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6 results on '"Ramasamy, Sudha"'

2. Online Hazard Detection in Reconfigurable Plug & Produce Systems

Author

Massouh, Bassam, Danielsson, Fredrik, Ramasamy, Sudha, Khabbazi, Mahmood, Zhang, Xiaoxiao, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Silva, Francisco J. G., editor, Pereira, António B., editor, and Campilho, Raul D. S. G., editor

Published
2024
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3. Synergizing Sustainability: Integrated Nutrient Management and Intercropping for Optimal Coconut Cultivation in South India

Author

Arumugam Selva Rani, Sundararaj Subbulakshmi, Ramasamy Sudha, Kumaresan Kavitha, Shahul Hameed Nazreen Hassan, Murugiah Muthulakshmi, Kannan Sivagamy, and Samuel Suresh

Subjects
integrated nutrient management, sustainability, intercropping, crop productivity, net present value, benefit cost ratio, Plant culture, SB1-1110
Abstract

The study highlights the importance of integrating organic resources, such as vermicompost and biofertilizers with inorganic fertilizers to sustain coconut yields and manage costs. The experimental trial was conducted from 2016 to 2020 in a 47-year-old East Coast Tall coconut garden at Coconut Research Station in South India. The research evaluated four nutrient management treatments viz., T1 (75% Recommended Dose of Fertilizer (RDF) + 25% N organic), T2 (50% RDF + 50% N organic), T3 (100% N organic) and a control (100% RDF). Intercrops included black pepper (Piper nigrum), banana (Musa acuminata) and cocoa (Theobroma cacao). Organic manure significantly improved soil physical properties, water retention and overall soil health. The T2 treatment achieved the highest yields for coconut, cocoa, banana and black pepper. The added soil health parameters supported to these findings, with T2 showing the highest fungal (15.27 × 103 cfu/g of soil) and bacterial populations (17.25 × 105 cfu/g of soil), along with a significant earthworm population (26/m2), indicating enhanced soil ecosystem health. Additionally, soil moisture content was highest under T3 (100% organic) across various depths, followed by T2, highlighting the critical role of organic matter in improving soil moisture conservation. The economic feasibility analysis, including a net present value (NPV), benefit/cost ratio (B/C ratio) and an internal rate of return (IRR), revealed T2 to be the most economically viable nutrient management strategy. This study highlights the economic benefits of intercropping coconuts using an integrated nutrient management (INM) approach, demonstrating its superiority over traditional monocropping practices.

Published
2024
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6. Real-time implementation of deep reinforcement learning controller for speed tracking of robotic fish through data-assisted modeling

Author

Duraisamy, Palmani, Santhanakrishnan, Manigandan Nagarajan, Amirtharajan, Rengarajan, Ramasamy, Sudha, Duraisamy, Palmani, Santhanakrishnan, Manigandan Nagarajan, Amirtharajan, Rengarajan, and Ramasamy, Sudha

Abstract

This article proposes real-time speed tracking of two-link surface swimming robotic fish using a deep reinforcement learning (DRL) controller. Hydrodynamic modelling of robotic fish is done by virtue of Newtonian dynamics and Lighthill’s kinematic model. However, this includes external unsteady reactive forces that cannot be modeled accurately due to the distributed nature of hydrodynamic behavior. Therefore, a novel data-assisted dynamic model and control method is proposed for the speed tracking of robotic fish. Initially, the cruise speed motion data are collected through experiments. The water-resistance coefficient is estimated using the least mean square fit, which is then adopted in the model. Subsequently, a closed-loop discrete-time DRL controller trained through a soft actor-critic (SAC) agent is implemented through simulations. SAC overcomes the brittleness problem encountered by other policy gradient approaches by encouraging the policy network for maximum exploration and not assigning a higher probability to any single part of actions. Due to this robustness in the policy learning, the convergence error becomes low in RL-SAC than RL-DDPG controller. The simulation results verify that the DRL-SAC control with data-assisted modelling substantially improves the speed tracking performance. Further, this controller is validated in real-time, and it is observed that the SAC-trained controller tracks the desired speed more accurately than the DDPG controller.

Published
2024
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