Interval methods for verified integration of initial value problems (IVPs) for ODEs have been used for more than 40 years. For many classes of IVPs, these methods have the ability to compute guaranteed error bounds for the flow of an ODE, where traditional methods provide only approximations to a solution. Overestimation, however, is a potential drawback of verified methods. For some problems, the computed error bounds become overly pessimistic, or integration even breaks down. The dependency problem and the wrapping effect are particular sources of overestimations in interval computations. Berz (see [1]) and his co-workers have developed Taylor model methods, which extend interval arithmetic with symbolic computations. The latter is an effective tool for reducing both the dependency problem and the wrapping effect. By construction, Taylor model methods appear particularly suitable for integrating nonlinear ODEs. In this paper, we analyze Taylor model based integration of ODEs and compare Taylor model with traditional enclosure methods for IVPs for ODEs. More advanced Taylor model integration methods are discussed in the algorithm (1). For clarity, we summarize the major steps of the naive Taylor model method as algorithm 1.
In this paper, the Normality set will be investigated. Then, the study highlights some concepts properties and important results. In addition, it will prove that every operator with normality set has non trivial invariant subspace of .
Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You on
... Show MoreIn this paper we describe several different training algorithms for feed forward neural networks(FFNN). In all of these algorithms we use the gradient of the performance function, energy function, to determine how to adjust the weights such that the performance function is minimized, where the back propagation algorithm has been used to increase the speed of training. The above algorithms have a variety of different computation and thus different type of form of search direction and storage requirements, however non of the above algorithms has a global properties which suited to all problems.
Vehicular ad hoc network (VANET) is a distinctive form of Mobile Ad hoc Network (MANET) that has attracted increasing research attention recently. The purpose of this study is to comprehensively investigate the elements constituting a VANET system and to address several challenges that have to be overcome to enable a reliable wireless communications within a vehicular environment. Furthermore, the study undertakes a survey of the taxonomy of existing VANET routing protocols, with particular emphasis on the strengths and limitations of these protocols in order to help solve VANET routing issues. Moreover, as mobile users demand constant network access regardless of their location, this study seeks to evaluate various mobility models for vehi
... Show MoreThe problem of dark matter in galaxies is still one of the most important unsolved problems in the contemporary extragalactic astronomy and cosmology. The existence of a significant dynamic difference between the visible mass and the conventional mass of galaxies firmly establishes observational result. In this paper an unconventional explanation will be tested as an alternative to the cold dark matter hypothesis; which is called the modified Newtonian dynamics (MOND).
In this paper covers the simulation of galactic evolutions; where the two hypotheses are tested via the rotation curves. N-body simulation was carried adopting different configuration lik
Vol. 6, Issue 1 (2025)