The Impacts of the Applications of Artificial Intelligence in Maritime Logistics

Aylak, Batin Latif

doi:10.31590/ejosat.1079206

Cited by 3 publications

(1 citation statement)

References 69 publications

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“…The number of applications of machine learning (ML) and other artificial intelligence (AI) methods have been growing in recent years in maritime applications. Many authors, including Aylak (2022) [1] comment on the wide-reaching impacts of such models applied in logistics, but such models are present in maritime engineering as well. Karatug and Arslanoglu (2022) comment on the application of this machine learning for condition-based maintenance and fault diagnosis in ship engine systems, demonstrating that high-precision models can be developed for these tasks.…”

Section: Introductionmentioning

confidence: 99%

Use of Synthetic Data in Maritime Applications for the Problem of Steam Turbine Exergy Analysis

Šegota

Mrzljak

Anđelić

et al. 2023

JMSE

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Machine learning applications have demonstrated the potential to generate precise models in a wide variety of fields, including marine applications. Still, the main issue with ML-based methods is the need for large amounts of data, which may be impractical to come by. To assure the quality of the models and their robustness to different inputs, synthetic data may be generated using other ML-based methods, such as Triplet Encoded Variable Autoencoder (TVAE), copulas, or a Conditional Tabular Generative Adversarial Network (CTGAN). With this approach, a dataset can be trained using ML methods such as Multilayer Perceptron (MLP) or Extreme Gradient Boosting (XGB) to improve the general performance. The methods are applied to the dataset containing mass flow, temperature, and pressure measurements in seven points of a marine steam turbine as inputs, along with the exergy efficiency (η) and destruction (Ex) of the whole turbine (WT), low-pressure cylinder (LPC) and high-pressure cylinder (HPC) as outputs. The achieved results show that models trained on synthetic data achieve slightly worse results than the models trained on original data in previous research, but allow for the use of as little as two-thirds of the dataset to achieve these results. Using R2 as the main evaluation metric, the best results achieved are 0.99 for ηWT using 100 data points and MLP, 0.93 for ηLPC using 100 data points and an MLP-based model, 0.91 for ηHPC with the same method, and 0.97 for ExWT, 0.96 for ExLPC, and 0.98 for ExHPC using a the XGB trained model with 100 data points.

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Section: Introductionmentioning

confidence: 99%