Sree Buddha College of Engineering is a self-financing engineering college under Sree Buddha Foundation, Kollam. This college is located in Elavumthitta of Pathanamthitta District and is affiliated to APJ Abdul Kalam Kerala Technological University. Sree Buddha College of Engineering, Pattoor (Alapuzha District), Sree Buddha Central School, Karunagappally and Sree Buddha Central School, Pattoor are some of the Centres of Excellence Associated by the Foundation. Currently, there are four engineering streams in this college: Electrical and electronics engineering, Civil Engineering, Computer Science, Mechanical Engineering & Electronics and Communication Engineering..
Precast concrete connections are often critical points of vulnerability during seismic events if not adequately designed. This study investigates the cyclic behaviour of precast column-to-foundation pocket connections using two distinct reinforcement detailing strategies. In the first configuration (PC I), the connection was based on the model proposed by Canha et al. (2012), enhanced with additional corner dowel bars. The second configuration (PC II) adopted independent reinforcement for each transverse pocket wall. A G + 3 reinforced concrete structure was modelled according to Singapore codes, with detailing compliant with IS 456 and IS 13,920. Numerical simulations were conducted on 1:2 scaled specimens using ABAQUS 6.14, and results were compared with experimental data. Key parameters analyzed include ultimate load capacity, strain behaviour in concrete and steel, energy dissipation, and displacement ductility. The simulation accurately reproduced experimental trends (Hemamathi & Jaya, 2021), with average deviation within 15%. The findings provide valuable insights into the seismic performance of precast pocket connections and inform more resilient detailing practices.
The present research aims to develop an Electromagnetic interference (EMI) shielding which is light weight by natural fiber, and filler reinforced polymer composite. The novelty of this study producing composite using waste biomass by treating the fiber and filler surface modification. As well as analysing the mechanical, dielectric, and EMI shielding efficiency of the composite in accordance to the ASTM (American Society for Testing and Materials) standard. The study founded that composite VNB2, with 2.0 vol.
This review paper compiles and provides a review of the latest and relevant research on the nanoparticle-reinforced low-temperature SnBi solder. The physical, electromigration and thermomigration, microstructural, interfacial and mechanical properties of a solder are vital to the reliability of the electronic components. Generally, reinforcement of nanoparticles is beneficial to the properties of low-temperature SnBi solder. Literature on the types of nanoparticles added to SnBi focusing on these properties is available but is not outlined in a competent review. This paper provides a significant review on this topic to provide insight into SnBi solder alloy as an alternative solder in the electronic packaging industry.
One pivotal aspect of Electric Vehicle (EV) evolution is the development of advanced traction systems that efficiently convert and manage power for vehicle propulsion. The integration of a Permanent Magnet Synchronous Motor (PMSM) with EV is recognized as an ideal choice for propulsion due to its efficiency and performance characteristics. This study focuses on the advancement of EV traction systems by introducing a Photovoltaic based Hybrid Modified Boost–Cuk Converter that operates without conventional batteries. The proposed converter has been developed to efficiently manage power flow between the EV traction system and the grid. The control of the converter’s DC link is optimized using a combination of Particle Swarm Optimization and Adaptive Neuro-Fuzzy Inference System, resulting in enhanced performance and energy utilization. The output from the converter is applied to a Three-Phase Voltage Source Inverter (VSI), which interfaces with PMSM within the EV traction system. The speed of the PMSM is controlled by a Proportional-Integral controller, ensuring precise and efficient control of the motor’s operation. To achieve effective modulation of the VSI output, a Space Vector Pulse Width Modulation generator is employed. This technology refines the quality of the output waveform, leading to smoother motor operation and reduced harmonic distortion. Additionally, the research integrates a Bidirectional Single-Phase VSI connected to the grid which performs energy storage and supplies energy during periods of deficiency. The obtained outputs reveal that the proposed framework with the integration of advanced control strategies ensures efficient motor operation contributing to the development of sustainable and efficient EV traction concept. The overall system is implemented employing MATLAB Simulink, the obtained outcomes prove that developed system achieves maximum efficiency and reduced THD value of 95.5
Navigation is an important skill required for an autonomous robot, as information about the location of the robot is necessary for making decisions about upcoming events. The objective of the localization technique is “to know about the location of the collected data.” In previous works, several deep learning methods were used to detect localization, but none of them gives sufficient accuracy. To address this issue, an Enhanced Capsule Generation Adversarial Network and optimized Dual Interactive Wasserstein Generative Adversarial Network for landmark detection and localization of autonomous robots in outdoor environments (ECGAN-DIWGAN-RSO-LAR) is proposed in this manuscript. Here, the outdoor robot localization dataset is taken from the Virtual KITTI dataset. It contains two phases, which are landmark detection and localization. The landmark detection phase is determined using Enhanced Capsule Generation Adversarial Network for detecting the landmark of the captured image. Then the robot localization phase is determined using Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) for determining the robot location coordinates as well as compass orientation from identified landmarks. After that, the weight parameters of the DIWGAN are optimized by Rat Swarm Optimization (RSO) algorithm. The proposed ECGAN-DIWGAN-RSO-LAR is implemented in Python. The efficiency of the proposed ECGAN-DIWGAN-RSO-LAR technique shows higher accuracy of 22.67%, 12.45 %, and 8.89% compared to the existing methods.
