Comparison of PSDA and CCA detection methods in a SSVEP-based BCI-system

Using steady-state visually evoked potential (SSVEP) in brain-computer interface (BCI) systems is the subject of a lot of research. One of the most popular and widely used detection method is using a power spectral density analysis (PSDA). Lately there have been some new methods emerging, one of them is using canonical correlation analysis (CCA) which seems to have some promising improvements and advantages compared to traditional SSVEP detection methods, like better signal-to-noise ratio (SNR), lower inter-subject variability and the possibility to use harmonic frequencies, i.e., a serie of frequencies which have the same fundamental frequency. In this research two different SSVEP detection methods, one using PSDA and one using CCA are compared. The results show that the CCA-based detection method performs significantly better than the PSDA-based detection method. The increase of performance can in particular be seen when using harmonic frequencies. While the PSDA-based detection method has difficulties detecting harmonic frequencies, the CCA-based detection method is able to detect harmonic frequencies

The Question of Generation Adequacy in Liberalised Electricity Markets

This paper presents an overview of the reasons why unregulated markets for the production of electricity cannot be expected to invest sufficiently in generation capacity on a continuous basis. Although it can be shown that periodic price spikes should provide generation companies with sufficient investment incentives in theory, there are a number of probable causes of market failure. A likely result is the development of investment cycles that may affect the adequacy of capacity. The experience in California shows the great social costs associated with an episode of scarce generation capacity. Another disadvantage is that generation companies can manipulate price spikes. This would result in large transfers of income from consumers to producers and reduce the operational reliability of electricity supply during these price spikes. We end this paper by outlining several methods that have been proposed to stabilise the market, which provide better incentives to generation companies and consumers alike.Generation adequacy, Liberalised electricity market

Model-Based Iterative Learning Control Applied to an Industrial Robot with Elasticity

In this paper model-based Iterative Learning Control (ILC) is applied to improve the tracking accuracy of an industrial robot with elasticity. The ILC algorithm iteratively updates the reference trajectory for the robot such that the predicted tracking error in the next iteration is minimised. The tracking error is predicted by a model of the closed-loop dynamics of the robot. The model includes the servo resonance frequency, the first resonance frequency caused by elasticity in the mechanism and the variation of both frequencies along the trajectory. Experimental results show that the tracking error of the robot can be reduced, even at frequencies beyond the first elastic resonance frequency