Position Of Vessel Research Articles

A Monocular Ranging Method for Ship Targets Based on Unmanned Surface Vessels in a Shaking Environment

Aiming to address errors in the estimation of the position and attitude of an unmanned vessel, especially during vibration, where the rapid loss of feature point information hinders continuous attitude estimation and global trajectory mapping, this paper improves the monocular ORB-SLAM framework based on the characteristics of the marine environment. In general, we extract the location area of the artificial sea target in the video, build a virtual feature set for it, and filter the background features. When shaking occurs, GNSS information is combined and the target feature set is used to complete the map reconstruction task. Specifically, firstly, the sea target area of interest is detected by YOLOv5, and the feature extraction and matching method is optimized in the front-end tracking stage to adapt to the sea environment. In the key frame selection and local map optimization stage, the characteristics of the feature set are improved to further improve the positioning accuracy, to provide more accurate position and attitude information about the unmanned platform. We use GNSS information to provide the scale and world coordinates for the map. Finally, the target distance is measured by the beam ranging method. In this paper, marine unmanned platform data, GNSS, and AIS position data are autonomously collected, and experiments are carried out using the proposed marine ranging system. Experimental results show that the maximum measurement error of this method is 9.2%, and the average error is 4.7%.

Prediction of the Relative Resource Abundance of the Argentine Shortfin Squid Illex argentinus in the High Sea in the Southwest Atlantic Based on a Deep Learning Model.

To analyze the impact of the marine environment on the relative abundance of Illex argentinus (high and low categories) in the southwest Atlantic, this study collected logbook data from Chinese pelagic trawlers from December 2014 to June 2024, including vessel position data and oceanographic variables such as sea surface temperature, 50 m and 100 m water temperature, sea surface salinity, sea surface height, chlorophyll-a concentration, and mixed layer depth. Vessel positions were used to enhance the logbook data quality, allowing an analysis of the annual trends in the resource center of this squid at a spatial resolution of 0.1° × 0.1° and a temporal resolution of ten days. The findings showed that the resource center is primarily located around 42° S in the north and between 45° S and 47° S in the south, with a trend of northward movement during the study period. Additionally, we constructed two ensemble learning models based on decision trees-AdaBoost and PSO-RF-aiming to identify the most critical environmental factors that affect its resource abundance; we found that the optimal model was the PSO-RF model with max_depth of 5 and n_estimators of 46. The importance analysis revealed that sea surface temperature, mixed layer depth, sea surface height, sea surface salinity, and 50 m water temperature are critical environmental factors affecting this species' resources. Given that deep learning models generally have shorter running times and higher accuracy than other models, we developed a CNN-Attention model based on the five most important input factors. This model achieved an accuracy of 73.6% in forecasting this squid for 2024, predicting that the population would first appear near the Argentine exclusive economic zone around mid-December 2023 and gradually move east and south thereafter. The predictions of the model, validated through log data, maintained over 70% accuracy during most periods at a time scale of ten days. The successful construction of the resource abundance forecasting model and its accuracy improvements can help enterprises save fuel and time costs associated with blind searches for target species. Moreover, this research contributes to improving resource utilization efficiency and reducing fishing duration, thereby aiding in lowering carbon emissions from pelagic trawling activities, offering valuable insights for the sustainable development of this species' resources.

