèches ont été étudiés. Pour le défaut de glissement, l’influence du type de renfort, des tensions dans les mèches, de l’armure du renfort et de l’orientation des mèches dans le renfort ont été explorés. Ces résultats ouvrent des perspectives concernant l’amélioration de la qualité des pièces composites

MODELLING THE FISHERIES OF LAKE MANZALA, EGYPT, USING PARAMETRIC AND NON-PARAMETRIC STATISTICAL METHODS

Much attention has been given to the economic aspects of the fisheries in Egypt, while building a statistical or mathematical model for fish production has received little attention. This study is devoted to a comprehensive assessment of Lake Manzala fisheries; past, present and future. Lake Manzala is one of the main fisheries resources in Egypt, and there is evidence that the fisheries have been over-exploited in recent years. The study objectives were to determine the factors that affect fish catches by individual vessels, to compare between parametric and non-parametric models of the fish catches, and to produce a mathematical model of stock behaviour which can be used to suggest policies to manage the Lake Manzala fishery. A new method of estimating the carrying capacity of the lake and intrinsic growth rate of Tilapia and its four species has been developed. Simulation had to be used to get error estimates of the biomass parameter estimates using the new method. Three catch strategies have been investigated and assessed, with discounted utility of future yields. Two ways of modelling individual vessel catches in relation to their effort characteristics, a parametric and non-parametric analysis, have been investigated. Using generalised additive model gave an improved fit to the survey data compared with the parametric analysis. It also gave a lower allowable fleet size which leads to more conservative management policy. A simulation approach was used to investigate the uncertainty in the predicted catches and stock levels, and to give insight into the risks associated with various levels of control. There was no evidence that a management strategy which aimed to fish at maximum sustainable yield would put the stock at risk

Resource-efficient fast prediction in healthcare data analytics: A pruned Random Forest regression approach

In predictive healthcare data analytics, high accuracy is both vital and paramount as low accuracy can lead to misdiagnosis, which is known to cause serious health consequences or death. Fast prediction is also considered an important desideratum particularly for machines and mobile devices with limited memory and processing power. For real-time health care analytics applications, particularly the ones that run on mobile devices, such traits (high accuracy and fast prediction) are highly desirable. In this paper, we propose to use an ensemble regression technique based on CLUB-DRF, which is a pruned Random Forest that possesses these features. The speed and accuracy of the method have been demonstrated by an experimental study on three medical data sets of three different diseases

Adaptive One-Class Ensemble-based Anomaly Detection: An Application to Insider Threats

The malicious insider threat is getting increased concern by organisations, due to the continuously growing number of insider incidents. The absence of previously logged insider threats shapes the insider threat detection mechanism into a one-class anomaly detection approach. A common shortcoming in the existing data mining approaches to detect insider threats is the high number of False Positives (FP) (i.e. normal behaviour predicted as anomalous). To address this shortcoming, in this paper, we propose an anomaly detection framework with two components: one-class modelling component, and progressive update component. To allow the detection of anomalous instances that have a high resemblance with normal instances, the one-class modelling component applies class decomposition on normal class data to create k clusters, then trains an ensemble of k base anomaly detection algorithms (One-class Support Vector Machine or Isolation Forest), having the data in each cluster used to construct one of the k base models. The progressive update component updates each of the k models with sequentially acquired FP chunks; segments of a predetermined capacity of FPs. It includes an oversampling method to generate artificial samples for FPs per chunk, then retrains each model and adapts the decision boundary, with the aim to reduce the number of future FPs. A variety of experiments is carried out, on synthetic data sets generated at Carnegie Mellon University, to test the effectiveness of the proposed framework and its components. The results show that the proposed framework reports the highest F1 measure and less number of FPs compared to the base algorithms, as well as it attains to detect all the insider threats in the data sets