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Spatial-temporal features are extracted from X-band marine radar backscatter image sequences via deep neural networks to estimate sea surface significant wave heights (SWHs). A convolutional neural network (CNN) is first constructed based on the pretrained GoogLeNet to estimate SWH using multiscale deep spatial features extracted from each radar image. Since the CNN-based model cannot analyze the temporal behavior of wave signatures in radar image sequences, a gated recurrent unit (GRU) network is concatenated after the deep convolutional layers from the CNN to build a convolutional GRU (CGRU)-based model, which generates spatial-temporal features for SWH estimation. Both the CNN and CGRU-based models are trained and tested using shipborne marine radar data collected during a sea trial off the East Coast of Canada, while simultaneous SWHs measured by nearby buoys are used as ground truths for model training and reference. Experimental results show that compared to the classic signal-to-noise ratio (SNR)-based method, both models improve estimation accuracy and computational efficiency significantly, with a reduction of RMSD by 0.32 m (CNN) and 0.35 m (CGRU), respectively. It is also found that under rainy conditions, CGRU outperforms SNR and CNN-based models by reducing the RMSD from around 0.90 to 0.54 m.

Photometric stereo is a widely used optical 3-D reconstruction method, which has advantages for high-resolution and well-detailed 3-D reconstruction even in weak texture regions. However, in underwater photometric stereo, the light refraction and attenuation using the camera and point light sources usually result in significant errors in shape recovery. In this article, we present a novel solution to address these challenges for improving the performance of underwater photometric stereo by combining it with underwater laser triangulation. First, we employ underwater laser triangulation as references to correct the global surface-shape distortion caused by nonuniform close-range illumination. Second, we propose to use an underwater camera refractive model to eliminate nonlinear refraction distortion. Third, we build a device implementing the proposed method for 3-D seabed reconstruction in on-site sea trials. Experimental results demonstrate that the proposed approach is able to produce accurate 3-D reconstruction results in the underwater environment.

Underwater acoustic localization (UWAL) is widely applied in ocean exploration and a key issue for the high rate time of arrival (TOA) based UWAL method is to select the direct signal in the multi-path acoustic channel. However, the existing direct signal selection methods suffer from a deteriorate of the selection accuracy since the received signals from adjacent periods are mixed with each other. Addressing the problem, we propose a high rate direct signal selection algorithm in this paper. First, we choose the amplitude, TOA, bandwidth, and Doppler frequency of the detected pulses as the input features. Secondly, a direct signal classifier is established utilizing the decision tree. Finally, the localization model is built based on the TDOA information of the direct signal, and the localization precision analysis evaluates the performance of the UWAL method. Sufficient experiments including simulation, tank experiment, and sea trial are implemented to verify the effectiveness of the proposed method.

Verification and validation of Computational Fluid Dynamics (CFD) simulations of a full-scale ship trial are presented in this study. Speed/power trials were carried out according to industry standards for three different power settings. Measured data was corrected for environmental effects to obtain ideal trial runs. Ship-scale unsteady RANS CFD simulations were conducted. Grid refinement sensitivity was evaluated for each power setting. Furthermore, time-step sensitivity was assessed for the selected grids. Finally, assumptions regarding symmetry condition and turbulence model were verified. Simulated results were in good agreement with the test data, thus illustrating the capabilities of numerical methods to determine ship performance at full scale.

MARPOL 73/78, Chapter IV is considered to reveal the emission control engineering on ships. The probability model of the fuel oil consumption is established based on the machine learning technique. The proposed methods are applied into this research in order to establish the probability model of fuel oil consumption. The combination of Monte Carlo (MC) simulation method with Artificial Neural Networks (ANNs) is an optimal solution to deal the fuel consumption of marine main diesel engine. The sample data has been established based on the Monte Carlo simulation method. The model of fuel oil consumption is designed by Artificial Neural Network method. The proposed prediction model of fuel oil consumption is based on a back-propagation training algorithm of ANNs method. The research results of proposed model have been verified with the actual operation data that have been collected from a certain bulk carrier of VINIC shipping transportation company in Vietnam. The collected data is the actual operation parameters from the noon-log report of voyage during two years of the ship. The probability model of fuel oil consumption for main diesel engine is very useful in the field of ships energy efficiency management with higher accurancy than the other previous models.

Recently, shipyards have been booming in the product carrier (PC) market of the global shipbuilding industry. Due to the rising market conditions, orders for large container vessels and PCs are also steadily increasing. According to the industry, freight rates for LR tankers (70K) and MR tankers (50K) in the shipbuilding industry have rose to the highest level during the previous year. In order to secure the competitiveness of PCs, various core items are required, and Korean shipbuilders have been leading the market for a long time based on their knowledge of the design and production of low-fuel ships. In recent years, there have been frequent cases of coating cracks in the cargo hold after sea trial. All relevant rules presented by the classification so far are structural design, safety evaluation, and inspection standards, and coating cracks are considered a problem for coating makers. In other words, they must establish a standard coating measure agreed upon by all parties (owner, shipbuilder, and coating maker); therefore, solutions have been proposed by each shipping company. In this study, the causes of coating cracks occurring in the cargo hold of PCs during tank tests in sea trials were analyzed and measures to prevent them were studied. The main point of this study is assumed that coating crack are caused by mechanical load induced by structural behavior, and the numerical analysis methodology is newly introduced. Based on the results of the numerical analysis, it was confirmed that there is a high probability of coating cracks in the critical area where high stress occurs in the cargo hold. Therefore, the results obtained in this study will be useful to prevent coating cracks in future PC designs.

