Informatics Publishing Limited

P. Lakshmi ¹, D. Geetha ²

Affiliations
1 Department of Computer Science, Sree Saraswathi Thyagaraja Arts and Science College, Pollachi, IN
2 Department of MCA, Sree Saraswathi Thyagaraja Arts and Science College, Pollachi, IN

Abstract

In network security, intrusion detection system plays an important role as it is able to detect various types of attacks in the network. The main idea of the intrusion detection system is to recognize the malicious attacks which intimidate the security from the information system’s normal activities. The intrusion detection system can be formulated basically as a problem of binary classification, so that it can be solved using effective classification technique. Support Vector Machine (SVM) is the most prominent classification algorithms in the area of data mining, but it has limitation such as extensive training time. To rectify this limitation, a modified version of SVM is introduced in this work. In this work, classification is done using modified SVM and evaluation of the proposed method is done using KDD dataset by conducting experiments. The experimental result proved that the extensive time is reduced using modified SVM by performing proper dataset.

Keywords

Data Mining Technique, Intrusion Detection System, Support Vector Machine, Modified Support Vector Machine.

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R. Kalaivani ¹, K. Sudhagar ², P. Lakshmi ³

Affiliations
1 DEEE, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Chennai - 600062, Tamil Nadu, IN
2 Murugappa Polytecnic College, Avadi, Chennai - 600062, Tamil Nadu, IN
3 DEEE, CEG, Anna University, Chennai - 600025, Tamil Nadu, IN

Abstract

This paper presents a novel Neural Network (NN) based vibration control of a Vehicle Active Suspension System (VASS) when subjected to road disturbance for enhancing the travelling comfort to the passengers. The simulation for the vibration control of VASS with Proportional Integral and Derivative (PID) controller is used for training the NN. The nonlinearities of the system parameters can be effectively handled by the NN and also it can deal with unfocused data by considering the prejudiced phenomena such as logical reasoning and perception beyond its domain. The idea of this work is to design, simulate and compare the performance of NN based VASS with that of the uncontrolled suspension system (passive) and the VASS controlled with PID controller. The Root Mean Square (RMS) value of body acceleration as the performance index for genetic optimization, the simulation is carried out using MATLAB/SIMULINK software. Simulation results such as sprung mass displacement, body acceleration, suspension deflection, tyre deflection and Power Spectral Density (PSD) of body acceleration shows the effectiveness of NN in suppression of the vibration of vehicle body compared to passive system, PID based VASS and optimized PID controller.

Keywords

Genetic Algorithms, Neural Networks, Simulation, Suspensions, Vibration Control

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S. Rajendiran ¹, P. Lakshmi ¹, B. Rajkumar ¹

Affiliations
1 College of Engg., Anna University, Chennai, IN

Abstract

Vehicle vibration can be controlled by Active Suspension System (ASS). The performances of ASS are better than the conventional Passive Suspension System (PSS). The effectiveness of ASS is based on the type of controllers used. In this paper, a quarter car model with ASS is considered for analysis. To reduce the vibration and improve the ride quality, Fractional order Fuzzy Sliding Mode Controller (FrFSMC) is proposed and its performances are compared with Fuzzy Sliding Mode Controller (FSMC) and passive system. While testing the performance of the controllers three types of road disturbances are given to the quarter car model to stimulate the vibration. The results of the proposed controllers are also compared against the existing Gray Fuzzy Sliding Mode Controller (GFSMC). From the time responses and ischolar_main mean square indices, FrFSMC performs better than the FSMC, GFSMC and PSS.

Keywords

Active Suspension System, Fractional Order Fuzzy Sliding Mode Control, Fuzzy Sliding Mode Control, Quarter Car Model, Vibration Control.

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