-Glaucoma is one of the most common causes of blindness which is caused by increase of fluid pressure in the eye which damages the optic nerve and eventually causing vision loss. An automated technique to diagnose glaucoma disease can reduce the physicians' effort in screening of Glaucoma in a person through the fundal retinal images. In this paper, optimal hyper analytic wavelet transform for Glaucoma detection technique from fundal retinal images is proposed. The optimal coefficients for transformation process are found out using the hybrid GSO-Cuckoo search algorithm. This technique consists of pre-processing module, optimal transformation module, feature extraction module and classification module. The implementation is carried out with MATLAB and the evaluation metrics employed are accuracy, sensitivity and specificity. Comparative analysis is carried out by comparing the hybrid GSO with the conventional GSO. The results reported in our paper show that the proposed technique has performed well and has achieved good evaluation metric values. Two 10-fold cross validated test runs are performed, yielding an average fitness of 91.13% and 96.2% accuracy with CGD-BPN (Conjugate Gradient Descent-Back Propagation Network) and Support Vector Machines (SVM) respectively. The techniques also gives high sensitivity and specificity values. The attained high evaluation metric values show the efficiency of detecting Glaucoma by the proposed technique.
Magnetic levitation (Maglev) is becoming a popular transportation topic all around the globe. Maglev systems have been successfully implemented in many applications such as frictionless bearings, high-speed maglev trains, and fast tool servo systems. Due to the features of the instability and nonlinearity of the maglev system, the design of controllers for a maglev system is a difficult task. Literature shows, many authors have proposed various controllers to stabilize the position of the object in the air. The main drawback of these controllers is that symmetric constraints like stability conditions, decay rate conditions, disturbance rejection and eddy-current based force due to the motion of the levitated object are not taken into consideration. In this paper, a linear matrix inequality based fuzzy controller is proposed to reduce vibration in the object. The efficacy of the proposed controller tests on a maglev system considering symmetric constraints and the eddy current based force.
This Paper presents a comparative study of Z-N method and Genetic Algorithm method (GA) to determine the optimal proportional-integral-derivative (PID) controller parameters, for speed control of a Field Oriented Control (FOC) induction motor; the GA algorithm has been programmed and implemented in MATLAB. Z-N method and trial and error and open loop IM has been modelled in MATLAB (SIMULINK).comparing with traditional ZieglerNicholson method, it has been observed that during optimizing the controller parameters of a FOC IM drive with evolutionary algorithms (EA), the performance of the controller is improved for the step input in speed control as well as for speed tracking problem more efficiently under no load condition, if the load is placed on IM, the performance characteristics have changed for ZN and trial and error method, but even if load change occur, there is no much variation in the evolutionary algorithms (GA) than and Ziegler -Nicholson method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.