The buoyancy of traditional Underwater Glider (UG) with a rigid hull is affected by the changing marine parameters, making it difficult for the vehicle to dive deeper with good motion characteristics. In the present work, the development of a novel UG for neutral buoyancy, which is equipped with a Flexible-Liquid-Rigid (FLR) composite hull, is presented. The innovative hull that imitates the buoyancy regulation mechanism of marine organisms is easily adaptable to the changing marine environment. The rigid part of the composite hull withstands hydrostatic pressure for inner function modules of the vehicle. The liquid of the composite hull mainly serves as a buoyancy compensation to mitigate the effect of buoyancy variation caused by the changing marine parameters. The flexible covering provides a good hydrodynamic shape for the vehicle under the internal liquid pressure. The buoyancy variation due to the composite hull compression as well as changes of seawater density and temperature was comprehensively considered in the design process and integrated to the dynamic model. In addition, sea trials were performed to verify the motion characteristics of the proposed vehicle. The results reveal that the Flexible-bodied Underwater Glider (FUG) has a better gliding performance in practice.

In this study, a model is developed to simulate the dynamics of an internal combustion engine, and it is calibrated and validated against reliable experimental data, making it a tool that can effectively be adopted to conduct emission predictions. In this work, the Ricardo WAVE software is applied to the simulation of a particular marine diesel engine, a four-stroke engine used in the maritime field. Results from the bench tests are used for the calibration of the model. Finally, the calibration of the model and its validation with full-scale data measured at sea are presented. The prediction includes not only the classic engine operating parameters for a comparison with surveys but also an estimate of nitrogen oxide emissions, which are compared with similar results obtained with emission factors. The calibration of the model made it possible to obtain an overlap between the simulation results and real data with an average error of approximately 7% on power, torque, and consumption. The model provides encouraging results, suggesting further applications, such as in the study on transient conditions, coupling of the engine model with the ship model for a complete simulation of the operating conditions, and optimization studies on consumption and emissions. The availability of the emission data during the sea trial and validated simulation results are the strengths and novelties of this work.

The extreme operational environmental conditions and aging conditions of subsea structures pose a risk to their structural integrity and is critical to their safety. Non-destructive testing is essential to identify defects developing within the structure, allowing repair in a timely manner to mitigate against failures that cause damage to the environment and pose a hazard to human operators. However, in order to be cost effective, inspections must be carried out without taking the risers out of service. This poses significant safety risks if undertaken manually. This paper presents the development of an automated inspection system for flexible risers that are used to connect wellheads on the seafloor to the offshore production and storage facility. Due to the complex structure of risers, radiography is considered as the best technique to inspect multiple layers of the risers. However, radiography inspection in turn requires a robotic system for in-situ inspection with higher payload capacity, precise movement of source and detector which is able to withstand an extreme operational environment. The deployment of a radiography inspection system has been achieved by developing a customized subsea robotic system called RiserSure that can precisely provide the scanning motion of a gamma ray source and digital detector moving in alignment. The prototype has been tested on a flexible riser during shallow water sea trials with the system placed around a riser by a remotely operated vehicle. The results from the trials show that the internal inner and outer tensile armour layer and defects in the riser can be successfully imaged in real operational conditions.

This study investigated a newly developed soft-type turtle releasing device (Soft-TRD) for setnets. Some setnet fish chambers (bag nets) are open at the sea surface, while others are closed (with ceiling netting) and submerged. Sea turtles that stray into submerged bag nets often drown because the ceiling netting prevents them from swimming up to the surface to breathe. The Soft-TRD has a slit that functions as an escape outlet at the center of a 2 m × 2 m netting. The escape slit remains closed, due to the buoyancy of a float attached at each end of the slit, and prevents fish from getting out. Turtles can push the escape slit upward, which opens and allows the turtles to escape. To decide the optimum design of the Soft-TRD, a turtle releasing trial was conducted in an outdoor water tank. Four loggerhead turtles (Caretta caretta) (standard carapace length (SCL): 67.1–72.8 cm) and five green turtles (Chelonia mydas) (SCL: 42.4–63.4 cm) were used for the trial. A single turtle was placed into the experimental bag net where the Soft-TRD (six models in total) was mounted at the center part of the ceiling, and its behavior was observed. All turtles successfully escaped the model with a 150 cm length escape slit, 10 cm overlaying width, and a 4 kgf buoyancy float at each end. The escape slit immediately closed after the turtles escaped. A sea trial (same model) was also performed using a submerged bag net (30 × 10 × 10 m) for two months. Approximately 90% (seven of the eight) of the entrapped loggerhead turtles successfully escaped out through the Soft-TRD. Fishing was not interrupted by the introduction of this device. The use of the Soft-TRD would be helpful in releasing sea turtles from the setnets and contribute to sea turtle conservation efforts.

