Daily Archives: June 23, 2026

Authors: Ms. Vaishnavi Kishor Patil, Ms. Rutuja Rajkumar Waghmare, Ms. Priyanka Ubhad, Mr. Ramgopal Sahu

Abstract: Material handling plays a crucial role in industrial automation, warehouse management, and logistics operations. Conventional transportation methods based on manual carts and trolleys require significant human effort, resulting in reduced operational efficiency, increased labor dependency, and limitations in confined working environments. To address these challenges, this paper presents OmniLiftBot, an autonomous Mecanum-wheeled robotic platform designed for smart load transportation, vertical load handling, and real-time payload monitoring. The proposed system is built around an ESP32 microcontroller and integrates Mecanum wheels for omnidirectional mobility, a scissor-lift mechanism for controlled elevation of loads, a load cell with HX711 module for weight measurement, and an ultrasonic sensor for obstacle detection. A Wi-Fi-based interface enables path selection and system control, allowing flexible operation in indoor environments. The combination of mobility, lifting, sensing, and monitoring functionalities within a single platform enhances the versatility of the system while reducing manual intervention. Experimental evaluation of the developed prototype demonstrates reliable navigation, effective lifting operation, accurate payload monitoring, and safe obstacle detection under controlled conditions. The proposed solution offers a compact, cost-effective, and scalable approach for modern material handling applications and can serve as a foundation for future intelligent warehouse automation systems.

Published by: vikaspatanker

Authors: Tejas Rajendra Moule, Kunal Sanjay Patekar, Abhay Ramesh Mishra, Assistant Professor Ganesh Keshav Gaikwad

Abstract: The Kumbh Mela, one of the largest human congregations on Earth, presents significant challenges in crowd management, health response, and transportation logistics. Traditional management systems[1] often rely on manual surveillance and limited communication mechanisms, which are insufficient for real-time risk detection and decision-making. Kumbh Connect proposes a comprehensive AI-powered framework that integrates computer vision, predictive analytics, Internet of Things (IoT) sensors, and natural language processing to ensure safety, efficiency, and improved pilgrim experience. This paper surveys current research and technologies relevant to large-scale event management, identifies key challenges, and outlines potential directions for future development toward a more intelligent, connected, and secure Kumbh Mela environment.

DOI: https://doi.org/10.5281/zenodo.20808940

Published by: vikaspatanker

Authors: Gaurav Kankuse, Jay Deshmukh, Om Borse, N. D. Dhamale

Abstract: This project focuses on building a smart, easy- to-use Grammatical Error Correction (GEC) system for a native Indic language, specifically Marathi. The system leverages modern transformer-based AI models, such as IndicBERT and mBART, which are fine-tuned using local language data. The primary objective of the system is to identify and correct grammatical errors in sentences in real time. The proposed solution includes a simple web-based tool where users can input text, view suggested corrections along with brief explanations, and choose which changes to accept or reject. The study outlines the system design to automated text analysis. Preliminary observations indicate the feasibility of the proposed approach, with future work focusing on extensive experimental validation.

DOI: https://doi.org/10.5281/zenodo.20808637

Published by: vikaspatanker

Authors: Assistant Professor Shekhar P Kale, Assistant Professor Vishal K Paithankar

Abstract: Trapping of sediments in rivers is done by various methods as is is tedious job; But still many researches have shown different techniques. By using artificial obstacles for collection of trap we can minimize transfer and deposition of trap in our reservoir. Along with obstacles some river training works found to be useful for collection and deposition of trap at particular location so that it will not get transferred close to the dam site. This research suggests the experiment analysis of trap collection in the river channel prior to dam site. Perennial rivers in which there is no chance to collect or remove the trap in dry period. It is quite possible for seasonal rivers therefore collection of trap in wet season and removal of it in dry season is quite possible in most of the states of India. Reservoir sedimentation has become one of the major problems facing water resources development projects in many countries around the world. However, only a limited number of studies has been reported in this field, particularly addressing the trap efficiency of reservoirs. The most important practical and critical problem related to the performance of reservoirs is the estimation of storage capacity loss due to sedimentation process.. A small-scaled laboratory model was set-up in representing a reservoir and a series of tests were conducted by varying inflow rate, inflow sediment concentration, reservoir capacity and outflow rate. The experimental results were compared with the available theories

DOI: https://doi.org/10.5281/zenodo.20808352

Published by: vikaspatanker

Authors: Assistant Professor Shekhar P Kale, Assistant Professor Vishal K Paithankar

Abstract: The paper addresses the dual environmental challenges of urban waterlogging and the accumulation of non-biodegradable plastic waste 1. This study investigates the feasibility of developing sustainable pervious concrete by partially replacing natural coarse aggregates with waste plastic aggregates at varying levels of 5%, 10%, 15%, and 20%. Experimental specimens, cast as 150 mm x 150 mm x 150 mm cubes using 10 mm aggregates and a water-cement ratio of 0.35, were subjected to rigorous testing for compressive strength, permeability, and workability after 14 days of curing. The results indicate that while increasing the plastic content leads to a reduction in compressive strength and a slight decrease in permeability due to modifications to the void structure, a replacement level of up to 10% offers an optimum balance, maintaining sufficient structural integrity for light-load applications. Ultimately, this research demonstrates that integrating plastic waste into pervious concrete not only aids in groundwater recharge by effectively reducing surface runoff but also provides a viable waste management solution for sustainable infrastructure development.

