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Planes coordinates transformation between PSAD56 to SIRGAS using a Multilayer Artificial Neural Network

Tierra A., Romero R.

Geodesy and Cartography

2014

Vol. 63, no. 2

199--209

Prior any satellite technology developments, the geodetic networks of a country were realized from a topocentric datum, and hence the respective cartography was performed. With availability of Global Navigation Satellite Systems-GNSS, cartography needs to be updated and referenced to a geocentric datum to be compatible with this technology. Cartography in Ecuador has been performed using the PSAD56 (Provisional South American Datum 1956) systems, nevertheless it’s necessary to have inside the system SIRGAS (Sistema de Referencia Geocéntrico para las AmericaS). This transformation between PSAD56 to SIRGAS use seven transformation parameters calculated with the method Helmert. These parameters, in case of Ecuador are compatible for scales of 1:25 000 or less, that does not satisfy the requirements on applications for major scales. In this study, the technique of neural networks is demonstrated as an alternative for improving the processing of UTM planes coordinates E, N (East, North) from PSAD56 to SIRGAS. Therefore, from the coordinates E, N, of the two systems, four transformation parameters were calculated (two of translation, one of rotation, and one scale difference) using the technique bidimensional transformation. Additionally, the same coordinates were used to training Multilayer Artificial Neural Network -MANN, in which the inputs are the coordinates E, N in PSAD56 and output are the coordinates E, N in SIRGAS. Both the two-dimensional transformation and ANN were used as control points to determine the differences between the mentioned methods. The results imply that, the coordinates transformation obtained with the artificial neural network multilayer trained have been improving the results that the bidimensional transformation, and compatible to scales 1:5000.

Dostęp do nowoczesnych technologii, w tym GNSS umożliwiły dokładniejsze zdefiniowanie systemów odniesień przestrzennych wykorzystywanych m.in. w definiowaniu krajowych układów odniesień i układów współrzędnych. W Ekwadorze wykorzystywany jest system PSAD56 (Provisional South American Datum 1956), ale w ostatnim czasie zaszła konieczność zdefiniowania wewnętrznego(krajowego) systemu SIRGAS (Sistema de Referencia Geocéntrico para las AmericaS). Do transformacji pomiędzy oboma systemami powszechnie wykorzystuje się metodę Helmerta, stosując układ siedmioparametrowy. Transformacja taka pozwala na zachowanie dokładności wystarczającej do opracowania map topograficznych w skalach 1:25 000 lub mniejszych. W artykule do transformacji zastosowano sieci neuronowe, co umożliwiło podniesienie dokładności do skali 1:5 000.

Application of artificial neural networks in modelling of normalised structural steels mechanical properties

Dobrzański L. A., Honysz R.

Journal of Achievements in Materials and Manufacturing Engineering

2009

Vol. 32, nr 1

37-45

Purpose: This paper presents the application of artificial neural networks for mechanical properties prediction of structural steels after heat treatment. Design/methodology/approach: On the basis of such input parameteres, which are the chemical composition, the kind of mechanical and heat treatment and dimensions of elements, mechanical properties, such as strength, impact resistance or hardness are predicted. Findings: Results obtained in the given ranges of input parameters show very good ability of constructed neural networks to predict described mechanical properties for steels after heat treatment. The uniform distribution of descriptive vectors in all, training, validation and testing sets, indicate about the good ability of the networks to results generalisation. Practical implications: Created tool makes possible the easy modelling of described properties and allows the better selection of both chemical composition and the processing parameters of investigated materials. At the same time the obtainment of steels, which are qualitatively better, cheaper and more optimised under customers needs is made possible. Originality/value: The prediction possibility of the material mechanical properties is valuable for manufacturers and constructors. It allows preserving the customers quality requirements and brings also measurable financial advantages.