⚡ Category

eess

1712 papers

⚡ electrical engineering

Impact of Reservoir Inflow Velocity on Flow Dynamics and Turbine Design in Overtopping Breakwater Energy Conversion Systems

This study utilizes particle-based numerical simulations to demonstrate that ramp geometry and wave conditions significantly influence overtopping inflow velocities, revealing that linear ramps generate higher velocities while convex ramps enhance energy dissipation, thereby providing critical velocity-based insights for optimizing turbine design in overtopping breakwater energy conversion systems.

Yeddid Yonatan Eka Darma, Raditya Hendra Pratama, Ahmad Taufiqur Rohman, Samsu Dlukha Nurcholik, Ippei Oshima2026-06-25
⚡ electrical engineering

An Intelligent Well Log Based Lithology Identification Method Integrating Unsupervised Clustering and BiLSTM-CRF

This paper proposes a hybrid deep learning framework that integrates Gaussian mixture model-based unsupervised clustering with a BiLSTM-CRF network to achieve accurate and geologically consistent lithology identification from well logs, even in the presence of data imbalance, nonlinearity, and incomplete logging curves.

Zihao Mu, Liwei Song, Jiacheng Huang, Haiwei Mu, Xiaochang Lv, Guohua Zhang, Guangjie Fu, Yan Shi, Xinyu Liu, Yuhang Wan (…)2026-06-25
⚡ electrical engineering

A non-planar slicing and hybrid path generation method for robotic wire arc additive remanufacturing of large sprockets​​

This paper proposes a software–hardware integrated system featuring a non-planar slicing and hybrid path generation method that utilizes curved-layer conformal slicing and coordinate transformation to achieve high-quality, defect-free remanufacturing of large sprockets by overcoming geometric mismatches and motion interference inherent in conventional planar slicing.

Jiahua Chen, Renpei Liu, Weihang Liu, Shuaikang Wang, Yuhang Zhu, Yanhong Wei2026-06-25
⚡ electrical engineering

Comparative study of the mechanical behavior a materials plates subjected to external force impact

This study analyzes the dynamic response of 2024-T3 aluminum aircraft plates to projectile impact, revealing that damage severity and failure mechanisms are governed by the interplay of projectile geometry, mass, material, and velocity, with conical tips causing perforation while blunt noses promote elastic deformation.

Abdessamed Bachiri, Nadia KADDOURI, Mustapha ARAB2026-06-25
⚡ electrical engineering

Selecting Optimal Cybersecurity Measures for Power Systems Using Complex Intuitionistic Fuzzy Rough Frank Aggregation Operators

This paper proposes a novel multi-criteria decision-making framework utilizing complex intuitionistic fuzzy rough Frank aggregation operators to effectively select optimal cybersecurity measures for power systems by addressing data uncertainty and imprecision, as validated through a case study and comparative analysis.

Ahmad Idrees, Meraj Ali Khan, Ibrahim Al-Dayel, Tahir Mahmood2026-06-25
⚡ electrical engineering

Multi-Microgrid Electricity–Heat Energy Sharing Based on Spatio-Temporal Distributionally Robust Optimization and Benefit Allocation

This paper proposes a distributed electricity–heat energy sharing framework for multi-microgrids that integrates spatio-temporally correlated distributionally robust optimization to handle source–load uncertainties and an asymmetric Nash bargaining mechanism to ensure fair benefit allocation, ultimately achieving low-cost, low-carbon, and robust coordinated operation.

Yuntao Yue, CuiPing Yang, Dong Liu2026-06-25
⚡ electrical engineering

Understanding the Governing Parameters of UHPC’s Strength Using Explainable Machine Learning

This study demonstrates that an XGBoost machine learning model, trained on 810 experimental mixtures and interpreted via SHAP analysis, achieves high predictive accuracy for Ultra-High-Performance Concrete (UHPC) compressive strength while identifying fiber content and curing age as the most influential governing parameters.

Nafis Niaz Chowdhury, Syeda Tabassum, Badhon Singha2026-06-25
⚡ electrical engineering

Mechanical Performance and Strength Prediction of Rubberized Silica Fume Concrete: An Experimental and Machine Learning Investigation for Sustainable Construction

This study investigates the mechanical performance of rubberized silica fume concrete through experimental testing and machine learning, demonstrating that while rubber powder and silica fume enhance specific properties and existing design codes underestimate strength, advanced predictive models like Artificial Neural Networks offer superior accuracy for estimating compressive and flexural strength to support sustainable construction.

M. Shariq, A. Fuzail Hashmi, M. Ayaz, M. Tanveer, M. Asif Ansari2026-06-25