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P. Palanichamy
AMET University, Chennai

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Remotely Operated Submarine Vehicle Control Using Fuzzy Logic P. Palanichamy
Indonesian Journal of Electrical Engineering and Computer Science Vol 8, No 2: November 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v8.i2.pp582-585

Abstract

Submerged marine investigation still remains a puzzle. The motivation behind this paper is to address the issues in plan and improvement of submerged vehicles with hindrance shirking and moving help for administrator in marine condition utilizing fluffy rationale controller. A symmetrical, practical and little measured submerged Remotely Operated Vehicle (ROV) with three thrusters is intended for testing control calculation and execution of framework. Kinematics of the ROV is produced relating rate of revolution of thrusters with ROV'S straight and precise increasing speed. A near investigation of the framework's reaction for basic if-else rationale and with fluffy rationale controller for route is made and assessed. A Graphical User Interface (GUI) with live bolster is given to the administrator to more extensive scope of visual perception of submerged condition and navigational guide.
Application of Artificial Neural Network in Electrical Power System P. Palanichamy
Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 1: January 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v9.i1.pp77-80

Abstract

The artificial neural network used to detect the fault in electrical machines and can increase the function of new entry detection when compared to the conventional method. In proposed artificial neural network has increased the precision and stability of system performance. The time-area vibration signs of a pivoting machine with ordinary and flawed apparatuses are handled for highlight extraction. The separated elements from unique and preprocessed signs are utilized as contributions to both classifiers in view of ANNs and SVMs for two-class (typical or blame) acknowledgment. The quantity of hubs in the concealed layer, if there should be an occurrence of ANNs, and the extend basis work section parameter, in the event of SVMs, alongside the choice of information components are enhanced utilizing genetic algorithm (GAs).
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