No abstract

No abstract

No abstract

Abstract. In this paper we present a novel GA-ICA method which converges to the optimum. The new method for blindly separating unobservable independent component signals from their linear mixtures (Blind Source Separation BSS), uses genetic algorithms (GA) to find the separation matrices which minimize a cumulant based contrast function. The paper also include a formal prove on the convergence of the proposed algorithm using guiding operators, a new concept in the genetic algorithms scenario. This approach is very useful in many fields such as biomedical applications i.e. EEG which usually use a high number of input signals. The Guiding GA (GGA) presented in this work converges to uniform populations containing just one individual, the optimum.

This paper presents results from a power quality audit conducted at a highly automated plant over last year. It was found that the main problems for the equipment installed were voltage sags. Voltage sag analysis is a complex stochastic issue, since it involves a large variety of random factors, such as: type of short-circuits in the power system, location of faults, protective system performance and atmospheric discharges. Among all categories of electrical disturbances, the voltage sag (dip) and momentary interruption are the nemeses of the automated industrial process. The paper analyzes the capabilities of modern power supplies; the convenience of "embedded solution" is also discussed. Finally it is addressed the role of the Standards on the protection of electronic equipment.I.