M.Tech, B-Tech

K.Sireesha is serving as Assistant Professor ( Sr. Gr) in the Department of Electrical and Electronics Engineering, Amrita School of Engineering, Bengaluru. Currently she is pursuing Ph.D in the area of Battery management systems.  


2013 M.Tech (VLSI Design) Amrita Vishwa Vidyapeetham, India
2003 B.Tech(EEE) JNTUK University, India


Publication Type: Journal Article

Year of Publication Title


V. Sriram, R Prakash, K., Gunasekaran, M., M. K. Haridharan, Kothandapani, K., and K. Sireesha, “A new method to estimate Weibull parameter for the fatigue life of self compacting fibre reinforced concrete beams”, International Journal of Civil Engineering and Technology, vol. 8, pp. 326-331, 2017.[Abstract]

Fatigue life data is the important factor for designing, high rise buildings, bridge decks, rapid transportation systems and precast structural elements and it should to be modelled accurately. To attain a appropriate modelling data, it is vital to choose a suitable computation method. Two parameter Weibull distribution is frequently used statistical tool for modelling the flexural fatigue failure life exactly. In this article, a flexural fatigue failure life of self-compacting fibre reinforced concrete (FRC) were statistically commanded. Subsequently, a novel energy pattern factor method (NEPFM) has been proposed for the computation of Weibull shape parameter from the data of earlier researcher. The validity of the proposed NRPFM is verified with power density method and other researchers. The results revealed that the proposed NEPFM is appropriate and efficient to calculate the Weibull shape parameter for the flexural fatigue failure applications.

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C. Rajesh, Kranthi, K., Kishore, P., and K. Sireesha, “Intelligent Vehicle Security and SOS Messaging System with Embedded GSM Module”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 4, no. 6, 2015.

Publication Type: Conference Paper

Year of Publication Title


K. Kiranvishnu, Dr. J. Ramprabhakar, and K. Sireesha, “Comparative study of wind speed forecasting techniques”, in 2016 - Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy, PESTSE 2016, 2016.[Abstract]

Wind is one of the prominent renewable source, as it is clean energy and abundantly available, however there are immense issues owing to varying nature of wind flow. The said issues has attracted many researchers to work pertinent to wind forecasting models and hence it is possible for wind power forecast based on the capacity of wind turbine based energy conversion system. With the devised forecasting techniques, it is also possible to schedule required demand to match the generation. Forecasting is an important aid in wind speed prediction. The wind speed forecasting can effectively reduce or avoid the adverse effect of wind farm on power grid and it will also ensure the safe and economic operations of the power system. This work is aimed to design an appropriate model for wind forecasting with less mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square error (MSE) in comparison to back propagation neural network (BPNN) and linear regression technique. The simulation of the mentioned and proposed models are done and the results are presented to validate the effectiveness of the proposed technique

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