Artificial neural networks for acquisition and processing of sensors data in a radiotherapy application. (2018)

• Record Type: Journal Article
• Authors: Lamamra, Kheireddine; Allam, Abdelkrim; Afiane, M'hammed
• Is Part Of: International journal of simulation and process modelling. Volume 13:Number 1(2018)
• Page Start: 15
• Page End: 23
• Publication Date: 2018
• Subjects: data acquisition and processing -- temperature sensor -- radiotherapy rooms control -- artificial neural network -- modelling; Management -- Computer simulation -- Periodicals; Mathematical models -- Periodicals; Operations research -- Periodicals; Simulation methods -- Periodicals; 003.05
• Languages: English
• ISSNs: 1740-2123

Abstract

This paper presents a practical part of work that we have begun to realise for several months and it is planned for several steps. In this paper, a part devoted to acquisition and processing of coded data from temperature sensor of type MS6503 used in radiotherapy rooms of the Hospital Pierre and Marie Curie Centre (PMCC) is presented. The aim is to acquire and check remotely the temperatures of rooms to trigger alarms and their control thereafter in order to avoid mistakes of manipulation which are deadly for patients if they happen or arise. For this, a system modelling is made before proceeding to the implementation in practice. During the implementation, several problems have occurred such as the legibility of the received data that has been encrypted. To overcome this problem, an artificial neural network type of multi-layer perceptron (MLP) is used to acquire and decrypt the temperature data received from the sensors placed in the treatment rooms. The obtained results show that the neural network used has decrypted well the received data. It is the reason for which this technique has been implemented in the realised solution.