This paper focuses on modeling and predicting the Internet end-to-end (e2e) delay multi-step ahead using Recurrent Neural Networks (RNNs). In this work, Round- Trip Time (RTT) is used as the basic metric to forecast the Internet e2e delay. A method for delay prediction model is developed using RNNs, able to model nonlinear systems. By observing the delay between two Internet nodes, RTT data has been collected as a time series during several days. Then this discrete-time series data has been organized into two parts, the first one is used as a training/learning set of the RNN, whereas the rest of data is used for the testing/evaluation of the RNN performance. To achieve this purpose, a learning phase has been performed to provide a mathematical characterization of RTT during one or several reference days. The test phase consists of iteratively forecasting RTT acquired during the test day. Simulation results illustrate that the suggested model is adaptive and it tracks RTT dynamics rapidly and accurately, even for long time ahead prediction.
In the reliability modeling field, we sometimes encounter systems with uncertain structures, and the use of fault trees and reliability diagrams is not possible. To overcome this problem, Bayesian approaches offer a considerable efficiency in this context. This paper introduces recent contributions in the field of reliability modeling with the Bayesian network approach. Bayesian reliability models are applied to systems with Weibull distribution of failure. To achieve the formulation of the reliability model, Bayesian estimation of Weibull parameters and the model's goodness-of-fit are evoked. The advantages of this modelling approach are presented in the case of systems with an unknown reliability structure, those with a common cause of failures and redundant ones. Finally, we raise the issue of the use of BNs in the fault diagnosis area.
The medical diagnosis process requires several steps to identify the types of cardiac pathologies. The segmentation step is used to determine the measurements for cardiac abnormalities on the short axis of the 4D acquired in MRI, but this phase remains limited on the blood flow sequences. The MRI modality allow to the experts to quantify the stenosis and the regurgitation of aortic blood flow. The parameters extracted from the flow sequences, after segmentation, make it possible to identify the valvular pathologies, but they are not sufficient to complete the medical prognosis as well as the lack of precision of these measurements. In this paper, we propose to make a coupling between the 4D cardiac cuts with their study of blood flow through the technique of registration and reconstruction. The interest of the purpose of the blood flow as fifth dimension is to improve the accuracy of the cardiac parameters and the extracting of measurements for valvulopathies in the process of assistance to the decision for the experts An example was introduced in this context through the development of cloud services for patient-specific simulations of blood flows through aortic valves as well as an OsiriX software for 5D visualization that combines a 4D sequence and the functional flow dimension. In this framework, we proposed a processing chain to lead towards a 5D solution. Another problem is raised is the choice of the appropriate architecture to solve the problem of hybrid parallelization for the processing of these cardiac images. To test the constraints of time of the concept of 5D, we need a GPU graphics processor acquired in MRI, as well as a CPU processor to perform the complexity of calculation and the operations applied to the algorithms of image processing.
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