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All Journal IAES International Journal of Robotics and Automation (IJRA) Bulletin of Electrical Engineering and Informatics International Journal of Engineering, Science and Information Technology
Deepak Kumar
National Institute of Technology, Hamirpur (HP)
Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Power System Restoration Using Multilayer Perceptron Kumar, Deepak
International Journal of Engineering, Science and Information Technology Vol 1, No 1 (2021)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (811.256 KB) | DOI: 10.52088/ijesty.v1i1.35

Abstract

In recent years, power systems are being operated nearer to their limits due to economic competition and deregulation. Also, nowadays the challenge is to include large and ever increasing amounts of decentralized generated power into the existing transmission network and at the same time comply with the electricity market transmission demands. Both factors increase the risk of blackout. After which, power needs to be restored as quickly and reliably as possible and, accordingly, detailed power system restoration plans are required. The multilayer perceptron network is chosen for a more precise examination.
Enhancing manufacturing efficiency: leveraging CRM data with Lean-based DL approach for early failure detection Kalluri, Venkata Saiteja; Malineni, Sai Chakravarthy; Seenivasan, Manjula; Sakkarai, Jeevitha; Kumar, Deepak; Ananthan, Bhuvanesh
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8757

Abstract

In the pursuit of enhancing manufacturing competitiveness in India, companies are exploring innovative strategies to streamline operations and ensure product quality. Embracing Lean principles has become a focal point for many, aiming to optimize profitability while minimizing waste. As part of this endeavour, researchers have introduced various methodologies grounded in Lean principles to track and mitigate operational inefficiencies. This paper introduces a novel approach leveraging deep learning (DL) techniques to detect early failures in manufacturing systems. Initially, realtime data is collected and subjected to a normalization process, employing the weighted adaptive min-max normalization (WAdapt-MMN) technique to enhance data relevance and facilitate the training process. Subsequently, the paper proposes the utilization of a triple streamed attentive recalling recurrent neural network (TSAtt-RRNN) model to effectively identify Leanbased manufacturing failures. Through empirical evaluation, the proposed approach achieves promising results, with an accuracy of 99.23%, precision of 98.79%, recall of 98.92%, and F-measure of 99.2% in detecting early failures. This research underscores the potential of integrating DL methodologies with customer relationship management (CRM) data to bolster early failure detection capabilities in manufacturing, thereby fostering operational efficiency and competitive advantage.
Selection of smooth motion profile for a tube locator module of an inspection device Perumalsamy, G.; Visweswaran, P.; Kumar, Deepak; Winston, S. Joseph; Murugan, S.
IAES International Journal of Robotics and Automation (IJRA) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v11i3.pp181-195

Abstract

The Prototype Fast Breeder Reactor steam generators inspection system has seven modules. In this, tube locator module is a planar serial two-link robotic arm, which is used to place the eddy current probe above the steam generators tube hole in the tube sheet region. The trajectory planning of the two-link robotic arm is one of the important tasks, so the peak velocity, peak acceleration, peak jerk of various motion profiles for a given distance has to be selected properly for smooth motion and to avoid actuator saturation. The fifth-order polynomial gives lower acceleration and velocity than the jerk-limited S-curve. In this paper, the comparison of peak values of kinematic variables (velocity, acceleration, and jerk) for different motion profiles has been presented.
Co-Authors Ananthan, Bhuvanesh Kalluri, Venkata Saiteja Malineni, Sai Chakravarthy Murugan, S. Perumalsamy, G. Sakkarai, Jeevitha Seenivasan, Manjula Visweswaran, P. Winston, S. Joseph