Search Results (10)

Understanding the impact of environmental factors, particularly seaway, on marine units is critical for developing efficient control and decision support systems. To this end, the concept of wave buoy analogy (WBA), which utilizes ships as sailing buoys, has captured practitioners’ attention due to its cost-effectiveness and extensive coverage. Despite extensive research, real-time sea-state estimation (SSE) has remained challenging due to the large observation window needed for statistical inferences. The current study builds on previous work, aiming to propose an AI framework to reduce the estimation time lag between exciting waves and respective estimation by transforming temporal/spectral features into a manipulated scalogram. For that, an adaptive ship response predictor and deep learning model were incorporated to classify seaway while minimizing network complexity through feature engineering. The system’s performance was evaluated using data obtained from an experimental test on a semi-submersible platform, and the results demonstrate the promising functionality of the approach for a fully automated SSE system. For further comparison of features of low- and high-fidelity modeling, the deficits with the feature transformation of the existing SSE models are discussed. This study provides a foundation for improving online SSE and promoting the seaway acquisition for stationary marine units. Full article

Semi-submersible platforms are used in the offshore oil and gas industry. They are specialised marine vessels that float on submersed drafts, which are composed of pontoons and columns and can serve as habitats for biofouling marine benthic communities. When these vessels sail from one place to another, either by using their own propellers or being towed, they can act as vectors for introducing non-native marine species. To establish themselves in new areas, these exotic species require suitable benthic habitats. Artificial substrates, such as harbour infrastructure where such vessels are moored, appear to be highly suitable for this purpose. In the present study, a mooring buoy and a harbour piling at Curaçao (southern Caribbean), frequently used by semi-submersible platforms, were found to be colonised by the sun corals Tubastraea coccinea and T. tagusensis at shallow depths. This report presents the first record of T. tagusensis as an introduced non-native species in the southern Caribbean, highlighting the potential role of harbour infrastructure in facilitating coral settlement at depths shallower than those typically observed. These findings underscore the ecological impact of artificial substrates in supporting invasive species and emphasise the need for monitoring programs and defouling facilities. Full article

This paper focuses on the characterization of radio propagation, and data communication in a marine environment. More specifically, we consider signal propagation when three different sub-gigahertz industrial, scientific, and medical (ISM) bands, i.e., 169 MHz, 434 MHz, and 868 MHz, are used. The main focus of the paper is to evaluate the path loss (PL), i.e., the power loss that a propagation radio wave would experience when communication occurs between a sail boat and a buoy. We describe the measurement results obtained performing three different radio power measurement campaigns, at the three different aforementioned ISM sub-gigahertz bands. We also want to correlate the radio propagation quality with the weather conditions present in the measurement areas. The obtained results show that higher distances are achieved by transmitting at lower frequencies, i.e., 169 MHz, and, on average, the propagation is directly dependent from the dew point index. Full article

Using a Doppler radar to measure river surface velocity is a safe and effective technique. However, the measurement would be severely affected by undesired targets that enter the illuminated area of radar. The issue is worsened when measuring the surface velocities of wide rivers because undesired targets such as boats and ships are more likely to be present. The buoy boats fixed on the river surface and cargo ships sailing on the river would generate ground clutter and moving target interference, respectively. The clutter and interference can mask the signal produced by the Bragg scattering and seriously bias the extraction result of river surface velocity. This paper proposes two effective methods to remove ground clutter and moving target interference, respectively. One is an improved phase-based method that eliminates ground clutter after obtaining its boundaries through the phase in the frequency domain, and another is an improved constant false alarm rate (CFAR) detector that combines smallest-of selection logic and a multi-step deletion scheme to detect and remove interference in the time-Doppler spectrum. The experimental data measuring the surface velocity of the Yangtze River with a coherent S-band radar in July 2022 are used to verify the proposed methods. The results show that the proposed methods can effectively remove ground clutter and moving target interference, respectively. After clutter and interference cancellation, a more reasonable result of river surface velocity distribution can be extracted. Therefore, the methods proposed in this paper can be used to remove clutter and interference when extracting the surface velocity of rivers with numerous undesired targets. Full article

Aqua farms will be the most frequently encountered obstacle when autonomous ships sail along the coastal area of Korea. We used YOLOv5 to create a model that detects aquaculture buoys. The distances between the buoys and the camera were calculated based on monocular and stereo vision using the detected image coordinates and compared with those from a laser distance sensor and radar. A dataset containing 2700 images of aquaculture buoys was divided between training and testing data in the ratio of 8:2. The trained model had precision, recall, and mAP of 0.936%, 0.903%, and 94.3%, respectively. Monocular vision calculates the distance based on camera position estimation and water surface coordinates of maritime objects, while stereo vision calculates the distance by finding corresponding points using SSD, NCC, and ORB and then calculating the disparity. The stereo vision had small error rates of −3.16% and −14.81% for short (NCC) and long distances (ORB); however, large errors were detected for objects located at a far distance. Monocular vision had error rates of 2.86% and −4.00% for short and long distances, respectively. Monocular vision is more effective than stereo vision for detecting maritime obstacles and can be employed as auxiliary sailing equipment along with radar. Full article

