Varun Vats - Academia.edu (original) (raw)
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Papers by Varun Vats
—Voltage interruption can be happened by several reasons like blackout, brownout, noise, spike et... more —Voltage interruption can be happened by several reasons like blackout, brownout, noise, spike etc. Those incidents have consequential effect on computer's hardware such as data losses, burning of sensitive equipment etc. So, UPS broadly called an uninterruptible power supply device is used in computer to provide a backup power source which will maintain the consistency in voltage and flow of electricity which in turn will provide consistent power input. Batteries are used as energy storage device in UPS which store electric energy during charging state and discharge that electric energy during any kind of interruption to provide backup as additional power source for the computer. So, the most important integral part of UPS is the battery pack which is used as energy storage device in UPS. Main goal of this paper is to design the suitable battery pack for UPS so that it can give maximum backup range to the computer within a certain economical range or cost limit. Also, another goal is to analyze the electrical and thermal parameters of the designed battery pack and cell and suggest better solutions to make the wet battery cell maintenance free (no need of addition of water). Index Terms—uninterruptable power supply, energy storage device, lead acid battery, battery maintenance, wet cell battery.
Machine learning is a branch of Artificial Intelligence(AI) which is heavily used in the field of... more Machine learning is a branch of Artificial Intelligence(AI) which is heavily used in the field of data science. It has a strong potential in health-related data analysis for automated disease prediction. The work focuses on three different machine learning techniques, i.e., DBSCAN, K-Means, and Affinity Propagation to compare their prediction accuracy and computational complexity. The study concentrates on liver disease-related health care data set and uses the Silhouette coefficient for comparative performance measurement of the three techniques mentioned above. The Silhouette coefficient determines prediction accuracy giving K-Means as the optimal method. The overall results will then be analyzed on the basis of prediction accuracy and computational complexity to determine the best technique for prediction of liver diseases using unsupervised machine learning.
—Voltage interruption can be happened by several reasons like blackout, brownout, noise, spike et... more —Voltage interruption can be happened by several reasons like blackout, brownout, noise, spike etc. Those incidents have consequential effect on computer's hardware such as data losses, burning of sensitive equipment etc. So, UPS broadly called an uninterruptible power supply device is used in computer to provide a backup power source which will maintain the consistency in voltage and flow of electricity which in turn will provide consistent power input. Batteries are used as energy storage device in UPS which store electric energy during charging state and discharge that electric energy during any kind of interruption to provide backup as additional power source for the computer. So, the most important integral part of UPS is the battery pack which is used as energy storage device in UPS. Main goal of this paper is to design the suitable battery pack for UPS so that it can give maximum backup range to the computer within a certain economical range or cost limit. Also, another goal is to analyze the electrical and thermal parameters of the designed battery pack and cell and suggest better solutions to make the wet battery cell maintenance free (no need of addition of water). Index Terms—uninterruptable power supply, energy storage device, lead acid battery, battery maintenance, wet cell battery.
Machine learning is a branch of Artificial Intelligence(AI) which is heavily used in the field of... more Machine learning is a branch of Artificial Intelligence(AI) which is heavily used in the field of data science. It has a strong potential in health-related data analysis for automated disease prediction. The work focuses on three different machine learning techniques, i.e., DBSCAN, K-Means, and Affinity Propagation to compare their prediction accuracy and computational complexity. The study concentrates on liver disease-related health care data set and uses the Silhouette coefficient for comparative performance measurement of the three techniques mentioned above. The Silhouette coefficient determines prediction accuracy giving K-Means as the optimal method. The overall results will then be analyzed on the basis of prediction accuracy and computational complexity to determine the best technique for prediction of liver diseases using unsupervised machine learning.