Browsing by Author "Sinha, S."

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A Zig-Zag Multiwinding Transformer Based AC-DC Converter for EV Battery Charger Using Interleaved Buck DC-DC Converter
(Institute of Electrical and Electronics Engineers Inc., 2024) Sinha, S.; Kalpana, R.
Electric vehicles (EVs) are depending on reliable and efficient battery charging infrastructure. This work provides a 2 set of three phase ac-dc converter-based power-quality (PQ) compliant IEEE-519 battery charging system. The zigzag multi-winding transformers (ZZMWTs) will play a role in reducing total harmonic distortion (THD) without the use of any power factor correctors or filter circuits. The secondary side star and delta arrangement of the ZZMWTs will have a phase shift of Â± 150. This paper focuses on designing, optimizing, and managing a DC-DC interleaved buck converter for EV battery charging in both constant current (CC) and constant voltage (CV) modes. The planned EV charger performance is assessed using the MATLAB/Simulink environment in terms of THD and PF. A laboratory prototype hardware arrangement of the suggested battery charger is used to validate the results while providing controlled feedback. To support the theoretical analysis, further experimental findings from the lab prototype are presented. Â© 2024 IEEE.
Code-switching automatic speech recognition using modified ESPNet
(American Institute of Physics Inc., 2023) Sinha, S.; Spoorthy, V.; Koolagudi, S.G.
Recently, a drastically increased focus has been observed in multilingual Automatic Speech Recognition (ASR). To cater to multiple low resource languages, a speech recognition system is used. This is performed by taking advantage of low amounts of labeled corpora in multiple languages has. The prosperity of low-resource multilingual and code-switching ASR often depends on the variety of languages in terms of linguistic characteristics as well as the amount of data available. This work focuses on modifying the multilingual and code-switching ASR system through two different subtasks including a total of seven Indian languages. To counter this the model has been provided with several hours of transcribed speech data, comprising of train and test sets, in these languages including two code-switched language pairs, Hindi-English and Bengali-English. In this work, a modified ESPNet architecture is proposed to perform multilingual ASR which improved the performance of the baseline system resulting in accuracy of Word Error Rate (WER) is 27.69%. Â© 2023 Author(s).
Exploring and understanding the microwave-assisted pyrolysis of waste lignocellulose biomass using gradient boosting regression machine learning model
(Elsevier Ltd, 2024) Sinha, S.; Sankar Rao, C.; Kumar, A.; Venkata Surya, D.; Basak, T.
The production of bio-oil is a complex process influenced by various parameters. Optimizing these parameters can significantly enhance bio-oil yield, thus improving process efficiency. This study aims to develop a predictive model for bio-oil yield using the Gradient Boosting Regression (GBR) technique. It also seeks to identify the key factors affecting bio-oil yield and determine the optimal conditions for maximizing production. The GBR model was constructed using data collected from the literature. The model's performance was evaluated based on its determination coefficients for training and testing datasets. Optimization studies were conducted to identify the best conditions for bio-oil production. The GBR model demonstrated high precision, with determination coefficients of 0.983 and 0.913 for the training and testing datasets, respectively, indicating its effectiveness in predicting bio-oil yield. The optimal conditions for maximizing bio-oil yield were identified as 20 min of pyrolysis time, a temperature of 771 °C, and 524W of microwave power. The two-way PDP analysis provided valuable insights into the interactive effects of temperature with other factors, enhancing the understanding of the dynamics of the bio-oil production process. This study not only identifies the most impactful variables for bio-oil yield but also offers critical guidance for optimizing the production process. © 2024 Elsevier Ltd
Influence of Angle of Internal Friction and Slope Face Angle on Kinematic Failures in Marble Mines: A Predictive Approach
(Springer, 2025) Sinha, S.; Tripathi, A.K.; Akhil, A.; Kumar, M.
This study investigates the kinematic stability of slopes in two opencast marble mines, focusing on the variation of dip angles of the slope and angles of internal friction on overall slope stability. The research draws on joint orientation data collected from the mines to perform detailed kinematic analyses, examining different slope faces at various dip directions that gave a probability of failure. A crucial part of the study involved statistical analysis by curve-fitting model to establish a relationship between the dip angle (or overall pit angle) and the angle of internal friction. The relationship was found to be nonlinear following a trend of 3rd-degree polynomial equation. Additionally, sensitivity analysis was conducted to further understand the relationship between these critical parameters. The sensitivity index was calculated by finite difference method where the parameters dip angle and angle of internal friction were taken into consideration by keeping one of the parameter constants and varying the other parameter and vice versa to find out the dependency of the varying parameter on the probability of failure. This multifaceted approach not only validates the importance of these variables, but also provides a predictive framework for assessing slope failure risks. © The Author(s), under exclusive licence to Indian Geotechnical Society 2025.
Microwave assisted catalytic co-pyrolysis of banana peels and polypropylene: experimentation and machine learning optimization
(Royal Society of Chemistry, 2025) Rajpurohit, N.S.; Sinha, S.; Ramesh, R.; Sankar Rao, C.; Harshini, H.
The growing accumulation of agricultural and plastic waste poses serious environmental challenges, necessitating sustainable and efficient valorization strategies. This study investigates the microwave-assisted catalytic co-pyrolysis of banana peels and polypropylene, using graphite as a susceptor and potassium hydroxide as a catalyst. Experiments were conducted by varying biomass and plastic quantities and microwave power levels to study their effects on product yields and thermal performance. The process effectively converted waste materials into valuable products, with oil yield increasing with microwave power and optimized biomass-to-plastic ratios. The rate of mass loss and heating rate were found to significantly influence overall conversion efficiency. A support vector regression (SVR) model was developed to predict yields based on input parameters, achieving a coefficient of determination ranging from 0.81 to 0.99, which demonstrates the reliability of machine learning in capturing complex thermochemical behavior. 3D plots illustrated the nonlinear effects of process variables on yields. Fourier Transform Infrared Spectroscopy (FTIR) and X-ray Diffraction (XRD) analyses of char confirmed functional groups and crystalline phases, suggesting its suitability for applications like adsorbents or catalysts. Brunauer-Emmett-Teller (BET) analysis showed multilayer adsorption, while thermogravimetric analysis (TGA) highlighted distinct thermal degradation patterns of the feedstocks. These results affirm the promise of integrating experiments with ML for efficient waste-to-energy conversion. © 2025 The Royal Society of Chemistry.