Intelligent ship inspection analytics: Ship deficiency data mining for port state control

Demirci, S. M. Esad; Çiçek, Kadir

doi:10.1016/j.oceaneng.2023.114232

Cited by 13 publications

(1 citation statement)

References 43 publications

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“…The VHF voice communication at sea employs analog communication technology. However, in accordance with the international norms established by IMO, ships worldwide utilize shipboard VHF radio systems that adhere to identical technical standards and are verified by classification societies [11]. Unfortunately, these systems lack the capability to incorporate unique signal features during the modulation process for radio signal identification [12], resulting in a typical non-cooperative signal of the same type.…”

Section: Introductionmentioning

confidence: 99%

Identification of Shipborne VHF Radio Based on Deep Learning with Feature Extraction

Chen,

Liu

2024

JMSE

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In the feature identification of maritime VHF radio communication signals, shipborne VHF communication technology follows the same international technical standards formulated by IMO, uses analog communication technology and uses the same communication channel in the same area, and cannot effectively achieve signal feature identification by adding feature elements in the process of signal modulation. How to effectively identify the ship using VHF radio has always been a technical difficulty in the field of ship perception. In this paper, based on the convolutional neural network, combined with the feasibility of CAM feature extraction and BiLSTM feature extraction in non-cooperative signal recognition, a deep learning recognition model of shipborne VHF radio communication signals is established, and the deep learning approach is employed to discern the features of VHF signals, thereby accomplishing the identification and classification of transmitting VHF radio stations. Several experiments are designed according to the characteristics of ship communication scenes at sea. The experimental data show that the method proposed in this paper can provide a new feasible path for ship target perception in terms of radio signal characteristics and identification.

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Section: Introductionmentioning

confidence: 99%

Identification of Shipborne VHF Radio Based on Deep Learning with Feature Extraction

Chen,

Liu

2024

JMSE

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Interpreting the influential factors in ship detention using a novel random forest algorithm considering dataset imbalance and uncertainty

Xiao,

Jin,

et al. 2024

Engineering Applications of Artificial Intelligence

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Evaluation of pollution prevention related deficiencies of ships using association rule mining

Sevgili

2024

Marine Pollution Bulletin

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Intelligent ship inspection analytics: Ship deficiency data mining for port state control

Cited by 13 publications

References 43 publications

Identification of Shipborne VHF Radio Based on Deep Learning with Feature Extraction

Identification of Shipborne VHF Radio Based on Deep Learning with Feature Extraction

Interpreting the influential factors in ship detention using a novel random forest algorithm considering dataset imbalance and uncertainty

Evaluation of pollution prevention related deficiencies of ships using association rule mining

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