Assessment of the vessel position dead reckoning method error

A method of the calculation of vessel dead reckoning coordinates with the accuracy assessment is proposed. It is based on previously derived formulas for the assessment of the uncertainty boundaries for vessel geodetic latitude and longitude during its motion along the ellipsoid surface. The above formulas are used as follows. At first, the latitude and its precision index are calculated. After, they are used for the calculation of the longitude and its precision index. As the result, it is possible to determine uncertainty intervals for vessel coordinates derived with dead reckoning. The implementation of this method for testing an electronic chart display and information system Navi-Sailor 4000 as to the calculation of vessel dead reckoning coordinates with it is shown. The provision of the information about vessel heading and speed through water is carried out to the system in real-time mode by means of a signal simulator. The speed and heading are remaining unchanged during one-hour time interval. During the above time interval, the data about dead reckoning coordinates are recording into a log-file, then the data are converted in a text format and used for the test. The dead reckoning coordinates, calculated by the system, are in the limits of uncertainty assessed with the proposed method. A limitation of the method is that it does not take into account the influence of errors due to rounding because of the finiteness of the computer bit grid. After the elimination of the above limitation, it seems to be possible to use a modernized version of the method as a part of a procedure for testing the navigational systems which give the information about vessel dead reckoning coordinates.

Intelligent monitoring of marine vessel dynamics based on data mining

This study explores vessel navigation's universal characteristics and individual anomaly behavior from dispatch to destination. With the help of information technology, it introduces big data and machine learning technologies into the work of vessel traffic management and conducts statistical analysis and monitoring studies of vessel behavior from a data-driven perspective. The study was conducted by modeling vessel speed, position, course, and other navigational characteristics behavior through comprehensive statistical analysis of vessel AIS, radar, and VTS data. The study was based on the monitoring methods for anomalous behavior of vessel speed, position, and heading inconsistencies with the expected behavior identified by the model.

Mechanical positional information guides the self-organized development of a polygonal network of creases in the skin of mammalian noses.

The glabrous skin of the rhinarium (naked nose) of many mammalian species exhibits a polygonal pattern of grooves that retain physiological fluid, thereby keeping their nose wet and, among other effects, facilitating the collection of chemosensory molecules. Here, we perform volumetric imaging of whole-mount rhinaria from sequences of embryonic and juvenile cows, dogs, and ferrets. We demonstrate that rhinarial polygonal domains are not placode-derived skin appendages but arise through a self-organized mechanical process consisting of the constrained growth and buckling of the epidermal basal layer, followed by the formation of sharp epidermal creases exactly facing an underlying network of stiff blood vessels. Our numerical simulations show that the mechanical stress generated by excessive epidermal growth concentrates at the positions of vessels that form rigid base points, causing the epidermal layers to move outward and shape domes-akin to arches rising against stiff pillars. Remarkably, this gives rise to a larger length scale (the distance between the vessels) in the surface folding pattern than would otherwise occur in the absence of vessels. These results hint at a concept of "mechanical positional information" by which material properties of anatomical elements can impose local constraints on an otherwise globally self-organized mechanical pattern. In addition, our analyses of the rhinarial patterns in cow clones highlight a substantial level of stochasticity in the pre-pattern of vessels, while our numerical simulations also recapitulate the disruption of the folding pattern in cows affected by a hereditary disorder that causes hyperextensibility of the skin.

Maximum correntropy polynomial chaos Kalman filter for underwater navigation

This paper develops an underwater navigation solution that utilizes a strapdown inertial navigation system (SINS) and fuses a set of auxiliary sensors such as an acoustic positioning system, Doppler velocity log, depth meter, and magnetometer to accurately estimate an underwater vessel's position and orientation. The conventional integrated navigation system assumes Gaussian measurement noise, while in reality, the noises are non-Gaussian, particularly contaminated by heavy-tailed impulsive noises. To address this issue, and to fuse the system model with the acquired sensor measurements efficiently, we develop a square root polynomial chaos Kalman filter based on maximum correntropy criteria. The proposed method uses Hermite polynomial chaos expansion to tackle the nonlinearity, and it has the potential to estimate the states in a more accurate way in presence of a non-Gaussian measurement noise. The filter is initialized using acoustic beaconing to accurately locate the initial position of the vehicle. The computational complexity of the proposed filter is calculated in terms of flops count. The proposed method is compared with the existing maximum correntropy sigma point filters in terms of estimation accuracy and computational complexity. It is found from the simulation results that the proposed method is more accurate compared to the conventional deterministic sample point filters and Huber's M-estimator.