Underwater acoustic target recognition based on ship-radiated noise is difficult owing to the complex marine environment and the interference by multiple targets. As an important technology for target recognition, deep-learning has high accuracy but poor interpretability. In this study, an attention-based neural network (ABNN) is proposed for target recognition in the pressure spectrogram with multi-source interference using an attention module to inspect the inner workings of the neural network. From data obtained during a September 2020 sea trial, the ABNN exhibited a gradual focus on the frequency-domain feature of the target ship and suppressed environmental noises and marine vessel interference, which led to high accuracy in the target detection and recognition.

Underwater autonomous navigation technology is a key link for AUV to complete various underwater missions successfully. In this paper, we have proposed a position correction model based on hybrid gated RNN, which does not need to establish a motion model like typical navigation algorithms, to avoid modeling errors in the navigation process. According to the low update rate of GPS and the slip data caused by the coverage of GPS antenna during the process of floating and diving, it does not meet the actual requirements of navigation and control. We proposed to use improved EKF and iSAM as the output of the training dataset to filter out GPS slips, then the position error is corrected at the same time. First, the normalized input sequence is fed to the two-layer hybrid gated RNN with BiLSTM output mode as sequence and LSTM output mode as last, and then the processed data is fed to the two fully connected layers with a dropout layer added in the middle. After that, the output of the position correction model can be obtained through the regression layer. To confirm the validity of the proposed model, we conducted sea trials to compare the proposed model with EKF and iSAM. The experimental results prove that the proposed position correction model performs better than EKF and iSAM in all aspects, and the correction model trained with iSAM is more effective than that trained with EKF.

The article discusses the main methodologies and key technologies behind the ROBUST project, funded by the European Union within the Horizon 2020 framework, whose goal is the development of an autonomous underwater vehicle manipulator system for deep sea mining exploration. This article focuses on the developed control and perception framework and related mechatronics, and presents the experimental results from the sea trials in Sardinia, Italy, supporting the proposed methodologies.

The underwater glider is a new type of unpowered, unmanned, moving observational platform with advantages of low-noise level, long operation time, far sustainable range, and high cost-effectiveness. In the paper, based on the underwater glider platform integrated with single vector acoustic sensor, an underwater acoustic glider platform is developed with the ability to detect target direction and observe the ambient noise. The acoustic measurement system and the self-noise of the glider platform under each working condition are tested to analyze the self-noise levels of the acoustic system and the primary noise sources of the platform, and conduct the vibration and noise reduction processing of the platform and optimize the working mode of the acoustic system. The test result shows that the underwater acoustic glider with the optimization has the ability to observe the ambient noise only on the pressure hydrophone channel. With the data sampled from one of the underwater gliders of the sea trial organized in a certain area of the South China Sea in August 2019, authors analyze the variation of spectrum levels of the ambient noise with the depth and the time at the center of seven frequency points (63 Hz, 100 Hz, 200 Hz, 400 Hz, 800 Hz, 1.6 kHz and 3.15 kHz) and discuss the influence of the sailing vessel close to the glider on it. The experimental result shows that the underwater acoustic glider, as an unscrewed moving platform, can be well used to monitor the ambient noise properties over a long term.

A novel system was developed to deploy settlement panels to monitor biofouling growth in situ and evaluate antifouling coatings at depths representative of operational conditions of full-scale marine renewable energy devices. Biofouling loading, species diversity, and succession were assessed at depths ranging from 25-40 m at four tests sites in Orkney (UK) featuring extreme wave and tidal current exposure to more sheltered conditions. Evaluations were carried out over a period of 8 months with intermediate retrieval of samples after 3 months. Early pioneer fouling communities, comprised of colonial hydroids, were succeeded by tube-forming amphipods across sites while solitary tunicates dominated in greater shelter. The highest biofouling loading was observed on high-density polyethylene (HDPE) panels (6.17 kg m-2) compared with coated steel (3.34 kg m-2) panels after 8 months. Distinct assemblages were present at exposed vs sheltered sites. Better understanding of fouling and antifouling strategies may provide guidance to more effectively manage biofouling impacts in this sector.