This study introduces an alternative to the existing methods for measuring ocean currents based on a recently developed technology. The SailBuoy is an unmanned surface vehicle powered by wind and solar panels that can navigate autonomously to predefined waypoints and record velocity profiles using an integrated downward-looking acoustic Doppler current profiler (ADCP). Data collected on two validation campaigns show a satisfactory correlation between the SailBuoy current records and traditional observation techniques such as bottom-mounted and moored current profilers and moored single-point current meter. While the highest correlations were found in tidal signals, strong current, and calm weather conditions, low current speeds and varying high wave and wind conditions reduced correlation considerably. Filtering out some events with the high sea surface roughness associated with high wind and wave conditions may increase the SailBuoy ADCP listening quality and lead to better correlations. Not yet resolved is a systematic offset between the measurements obtained by the SailBuoy and the reference instruments of ±0.03 m/s. Possible reasons are discussed to be the differences between instruments (various products) as well as changes in background noise levels due to environmental conditions. Full article

In the existing research on the intelligent navigation of ships, navigation route planning often regards light buoys as fixed obstructions. However, due to factors such as water ripples, the position of the buoys keeps periodically changing. If the buoys are set to a fixed range of avoidance areas in the process of ship navigation, it is easy to allow a collision between the ship and the light buoys. Therefore, based on historical motion trajectory data of the buoys, a SARIMA-based time-series prediction model is proposed to estimate the offset position of a given buoy in a specified time. Furthermore, the collision-free path planning approach is presented to dynamically recommend an accurate sailing path. The results of the simulation experiment show that this method can effectively deal with collisions of ships caused by the offset position of the light buoys during the navigation of the large and low-speed autonomous ships. Full article

This paper illustrates a methodology to get a reliable estimation of the local wave properties, based on the reconstruction of the motion of a moving sailboat by means of GNSS receivers installed on board and an original kinematic positioning approach. The wave parameters reconstruction may be used for many useful practical purposes, e.g., to improve of autopilots, for real-time control systems of ships, to analyze and improve the performance of race sailboats, and to estimate the local properties of the waves. A Class 40 oceanic vessel (ECO40) left from the port of “Riva di Traiano” located close to Rome (Italy) on 19 October 2014 to perform a non-stop sailing alone around the world in energy and food self-sufficiency. The proposed system was installed on ECO40 and the proposed method was applied to estimate the wave properties during a storm in the Western Mediterranean Sea. The results compared against two sets of hindcast data and wave buoy records demonstrated the reliability of the method. Full article

Problems related to quality (and quantity) of water in natural resources or in artificial reservoirs are frequently arising and are at the center of attention of authorities and governments around the world. Many times the monitoring is not performed in an efficient time frame and a precise manner, whereas the adoption of fast and punctual solutions would undoubtedly improve the water quality and consequently enhance the life of people. To minimize or diminish such kinds of problems, we propose an architecture for sensors installed in a robotic platform, an autonomous sail boat, able to acquire raw data relative to water quality, to process and make them available to people that might be interested in such information. The main contributions are the sensors architecture itself, which uses low cost sensors, with practical experimentation done with a prototype. Results show data collected for points in lakes and rivers in the northeast of Brazil. This embedded system is fixed in the sailboat robot with the intention to facilitate the study of water quality for long endurance missions. This robot can help monitoring water bodies in a more consistent manner. Nonetheless the system can also be used with fixed vases or buoys in strategic points. Full article

Daily wind patterns and their relational associations with other metocean (oceanographic and meteorological) variables were algorithmically computed and extracted from a year-long wind and weather dataset, which was collected hourly from an ocean buoy located in the Penghu archipelago of Taiwan. The computational algorithm is called data cloud geometry (DCG). This DCG algorithm is a clustering-based nonparametric learning approach that was constructed and developed implicitly based on various entropy concepts. Regarding the bivariate aspect of wind speed and wind direction, the resulting multiscale clustering hierarchy revealed well-known wind characteristics of year-round pattern cycles pertaining to the particular geographic location of the buoy. A wind pattern due to a set of extreme weather days was also identified. Moreover, in terms of the relational aspect of wind and other weather variables, causal patterns were revealed through applying the DCG algorithm alternatively on the row and column axes of a data matrix by iteratively adapting distance measures to computed DCG tree structures. This adaptation technically constructed and integrated a multiscale, two-sample testing into the distance measure. These computed wind patterns and pattern-based causal relationships are useful for both general sailing and competition planning. Full article