Performance of Prenatal Ultrasound Screening for the Relative Positioning of Mesenteric Vessels.

Abnormal relative positioning of the superior mesenteric artery (SMA) and vein (SMV) can lead to intestinal malrotation that predisposes to midgut volvulus. The aim of this study was to assess the prenatal ultrasound ability to visualize the relative position of SMA and SMV in normal pregnancies. Prospective cohort study performed in Montpellier University Hospital Centre, including 80 fetuses during routine 3rd trimester ultrasound scan. For each fetus included, the relative position of the vessels on an axial image was defined as SMV on the right, forward, or on the left of SMA. Doppler imaging was additionally used if necessary. Data were compared to the neonatal abdominal scans performed by pediatric radiologist. The superior mesenteric vessels were identified in 79 fetuses. Prenatal findings showed a usual relative position of the vessels, that is, the vein on the right of the artery, in 96.2%. In 2 cases, the vein was strictly in front of the artery, and in 1 case, the vein was on the left side of the artery. Seventy-four neonates were examined and comparison with prenatal finding showed a perfect agreement (Kappa coefficient of 100%). An intestinal malrotation was postnatally diagnosed corresponding to the case where vein was on the left side of the artery. This study showed that the relative position of the SMA and SMV could be assessed using ultrasound prenatal examination with a perfect agreement with postnatal findings. In case of abnormal vessels positioning more examinations should be promote including prenatal MRI and postnatal conventional radiologic examinations to confirm intestinal malrotation.

Removal of Pb(II) pollution by algae-Fe-Ni magnetic nanocomposite

A magnetic nanocomposite material was synthesized by combining Chaetomorpha Algae with a Fe–Ni alloy. The dry algae, Fe, and Ni were mechanically alloyed using a ball milling technique in an argon atmosphere. The resulting Algae-Fe-Ni Magnetic Nanocomposite (AFNMN) was characterized using various analytical techniques, including particle size distribution (PSD) analysis, Brunauer-Emmett-Teller (BET) surface area analysis, scanning electron microscopy (SEM), differential scanning calorimetry (DSC), vibrating sample magnetometry (VSM), atomic absorption spectroscopy (AAS), and Fourier transform infrared (FTIR) spectroscopy. Multiple isotherm models were employed to study the adsorption of Pb(II) ions onto the AFNMN material. Density functional theory (DFT) was applied to predict the most stable amino acids for the removal of Pb(II) pollutants from a set of 14 amino acids. To optimize the Pb(II) uptake performance, three key experimental parameters were fine-tuned: pH, temperature, and reactor vessel geometry. Four different optimization methods were utilized, including response surface methodology (RSM), fuzzy logic, adaptive network-based fuzzy inference system (ANFIS), and artificial neural network genetic algorithms (ANN-GA). Additionally, a heat transfer simulation was conducted to determine the optimal vessel positions for achieving the desired temperature variations within the reactor during the adsorption process.

Assessment of hemodynamic parameters based on the results of ultrasound Dopplerography in different variants of the position of vessels in the area of bifurcation of the common carotid arteries

