A probabilistic demand side management approach by consumption admission control

Nova generacija električne mreže pod nazivom pametna mreža (Smart Grid) je nedavno zamišljena vizija čišćeg, učinkovitijeg i jeftinijeg elektroenergetskog sustava. Jedan od najvećih izazova električne mreže je da bi proizvodnja i potrošnja trebale biti uravnotežene u svakome trenutku. U radu se uvodi novi koncept za kontrolu potrošnje sredstvima automatski omogućavanih/onemogućavanih električnih aparata kako bi bili sigurni da je potrošnja usklađena s raspoloživim zalihama, na temelju statističkih karakterizacija potreba. U našem novom pristupu, umjesto uporabe tvrdih granica procjenjujemo vjerojatnost kraja distribucije potrošnje i sustava kontrole pomoću načela i rezultata statističkog upravljanja resursima.New generation electricity network called Smart Grid is a recently conceived vision for a cleaner, more efficient and cheaper electricity system. One of the major challenges of electricity network is that generation and consumption should be balanced at every moment. This paper introduces a new concept for controlling the demand side by the means of automatically enabling/disabling electric appliances to make sure that the demand is in match with the available supplies, based on the statistical characterization of the need. In our new approach instead of using hard limits we estimate the tail probability of the demand distribution and control system by using the principles and the results of statistical resource management

Minimum Probability of Error-Based Equalization Algorithms for Fading Channels

Novel channel equalizer algorithms are introduced for wireless communication systems to combat channel distortions resulting from multipath propagation. The novel algorithms are based on newly derived bounds on the probability of error (PE) and guarantee better performance than the traditional zero forcing (ZF) or minimum mean square error (MMSE) algorithms. The new equalization methods require channel state information which is obtained by a fast adaptive channel identification algorithm. As a result, the combined convergence time needed for channel identification and PE minimization still remains smaller than the convergence time of traditional adaptive algorithms, yielding real-time equalization. The performance of the new algorithms is tested by extensive simulations on standard mobile channels.</p

Socio-economic inequalities in suicide in Europe: the widening gap

Background Suicide has been decreasing over the past decade. However, we do not know whether socioeconomic inequality in suicide has been decreasing as well. Aims We assessed recent trends in socioeconomic inequalities in suicide in 15 European populations. Method The DEMETRIQ study collected and harmonised register-based data on suicide mortality follow-up of population censuses, from 1991 and 2001, in European populations aged 35-79. Absolute and relative inequalities of suicide according to education were computed on more than 300 million person-years. Results In the 1990s, people in the lowest educational group had 1.82 times more suicides than those in the highest group. In the 2000s, this ratio increased to 2.12. Among men, absolute and relative inequalities were substantial in both periods and generally did not decrease over time, whereas among women inequalities were absent in the first period and emerged in the second. Conclusions The World Health Organization (WHO) plan for 'Fair opportunity of mental wellbeing' is not likely to be met

Minimum Probability of Error-Based Equalization Algorithms for Fading Channels

Novel channel equalizer algorithms are introduced for wireless communication systems to combat channel distortions resulting from multipath propagation. The novel algorithms are based on newly derived bounds on the probability of error (PE) and guarantee better performance than the traditional zero forcing (ZF) or minimum mean square error (MMSE) algorithms. The new equalization methods require channel state information which is obtained by a fast adaptive channel identification algorithm. As a result, the combined convergence time needed for channel identification and PE minimization still remains smaller than the convergence time of traditional adaptive algorithms, yielding real-time equalization. The performance of the new algorithms is tested by extensive simulations on standard mobile channels