DOI: https://doi.org/10.5281/zenodo.20808288

Published by: vikaspatanker

Authors: Nilesh.P.Dabe, Yogesh R. Patni, Deepak Kadam, Kulkarni Kirti S

Abstract: Accurate electricity load forecasting is critical for maintaining stability, reliability, and cost efficiency in modern smart grids, especially with the growing integration of renewable energy sources. However, the inherent intermittency and uncertainty of renewables such as solar and wind introduce significant challenges for traditional forecasting models. This paper proposes a Hybrid Generative AI–Enhanced Load Forecasting Model that combines Generative Adversarial Networks (GANs) with deep learning architectures to improve prediction accuracy under varying renewable energy conditions. The generative component synthesizes high-variance energy patterns that capture extreme fluctuations, while the predictive module leverages a hybrid CNN–LSTM network for temporal–spatial learning. Experimental results on real-world datasets demonstrate substantial improvements, with reductions of 40.1% in MAE, 38.2% in RMSE, and enhanced robustness against high-uncertainty renewable inputs. The proposed model also reduces load–supply mismatch by 42.4% and energy imbalance cost by 41.3%, leading to more efficient power distribution and operational cost savings. These findings highlight the potential of Hybrid Generative AI to significantly enhance smart grid forecasting performance and support resilient, data-driven energy management strategies.

DOI: https://doi.org/10.5281/zenodo.20808188

Published by: vikaspatanker

Authors: Nilesh.P.Dabe, Yogesh R. Patni, Deepak Kadam, Kulkarni Kirti S

Abstract: Precise prediction of residential power consumption, and effective management of load are important tasks in smart grid. The current research proposes a novel hybrid model of ANN with NSGA-II to solve the multi-objective optimization problems for smart grid operations. The model incorporates four important inputs to simultaneously predict forecast demand and load management reliability: time-of-day, temperature, consumer type, and historical load. The ANN model optimized by NSGA-II offers improved forecasting, resulting in the best fitness value of 855.176 kWh, and the resulting high correlation coefficient R = 0.97432 for the load forecasting. Meanwhile, the model also maintained a high level of load management reliability as present an best Fitness 86.7012 % and a correlation R = 0.93381. Pareto front analysis demonstrated a trade-off solution between forecast accuracy (855.928–855.934 kWh) and reliability (84.043% to 84.086%) and therefore it is flexible in advising grid operator. This NSGA-II-ANN hybrid approach has wide range of applications for real-time load prediction, and better resource allocation and control for increasing smart grid stability in dynamic operation condition.

DOI: https://doi.org/10.5281/zenodo.20808126

Published by: vikaspatanker

Authors: Research scholar Dinesh V Malkhede, Associate professor Dr. Prabhat Sharma

Abstract: Demand Side Management (DSM) has emerged as a critical component of within smart grid frameworks to optimize energy efficiency and mitigate peak load scenarios, and facilitate the integration of renewable energy sources. With the evolution of smart grids, advanced communication infrastructures, intelligent control algorithms, and dynamic pricing mechanisms have significantly transformed DSM strategies. This study explores demand side management by examining its key concepts, goals, and implementation practices, while highlighting pricing-based demand response, optimized appliance scheduling, and smart energy management systems. The review synthesizes recent research contributions covering heuristic, metaheuristic, and artificial intelligence–based approaches, including game theory, evolutionary algorithms, and deep reinforcement learning. The review places special focus on residential DSM, electric vehicle integration, and energy storage technologies, while also outlining major challenges, open research problems, and future research opportunities relevant to researchers and industry professionals.

DOI: https://doi.org/10.5281/zenodo.20808055

Published by: vikaspatanker

Authors: Assistant Professor Bhupendra Deshmukh, Associate Professor Mohite Utkarsha Laxman, Assistant Professor Diksha M Ahire

Abstract: During islanded operation, AC microgrids operate without grid support, making voltage regulation a critical challenge due to load variations, intermittent renewable generation, and inverter-dominated dynamics. In such conditions, maintaining stable voltage becomes difficult without effective local control mechanisms. This paper presents a decentralized voltage control approach based on a PI-dominant PID controller applied at the primary control level. The proposed controller regulates the inverter output voltage to handle disturbances arising from load changes and renewable energy fluctuations, including photovoltaic and fuel cell sources. The control strategy is simple, does not require communication infrastructure, and is suitable for practical implementation. Simulation results obtained using MATLAB/Simulink demonstrate that the proposed method improves voltage stability, minimizes oscillations, and maintains acceptable performance under varying operating conditions.

DOI: https://doi.org/10.5281/zenodo.20807928

Published by: vikaspatanker