International Journal of Chaos, Control, Modelling and Simulation

SEASONAL VARIATION OF CARRYING CAPACITY ON DISEASED MODEL CAN CAUSE SPECIES EXTINCTION Prodip Roy1, Banshidhar Sahoo2, and Swarup Poria3 1,2,3 Department of Mathematics, Mahadevananda Mahavidyalaya, Kolkata-120, India. ABSTRACT Most natural populations experience fluctuations in biological and environmental factors which causes carrying capacity variation. In this paper, we have introduced a diseased prey-predator model with periodically varying carrying capacity. We have studied the effects of different amplitudes of oscillation as well as different frequencies of oscillation on the dynamics of the model. We have done bifurcation analysis of the model with respect to the amplitude of oscillation and frequency of oscillation of the carrying capacity. We observe limit cycle, low periodic orbits, high periodic orbits and chaos in the model. We observe the existence of critical frequency and amplitude of oscillation of carrying capacity for which the prey population ex...

 International Journal of Chaos, Control, Modelling and Simulation (IJCCMS)     ISSN :  2319 - 5398 [Online] ; 2319 - 8990 [Print] http://airccse.org/journal/ijccms/index.html Aims and Scope:  The International Journal of Chaos, Control, Modelling and Simulation is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Chaos Theory, Control Systems, Scientific Modelling and Computer Simulation. In the last few decades, chaos theory has found important applications in secure communication devices and  secure data encryption. Control methods are very useful in several applied areas like Chaos, Automation, Communication, Robotics, Power Systems, and Biomedical Instrumentation. The journal focuses on all technical and practical aspects of Chaos, Control, Modelling and Simulation. The goal of this journal is to bring together researchers and practitioners from academia and industry...

SIMMECHANICS VISUALIZATION OF EXPERIMENTAL MODEL OVERHEAD CRANE,ITS LINEARIZATION AND REFERENCE TRACKING-LQR CONTROL  Thein Moe Win1 Tim Hesketh2 Ray Eaton3   School of Electrical Engineering & Telecommunication The University of New South Wales, High St, Kensington, NSW 2052, Australia ABSTRACT Overhead Crane experimental model using Simmechanic Visualization is presented for the robust antisway LQR control. First, 1D translational motion of overhead crane is designed with exact lab model measurements and features. Second, linear least square system identification with 7 past inputs/outputs is applied on collected simulation data to produce more predicted models. Third, minimize root mean square error and identified the best fit model with lowest RMSE. Finally, Linear Quadratic Regulator (LQR) and Reference tracking with pre-compensator have been implemented to minimize load swing and perform fast track on trolley positioning. KEYWORDS Simme...

A Comparative study of controllers for stabilizing a Rotary Inverted Pendulum Velchuri Sirisha and Dr. Anjali. S. Junghare  Electrical Engineering Department, Visvesvaraya National Institute of Technology, Nagpur, India Abstract  This paper describes comparative study of various controllers on Rotary Inverted Pendulum (RIP). PID, LQR, FUZZY LOGIC and H∞ controllers are tried on RIP in MatLab Simulink. The same four controllers have been tested on test bed of RIP system the controllers are compared from various aspects. The controllers in simulink are compared with the controllers in real time. Keywords Fuzzy Logic, H∞, LQR, PID, RIP  Original Source URL: http://airccse.org/journal/ijccms/current2014.html  http://airccse.org/journal/ijccms/index.html

Optimal Feature Selection from VMware ESXi 5.1 Feature Set Amartya Hatua Bangalore, India Abstract    A study of VMware ESXi 5.1 server has been carried out to find the optimal set of parameters which suggest usage of different resources of the server. Feature selection algorithms have been used to extract the optimum set of parameters of the data obtained from VMware ESXi 5.1 server using esxtop command. Multiple virtual machines (VMs) are running in the mentioned server. K-means algorithm is used for clustering the VMs. The goodness of each cluster is determined by Davies Bouldin index and Dunn index respectively. The best cluster is further identified by the determined indices. The features of the best cluster are considered into a set of optimal parameters. Index Terms   Clustering, Feature selection, K-means clustering algorithm, VMware ESXi 5.1  More Details :  http://airccse.org/journal/ijccms/current2014.html http://airccse.or...