Dejan Tanikić

An approach to evaluation of the extremely low-frequency magnetic field radiation in the laptop computer neighborhood by artificial neural networks

Abstract The paper considers the level of the extremely low-frequency magnetic field produced by laptop computers, which can be hazardous to the laptop users’ health. It proposes a new model for prediction of the laptop magnetic field radiation starting from a priori known laptop characteristics using artificial neural network. In the experiment, the magnetic field of ten different laptop computers is measured. The measuring of the magnetic field is performed at the points, which are relevant to laptop computer users. Prediction is obtained from testing laptops. The predicted magnetic field results are evaluated by correlation coefficient and clustered for detection of magnetic field ranges by self-organizing map. Obtained cluster set is compared with the cluster set computed on the measured magnetic field at the same laptop characteristics. Correlation coefficient showed good correspondence of the predicted values with the measured values. Furthermore, the comparison between the two cluster sets revealed a good alignment of magnetic field ranges in measured and predicted results.

Artificial Intelligence Techniques for Modelling of Temperature in the Metal Cutting Process

© 2012 Tanikić and Despotović., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Artificial Intelligence Techniques for Modelling of Temperature in the Metal Cutting Process

The artificial neural network based system for validation of thermocouples used in biomedicine

Machining operations are widely used in the orthopedic surgery. The temperature which occurs in the cutting zone, during the machining of the bones, may have many negative consequences in the postoperative period. Therefore, the measuring and the modeling of this parameter is a very important task. In this paper, the thermocouples are presented as a potential tool for the temperature measuring. The paper also deals with the system for validation of the thermocouples. The artificial neural network is used for modeling of the relationship between the electromotive force (as the thermocouple output) and the corresponding temperature. It is shown that the results of the modeling are in good correlation with the measured data.

Optimization of the extended water flow algorithm for the text-line segmentation

The paper proposed an approach for the optimization of the water flow algorithm for the text-line segmentation. Original method assumed the hypothetical water that flows to the document image frame from left to right and vice versa. It used the water flow angle as the only parameter. Algorithms extended version introduced a water flow function, which is given as the power function. It exploited two parameters: water flow angle α and exponent n. To optimize these two parameters artificial neural network has been used. Results are encouraging because of the improvement of the text-line segmentation results.

An Approach for Tuning the Parametric Water Flow Algorithm Based on ANN

The manuscript proposed an approach for the optimization of the parametric water flow algorithm. This algorithm introduced a water flow function as a basis for the text-line segmentation process. The function is established as the power function. It exploited two parameters: water flow angle α and exponent n. In order to tune these parameters, the artificial neural network has been used. Results are encouraging because of the improvement of the text-line segmentation for the handwritten text.