The issue of line simplification is one of the fundamental problems of generalisation of geographical information, and the proper parameterisation of simplification algorithms is essential for the correctness and cartographic quality of the results. The authors of this study have attempted to apply computational intelligence methods in order to create a cartographic knowledge base that would allow for non-standard parameterisation of WEA (Weighted Effective Area) simplification algorithm. The aim of the conducted research was to obtain two independent methods of non-linear weighting of multi-dimensional regression function that determines the “importance” of specific points on the line and their comparison to each other. The first proposed approach consisted in the preparation of a set of cartographically correct examples constituting a basis for teaching a neural network, while the other one consisted in defining inference rules using fuzzy logic. The obtained results demonstrate that both methods have great potential, although the proposed solutions require detailed parameterisation taking into account the specificity of geometric variety of the source data.
Unlike traditional passive filters, modern active filters have the following multiple functions; harmonic filtering, damping, isolation and termination, reactive-power control for power factor correction and voltage regulation, load balancing, voltage-flicker reduction, and/or their combinations. Significant cost reductions in both power semiconductor devices and signal-processing devices have inspired manufactures to put active filters on the market. This paper deals with general pure active filters for power conditioning, and specific hybrid active filters for harmonic filtering of three-phase diode rectifiers, as well as traditional passive filters.