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Sushmita Deka

Researcher at National Institute of Technology, Meghalaya

Publications -  7
Citations -  13

Sushmita Deka is an academic researcher from National Institute of Technology, Meghalaya. The author has contributed to research in topics: Impulse (physics) & Accelerometer. The author has an hindex of 1, co-authored 7 publications receiving 5 citations. Previous affiliations of Sushmita Deka include National Institute of Technology, Silchar.

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Machinability of Silicon and German Silver in Micro Electrical Discharge Machining: a Comparative Study

TL;DR: In this article, an experimental investigation of micro hole drilling on two different categories of material such as silicon and german silver using micro electrical discharge machining (μEDM) process is presented.
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Comparative assessment of modified deconvolution and neuro-fuzzy technique for force prediction using an accelerometer balance system

TL;DR: A comparison of two force prediction techniques which are used in dynamic calibration has been studied and it has been observed that both modified deconvolution and ANFIS can be used for force prediction without intensive calculations, however the accuracy of ANF IS is slightly higher than that of modified deconVolution.
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Measurement Technique for Ideal Selection of Sensors and Accurate Force Recovery on Aerodynamic Models

TL;DR: In this article, a multi-point calibration of a double cone model using tri-axial and uniaxial accelerometers to obtain the maximum number of responses with accurate prediction using a minimum number of accelerometers is described.
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Transient Dynamic Analysis of a Cantilever Rod with Axial Impulse Loading Using Finite Element Method

TL;DR: In this paper, the effect of a half sine impulse force applied on a cantilever rod in the axial direction has been discussed and the displacements at the tip of the rod were obtained based on two theories, the basic vibration formulae and FEM analysis.
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A new method for force prediction in an accelerometer force balance system using support vector regression

TL;DR: The present study describes the force prediction in an accelerometer force balance system using support vector regression (SVR), which was able to predict the forces quite accurately as compared to ANFIS and ANN.