Marine oil spills have a significant adverse impact on the economy, ecology, and human health. Rapid and effective oil spill monitoring action is extraordinarily important for controlling marine pollution. A marine oil spill detection scheme based on X-band shipborne radar image with machine learning is proposed here. First, the original shipborne radar image collected on Dalian 7.16 oil spill accident was transformed into a Cartesian coordinate system and noise suppressed. Then, texture features and SVM were used to indicate the effective monitoring location of ocean waves. Third, FCM was applied to classify the oil films and ocean waves. Finally, the oil spill detection result was transformed back to a polar coordinate system. Compared with an improved active contour model and another oil spill detection method with SVM, our method performed more intelligently. It can provide data support for marine oil spill emergency response.

Due to the recent rapid growth of ocean oil development and transportation, the offshore oil spill risk accident probability has increased unevenly. The marine oil spill poses a great threat to the development of coastal cities. Therefore, effective and reliable technologies must be used to monitor oil spills to minimize disaster losses. Based on YOLO deep learning network, an automatic oil spill detection method was proposed. The experimental data preprocessing operations include noise reduction, gray adjustment, and local contrast enhancement. Then, real and synthetically generated marine radar oil spill images were used to make slice samples for training the model in the YOLOv5 network. The detection model can identify the effective oil spill monitoring region. Finally, an adaptive threshold was applied to extract the oil slicks in the effective oil spill monitoring regions. The YOLOv5 detection model generated had the advantage of high efficiency compared with existing methods. The offshore oil spill detection method proposed can support real-time and effective data for routine patrol inspection and accident emergency response.

Harvesting wind energy from the ambient environment is a feasible method for powering wireless sensors and wireless transmission equipment. Triboelectric nanogenerators (TENGs) have proven to be a stable and promising technology for harvesting ambient wind energy. This study explores a new method for the performance enhancement and practical application of TENGs. An array of flag-type triboelectric nanogenerators (F-TENGs) for harvesting wind energy is proposed. An F-TENG consists of one piece of polytetrafluoroethylene (PTFE) membrane, which has two carbon-coated polyethylene terephthalate (PET) membranes on either side with their edges sealed. The PTFE was pre-ground to increase the initial charge on the surface and to enhance the effective contact area by improving the surface roughness, thus achieving a significant improvement in the output performance. The vertical and horizontal arrays of F-TENGs significantly improved the power output performance. The optimal power output performance was achieved when the vertical parallel distance was approximately 4D/15 (see the main text for the meaning of D), and the horizontal parallel distance was approximately 2D. We found that the peak output voltage and current of a single flag-type TENG of constant size were increased by 255% and 344%, respectively, reaching values of 64 V and 8 μA, respectively.

Marine oil spills are one of the major threats to marine ecological safety, and the rapid identification of oil films is of great significance to the emergency response. Marine radar can provide data for marine oil spill detection; however, to date, it has not been commonly reported. Traditional marine radar oil spill research is mostly based on grayscale segmentation, and its accuracy depends entirely on the selection of the threshold. With the development of algorithm technology, marine radar oil spill extraction has gradually come to focus on artificial intelligence, and the study of oil spills based on machine learning has begun to develop. Based on X-band marine radar images collected from the Dalian 716 incident, this study used image texture features, the BP neural network classifier, and threshold segmentation for oil spill extraction. Firstly, the original image was pre-processed, to eliminate co-channel interference noise. Secondly, texture features were extracted and analyzed by the gray-level co-occurrence matrix (GLCM) and principal component analysis (PCA); then, the BP neural work was used to obtain the effective wave region. Finally, threshold segmentation was performed, to extract the marine oil slicks. The constructed BP neural network could achieve 93.75% classification accuracy, with the oil film remaining intact and the segmentation range being small; the extraction results were almost free of false positive targets, and the actual area of the oil film was calculated to be 42,629.12 m². The method proposed in this paper can provide a reference for real-time monitoring of oil spill incidents.

As Arctic sea ice continues to melt and global demand for clean energy rises, Russia’s Liquefied Natural Gas (LNG) exports via the Northern Sea Route (NSR) are rapidly increasing. To ensure the operational safety of LNG carriers and safeguard the economic interests of stakeholders, including shipowners, a thorough assessment of the navigability of various ice-class LNG carriers along this route is essential. This study collected Arctic ice condition data from 2014 to 2023 and applied the Polar Operational Limit Assessment Risk Indexing System (POLARIS) methodology to calculate the Risk Index Outcome (RIO) for LNG carriers with No Ice Class, Arc4, and Arc7 ice classifications in Arctic waters. A navigability threshold of 95% RIO ≥ 0 was established to define navigable windows, and critical waters were identified where sections of the route remain in hazardous or risky conditions year-round. The results indicate that for No Ice Class vessels, Arc4 vessels, and Arc7 vessels, the navigable windows for westbound Route 1 and Route 2 under light, normal, and heavy ice conditions range from 70 to 133 days, 70 to 365 days, and 70 to 365 days, respectively, while for eastbound Route 3, the navigable windows range from 0 to 84 days, 0 to 238 days, and 7 to 365 days, respectively. The critical waters affecting the navigability of No Ice Class vessels, Arc4 vessels, and Arc7 vessels are primarily located in the Kara Sea, Laptev Sea and East Siberian Sea. This study, using the POLARIS methodology, provides valuable insights into the navigability of LNG carriers with different ice classes along the NSR, supporting the development and utilization of Arctic energy and shipping routes while offering decision-making support for stakeholders involved in Arctic maritime operations.

At present, sea ice remains a critical factor affecting the safety of vessel operations along the Northern Sea Route (NSR). However, inconsistencies between the navigability outcomes derived from the criteria for the admission of ships in the area of the Northern Sea Route (NSR criteria) and the polar operational limit assessment risk indexing system (POLARIS) methodology present challenges for navigational decision-making. This study aims to conduct a systematic comparison of the POLARIS methodology and the NSR criteria in evaluating the navigability of independently operating vessels classified as Arc4 to Arc9. Through comparative calculations of navigability and the navigability rates for six ice-class vessels across 27 districts using the two methods, this study reveals the consistencies and discrepancies in their navigability outcomes. Firstly, using the POLARIS methodology, the risk index outcome (RIO) is calculated for six ice-class vessels across 27 districts. For these districts, the navigability threshold is defined when 95% or more of the area exhibits an RIO greater than or equal to zero. Secondly, using the NSR criteria, navigability ratios for six ice-class vessels under varying ice conditions are evaluated. A navigability threshold is defined when 95% or more of the ice conditions in a district are classified as navigable. Finally, a quantitative comparison of the weekly navigability ratios obtained by the two methods is conducted to reveal the consistencies and discrepancies in the navigability outcomes of each ice-class vessel across different NSR districts. The results indicate that the consistency between the navigability outcomes of the two methods decreases with lower vessel ice classes, particularly in September and March. In general, the consistency of performance between the two methods in terms of navigability outcomes deteriorates as the vessel ice class decreases and ice conditions become more complex. This study provides a scientific foundation and data-based support for route planning and real-time decision-making in polar waters.