Indoor electric field level prediction model based on the artificial neural networks (original) (raw)

Estimating Base Station-Based Indoor and Outdoor Electric Field Levels by Artificial Neural Networks

European Journal of Technic, 2019

The measurement of the electric field and the magnetic field is significant in order to determine electromagnetic pollution level compared to standards. In Turkey, electric field limit value, which is emitted by base station is 13.5 V/m for one mobile communication operator. In addition, according to the regulation of medical devices, the limit of electric field value inside the hospital, where the medical devices are located, is 3 V/m. In this study, the measurement and evaluation of electromagnetic pollution inside and outside the Bursa Uludag University hospital building are performed and its compliance with national and international standards is examined. Moreover, the distribution of electric field in the environment is estimated by the artificial neural network and fuzzy logic methods considering the measurement results. The measured electric field values, estimated electric field values, and national standard values are compared.

Wireless LAN Electromagnetic Field Prediction for Indoor Environment Using Artificial Neural Network

Automatika

Original scientific paper A simple neural model for electromagnetic field prediction in indoor environment was created based on field strength measurements at 2.4 GHz, conducted at University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (FESB Split). Vertical rod antenna (omnidirectional in the horizontal plane) was placed in a faculty hallway and used as the electromagnetic field source. Electromagnetic field distribution was defined by commonly used rectangular grid of uniformly distributed measurement points. However, instead of commonly used Cartesian coordinates for measurement points location description, we used polar coordinates of distance and azimuth angle measured from the field source. These coordinates are found to be more suitable for the organization of input data, as the physical distribution of the field strength around the antenna depends on the same variables. This resulted with predictive ability improvement of the neural model, as confirmed by simulation results. Three-Layer Perceptron, trained with Levenberg-Marquardt (LM) algorithm, produced the best results.

Field strength prediction in indoor environment with a neural model

2001

This paper presents the results of our studies concerning the application of the neural networks to the field strength prediction in an indoor environment. The proposed model consists of a multilayer perceptron trained with measurements. The results of the prediction show good agreement with the measurements

Generalized regression neural network prediction model for indoor environment

Proceedings. ISCC 2004. Ninth International Symposium on Computers And Communications (IEEE Cat. No.04TH8769), 2004

This paper presents the results of our studies regarding the applications of the neural networks to the propagation path loss prediction in indoor environment. The proposed model consists of a Generalized Regression Neural Network trained with measurements. The results of the prediction made by the proposed model showed a good agreement with the measurements.

ANN prediction models for indoor environment

IEEE International Conference on Wireless and Mobile Computing, Networking and Communications 2006, WiMob 2006, 2006

This work presents the results of the studies concerning the application of the feedforward neural networks to the prediction of propagation path loss in indoor environment. The proposed models consist of a Multilayer Perceptron and a Generalized Regression Neural Network trained with measurements. The results of the prediction made by the proposed neural models show a good agreement with the measurements.

Artificial Neural Network Applications Use Measurements of Electrical Quantities to Estimate Electric Power

Journal of Physics: Conference Series, 2019

Prototype measurement of electrical quantities had been built and applied to the H building in faculty of engineering,University of Lampung. Electrical quantities Measurement of electrical quantities had been saved on TIK's server. However, it had not been used for estimation. Electric power is the electricity that tends to change following the electric load. So, electric power can be predictable or estimation based on measurement data in the past. The method of backpropogation artificial neural networks is a method that have a good approach to nonlinearity. The results of testing the estimation of electric power consumption had been done in the distribution panel of Electrical Engineering and Mechanical Engineering UNILA indicated that this method can be used to estimate electric power consumption for one month ahead with an accuracy of ±0,884%. Thus this research can be applied to real-time estimation processes that can be accessed and displayed by web in real-time.

Estimation of Magnetic Field Strength near Substation Using Artificial Neural Network

—In this paper, an efficient neural network based estimation technique has been studied to estimate the magnetic field strength near any power substation, and to assess the possible exposure to electromagnetic radiation received by the residents living near that substation. The measurement and the estimation were carried out in close proximity to different high powered equipment at four different substations near Brunei Darussalam. Initially, the measurement was performed using the TM-191 gaussmeter for all four 66/11kV substations. In the measurement process the highest magnetic field of 12.5mG was recorded near the lightning arrestor at Telisai substation and the lowest value of 0.1mG was recorded at Lamunin substation for the same equipment. Later on, the magnetic field strengths were estimated using single-layer and two-layer feed-forward artificial neural networks (ANN). The highest value of coefficient of determination was found to be 98% using single-layer ANN estimation while the coefficient of determination was found to be around 99% by using two-layer ANN estimation. These coefficients of determination values indicate that the artificial neural network can predict the magnetic field strength with high accuracy.  Index Terms—substation, magnetic field, coefficient of determination, single-layer and two-layer ANN

Electromagnetic field identification using artificial neural networks

This work presents a novel method based on artificial neural networks (ANNs) for the prediction of the transient electromagnetic field radiating by generators of electrostatic discharges constructed according to an IEC standard. Actual input and output data collected from measurements carried out in the High Voltage Laboratory of the National Technical University of Athens are used in the training, validation and testing process. The proposed ANN method which can easy and accurate assesses the electromagnetic field produced by electrostatic discharges by simply measuring the discharge current can be used by laboratories facing either a lack of suitable ESD test equipment or want to compare the results to their own measurements.

Predictions of the Electric Field Emissions around Power Transmission Lines by Using Artificial Neural Network Methods

American Scientific Research Journal for Engineering, Technology, and Sciences, 2016

In this study, Artificial Neural Network (ANN) Algorithms are used to estimate the electric field that occurred around the power transmission lines as an alternative approach. Firstly, electric field levels around the high voltage power transmission lines are measured, and then analytically calculated. Moreover, the field levels that occurred around these power lines have been predicted by using multilayer perceptron artificial neural network, radial basis function, and generalized regression neural network models. In the paper, 154 kV typical power transmission line used in Turkey are studied. Electric field levels occurred around the power transmission lines have been predicted with ANN models with high accuracy, particularly MLPNN algorithm predicted the electric field intensity with very high precision.