• Journal of Electrical Engineering and Technology
• /
• v.12 no.4
• /
• pp.1456-1470
• /
• 2017

This paper presents sensorless vector control of induction motor drives with an improved model reference adaptive system observer for rotor speed estimation and parameters identification from measured stator currents, stator voltages and estimated rotor fluxes. The aim of the proposed sensorless control method is to compensate simultaneously stator resistance and rotor time constant variations which are subject of large changes during operation. PI controllers have been used in the model reference adaptive system adaptation mechanism and in the closed loops of speed and currents regulation. The stability of the proposed observer is proved by the Lyapunov's theorem and its feasibility is verified by experimentation. The experimental results are obtained with an 1 kW induction motor using Matlab/Simulink and a dSPACE system with DS1104 controller board showing the effectiveness of the proposed approach in terms of dynamic performance.

• International journal of advanced smart convergence
• /
• v.1 no.1
• /
• pp.6-13
• /
• 2012

An improved algorithm for image quality assessment is presented. First a simple model of human visual system, consisting of a nonlinear function and a 2-D filter, processes the input images. This filter has one user-defined parameter, whose value depends on the reference image. This way the algorithm can adapt to different scenarios. In the next step the average value of locally computed correlation coefficients between the two processed images is found. This criterion is closely related to the way in which human observer assesses image quality. Finally, image quality measure is computed as the average value of locally computed correlation coefficients, adjusted by the average correlation coefficient between the reference and error images. By this approach the proposed measure differentiates between the random and signal dependant distortions, which have different effects on human observer. Performance of the proposed quality measure is illustrated by examples involving images with different types of degradation.