This work indicates two novel ocean current observation methods that based on a glider type mobile platform. Glider with current meters are ideal current observation solutions for marine scientists since they have merits of large dynamic area and low power consumption, but their measurement accuracy is insufficient now. The proposed 1st solution, single point current meter compensated inversion method (SPCMCIM), is a revolution of the conventional inverse method by solving the acoustic Doppler to current profiler (AD2CP) induced turbulence problem. It’s a cooperated AD2CP and single point current meter (SPCM) algorithm that compensates the contaminated first AD2CP layer data. The physical position of the SPCM is also optimized according to a finite element analysis (FEA) study to minimize the interference generated by the SPCM. The 2nd method is solving the area restriction problems that AD2CP can just apply to non-pure water environment namely single point and inertial measurement inversion method (SPIMIM). It utilizes the SPCM as the only current observation source and takes advantages of physical properties of ocean currents. The currents are constitute of stable layers and each of them has a stable velocity, so the glider’s velocity variation can be obtained by reading the SPCM. The velocity variation in the transition process between layers can be acquired by the low cost inertial sensors. Finally, these theoretical studies were verified in an onsite sea trial. The SPCM and AD2CP was mounted to an underwater glider to measure the currents and a ship equipped AD2CP was used to provide the arbitrary data. Both of the proposed method can effectively measure the current velocity and induced significant enhancement than the classical inversion method. In particular, the first method conducted improvement of 70% accuracy by average. The 2nd one also shows an obvious improvement and has the advantage to deploy to high purity water area like deep lakes or north pole.

Doppler Velocity Logs (DVL) is an important information source for underwater vehicle navigation because it can provide accurate velocity information. As the main system, SINS/DVL integrated navigation is widely used in the underwater vehicle. However, DVL scale factor error, and misalignment with inertial measurement unit (IMU) are the two sources of integration inaccuracy. A novel calibration method aided GNSS is proposed in this paper, which is designed based on velocity observation and position observation respectively. The proposed method combines the Quasi-Newton method and Quaternions method, and we name it Quasi-Newton Quaternion (QNQ) calibration method. In order to verify the performance of the proposed method, another calibration method based on the Kalman filter is introduced in simulation, vehicle tests, and Sea trial. The test results indicate that the proposed calibration method can perform a good estimation of DVL error.

Underwater glider (UG) is widely applied for long-term ocean observation, the gliding range of which is mainly influenced by its design. In this paper, the design parameters that have obvious influence on the gliding range, including the buoyancy factor, compressibility of the pressure hull, hydrodynamic coefficients, and motion parameters, are selected based on the gliding range model of UG. Due to their complicate coupling relationship in the design of the UG, the multidisciplinary optimization (MDO) design framework integrating the collaborative optimization (CO) method and approximate model technology is adopted to optimize the key parameters by taking the gliding range as the optimization target. The results show that the optimization leads to an increase of the gliding range of Petrel-L as much as 83.3% when the hotel load is 0.5 W, which is verified by the sea trial. The optimization is applicable to other types of underwater gliders.

One of the challenges of Internet of Underwater Things (IoUT) is the design of nodes for collecting information from underwater, with features of low cost, long-term, long range, voyage routing, and real-time communications. Wave glider, has shown great potential acting as IoUT nodes through its persistent, long range travelling, and flexibility underwater. In this paper, we propose an architecture of Internet of Underwater Things, involving multiple wave gliders as nodes for acoustical observation. We present target localisation method via acoustical observation of nodes with multiple wave gliders deployed underwater, by which precision of bearing estimation of each node is required to achieve high precision of localisation. With the data collected, we apply a compensation method of bearing estimate when the hydrophone array is rotating during the observation. The feasibility of acoustical observation of wave gliders has been validated through both simulation and sea trial data, which is of great potential to be nodes for constructing IoUT.

The presence of rain may blur surface wave signatures and cause additional radar backscatter, which negatively affects the performance of ocean remote sensing applications (e.g., ocean surface wind and wave parameter measurement) using X-band marine radars. In this article, a novel end-to-end model is developed to detect and locate rain-contaminated pixels in X-band marine radar images based on a type of deep neural network called SegNet, which is able to segment rain-contaminated regions by classifying each pixel into three classes: rain-free, rain-contaminated, and wind-dominated rain cases. Shipborne marine radar images collected during a sea trial on the East Coast of Canada are first preprocessed and then utilized to train an ensemble of SegNet-based networks. The final classification result of each pixel will be the class chosen by most individual networks. Testing results using images obtained from both shipborne and shore-based marine radar systems manifest that the proposed model effectively segment between rain-free, rain-contaminated, and wind-dominated rain regions, with a pixel classification accuracy of 94.6% and 90.4% for Decca and Koden radar images, respectively.

A fundamental property of most sea conditions is that sets of large waves alternate with sets of smaller waves, known as quiescent periods. Exploiting the quiescent periods can expand the sea states under which the execution of many maritime operations can be considered safe. Building upon the work of Belmont et al. , we extend the methodology to fully describe the statistical distribution of the quiescent periods in terms of their time duration using sea spectra derived from current ship readings as well as metocean forecast. This provides the basis for a short- or long-term planning tool that complements deterministic wave prediction technology for executing wave-height-limited maritime operations. The method is evaluated using spectra calculated from field measurements in a dedicated sea trial and from standard parameterized oceanographic forms (JONSWAP and Pearson–Moskowitz).

