Effect of ship loading on marine diesel engine fuel consumption for bulk carriers based on the fuzzy clustering method

Tran, Tien Anh

doi:10.1016/j.oceaneng.2020.107383

Cited by 39 publications

(16 citation statements)

References 37 publications

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“…All these models are supervised ML models, where the data label, i.e., the fuel consumption rate, is used in model training. Some unsupervised ML models are also used, such as self-organizing maps (SOM) , the Gaussian mixture model (GMM) (Petersen, 2011), and fuzzy c-means clustering (Tran, 2020) increase, and the squared bias tends to decrease. However, if the model is too complex, it will adapt too much to the training data and will not generalize well to unseen data (i.e., it will have large test errors).…”

Section: Review Of Bbms Based On Machine Learning (Ml)mentioning

confidence: 99%

Data analytics for fuel consumption management in maritime transportation: Status and perspectives

Yan

Wang

Psaraftis

2021

Transportation Research Part E: Logistics and Transportation Re

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Section: Review Of Bbms Based On Machine Learning (Ml)mentioning

confidence: 99%

Data analytics for fuel consumption management in maritime transportation: Status and perspectives

Yan

Wang

Psaraftis

2021

Transportation Research Part E: Logistics and Transportation Re

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“…Wang and Yang ( 2020 ) used fuzzy clustering and an accelerated genetic algorithm to analyze and assess sustainable and influencing factors for 27 European Union countries' renewable energy. Fuzzy C means and multi-criteria decision-making process were applied by (Tran 2020 ) to design the optimal loading of ships and diesel fuel consumption of marine ships. Tanoto et al ( 2020 ) applied a hybrid of k-means clustering, neural network based-self organizing map to group technology mixes with similar patterns.…”

Section: Applications Of Clusteringmentioning

confidence: 99%

Data clustering: application and trends

Oyewole

Thopil

2022

Artif Intell Rev

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Clustering has primarily been used as an analytical technique to group unlabeled data for extracting meaningful information. The fact that no clustering algorithm can solve all clustering problems has resulted in the development of several clustering algorithms with diverse applications. We review data clustering, intending to underscore recent applications in selected industrial sectors and other notable concepts. In this paper, we begin by highlighting clustering components and discussing classification terminologies. Furthermore, specific, and general applications of clustering are discussed. Notable concepts on clustering algorithms, emerging variants, measures of similarities/dissimilarities, issues surrounding clustering optimization, validation and data types are outlined. Suggestions are made to emphasize the continued interest in clustering techniques both by scholars and Industry practitioners. Key findings in this review show the size of data as a classification criterion and as data sizes for clustering become larger and varied, the determination of the optimal number of clusters will require new feature extracting methods, validation indices and clustering techniques. In addition, clustering techniques have found growing use in key industry sectors linked to the sustainable development goals such as manufacturing, transportation and logistics, energy, and healthcare, where the use of clustering is more integrated with other analytical techniques than a stand-alone clustering technique.

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“…The external factors especially impact on the ship energy efficiency management. The study of ship loading has been considered firstly for ship energy efficiency management through evaluating the fuel consumption (Tran, 2020). Consequently, the navigation environment conditions have been investigated into the ship energy efficiency model (Tran, 2019c).…”

Section: Research Gapmentioning

confidence: 99%

Effect of ship loading on marine diesel engine fuel consumption for bulk carriers based on the fuzzy clustering method

Cited by 39 publications

References 37 publications

Data analytics for fuel consumption management in maritime transportation: Status and perspectives

Data analytics for fuel consumption management in maritime transportation: Status and perspectives

Data clustering: application and trends

The novelty numerical simulation method for reducing the fuel oil consumption and the greenhouse gas emission in shipping transportation industry

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