Group items tagged

The Art of Breaking Ice.

The Panama Canal Authority (ACP) announced today that the Panama Canal Expansion will be officially inaugurated on Sunday, June 26, 2016. The announcement was made this morning during the inauguration ceremony of the Canal's state-of-the-art Scale Model Maneuvering Training Facility, which will provide additional hands-on experience to pilots and tugboat captains to operate in the Expanded Panama Canal.

Globally, movements of commercial vessels can facilitate the spread of marine non-indigenous species (NIS) beyond their current biogeographic ranges. Authorities at potential destination locations employ a number of biosecurity risk assessment strategies to estimate threat levels from potential origin locations, vulnerability levels of specific destination regions, or the consequences of successful establishment of particular NIS species. Among the many factors and processes that have an influence on the probability that NIS will survive transport and establish successfully at new locations, vessel type has been identified as an important risk factor. Different vessel types have different structural and operational characteristics that affect their overall level of marine biosecurity risk. Several recent studies have examined subsets of vessel types or vessel characteristics for their ability to spread NIS. While high-quality information is available via these endeavors, it is fragmented and not readily available as an integrated resource to support biosecurity regulators or other end-users. In this study, we synthesize available empirical data on a wide range of vessel types and characteristics to develop a framework that allows systematic quantification of the relative risk of NIS transfer by common commercial vessel types. We explain our approach for constructing the framework, from selection of key risk factors for inclusion, to selection of which datasets to use for those risk factors. The framework output is a set of risk scores which denote the relative biosecurity risk of common commercial vessel types. To demonstrate a potential application of our framework, we applied the risk scores to vessel visit data for commercial ports around New Zealand and assigned a relative risk level per port based on the arrival frequencies of different vessel types. The resulting per-port risk levels matched closely with the results of a prior benchmark study that employed sta

Seabed fluid flow is a widespread and important natural phenomenon in marine environments, which involves complex multi-physics, multi-process and multi-scale processes. The developments in offshore geophysical technology have facilitated the discovery of the widespread emissions of seabed fluids. For an overview on the state-of-the-art seabed fluid flow research and for obtaining a perspective on future research in the China Seas, we reviewed the data, reports, and publications particularly that associated with cold seeps such as pockmarks, seeps, domes, mud volcanoes, and gas hydrates in the Bohai Sea, the Yellow Sea, the East China Sea, and the South China Sea. This study presents the first report for seabed fluid flow on all China Seas with the basic information required to undertake additional analytical studies of these features. Furthermore, we explore processes responsible for them and their implications. Although the seabed fluid flow is widespread, dynamic, and influential, it is still poorly examined and understood. To understand seabed fluid flow in both time and space, it is important to investigate how and why these seabed fluids form and migrate.

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.

Compared to structured ocean environments, unstructured ocean environments are inherently more complex. In such unstructured environments, the presence of narrow waterways poses unique navigational hurdles for autonomous surface vehicles (ASVs) due to their restricted connectivity. Current path planning algorithms designed for unstructured environments, particularly those characterized by narrow spaces, often face difficulties in efficiently exploring the target area while producing high-quality paths. In this study, we tackle the aforementioned complexities by incorporating progressive sampling and point cloud clustering, which jointly expedite the detection of constrained waterways in unstructured marine environments. More specifically, we generate multiple random trees from these sampling points, thereby bolstering both navigational accuracy and overall computational efficiency. Building upon these core techniques, we introduce a novel extension of the traditional rapidly-exploring random trees (RRT) connect algorithm-referred to as multiple RRT-connect (multi-RRT-connect)-aimed at swiftly determining a viable path between prescribed start and goal coordinates. As the number of samples expands, the random trees gradually enlarge and interlink, mirroring the functionality of classic RRT-connect and ultimately forming a continuous corridor. Subsequently, the derived path undergoes iterative refinement and optimization, culminating in a significantly reduced trajectory length.We subjected the proposed algorithm to rigorous testing through comprehensive simulations alongside meticulous comparisons with established state-of-the-art solutions. The results highlight the algorithm's distinct advantages across multiple dimensions such as path construction success, computational efficiency, and trajectory refinement quality, thereby underscoring its potential to advance autonomous navigation in challenging maritime settings.

Although side-scan sonar can provide wide and high-resolution views of submarine terrain and objects, it suffers from severe interference due to complex environmental noise, variations in sonar configuration (such as frequency, beam pattern, etc.), and the small scale of targets, leading to a high misdetection rate. These challenges highlight the need for advanced detection models that can effectively address these limitations. Here, this paper introduces an enhanced YOLOv9(You Only Look Once v9) model named SOCA-YOLO, which integrates a Small Object focused Convolution module and an Attention mechanism to improve detection performance to tackle the challenges. The SOCA-YOLO framework first constructs a high-resolution SSS (sidescan sonar image) enhancement pipeline through image restoration techniques to extract fine-grained features of micro-scale targets. Subsequently, the SPDConv (Space-to-Depth Convolution) module is incorporated to optimize the feature extraction network, effectively preserving discriminative characteristics of small targets. Furthermore, the model integrates the standardized CBAM (Convolutional Block Attention Module) attention mechanism, enabling adaptive focus on salient regions of small targets in sonar images, thereby significantly improving detection robustness in complex underwater environments. Finally, the model is verified on a public side-scan sonar image dataset Cylinder2. Experiment results indicate that SOCA-YOLO achieves Precision and Recall at 71.8% and 72.7%, with an mAP50 of 74.3%. It outperforms the current state-of-the-art object detection method, YOLO11, as well as the original YOLOv9. Specifically, our model surpasses YOLO11 and YOLOv9 by 2.3% and 6.5% in terms of mAP50, respectively. Therefore, the SOCA-YOLO model provides a new and effective approach for small underwater object detection in side-scan sonar images.

Damen : un nouveau remorqueur hybride livré à Kotug.

Damen : un nouveau remorqueur hybride livré à Kotug.