Introduction. Ultrasound imaging allows us to study the influence of topographic and anatomical variability of the main vessels of the neck on hemodynamic parameters in the area of bifurcation of the common carotid artery (CCA). Aim. To assess the significance of changes in hemodynamic parameters on the damage of the main arteries near the bifurcation of the common carotid artery in different variants of the mutual position of vessels according to the results of ultrasound Dopplerography. Materials and methods. 865 volunteers were examined using SonoAce R7 and Logiq F6. The results were summarized, systematized and processed in Microsoft Excel 2007 spreadsheets and subjected to statistical analysis. Among the participants, 5 main variants of the mutual position of the vessels were identified: A – typical; Б – the external carotid artery (ECA) is located ventrally; В – the lateral position of the external carotid artery; Г – divergence or intersection of the carotid arteries near bifurcation; Д – joint medial deviation of both carotid arteries. Results. A total of 1730 vascular complexes were evaluated. The smallest diameter of the external carotid arteries was observed in groups A and B – 3.10 mm. The largest value of the diameter of the internal carotid arteries (ICA) was in group D – 4.20 mm and in group B – 4.10 mm. The diameter of the common carotid arteries in groups A and B was 5.50 mm, in groups D and D – 5.60 mm, and in group B – 5.70 mm. The volume velocity of blood flow had higher values in group D. The lowest indicators of volumetric blood flow velocity were noted in group A, for common carotid and internal carotid arteries – in group G. Conclusions. The smallest average and median diameter of the external carotid arteries was observed in types A and B. The highest median diameter of the internal carotid arteries was in group D (4.20 mm). The median value of the diameter of the common carotid arteries was the maximum in group B (5.70 mm). The presented observation results demonstrate the effect of anatomical variation on the morphometric and functional characteristics of the vascular bed, reflecting the change in the calculated volumetric velocity of blood flow.

Wave drag and wave patterns by ships moving in a single-file formation

To minimize energy expenditure for each individual, animals adopt distinctive formations, such as fish schooling, “V” formation by flying birds, and single-file formation by waterfowls. The phenomenon of ducklings following their mothers in a single-file configuration has been revealed by the mechanisms of wave-riding and wave-passing. Drawing inspiration from this phenomenon, an investigation is undertaken on ships moving in a single-file formation. The objective is to quantify how much energy can be saved in different configurations. In this study, a three-dimensional boundary element method incorporating linear free-surface boundary conditions is used to obtain the wave drag and wave patterns. It is found that when constructive wave interference occurs in a two-ship formation, the wave resistance of the trailing ship increases and the leading ship experiences a decrease in its wave drags, especially when the two ships are in close proximity. Mutual benefit arises when destructive wave interference occurs between two ships. In addition, increasing the size of the trailing vessel facilitates the effect of wave-riding by the leading ship, but this effect becomes less pronounced as the speed increases. In a multi-ship formation configuration, changing the size of the leading ship will have a localized effect on the wave-passing, but the fleet will eventually tend to a dynamic equilibrium. When the position of the first trailing vessel is changed, there is similarly a localized effect on the wave-passing. Adjusting the first trailing ship to the position of the constructive wave interference is not favorable to reducing its own drag but enhances the wave-riding effect of its close follower. Finally, to achieve wave-passing, the trailing ship does not necessarily have to occupy an optimum position. This can still be accomplished if the trailing ship moves backward by an integer multiple of wavelength.

A simple new approach for mapping an ultrasonic tank for sonochemistry

The most used piece of equipment for sonochemistry is the ultrasonic cleaning bath. However, what is sometimes forgotten by scientists new to sonochemistry is the vital importance of the shape and positioning of any reaction vessel in the bath to obtain the most efficient and reproducible results. In experiments using an ultrasonic bath, a glass vessel (reactor) is inserted into the water contained in the bath. The water acts as the coupling medium for the transfer of acoustic energy from the transducer to the vessel (termed indirect sonication). The position of the reaction vessel above the base of the US bath can change the energy transmitted into it over a wide range of values (in our system between 100–500 J). We have carried out a study of the vertical distribution of the ultrasound field in a common type of ultrasound bath, comparing conventional sonochemistry dosimeters with a new and very simple approach using the Ultrasonic Capillary Effect (UCE) which can be performed in any laboratory. The technique involves the use of a capillary tube, to locate the vertical positions of acoustic pressure maxima above a single transducer on the base of the bath. The results are compared with those obtained using calorimetry, iodimetry, a cavitometer and the perforation of aluminium foil. The results show that the optimum position for the reaction vessel can be located very simply using UCE.