It is important to verify that the contracted speed–power performance of a ship is satisfied in sea trials. International Organization for Standardization (ISO) has published the procedure for measuring and assessing ship speed during sea trials. The results obtained from actual sea conditions inevitably include various uncertainty factors. In this study, double run tests were performed on one container ship to analyze the uncertainty of sea trial on three maximum continuous rating conditions. The uncertainty factors and scale of uncertainty were examined based on the measured raw data during sea trial. The results indicate that the expanded uncertainty for ideal power performance is approximately ±1.4% at 95% confidence level (coverage factor k = 2) and most of the uncertainty factors were because of the shaft power measurement system.

Fronts between Arctic- and Atlantic-origin waters are characterized by strong lateral gradients in temperature and salinity. Ocean processes associated with fronts are complex with considerable space and time variability. Therefore, resolving the processes in frontal zones by observation is challenging but important for understanding the associated physical–biological interactions and their impact on the marine ecosystem. The use of autonomous robotic vehicles and in situ data-driven sampling can help improve and augment the traditional sampling practices, such as ships and profiling instruments. Here, we present the development and results of using an autonomous agent for detection and sampling of an Arctic front, integrated on board an autonomous underwater vehicle. The agent is based on a subsumption architecture implemented as behaviors in a finite-state machine. Once a front is detected, the front tracking behavior uses observations to continuously adapt the path of the vehicle to perform transects across the front interface. Following successful sea trials in the Trondheimsfjord, the front-tracking agent was deployed to perform a full-scale mission near 82 $^{\circ}$ N north of Svalbard, close to the sea ice edge. The agent was able to detect and track an Arctic frontal feature, performing a total of six crossings while collecting vertical profiles in the upper 90 m of the water column. Measurements yield a detailed volumetric description of the frontal feature with high resolution along the frontal zone, augmenting ship-based sampling that was run in parallel.

A joint multiple-input multiple-output (MIMO) processing framework is proposed to exploit spatial diversity and moving cues for the enhancement of target detection in a shallow ocean environment. Orthogonal signals are transmitted to illuminate different aspects of a target and beamforming operation is carried out over the received data for estimating target bearing. The target range is achieved by a replica correlation integrator where the beamformer outputs are matched with transmitted signals. After meanshift clustering algorithm is carried out over the bearing-range spectrums to generate the clutter centers, the potential trajectories of targets can be tracked by an improved Bernoulli filter with inputs of these centers. The at-sea experimental results have shown the effectiveness of the joint processing framework in MIMO detection of moving targets.

As well known, ships which have complex production processes are subject to various tests made on every stage in many fields from the beginning to the end of the production. After the tests are completed successfully, the ship is delivered to the ship-owner. “Sea trial” being the last stage of these tests, is examined in detail in this study. The purpose of this study is to plan the tests performed in the sea trial by the means of computer programs and to suggest shorter completion period for the tests.Thus, reducing the total cost ofthe cruising. Moreover, shortening the duration ofthe cruise will be a factor that can speed up the delivery ofthe ship. For this purpose, the tests and processes performed during the sea trial are listed. A cruise process flow diagram including all the tests applied under normal conditions was created, and the data were entered into the SIMIO simulation program. As a result, it was determined that the total cruising time was 28,0989 hours. After that, a new flow diagram was created by making some improvements in the current testing process, and a new simulation model was built up. In the new simulation model, total time spent to complete the tests were 25,3567 hours, so the testing time was shortened by 2.75 (9,76%) hours.

The calculation of total resistance value is important of ship because affects the speed of fluid flow that occurs, as well as the speed of the ship.So that in the designing new ships, sea trials are needed to find out whatever the preparedness of the ship is planned. The purpose is to obtain the value of the total resistance and the coefficient of ship resistance KM. Kendhaga Nusantara 6 uses the calculation method for each ship draught/loading condition. The Method used is experimental method with numerical value approach, Denny Mumford theory and Froude's number. The results show that the largest Total Resistance (Rt) is 5646,02 kN, it was found when speed of ship was 12 knots and draught ship 3,5 meters, with a coefficient value (Ct) of 7,757 x 10-3. The difference in value (Ct) is 0,032x10-3 or 0.41%, and it is still in the criteria because it is below 5%.

To resolve the severe wear problems of steering engine and fin stabilizer, the nonlinear response characteristics of steering engine and fin stabilizer are studied by analyzing sea trial data of ship Yukun. The nonlinear response characteristics are summarized and applied to a rudder and fin hybrid control system. Decoupling, $$\hbox {H}_\infty$$ and $${\rm H}_2$$ controllers are designed to achieve low control frequency for it. Simulations show that all the three controllers can achieve excellent course-keeping and roll damping performances, and their control frequencies and amplitudes are consistent with those of nautical practice. It can be found that $$\hbox {H}_2$$ controller achieves the best response performance of course-keeping and energy saving performance for rudder engine; $$\hbox {H}_\infty$$ controller achieves the best response performance for roll damping; Decoupling controller has the best energy saving performance for fin stabilizer. When the Beaufort wind force scale is increased from 6 to 8 and model perturbation is involved, $$\hbox {H}_2$$ controller shows the best robustness of control performance and control input. In addition, the designed low-order controllers can reduce hardware cost for autopilot realization in nautical field.

