Hamed Majidian

Lecturer (Casual)

University of Nottingham

Australia

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Hamed Majidian is a Lecturer (Casual) at the University of Nottingham, Australia. His research focuses on hydrodynamics and machine learning applications, with recent articles covering topics such as drag-reducing appendages for container ships, wave structure interaction analysis, and real-time sea-state estimation. His work aims to enhance computational efficiency and data extraction methods in marine environments.

Articles (11)

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2022

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2022

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2022

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2020

Collaborators (2)

Damon Howe

Lecturer

University of Tasmania Launceston Campus

AUSTRALIA

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Hossein Enshaei

Senior Lecturer

University of Tasmania Launceston Campus

AUSTRALIA

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Hamed Majidian

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Articles (11)

Part B: Innovative Data Augmentation Approach to Boost Machine Learning for Hydrodynamic Purposes—Computational Efficiency

Real-Time Wave Identification for Stationary Marine Units: A Deep Transfer Learning Approach With Adaptive Windowing

CFD-based design of novel drag-reducing appendages for container ships

Intelligent Computerized Video Analysis for Automated Data Extraction in Wave Structure Interaction; A Wave Basin Case Study

Online short-term ship response prediction with dynamic buffer window using transient free switching filter

Part A: Innovative Data Augmentation Approach to Enhance Machine Learning Efficiency—Case Study for Hydrodynamic Purposes

An Integrated Framework for Real-Time Sea-State Estimation of Stationary Marine Units Using Wave Buoy Analogy

Part. A: A review of the real-time sea-state estimation, using wave buoy analogy

Aerodynamic study of pyramid stacking configuration on mega-sized container ships using CFD to optimize lateral drag

Part. B: A review of the real-time sea-state estimation, using wave buoy analogy; a decuple benchmark and future outlook

Comparative study of wind resistance of a 2D stack of featured ocean containers and a 3D forty feet unit

Collaborators (2)

Damon Howe

Hossein Enshaei

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