KRISHNAVENI. S

@drmgrdu.ac.in

Associate Professor / EEE
DR M.G.R EDUCATIONAL RESEARCH INSTITUTE

KRISHNAVENI. S


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https://researchid.co/krishnaveni.eee

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment, Energy Engineering and Power Technology, Fuel Technology
5

Scopus Publications

Scopus Publications

  • Hospital Patient Monitoring and Fall Prediction System Using Medical Intranet of Things (IoT)
    M. Swarna, M. Shanmugakumar, S. Krishnaveni
    Communications in Computer and Information Science, 2025
  • Power management technique for energy-efficient communication systems in telemedicine
    K. Sujatha, N. P. G. Bhavani, Rajeswary Hari, K. Senthil Kumar, N. Jayachitra, S. Krishnaveni, K. S. Thivya, A. Kalaivani, B. Rengammal Sankari
    Green Technological Innovation for Sustainable Smart Societies Post Pandemic Era, 2021
  • A Machine Learning Approach for Wind Speed Forecasting
    S. Krishnaveni, Jay Singh, Kartik Verma, Ayush Pachaury, Garvit Kashyap, Anita Bhatia
    2021 International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2021, 2021
    Renewable energy production is increasing worldwide. In all the available renewable sources of energy wind energy has a major stake after hydro energy. However, intermittent nature of the wind speed is posing a constraint for the production of wind energy as stochastic wind power will de-stabilize the energy grid. The increase in demand of renewable sources of energy makes wind energy manufacturing a good investment in the upcoming future. To scheme an efficient regulation of the wind energy development, prediction of wind speed is quite beneficial. In this study, wind speed is predicted to mitigate and minimize the uncertainty in the production of wind power. So accordingly, four different machine learning techniques are applied and evaluated in this work for the Las Vegas region of U.S.A.
  • Analysis and control of the motor vibration using arduino and machine learning model
    S. Krishnaveni, S. Senthil Raja, T. Jayasankar, P. Sathish Babu
    Materials Today Proceedings, 2021
  • Automation of DMPS manufacturing by Using LabView and PLC
    Fareeza F, Chunchu Rambabu, S. Krishnaveni, Abel Chernet Kabiso
    International Journal of Electrical and Computer Engineering, 2018
    <p>This Paper is to enable the Siemens (Programmable Logic Control) CPU 313-5A to communicate with the Lab VIEW and to control the process accuracy by image processing. The communication between CPU 313-5A and Lab VIEW is via OPC (OLE for Process Control).Process Accuracy is achieved with the use of Labview Image Processing and Gray Scale matching Pattern. Accuracy in the gray scale matching will purely depend on the calibration of the camera with respect to the corresponding image. The digital output from the labview is communicated to PLC via Ethernet Protocol for the industrial process control. With the use of Labview the dead time while using the normal image vision module in PLC can be minimized. Labview uses the gray scale matching technique which is more accurate than the normal image vision module used in PLC.</p>