Small-scale ocean observations play an important role in oceanographic research. The station-keeping method based on the underwater mobile platform has been proposed as a means of small-scale ocean observation with the advantages of low cost and high flexibility. However, few studies have carried out complete modeling of the small-scale ocean observation process of mobile platforms and proposed a corresponding station-keeping method based on this. In this article, a hybrid underwater profiler (HUP) was tested under two working conditions for its ability of maintaining position during designated area persistent monitoring (DAPM) within a specific area of the sea over a long period. The DAPM problem is modeled and the cost function that evaluates the performance of DAPM is defined. If the underwater profiler cannot measure data about underwater currents, it can maintain its position by setting constant gliding and yaw angles based on the depth-averaged current measured each time it surfaces. If it has instruments to observe ocean currents at each depth during a dive, the profiler can maintain its position using a strategy based on a quasi-Newton method. Simulation results show that the gliding strategies effectively assist the HUP in monitoring a designated area. Additionally, sea trials were performed without instrumentation to measure ocean currents and the effectiveness of the simpler gliding strategy was clearly demonstrated.

Various methods have been developed and used for monitoring marine benthic habitats, such as coral reefs and seagrass meadows. However, the efficiency of general survey methods [e.g., line intercept transects and autonomous underwater vehicles (AUVs)] still is not high. In this article, we propose a practical coral-coverage estimation method combining an effective survey system [Speedy Sea Scanner (SSS)] and a deep-learning-based estimation method. The SSS is a towed-type system with six cameras arrayed on the platform. The depth rating of the system in our trial was 50 m. The length of the array baseline was 4.4 m, and six cameras were placed on the platform with equal spacing. The sea trial was conducted at Kujuku-Shima, Japan, on September 30, 2017. We successfully generated 3-D models and high-quality orthophotos of the seafloor with high resolution of about 1.5 mm/pixel. The survey efficiency of the SSS was about 7000 m2/h. In addition, the experimental results of coral-coverage estimation showed that the corals can be distinguished with accuracy of about 80% in places with relatively high transparency, and the error of coverage estimation was 10% or less. The proposed coral-coverage estimation method is more efficient than other survey techniques and costs less than AUV surveying; therefore, it is expected to become a promising tool for marine environmental surveying.

The prediction of water current (WC) is key in understanding oceanographic phenomena, and serves as a corner stone for maritime applications. While numerical models are capable to characterize the main phenomenon effecting the current, a good estimate of the current’s velocity requires accurate environmental information such as topographic maps, temperature, and wind velocity. Without such information, common practice involves deploying a large number of drifting devices to measure the WC in good resolution. In this paper, we purpose a statistical approach to predict the WC’s velocity field, namely, the relation between the drifters’ locations and their drifting velocity to estimate the velocity of the WC across a given environment. We offer two statistical approaches. Assuming the above relation is linear, the first uses a weighted least squares to estimate the parameters of the velocity field. The second uses a support vector regression to predict the WC by a classifier. The first approach is more traceable, while the second can manage a non-linear space-speed relation of the WC. Results are studied numerically using an WC model, and experimentally from a sea trial performed across the shores of San Diego including 13 acoustically localized submerged drifters. In both cases, performances show a good agreement between the ground truth of the WC and the predicted one.

In this paper, a sliding mode-based guidance strategy is proposed for the control of an autonomous vehicle. The aim of the autonomous vehicle deployment is the study of unknown environmental spatial features. The proposed approach allows the solution of both boundary tracking and source-seeking problems with a single autonomous vehicle capable of sensing the value of the spatial field at its position. The movement of the vehicle is controlled through the proposed guidance strategy, which is designed on the basis of the collected measurements without the necessity of preplanning or human intervention. Moreover, no a priori knowledge about the field and its gradient is required. The proposed strategy is based on the so-called suboptimal sliding mode controller. The guidance strategy is demonstrated by computer-based simulations and a set of boundary tracking experimental sea trials. The efficacy of the algorithm to autonomously steer the C-Enduro surface vehicle to follow a fixed depth contour in a dynamic coastal region is demonstrated by the results from the trial described in this paper.

Typically, the dominant load for the design of planing hulls is slamming, while sailing fast at head seas. Slamming is a violent fluid-structure interaction, where dynamics, hydro-elasticity, and n...

This paper deals with modelling ship speed and fuel consumption using artificial neural network (ANN) techniques. These tools allowed us to develop ANN models that can be used for predicting both the fuel consumption and the travel time to the destination for commanded outputs (the ship driveline shaft speed and the propeller pitch) selected by the ship operator. In these cases, due to variable environmental conditions, making decisions regarding setting the proper commanded outputs to is extraordinarily difficult. To support such decisions, we have developed a decision support system. Its main elements are the ANN models enabling ship fuel consumption and speed prediction. To collect data needed for building ANN models, sea trials were conducted. In this paper, the decision support system concept, input and variables of the ship driveline system models, and data acquisition methods are presented. Based on them, we developed appropriate ANN models. Subsequently, we performed a quality assessment of the collected data set, data normalization and division of the data set, selection of an ANN model architecture and assessment of their quality.

