Hossein Enshaei

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Senior Lecturer

University of Tasmania Launceston Campus

Australia

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About

Hossein Enshaei is a Senior Lecturer at the University of Tasmania Launceston Campus in Australia. His research areas encompass innovative data augmentation techniques for machine learning applications in hydrodynamic studies, real-time wave identification for marine units, and the challenges associated with hydrogen ports. He has published extensively, with notable articles addressing power management strategies for electric ferries and risk assessments in LNG and ammonia bunkering operations.

Recent Grants

Grant: Close

Development of shipboard fuel, engine performance and exhaust monitoring system with telemetry capability

Open Date: 2012-08-01

Close Date: 2014-07-01

Grant: Close

North Sea Ballast Water Opportunity

Open Date: 2010-01-01

Close Date: 2014-06-01

Grant: Close

The provision of a Strategic Environmental assessment of a change in Ballast Water Management Policy

Open Date: 2009-09-01

Close Date: 2010-04-01

Grant: Close

The Control of the Spread of Non Indigenous Species through Ballast Water

Open Date: 2008-01-01

Close Date: 2008-09-01

Articles (11)

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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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2023

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2023

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2022

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2022

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2022

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2021

Collaborators (9)

Wenming Shi

Lecturer

University of Tasmania Australian Maritime College

AUSTRALIA

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Damon Howe

Lecturer

University of Tasmania Launceston Campus

AUSTRALIA

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Shu-Ling Chen

Associate Professor

University of Tasmania

AUSTRALIA

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zhuohua qu

Reader

Liverpool John Moores University Liverpool Business School

UNITED KINGDOM

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Hamed Majidian

Lecturer (Casual)

University of Nottingham

AUSTRALIA

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Wei (Vera) Zhang

Lecturer

University of Tasmania Australian Maritime College

AUSTRALIA

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Nagi Abdussamie

Lecturer

University of Tasmania

AUSTRALIA

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Shantha Gamini Jayasinghe

Lecturer in maritime electrical enginrring

University of Tasmania Launceston Campus

AUSTRALIA

View details

Hongjun Fan

AUSTRALIA

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Hossein Enshaei

Research Interests

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About

Recent Grants

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

Opportunities and Challenges of Hydrogen Ports: An Empirical Study in Australia and Japan

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

Towards a future electric ferry using optimisation-based power management strategy in fuel cell and battery vehicle application — A review

Standard Wave Scatter Table Limitation for Evaluating SGISC Based on Hindcast Data Analysis

Quantitative risk assessment for ammonia ship‐to‐ship bunkering based on Bayesian network

Formation of Dataset for Fuzzy Quantitative Risk Assessment of LNG Bunkering SIMOPs

Human Error Probability Assessment for LNG Bunkering Based on Fuzzy Bayesian Network-CREAM Model

Comparison of the Economic Performances of Three Sulphur Oxides Emissions Abatement Solutions for a Very Large Crude Carrier (VLCC)

Collaborators (9)

Wenming Shi

Damon Howe

Shu-Ling Chen

zhuohua qu

Hamed Majidian

Wei (Vera) Zhang

Nagi Abdussamie

Shantha Gamini Jayasinghe

Hongjun Fan

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