Abstract: Medical image segmentation based on image smoothing followed by edge detection assumes a great degree of importance in the field of Image Processing. In this regard, this paper proposes a novel algorithm for medical image segmentation based on vigorous smoothening by identifying the type of noise and edge diction ideology which seems to be a boom in medical image diagnosis. The main objective of this algorithm is to consider a particular medical image as input and make the preprocessing to remove the noise content by employing suitable filter after identifying the type of noise and finally carrying out edge detection for image segmentation. The algorithm consists of three parts. First, identifying the type of noise present in the medical image as additive, multiplicative or impulsive by analysis of local histograms and denoising it by employing Median, Gaussian or Frost filter. Second, edge detection of the filtered medical image is carried out using Canny edge detection technique. And third part is about the segmentation of edge detected medical image by the method of Normalized Cut Eigen Vectors. The method is validated through experiments on real images. The proposed algorithm has been simulated on MATLAB platform. The results obtained by the simulation shows that the proposed algorithm is very effective which can deal with low quality or marginal vague images which has high spatial redundancy, low contrast and biggish noise, and has a potential of certain practical use of medical image diagnosis.
Abstract: Fuzzy Load forecasting plays a paramount role in the operation and management of power systems. Accurate estimation of future power demands for various lead times facilitates the task of generating power reliably and economically. The forecasting of future loads for a relatively large lead time (months to few years) is studied here (long term load forecasting). Among the various techniques used in forecasting load, artificial intelligence techniques provide greater accuracy to the forecasts as compared to conventional techniques. Fuzzy Logic, a very robust artificial intelligent technique, is described in this paper to forecast load on long term basis. The paper gives a general algorithm to forecast long term load. The algorithm is an Extension of Short term load forecasting method to Long term load forecasting and concentrates not only on the forecast values of load but also on the errors incorporated into the forecast. Hence, by correcting the errors in the forecast, forecasts with very high accuracy have been achieved. The algorithm, in the paper, is demonstrated with the help of data collected for residential sector (LT2 (a) type load: Domestic consumers). Load, is determined for three consecutive years (from April-06 to March-09) in order to demonstrate the efficiency of the algorithm and to forecast for the next two years (from April-09 to March-11).
Abstract: Recent developments in Soft computing techniques,
power electronic switches and low-cost computational hardware have
made it possible to design and implement sophisticated control
strategies for sensorless speed control of AC motor drives. Such an
attempt has been made in this work, for Sensorless Speed Control of
Induction Motor (IM) by means of Direct Torque Fuzzy Control
(DTFC), PI-type fuzzy speed regulator and MRAS speed estimator
strategy, which is absolutely nonlinear in its nature. Direct torque
control is known to produce quick and robust response in AC drive
system. However, during steady state, torque, flux and current ripple
occurs. So, the performance of conventional DTC with PI speed
regulator can be improved by implementing fuzzy logic techniques.
Certain important issues in design including the space vector
modulated (SVM) 3-ะค voltage source inverter, DTFC design,
generation of reference torque using PI-type fuzzy speed regulator
and sensor less speed estimator have been resolved. The proposed
scheme is validated through extensive numerical simulations on
MATLAB. The simulated results indicate the sensor less speed
control of IM with DTFC and PI-type fuzzy speed regulator provides
satisfactory high dynamic and static performance compare to
conventional DTC with PI speed regulator.