To solve the beamforming performance degradation problem of the towed linear array caused by the shape distortion, we propose a robust beamforming way for distorted towed linear array. The proposed method basically uses the segmented processing, which can be divided into 3 steps. Firstly, towed linear array is divided into sub-arrays. Then, the weights of sub-arrays are obtained and outputs of beamforming of sub-arrays are obtained. In the end, according to the principle of the product theorem, the correlation calculations are performed on the output of the sub-array. Due to the adoption of sub-array correlation calculations, the proposed method can upgrade beamforming performance. Compared with the traditional distortion array processing method, the proposed method can improve the local signal-to-interference plus noise ratio (SINR) by about 2–3 dB. Simulation and sea trial data verify the effectiveness of the proposed method.

Sea trial tests are necessary to verify speed-power performance, and are an import contract between ship owners and shipyards. The International Organization for Standardization (ISO) published ISO 15016:2015, which specifies the correlation method between model and full-scale ships. The results of sea trials have been questioned because of the uncertainty of speed and power measurements, especially when sea conditions differ from ideal calm water conditions. In this paper, such uncertainties were investigated by utilizing the standard speed-power trial analysis procedure defined in ISO 15016:2015 through Monte Carlo simulations. It was found that the expanded uncertainty of the delivered power (PDid) at 95 % confidence interval (k = 2) was ±1.5 % under 75 % MCR conditions.

The study of extending navigation range of autonomous underwater vehicles (AUVs) is popular for ocean exploration. A common method is to use the underwater docking station to recover and recharge AUVs. This paper uses the simple but effective inductive power transmission (IPT) system to realize underwater wireless charging for an AUV docking system. A design procedure is provided to select the optimal underwater operating frequency of the IPT system in consideration of eddy-current loss in seawater. To solve the power failure problem of the control power system, an inrush current limiter (ICL) is proposed on the basis of the dynamic model of the IPT system. The test results in the laboratory experiment and sea trial both verify the advantages of the frequency design method and the effectiveness of the ICL.

Since rain alters the histogram pattern of radar images, rain-contaminated radar data can be identified. In this article, a support vector machine (SVM)-based method for rain detection using X-band marine radar images is presented. First, the normalized histogram bin values for each image are extracted and combined into feature vector. Then, SVMs are employed to classify between rain-free and rain-contaminated images. Radar images and simultaneous rain rate data collected from a sea trial in North Atlantic Ocean are utilized for model training and testing. Comparison with the zero pixel percentage (ZPP) threshold method shows that the SVM-based method obtains higher detection accuracy, with 98.4% for the Decca radar data and 99.7% for the Furuno radar. It is also found that as the total number of bins does not significantly affect detection accuracy, the proposed method can be applied to different radar systems directly with a suitable number of bins. In addition, compared to the ZPP threshold method, the SVM-based method proves to be more robust even with limited training samples.

The colonization of underwater environments by exotic seaweeds is causing major ecological problems around the world. This project, referred to AMALIA, aims to transform this current ocean threat into an opportunity by adding value to the macroalgae present off the northwest of the Iberian Peninsula. To do so and to observe the presence of seaweeds in situ, an ocean modular submersible platform was developed. This platform was designed to be capable of detecting and surveying surges of invasive seaweeds while withstanding sea conditions. Conceptual designs followed by a screening process were performed, taking into consideration criteria such as operational range and modularity. An open-frame lander was considered and further developed using buckling criteria. In parallel, a state-of-the-art monitoring system was created using spectral imaging, allowing for the future creation of a macroalgae identification system. In addition, sensorial systems for characterizing growth conditions were introduced. Laboratory trials were executed to assess the capability of the system, and sea trials are currently being performed. Numerical simulations and laboratory trials indicate that the structure is fully capable of being deployed for shallow-water environments with a state-of-the-art invasive seaweed monitoring system while maintaining a high degree of modularity.

The research addresses the problem of an ultra-large container ship mathematical model adjustment based on sea trials. In order to verify the model’s adequacy, simulated data had to be compared to the trial report data, which was obtained in ballast condition with significant trim. In such circumstances, model coefficients cannot be calculated by known methods and have to be corrected as per trial data. It is proposed to determine translational motion coefficients first. To get optimal results, it was also proposed to divide the objective function into kinematic and dynamic components, with each component being assigned a weighting factor. A separate objective function component was assigned to the zig-zag maneuver, which includes the first and second overshoot angles.

