Faculty Publications
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Item Compact wideband microstrip circular patch antenna for 6G application(Institute of Electrical and Electronics Engineers Inc., 2023) Mahapatra, R.K.; Shet, N.S.V.; Satapathi, G.S.; Manjukiran, B.; DImri, P.; Shetty, A.N.; Shettigar, S.; Patro, B.S.; Senapati, A.; Srichandan, R.In order to create an antenna with a wide band range, the work provided in this paper displays the parametric analysis for the circular patch antenna designs. Microstrip line in the 50 ohm range is used in the developed antenna. To achieve it, three proposed designs were put forth, out of which design2 achieves a broadband of below -10dB return loss range from 20.33 to 47.11 GHz with a bandwidth of 26.78 GHz towards 6G. The HFSS (High Frequency Structure Simulator) is used on intel core i5, 8 GB RAM, Windows 11 to simulate the suggested antenna designs. © 2023 IEEE.Item Design and Analysis of Microstrip Wideband Filter(Institute of Electrical and Electronics Engineers Inc., 2023) Mahapatra, R.K.; Kaliyath, Y.; Shet, N.S.V.; Satapathi, G.S.; Manjukiran, B.; DImri, P.; Shetty, A.N.; Srichandan, R.; Patro, B.S.; Senapati, A.This paper deals with the study on conventional wideband bandpass filter (BPF) and the bandpass filter designed using the split ring resonator structure. The proposed design using the SRR consists of 3 SRR on which the filter is mounted. This is designed using the HFSS software. The material with in the dielectric constant of 4.36 and the loss tangent of 0.01 is used for the substrate material. The substrate height is varied with the dimension of 4.9 x 2.9 kept constant. The result observed for the BPF on SRR with increase substrate height has shown better results better return loss characteristics as compared to the other design. © 2023 IEEE.Item Stability Assessment of Vertical Remnant Pillars In Cut and Fill Mining Method with Numerical Modelling(Avestia Publishing, 2025) Mohanto, S.; Upare, A.; Santosh, M.; Panchal, S.; Senapati, A.Cut and fill mining method involves extraction of orebody in horizontal slices in weak rock formations. The void created as a result of excavation is backfilled and vertical pillars are left at intervals if the overlying roof is weak. This method is advantageous in terms of ore recovery and safety, making it a preferred method for steeply dipping orebodies in challenging underground environments. These remnant pillars left intact plays a crucial role in supporting the overlying strata and protecting a safe environment for ore exploitation. The stability of these pillars is important since pillar failure results in catastrophic consequences including subsidence or even loss of lives. Hence, the pillar dimension is one of the important parameters which governs the stability of the overlying strata in cut and fill mining method. The present study focuses on the assessment of vertical pillar stability with 5 m × 5 m dimension left intact throughout the entire depth of orebody in cut and fill post pillar mining method considering three-dimensional finite element analyses. Based on the simulation results obtained from numerical modeling, it was found that the pillar dimension of 5 m × 5 m was stable enough for the considered geo-mining condition with factor of safety above unity. © 2025 Avestia Publishing. All rights reserved.Item Experimentation and Statistical Prediction of Dust Emission in Iron Ore Mines using Supervised Machine Learning (Regression) Modelling(World Researchers Associations, 2025) Rajib, P.; Harsha, V.; Senapati, A.; Sahas, S.V.In India, the mine area and the processing plant of materials such as iron ore and coal will cause dust emissions. The fugitive dust emission creates a hazardous working environment for the workers. Dust emissions will cause pulmonary-related diseases to the workers and also to the people living in nearby areas of the mine. Environmental effects such as air pollution occur due to the dispersion of particulate matter over the permissible limit in the processing area. This study evaluates dust emission levels and air quality control measures in an iron ore mine (A), Karnataka, India. Fugitive and workplace dust sampling was conducted following DGMS and MoEF and CC guidelines, with a specific focus on PM10 and PM2.5 particulate matter. Measurements revealed that dust concentrations in several mining areas exceeded the permissible limit of 1200 ?g/m³ as per the National Ambient Air Quality Standards (NAAQS, 2009). To analyze and predict these concentrations, supervised machine learning (regression) modeling including linear, polynomial (order 2) and polynomial (order 2) models, was applied. The results indicated that a third-order polynomial regression model provided the best fit for predicting dust concentrations, demonstrating lower error. The study emphasizes the necessity of more robust