Modelling

1 pages, 135 KiB

Open AccessEditorial

Modelling—International Open Access Journal of Modelling in Engineering Science

by Alfredo Cuzzocrea

Modelling 2020, 1(1), 77; https://doi.org/10.3390/modelling1010005 - 22 Sep 2020

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Modelling in engineering science embraces a wide area of theoretical results, methodological aspects and practical findings, with particular regard to computer-based modelling approaches [...] Full article

24 pages, 556 KiB

Open AccessArticle

Modelling and Planning Evolution Styles in Software Architecture

by Kadidiatou Djibo, Mourad Chabane Oussalah and Jacqueline Konate

Modelling 2020, 1(1), 53-76; https://doi.org/10.3390/modelling1010004 - 15 Sep 2020

Cited by 3 | Viewed by 2062

Abstract

The purpose of this study is to find the right model to plan and predict future evolution paths of an evolving software architecture based on past evolution data. Thus, in this paper, a model to represent the software architecture evolution process is defined. In order to collect evolution data, a simple formalism allowing to easily express software architecture evolution data is introduced. The sequential pattern extraction technique is applied to the collected evolution styles of an evolving software architecture in order to predict and plan the future evolution paths. A learning and prediction model is defined to generate the software architecture possible future evolution paths. A method for evaluating the generated paths is presented. In addition, we explain and validate our approach through a study on two examples of evolution of component-oriented software architecture. Full article

(This article belongs to the Special Issue Feature Papers of Modelling)

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14 pages, 2426 KiB

Open AccessArticle

Experimental Modelling of a Solar Dryer for Wood Fuel in Epinal (France)

by Merlin Simo-Tagne, Macmanus Chinenye Ndukwu and Martin Ndi Azese

Modelling 2020, 1(1), 39-52; https://doi.org/10.3390/modelling1010003 - 23 Aug 2020

Cited by 15 | Viewed by 2960

Abstract

A solar dryer for wood was constructed and modelled based on the climatic condition of Epinal, France, during the summer, spring and winter seasons. The solar dryer was able to raise the temperature of the drying air by 38 °C in the spring and summer season with a global effective efficiency of 39%. Modelling of the drying of the log of wood was based on the global mass transfer coefficient and the geometric form of the log which was mostly cylindrical was considered. Validation was undertaken with the log covered with the bark. The coefficient of variations of numerical points with the experimental values given by the model was less than 5% with a mean average error and a mean relative error of 2.33% and 4.53%, respectively. Full article

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17 pages, 2756 KiB

Open AccessArticle

Finite-Element Based Image Registration for Endovascular Aortic Aneurysm Repair

by Aymeric Pionteck, Baptiste Pierrat, Sébastien Gorges, Jean-Noël Albertini and Stéphane Avril

Modelling 2020, 1(1), 22-38; https://doi.org/10.3390/modelling1010002 - 11 Jul 2020

Cited by 6 | Viewed by 3247

Abstract

In this paper we introduce a new method for the registration between preoperative and intraoperative computerized tomography (CT) images used in endovascular interventions for aortic aneurysm repair. The method relies on a 3D finite-element model (FEM) of the aortic centerline reconstructed from preoperative CT scans. Intraoperative 2D fluoroscopic images are used to deform the 3D FEM and align it onto the current aortic geometry. The method was evaluated on clinical datasets for which a reference CT scan was available to evaluate the registration errors made by our method and to compare them with other registration methods based on rigid transformations. Errors were estimated based on the predicted locations of landmarks positioned at different branch ostia. It appeared that our method always reduced the registration errors of at least 20% compared to gold standard 3D rigid registration and permitted to reach a global precision of 3.8 mm and a renal precision of 2.6 mm, which is a significant improvement compatible with surgical specifications. Finally, the major asset of our method is that it only requires one fluoroscopic intraoperative 2D image to perform the 3D non-rigid registration. This would reduce patient irradiation and cut the costs compared to traditional methods. Full article

(This article belongs to the Section Modelling in Biology and Medicine)

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21 pages, 12832 KiB

Open AccessArticle

Time Series Clustering: A Complex Network-Based Approach for Feature Selection in Multi-Sensor Data

by Fabrizio Bonacina, Eric Stefan Miele and Alessandro Corsini

Modelling 2020, 1(1), 1-21; https://doi.org/10.3390/modelling1010001 - 28 May 2020

Cited by 6 | Viewed by 5706

Abstract

Distributed monitoring sensor networks are used in an ever increasing number of applications, particularly with the advent of IoT technologies. This has led to a growing demand for unconventional analytical tools to cope with a large amount of different signals. In this scenario, the modeling of time series in similar groups represents an interesting area especially for feature subset selection (FSS) purposes. Methods based on clustering algorithms are very promising for FSS, but in their original form they are unsuitable to manage the complexity of temporal dynamics in time series. In this paper we propose a clustering approach, based on complex network analysis, for the unsupervised FSS of time series in sensor networks. We used natural visibility graphs to map signal segments in the network domain, then extracted features in the form of node degree sequences of the graphs, and finally computed time series clustering through community detection algorithms. The approach was tested on multivariate signals monitored in a 1 MW cogeneration plant and the results show that it outperforms standard time series clustering in terms of both redundancy reduction and information gain. In addition, the proposed method demonstrated its merit in terms of retention of information content with respect to the original dataset in the analyzed condition monitoring system. Full article

(This article belongs to the Special Issue Feature Papers of Modelling)

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Modelling, Volume 1, Issue 1 (September 2020) – 5 articles

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