This paper proposes a method that synthesizes realistic sonar images using a Generative Adversarial Network (GAN). A ray-tracing-based sonar simulator first calculates semantic information of a viewed scene, and the GAN-based style transfer algorithm then generates realistic sonar images from the simulated images. We evaluated the method by measuring the similarity between the generated realistic images and real sonar images for several objects. We applied the proposed method to deep learning-based object detection, which is necessary to automate underwater tasks such as shipwreck investigation, mine removal, and landmark-based navigation. The detection results showed that the proposed method could generate images realistic enough to be used as training images of target objects. The proposed method can synthesize realistic training images of various angles and circumstances without sea trials, making the object detection straightforward and robust. The proposed method of generating realistic sonar images can be applied to other sonar-image-based algorithms as well as to object detection.

Autonomous underwater vehicles (AUVs) are programmable, robotic vehicles that can drift, drive, or glide through the ocean without real-time control by human operators. AUVs that also can follow a planned trajectory with a chosen depth profile are used for geophysical surveys, subsea pipeline inspection, marine archaeology, and more. Most AUVs are followed by a mother ship that adds significantly to the cost of an AUV mission. One pathway to reduce this need is to develop long-endurance AUVs by improving navigation, autonomy and energy storage. Long-endurance AUVs can open up for more challenging mission types than what is possible today. Fuel cell systems are a key technology for increasing the endurance of AUVs beyond the capability of batteries. However, several challenges exist for underwater operation of fuel cell systems. These are related to storage or generation of hydrogen and oxygen, buoyancy and trim, and the demanding environment of the ambient seawater. Protecting the fuel cell inside a sealed container brings along more challenges related to condensation, cooling and accumulation of inert gases or reactants. This paper elaborates on these technical challenges and describes the solutions that the Norwegian Defence Research Establishment (FFI) has chosen in its development of a fuel cell system for long-endurance AUVs. The reported solutions enabled a 24 h demonstration of FFI's fuel cell system under water. The remaining work towards a prototype sea trial is outlined.

Biofouling results in a range of adverse issues for ships and boats such as an increase in hydrodynamic drag force and fuel consumption and increased maintenance cost. To address this issue, toxic antifouling coatings have been developed. However, these toxic coatings can pose threats to marine life. This has led to research on less harmful techniques such as acoustic methods in the main implemented as laboratory trials. In fact, there have been relatively few sea trials and these are poorly documented. The current work has performed one of the few implemented field trials and the only one to be fully documented. In this sea trial, an optimised array of transducers generating ultrasound guided waves was attached to a hull representative plate in a port environment. The results of this work are evidenced by the significant reduction in-situ of a wide range of biofouling materials and organisms. The documented photographical evidence makes up for its scarcity in the published records of antifouling advances using acoustic techniques.

For enhancement of oceanic engineering technology and researchers, underwater robot competition has been held since 2016. Seventh competition in this year consists AUV league that university's vehicles automatically cruise at field and junior league that underwater craft is made. The paper reports competition regulations of AUV and junior league and results of the competition held in October 2019., The 2020 International Conference on Artificial Life and Robotics (ICAROB 2020), January 13-16, 2020, B-Con Plaza, Beppu, Oita

Using underwater gliders to measure underwater acoustic signals is a new measurement method emerging with the development of platform technology. Although underwater gliders are relatively quiet due to the absence of propellers, vehicle noise generated during motion is still inevitable, which will affect the recorded acoustic data. In this paper, we analyze the self-noise characteristics of underwater gliders based on simulated data by CFD technology for the hydrodynamic flow noise and experimental data acquired in an anechoic-water-tank experiment for the mechanical noise. The mechanical noise covers noises generated by buoyancy regulating (increasing and reducing), pitch regulating, rudder regulating and CTD pump working. According to the analysis results, the flow noise and CTD pump working noise could be ignored for the experimental data processing of sea trials. An experiment was conducted with an acoustic Sea-Wing underwater glider in the South China Sea from July 31 to September 4, 2018. Two kinds of noisy data were recorded, including target signals and ambient noise. All the target signals could be recognized after convolution filtering, except during the buoyancy regulating periods due to the high noise spectrum level. For the recorded ambient noise, in addition to the buoyancy regulating noise, the rudder and pitch regulating noises affected the recorded data. Then based on the acquired knowledge, a joint convolution filtering and thresholding method is proposed to remove the rudder and pitch noises from recorded noisy data. Kernels extracted from data acquired in the anechoic-water-tank experiment are used in the convolution filtering to localize each regulating action and energy thresholding is adopted to determine the duration of each regulation. All the rudder and pitch noises are removed from the recorded noisy data.

The autonomous under water vehicle (AUV) system uses sensors which have a very important role in this field, even work in very complex environment. In order to improve the reliability of the AUV systems it important to know about the sensor failure diagnostic technology. In this paper two methods has been proposed they are combining phase space reconstruction and extreme learning machine(ELM).They are mostly being used to predict the sensor output in order to achieve the sensor fault diagnosis for AUVS. This stimulation experiments based on sea trial data shows the proposed method can be diagnosed by the sensor faults and recover the signals after the faults occur in a period of time