POLA ANTREAN KAPAL PENANGKAPAN IKAN DI PELABUHAN PERIKANAN KARANGSONG, KABUPATEN INDRAMAYU

A catch unloading is a fishing vessel activity carried out regularly at the port. The high frequency of unloading activities leads to a long queue which requires fishing vessels to wait in significant time for their unloading turn. One of the fishing ports with a significant waiting time is Karangsong Port, where the unloading queue can reach three weeks. To reduce the waiting time, there is a need to identify the utilization level of unloading facilities and queue patterns, as well as calculate unloading time and its influencing factors at Karangsong fishing port. This case study was focused on vessel queuing patterns, utilization of port facilities and the length of unloading time. Data were collected through direct interviews and observations. Based on the calculation results, the utilization rate of the unloading dock is 20.16%, TPI is 36.63% and the port basin reaches 577.86%. The vessels queue in double lines with a single service facility. The average length of unloading time for >30 GT vessels is 2.60 hours while for ≤30 GT vessels is 11.92% faster which is recorded at 2.29 hours. Factors affecting the length of unloading time incorporate the vessel's size, crew size, number of catches, availability of scales, market demand as well as the vessel's position and number of vessels in the queue. Keywords : Queue, fish unloading, port facilities

Application of three-dimensional ultrasound technique in repairing dorsal foot wounds with medial sural artery perforator flaps

To investigate the accuracy of positioning perforator of medial sural artery with three-dimensional ultrasound technique guided by a wide band linear matrix array volume transducer probe before operation, and the effectiveness of the flap design based on this in repairing the dorsal foot wounds. Between January 2019 and December 2022, 30 patients with skin and soft tissue defects of the dorsal foot were treated. There were 19 males and 11 females, with an average age of 43.9 years (range, 22-63 years). There were 12 cases of traffic accident injury, 15 cases of heavy crushing injury, and 3 cases of machine injury. The time from injury to hospitalization was 1-8 hours (mean, 3.5 hours). The wounds in size of 5 cm×3 cm to 17 cm×5 cm were thorough debrided and covered with vacuum sealing drainage dressing. Then the wounds were repaired with the medial sural artery perforator flaps after no obvious infection observed. To obtain the complete three-dimensional image, the number and position of the medial sural artery perforator branches and the position of the main blood vessels in the muscle were detected and recorded by wide band linear matrix array volume transducer probe before operation. Suitable perforating branches were selected to design the flap and guide the flap incision on this basis. The size of the perforating flap ranged from 6 cm×4 cm to 18 cm×6 cm. The sensitivity and positive predictive value were calculated by comparing preoperative exploration with intraoperative observation of perforating branches, so as to evaluate the positioning accuracy of three-dimensional ultrasound technique. The donor sites were sutured directly in 25 cases and repaired with free skin grafting in 5 cases. The 60 perforating branches of medial sural artery were found before operation and 58 during operation in 30 patients. Among them, pre- and intra-operative perforations were consistent with 56. The sensitivity was 93.3% and positive predictive value was 96.6%. The intramuscular position and route of the main blood vessels were basically consistent with the pre- and intra-operative observation. All flaps survived and wounds healed by first intention. All incisions at the donor sites healed by first intention, and all skin grafts survived. All patients were follow up 9-24 months (mean, 14.7 months). The appearance, color, and texture of the flaps were good, and no obvious effect on wearing shoes and walking. At last follow-up, the American Orthopaedic Foot and Ankle Society (AOFAS) ankle-hind score ranged from 80 to 92, with an average of 87.5. The patient satisfaction was excellent in 29 cases and good in 1 case. The three-dimensional ultrasound technique guided by the wide band linear matrix array volume transducer probe can accurately locate the perforating branch of the medial sural artery, and the three-dimensional imaging is more intuitive, which can be used to guide the design and incision of the medial sural artery perforator flap.

Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix

This study proposes a vessel position prediction method using attention spatiotemporal graph convolutional networks, which addresses the issue of low prediction accuracy due to less consideration of inter-feature dependencies in current vessel trajectory prediction methods. First, the method cleans the vessel trajectory data and uses the Time-ratio trajectory compression algorithm to compress the trajectory data, avoiding data redundancy and providing feature points for vessel trajectories. Second, the Spectral Temporal Graph Neural Network (StemGNN) extracts the correlation matrix that describes the relationship between multiple variables as a priori matrix input to the prediction model. Then the vessel trajectory prediction model is constructed, and the attention mechanism is added to the spatial and temporal dimensions of the trajectory data based on the spatio-temporal graph convolutional network at the same time as the above operations are performed on different time scales. Finally, the features extracted from different time scales are fused through the full connectivity layer to predict the future trajectories. Experimental results show that this method achieves higher accuracy and more stable prediction results in trajectory prediction. The attention-based spatio-temporal graph convolutional networks effectively capture the spatio-temporal correlations of the main features in vessel trajectories, and the spatio-temporal attention mechanism and graph convolution have certain interpretability for the prediction results.

A Resetting Observer for Linear Time-Varying Systems With Application to Dynamic Positioning of Marine Surface Vessels

A Resetting Observer for Linear Time-Varying Systems With Application to Dynamic Positioning of Marine Surface Vessels

A Novel & Efficient LR LSTM AIS Route Data Prediction for Longer Range

The growth of technology has enabled different industries to generate an excessive amount of data- one such industry being the maritime sector. Sophisticated sensory systems installed on various vessels generate information at a very large scale which can further be used in optimizing operational efficiency, improving safety standards, and aiding in the decision-making process. Researchers have henceforth identified statistical learning methods and machine learning techniques as potent tools for excavating useful insights from this copious amount of data available. This research evaluates how these algorithms work by focusing exclusively on the analysis of sensory data collected from vessels within the maritime domain. A comparison study has been conducted between statistical learning methods (which includes regression analysis, and time series analysis) vis-a-vis machine learning approaches. The major objective of this study was to determine the most effective method for detecting anomalies while simplifying marine operations and optimizing vessel behavior. The scope of the conducted analysis is restricted to the prediction of the next trajectory points. Accurate prediction of vessel positions plays a crucial role in maritime operations, enabling efficient route planning, collision avoidance, and maritime traffic management. In this article, the authors propose a combination model that combines the benefits of Linear Regression (LR) and Long Short-Term Memory (LSTM) techniques to anticipate vessel positions based on Automatic Identification System (AIS) data. The proposed model takes advantage of the interpretability of LR and the temporal dependencies collected by LSTM to capture temporal dependencies, which improve prediction accuracy and reveal the underlying links between vessel features and future positions.

YOLOv8-BYTE: Ship tracking algorithm using short-time sequence SAR images for disaster response leveraging GeoAI

Ship tracking technology is crucial for emergency rescue in the event of a disaster. Quickly identifying the position and status of vessels is vital for rescue teams to be able to deploy efficiently in disaster areas. When responding to emergencies or natural disasters, ship tracking technology plays a critical role in supporting emergency rescue operations and resource allocation, improving the overall resilience of the maritime transportation system. However, the research on multi-object tracking (MOT) algorithms has primarily focused on optical image datasets. In contrast, image data from synthetic aperture radar (SAR) presents unique challenges, such as defocus interference, a high false alarm rate, and a lack of prior samples. To overcome these particular challenges, we propose a robust MOT algorithm developed for SAR images to achieve effective multi-vessel tracking under difficult imaging conditions. In particular, we optimize the YOLOv8 detection network by introducing a diffusion model-based training method for data augmentation. This method improves the robustness of the network to scaling, rotational and translational deformations. Moreover, an enhanced swin transformer is proposed as a feature extraction network, which strengthens the representation capability of the detection network. Furthermore, the state parameters within the KF technique are enhanced by directly capturing the details of the height and width of the tracking rectangle box. This refinement of the ByteTrack algorithm aims to achieve a more precise and accurate fit of the tracking rectangle to the ship, further improving the overall tracking performance. The experimental results from the ship detection and multiple objects tracking datasets show the impressive performance of the proposed model. With a precision of 97.60%, a recall of 96.36%, and an average precision of 96.72%, the model achieves exceptional detection accuracy with an 18% reduction in model parameters. Furthermore, significant improvements can be observed in key tracking metrics such as HOTA, MOTA and IDF1, with improvements of 4.8%, 8.5% and 6.8% respectively compared to the baseline algorithm, alongside a remarkable 37.5% reduction in IDS. It is noteworthy that the tracker works in real time, achieving an average analysis speed of 47 frames per second. The proposed MOT algorithm achieves state-of-art tracking performance on a SAR image dataset with short time sequences. Therefore, the proposed approach is a compelling solution for ship tracking in SAR imagery.

