Faculty Publications

Now showing 1 - 4 of 4

Selective cropper for geometrical objects in openflipper
(Springer Verlag service@springer.de, 2017) Maonica, B.; Das, P.; Ramteke, P.B.; Koolagudi, S.G.
Computer graphics remains one of the most exciting and rapidly growing computer fields. It includes geometry processing as a major part of it. Every element in Graphics can be processed using different algorithms for acquisition, reconstruction, analysis, manipulation, simulation, and transition of simple, primitive, and complex structures. One such commonly used function is cropping/clipping of geometrical objects. In this paper, an approach has been proposed for cropping a 3D object. This algorithm allows users to crop out selective portions of geometrical objects based on certain constraints like the axis, position, and amount to be cropped. The proposed algorithm has been provided as a plugin to the open-source software OpenFlipper and the results of the crop algorithm have been presented. Â© Springer Science+Business Media Singapore 2017.
Hybrid wavelet packet machine learning approaches for drought modeling
(Springer, 2020) Das, P.; Naganna, S.R.; Deka, P.C.; Pushparaj, J.
Among all the natural disasters, drought has the most catastrophic encroachment on the surrounding and environment. Gulbarga, one of the semi-arid districts of Karnataka state, India receives about 700 mm of average annual rainfall and is drought inclined. In this study, the forecasting of drought for the district has been carried out for a lead time of 1 month and 6 months. The multi-temporal Standardized Precipitation Index (SPI) has been used as the drought quantifying parameter due to the fact that it is calculated on the basis of one simplest parameter, i.e., rainfall and additionally due to its ease of use. The fine resolution daily gridded precipitation data (0.25º × 0.25º) procured from Indian Meteorological Department (IMD) of 21 grid locations within the study area have been used for the analysis. Forecasting of drought plays a significant role in drought preparedness and mitigation plans. With the advent of machine learning (ML) techniques over the past few decades, forecasting of any hydrologic event has become easier and more accurate. However, the use of these techniques for drought forecasting is still obscure. In this study, Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques have been employed to examine their accuracy in drought forecasting over shorter and longer lead times. Furthermore, two hybrid approaches have been formulated by coupling a data transformation method with each of the aforementioned ML approaches. At the outset, pre-processing of input data (i.e., SPI) has been carried out using Wavelet Packet Transform (WPT) and then used as inputs to ANN and SVR models to induce hybrid WP-ANN and WP-SVR models. The performance of the hybrid models has been evaluated based on the statistical indices such as R2 (co-efficient of determination), RMSE (Root Mean Square Error), and MAE (Mean Absolute Error). The results showed that the hybrid techniques have better forecast performance than the standalone machine learning approaches. Hybrid WP-ANN model performed relatively better than WP-SVR model for most of the grid locations. Also, the forecasting results deteriorated as the lead time increased from 1 to 6 months. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
Modelling stream flow and soil erosion response considering varied land practices in a cascading river basin
(Academic Press, 2020) Venkatesh, K.; Ramesh, H.; Das, P.
[No abstract available]
Waste dry cell derived photo-reduced graphene oxide and polyoxometalate composite for solid-state supercapacitor applications
(Royal Society of Chemistry, 2023) Maity, S.; Biradar, B.R.; Srivastava, S.; Chandewar, P.R.; Shee, D.; Das, P.; Mal, S.S.
In the modern era, realizing highly efficient supercapacitors (SCs) derived through green routes is paramount to reducing environmental impact. This study demonstrates ways to recycle and reuse used waste dry cell anodes to synthesize nanohybrid electrodes for SCs. Instead of contributing to landfill and the emission of toxic gas to the environment, dry cells are collected and converted into a resource for improved SC cells. The high performance of the electrode was achieved by exploiting battery-type polyoxometalate (POM) clusters infused on a reduced graphene oxide (rGO) surface. Polyoxometalate (K5[α-SiMo2VW9O40]) assisted in the precise bottom-up reduction of graphene oxide (GO) under UV irradiation at room temperature to produce vanadosilicate embedded photo-reduced graphene oxide (prGO-Mo2VW9O40). Additionally, a chemical reduction route for GO (crGO) was trialed to relate to the prGO, followed by the integration of a faradaic monolayer (crGO-Mo2VW9O40). Both composite frameworks exhibit unique hierarchical heterostructures that offer synergic effects between the dual components. As a result, the hybrid material's ion transport kinetics and electrical conductivity enhance the critical electrochemical process at the electrode's interface. The simple co-participation method delivers a remarkable specific capacity (capacitance) of 405 mA h g−1 (1622 F g−1) and 117 mA h g−1 (470 F g−1) for prGO-Mo2VW9O40 and crGO-Mo2VW9O40 nanocomposites alongside high capacitance retentions of 94.5% and 82%, respectively, at a current density of 0.3 A g−1. Furthermore, the asymmetric electrochromic supercapacitor crGO//crGO-Mo2VW9O40 was designed, manifesting a broad operating potential (1.2 V). Finally, the asymmetric electrode material resulted in an enhanced specific capacity, energy, and power of 276.8 C g−1, 46.16 W h kg−1, and 1195 W kg−1, respectively, at a current density of 0.5 A g−1. The electrode materials were tested in the operating of a DC motor. © 2023 The Royal Society of Chemistry.

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