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In this paper, a novel fusion network for automatic modulation classification (AMC) is proposed in underwater acoustic communication, which consists of a Transformer and depth-wise convolution (DWC) network. Transformer breaks the limitation of sequential signal input and establishes the connection between different modulations in a parallel manner. Its attention mechanism can improve the modulation recognition ability by focusing on the key information. DWC is regularly inserted in the Transformer network to constitute a spatial-temporal structure, which can enhance the classification results at lower signal-to-noise ratios (SNRs). The proposed method can obtain more deep features of underwater acoustic signals. The experiment results achieve an average of 92.1% at −4 dB ≤ SNR ≤ 0 dB, which exceed other state-of-the-art neural networks.

RINA Services, a world leader in marine classification and certification, recently awarded Carnival Maritime GmbH ISO 14001 and BS OHSAS 18001 certificates.

RINA Services, a world leader in marine classification and certification, recently awarded Carnival Maritime GmbH ISO 14001 and BS OHSAS 18001 certificates.

Introduction: Various types of ships sail at sea, and identifying maritime ship types through shipradiated noise is one of the tasks of ocean observation. The ocean environment is complex and changeable, such rapid environmental changes underline the difficulties of obtaining a huge amount of samples. Meanwhile, the length of each sample has a decisive influence on the classification results, but there is no universal sampling length selection standard.

Entanglements and vessel strikes impact large whales worldwide. Post-event health status is often unknown because whales are seen once or over short spans that conceal long-term health declines. Well-studied populations with high site fidelity verified by photo-ID offer opportunity to confirm deaths, health declines and recoveries. We used known outcome entanglements and vessel strikes of right whales (Eubalaena glacialis) and humpback whales (Megaptera novaeangliae) to model probabilities of deaths, health declines and recoveries with Random Forest (RF) classification trees. Variables included presence or absence of phrases from case narratives ('deep laceration', 'cyamid', 'healing', 'superficial') and a categorical variable for vessel size. Health status post-entanglement was correctly classified in 95.7% of right whale and 93.6% of humpback whale cases (expected by chance=50%). Health status post-vessel strike was correctly classified in 91.4% of right whale and 88.6% of humpback whale cases. Important variables included cyamid presence, emaciation, discolored skin, constricting entanglements, gear-free resightings, superficial or healing lacerations, and vessel size. Cross-validated RF models were applied to unknown outcome cases to estimate the probability of deaths, health declines and recoveries. Total serious injuries (probability of death or health decline > 0.50) assigned by RF were nearly equal to current injury assessment methods applied by biologists for known outcomes. However, RF consistently predicted higher serious injury totals for unknown outcomes, suggesting that current assessment methods may underestimate risk for cases lacking details or long-term observations. Advantages of the RF method include: 1) risk models are based on known outcomes; 2) unknown outcomes are assigned post-event health status probabilities; and 3) identification of important predictor variables improves data collection standards.

Strikes between vessels and cetaceans have significantly increased worldwide in the last decades. The Canary Islands archipelago is a geographical area with an important overlap of high cetacean diversity and maritime traffic, including high-speed ferries. Sperm whales (Physeter macrocephalus), currently listed as a vulnerable species, are severely impacted by ship strikes. Nearly 60% of sperm whales' deaths are due to ship strikes in the Canary Islands. In such cases, subcutaneous, muscular and visceral extensive hemorrhages and hematomas, indicate unequivocal antemortem trauma. However, when carcasses are highly autolyzed, it is challenging to distinguish whether the trauma occurred ante- or post-mortem. The presence of fat emboli within the lung microvasculature is used to determine a severe "in vivo" trauma in other species. We hypothesized fat emboli detection could be a feasible, reliable and accurate forensic tool to determine ante-mortem ship strikes in stranded sperm whales, even in decomposed carcasses. In this study, we evaluated the presence of fat emboli by using an osmium tetroxide (OsO4)-based histochemical technique in lung tissue of 24 sperm whales, 16 of them with evidence of ship strike, stranded and necropsied in the Canaries between 2000 and 2017. About 70% of them presented an advanced autolysis. Histological examination revealed the presence of OsO4-positive fat emboli in 13 out of the 16 sperm whales with signs of ship strike, and two out of eight of the "control" group, with varying degrees of abundance and distribution. A classification and regression tree was developed to assess the cut off of fat emboli area determining the high or low probability for diagnosing ship-strikes, with a sensitivity of 89% and a specificity of 100%. The results demonstrated: (1) the usefulness of fat detection as a diagnostic tool for "in vivo" trauma, even in decomposed tissues kept in formaldehyde for long periods of time; and (2) that, during

DNV GL, the world's leading classification society, has awarded AYRO an Approval in Principle (AiP) for its Oceanwings 3.6.3 wind assisted propulsion system for ships. The Oceanwings 3.6.3 system is designed to enable ship owners and operators to leverage wind energy to improve the energy balance of individual vessels and fleets, thereby significantly reducing carbon emissions.

Accurate and automated detection of maritime vessels present in aerial images is a considerable challenge. While significant progress has been made in recent years by adopting neural network architectures in detection and classification systems, these systems are usually designed specific to a sensor, dataset or location. In this paper, we present a system which uses multiple sensors and a convolutional neural network (CNN) architecture to test cross-sensor object detection resiliency. The system is composed of five main subsystems: Image Capture, Image Processing, Model Creation, Object-of-Interest Detection and System Evaluation. We show that the system has a high degree of cross-sensor vessel detection accuracy, paving the way for the design of similar systems which could prove robust across applications, sensors, ship types and ship sizes.

ABS Certify First Drone Specialist for Internal Vessel Inspections

Perte du "MOL Comfort" : du flambage constaté sur les sister-ships.

ClassNK Update on Loss of 'MOL Comfort'.

In general, a single scalar hydrophone cannot determine the orientation of an underwater acoustic target. However, through a study of sea trial experimental data, the authors found that the sound field interference structures of inbound and outbound ships differ owing to changes in the topography of the shallow continental shelf. Based on this difference, four different convolutional neural networks (CNNs), AlexNet, visual geometry group, residual network (ResNet), and dense convolutional network (DenseNet), are trained to classify inbound and outbound ships using only a single scalar hydrophone. Two datasets, a simulation and a sea trial, are used in the CNNs. Each dataset is divided into a training set and a test set according to the proportion of 40% to 60%. The simulation dataset is generated using underwater acoustic propagation software, with surface ships of different parameters (tonnage, speed, draft) modeled as various acoustic sources. The experimental dataset is obtained using submersible buoys placed near Qingdao Port, including 321 target ships. The ships in the dataset are labeled inbound or outbound using ship automatic identification system data. The results showed that the accuracy of the four CNNs based on the sea trial dataset in judging vessels' inbound and outbound situations is above 90%, among which the accuracy of DenseNet is as high as 99.2%. This study also explains the physical principle of classifying inbound and outbound ships by analyzing the low-frequency analysis and recording diagram of the broadband noise radiated by the ships. This method can monitor ships entering and leaving ports illegally and with abnormal courses in specific sea areas.