Dependence of reaction on vessel position, diameter, and liquid height in the sonochemical reactor with indirect irradiation

To investigate the sonochemical reaction performance caused by the indirect irradiation at 500 kHz, the glass vessel and a KI aqueous solution were used. Both the ultrasonic power and reaction rate had maximum values at every half wavelength of ultrasound. When the vessel position was adjusted to a larger absolute value of transducer impedance, the reaction rate became higher. The reaction rate and ultrasonic power increased as the vessel position moved closer to the transducer. The reaction rate first increased as the electric power applied to the transducer increased, reached a maximum value, and then decreased. This decrease phenomenon is called quenching of the sonochemical reaction. Before the quenching occurrs, the reaction rate per unit volume almost linearly increased with ultrasonic power density. The effects of the vessel diameter and liquid height on the relationship between the reaction rate per unit volume and the ultrasonic power density were small.

Barrier Lyapunov function-based adaptive optimized control for full-state and input-constrained dynamic positioning of marine vessels with simulation and model-scale tests

For practical marine engineering, ensuring system performance and avoiding violations of safety-related system constraints in various scenarios remains a challenging problem. To tackle this problem, this paper addresses adaptive neural network (NN) optimized control for dynamic positioning (DP) of marine vessels suffering from unknown model uncertainties and control gains, ocean disturbances, and full-state/input constraints. To realize model-free optimized control, the reinforcement learning (RL) with a critic–actor–identifier architecture is constructed on the basis of NN approximation, in which the actor, critic, and identifier NNs are designed to perform control actions, evaluate control performance, and estimate unknown dynamics, respectively. To effectively guarantee the system safety, the critics and actors in the corresponding subsystems are decomposed into a guaranteed safety-related constraint term with respect to the barrier Lyapunov function (BLF) and an unknown dynamic term to be learned. Such a control strategy can ensure that all the state variables are restricted to a predefined safety region, even in the learning control process. From the above logical ideas, the adaptive optimized kinematic and kinetic control laws are presented to guarantee that the whole vessel system is optimized. Moreover, the motion control responses of vessels in various scenarios are realistically simulated through convincing simulations and model-scale tests. The validation results prove the applicability and feasibility of the proposed DP control strategy for practical offshore engineering.

Identification of suspicious behavior through anomalies in the tracking data of fishing vessels

Automated positioning devices can generate large datasets with information on the movement of humans, animals and objects, revealing patterns of movement, hot spots and overlaps among others. However, in the case of Automated Information Systems (AIS), attached to vessels, observed strange behaviors in the tracking datasets may come from intentional manipulation of the electronic devices. Thus, the analysis of anomalies can provide valuable information on suspicious behavior. Here, we analyze anomalies of fishing vessel trajectories obtained with the Automatic Identification System. The map of silent anomalies, those that occur when positioning data are absent for more than 24 hours, shows that they are most likely to occur closer to land, with 87.1% of anomalies observed within 100 km of the coast. This behavior suggests the potential of identifying silence anomalies as a proxy for illegal activities. With the increasing availability of high-resolution positioning of vessels and the development of powerful statistical analytical tools, we provide hints on the automatic detection of illegal activities that may help optimize the management of fishing resources.