We evaluated the performance of our new classifier by comparing it to the standard CSP method that is typically used for the classification of band power features in motor imagery paradigms (see e.g. [3]).
Furthermore, because the actual effect of power depends on its source (French and Raven 1959), we examine its effects in terms of different power bases using the expanded classification of power bases (French and Raven 1959) by Raven and Kruglanski (1970 2: coercive power, reward power, expert power, informational power, legitimate power, and referent power.
The significance of detection and classification of power quality (PQ) events that disturbs the voltage and/or current waveforms in the electrical power distribution networks is well known.
Electric power rates vary with the classification of the user, the amount of power used, and, often, with the time of day, the season of the year, the power factor, and the power demand.
The classification of incipient faults in power transformers via Dissolved Gas Analysis (DGA) is, for instance, a very well known technique for this purpose.
The training and validation sets, with 70% and 15% of data, respectively, were used for ANN model building; and the third set, with the last 15%, was used to test on the out-of-sample set the classification power of a model.
Classification of power system faults is the first stage for improving power quality and ensuring the system protection.
If Q is the decision attribute D, and P is a subset of condition attributes, then γ P (D) represents the depended degree of the condition attribute subset P by the decision attribute D; that is, to what degree P can discriminate the distinct classes of D. In this sense, γ P (D) reflects the classification power of the subset P of attributes.
We describe an interactive software platform, Visual Integration for Bayesian Evaluation, that ingests or creates Bayesian posterior probability matrices, performs data fusion and allows the user to interactively evaluate the classification power of fusing various combinations of data sources, such as transcriptomic, proteomics, metabolomics, biochemistry and function.
Automatic classification of Power Quality Disturbances (PQDs) is a challenging concern for both the utility and industry.
