Using deep generative-based machine and ensemble learning framework for predicting ultimate bending capacity of circular steel tubes

AI-Driven Biomechanical Modeling for Knee Osteoarthritis Prediction: A Comprehensive Review of Finite Element and Machine Learning Approaches

Fatima Zahra Mekrane, Radouane Ouladsine, Abdelwahed Barkaoui, Raymond Ghandour

Hybrid AI Architectures for Solar–Wind–Battery Systems: Machine-Learning Control, Fuzzy Energy Management, Robust MPC, and Automated Source-Switching Hardware

Eyime Echeng Eyime, Samuel Chukwujindu Nwokolo, Fadhil Khadoum Alhousni, Edson L. Meyer, Chinedu Christian Ahia

Explainable Soft-Sensing of Dissolved Oxygen in a Multi-Station Aquatic Monitoring Network Using Ensemble Machine Learning and Neural Networks

Md. Abdullah Al Mamun Hridoy, Matteo Bodini, Chiara Bordin, Khan Md Arafat Adnan, Mohd. Saifur Rahman, Abdullah Ibne Shawkat, et al.

Optimal placement of DSTATCOMs and PID tuned controller in IEEE bus system for enhanced voltage stability and power loss reduction under RES integration using hybrid BWO-SSA algorithm

Kishankumar P. Patel, Nilaykumar A. Patel, Jitendra Chaudhari, Hiren Mewada

This paper provides an optimal method for cell location of DSTATCOMs as well as TCSCs in an IEEE bus network to improve voltage stability and power losses, especially dealing with RES, such as PV arrays and wind turbines. The placement optimization problem is solved using a new h…Read more

Compact spaced-dual-band filtering via ridge-gap waveguide for enhanced satellite communications

Neetirajsinh Chhasatia, Jitendra Chaudhari, Hiren Mewada, Amitkumar V. Patel, Keyur Mahant, Y.P. Kosta

This paper presents a novel spaced-dual-band filter based on ridge-gap waveguide (RGW) technology, designed with a cascaded structure of two ridge-gap waveguide units. The filter offers two distinct passbands, separated by a frequency gap of 4 GHz. In comparison to spaced-dual-ba…Read more

Fully automated pavement crack quantification and PCI estimation via a digital twin-ready vision-based deep learning pipeline

Mohammad Amin Talaghat, Amir Golroo, Vahid Shahhosseini, Mehdi Rasti