Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
 
Articles by R.M.S. Parvathi
Total Records ( 3 ) for R.M.S. Parvathi
  S. Pariselvam and R.M.S. Parvathi
  Fill the text co-operation is considered as one of the important factor that has a vital influence in the reliable data dissemination of packets between the wireless nodes present in an ad hoc scenario. This co-operation is also essential for maintaining both the forward and reverse routes between the source and the sink nodes. Due to the stringent availability of resources in MANETs, some of the participating nodes present in the ad hoc environment may exhibit a malicious behavior of intentionally dropping the data packets rather than forwarding to their neighbor nodes. This maliciousness of the nodes may result in the degradation in the network performance. In this study, researchers formulate and propose a Cornbach alpha coefficient based trust model which manipulates the trust of each and every node present in the topology based on Cornbach alpha coefficient. The experimental analysis of the proposed trust model is studied through the ns-2 simulation with the aid of evaluation parameters namely packet delivery ratio, control overhead, total overhead and throughput by varying the number of malicious nodes. This proposed scheme performs well when compared to the model present in the literature like split half reliability model.
  M. Sivakumar and R.M.S. Parvathi
  The present study proposes a fault diagnosis methodology of three phase inverter circuit base on Radial Basis Function (RBF) artificial neural network trained by Particle Swarm Optimization (PSO) algorithm. Using the appropriate stimulus signal, fault features are extracted from efficient points in frequency response of the circuit directly and then a fault dictionary is created by collecting signatures of different fault conditions. Trained by the examples contained in the fault dictionary, the RBF neural network optimized by PSO has been demonstrated to provide robust diagnosis to the difficult problem of soft faults in three phase inverter circuits. The experimental result shows that the proposed technique is succeeded in diagnosing and locating faults effectively.
  M. Sivakumar and R.M.S. Parvathi
  Growing industrial need makes the choice of fast response, accurate and efficient systems. The Multilevel inverter based Induction Drives (MLID) are the best solution for the industrial drive needs which reduces the harmonics and increases the efficiency of the system. And also the need for hybrid electric vehicles increases the need of an efficient traction system with the use of multilevel inverters. As multilevel inverter has many semi-conductor switches, it is difficult to identify the fault in it. In this study, a new fault diagnosis method by using the Total Harmonic Distortion (THD) of the voltage waveform and to classify the fault, Neural Network (NN) trained with back propagation, Genetic Algorithm (GA) and also with Particle Swarm Optimization (PSO) is applied and the results are compared. Results shows that NN trained with PSO gives fast response in training algorithm when compared to BP and GA. Here, a cascaded multi-level inverter based three-phase induction motor drive is taken as the test system. MATLAB Software is used to analyse the effectiveness of the test system and results are tabulated.
 
 
 
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility