ICT and energy are two economic domains that became among the most influential to the growth of modern society. These, in the same time, due to exploitation of natural resources and producing unwanted effects to the environment, represent a kind of menace to the eco system and the human future. Implementation of measures to mitigate these unwanted effects established a new paradigm of production and distribution of electrical energy named smart grid. It relies on many novelties that improve the production, distribution and consumption of electricity among which one of the most important is the ICT. Among the ICT concepts implemented in modern smart grid one recognizes the artificial intelligence and, specifically the artificial neural network. Here, after reviewing the subject and setting the case, we are reporting some of our newest results aiming at broadening the set of tools being offered by ICT to the smart grid. We will describe our result in prediction of electricity demand and characterization of new threats to the security of the ICT that may use the grid as a carrier of the attack. We will use artificial neural networks (ANNs) as a tool in both subjects. [Projekat Ministarstva nauke Republike Srbije, br. TR32004]

In this paper, we apply artificial neural networks (ANNs) to the diagnosis of a mixed-mode electronic circuit. In order to tackle the circuit complexity and to reduce the number of test points hierarchical approach to the diagnosis generation was implemented with two levels of decision: the system level and the circuit level. For every level, using the simulation-before-test (SBT) approach, fault dictionary was created first, containing data relating the fault code and the circuit response for a given input signal. Also, hypercomputing was implemented, i.e. we used parallel simulation of large number of replicas of the original circuit with faults inserted to achieve fast creation of the fault dictionary. ANNs were used to model the fault dictionaries. At the topmost level, the fault dictionary was split into parts simplifying the implementation of the concept. During the learning phase, the ANNs were considered as an approximation algorithm to capture the mapping enclosed within the fault dictionary. Later on, in the diagnostic phase, the ANNs were used as an algorithm for searching the fault dictionary. A voting system was created at the topmost level in order to distinguish which ANN output is to be accepted as the final diagnostic statement. The approach was tested on an example of an analog-to-digital converter

Body temperature is an important indicator that may indicate the possibility of the existence of various pathological conditions and diseases. In the head and neck area, an infrared camera allows accurate temperature measurements of all regions of interest. The analysis of temperature characteristics of the region of interest of the head and neck in healthy subjects in terms of comparison of values in relation to the side of the face in the same person, and the comparison of values relative to the sex of the subjects is the topic of this research. These analyses are performed to create temperature maps of the face and determine physiological values. The research was conducted with the participation of 30 healthy people, 16 women and 14 men of different ages. Thermal imaging was performed in controlled conditions with infrared thermographic camera Varioscan 3021ST, while the software package IRBIS Professional 2.2 was used for thermogram analysis. Results show that the temperatures in female subjects at the submandibular region are significantly lower than in male subjects with an average temperature difference of 0.46?C, and the temperatures in female subjects at the supraorbital region are on average 0.5?C higher than in male subjects.