dust suppression measures including installing a dry fog dust suppression system, to guarantee safe working conditions and adherence to environmental regulations, even in the face of efforts to reduce dust exposure. © 2025, World Researchers Associations. All rights reserved.Item Human-in-the-Loop Data Analytics for Classifying Fatal Mining Accident Causes Using Natural Language Processing and Machine Learning Techniques(Springer Science and Business Media Deutschland GmbH, 2025) Sharma, A.; Kumar, A.; Vardhan, H.; Mangalpady, A.; Mandal, B.B.; Senapati, A.; Akhil, A.; Saini, S.Mining remains one of the most hazardous industries globally, marked by frequent fatalities resulting from complex operational risks. While accident investigation reports hold valuable insights for improving safety practices, the manual coding of fatality narratives remains labor-intensive, inconsistent, and impractical for large datasets. Although natural language processing (NLP) and machine learning (ML) techniques have gained traction for automating the analysis of safety narratives in other high-risk industries, their application to mining accident data, particularly within the Indian context, remains limited. Addressing this gap, the present study proposes a ML framework for the semi-automated classification of fatal accident causes from unstructured text narratives reported by the Directorate General of Mines Safety (DGMS) between 2016 and 2022. A total of 401 fatal accident descriptions were pre-processed and vectorized using Bag-of-Words, TF-IDF, and Word2Vec techniques, followed by model evaluation across multiple algorithms. A semi-automated classification scheme was developed to balance efficiency with expert oversight, where high-confidence predictions were assigned automatically and uncertain cases were flagged for manual review. Logistic regression combined with TF-IDF unigram features achieved the highest performance, with an F1 score of 0.78 and an accuracy of 0.81. Overall, the developed framework successfully auto-coded 68.75% of cases with 94% accuracy, 0.93 recall, and 0.91 precision. Word cloud visualizations were also employed to capture dominant words associated with different cause categories. The proposed framework offers a practical and operationally feasible solution for assigning fatality causes in the mining sector, contributing to active safety management, surveillance, and policy formulation. © Society for Mining, Metallurgy & Exploration Inc. 2025.Item An uncertainty-aware decision support system: Integrating text narratives and conformal prediction for trustworthy accident code classification(Institution of Chemical Engineers, 2025) Kumar, A.; Senapati, A.; Upadhyay, R.; Chatterjee, S.; Bhattacherjee, A.; Samanta, B.It is imperative to assign accident classification codes to the Mine Safety and Health Administration (MSHA) accident data for effective data analysis and risk assessment. Although trained personnel are capable of performing this task, the manual process is both time-consuming and resource-intensive. Automating the classification process with machine learning (ML) algorithms promises to expedite code assignment. However, ML predictions typically lack uncertainty metrics. This study proposes an uncertainty-aware hierarchical classification framework that assists human experts in efficiently and accurately assigning accident codes. Several text representation techniques combined with different ML algorithms were employed within a hierarchical architecture to assign classification codes. Low-frequency codes were consolidated into a single category, with a primary classifier distinguishing between these and a secondary classifier further classifying the grouped categories. Regularized Adaptive Prediction Sets (RAPS) was integrated to quantify uncertainty. Highly confident predictions yielding single-class sets were automatically classified, whereas multi-class sets were flagged for manual review. Primary Classifier with XGBoost with word2vec text representation achieved the best performance, with 95.12 % coverage, 37.02 % single-class prediction sets at 96.11 % accuracy, and an average prediction set size of 2.39. Whereas the secondary classifier, a logistic regression model with TF-IDF representation, yielded 96.19 % coverage, an average set size of 1.80, and 53.66 % single-class prediction sets with 98.90 % accuracy. Additionally, sensitivity analysis determined that a 95 % coverage guarantee offers the best trade-off between prediction set size and coverage. The framework effectively integrates conformal prediction to quantify uncertainty and aid human experts in improving the decision-making process in safety management. Although the framework is broadly applicable across different sectors, it needs to be retrained on domain-specific data for effective use. © 2025 The Institution of Chemical Engineers