Journal of Marine Science and Engineering

💡 #Newpaper in 2024 #@Ca’ Foscari University of Venice #@Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici 🌊 Title: Bayesian Network Analysis for #ShorelineDynamics, Coastal #WaterQuality, and Their Related Risks in the Venice Littoral Zone, Italy 🔑 Keywords: #multiriskassessment; #sealevel rise; #shoreline change; #climatechangeadaptation 🔗 paper link: https://lnkd.in/dwrpbqGs 📜 Abstract： The coastal environment is vulnerable to natural hazards and human-induced stressors. The assessment and management of coastal risks have become a challenging task, due to many environmental and socio-economic risk factors together with the complex interactions that might arise through natural and human-induced pressures. This work evaluates the combined effect of climate-related stressors on low-lying coastal areas by applying a multi-risk scenario analysis through a Bayesian Network (BN) approach for the Venice coast. Based on the available open-source and remote sensing data for detecting shoreline changes, the developed BN model was trained and validated with oceanographic variables for the 2015–2019 timeframe, allowing us to understand the dynamics of local-scale shoreline erosion and related water quality parameters. Three “what-if” scenarios were carried out to analyze the relationships between oceanographic boundary conditions, shoreline evolution, and water quality parameters. The results demonstrate that changes in sea surface height and significant wave height may significantly increase the probability of high-erosion and high-accretion states. Moreover, by altering the wave direction, the water quality variables show significant changes in the higher-risk class. The outcome of this study allowed us to identify current and future coastal risk scenarios, supporting local authorities in developing adaptation plans.

💡 #Newpaper in 2024 #@Universidade de Lisboa #@Technical University of Varna 🌊 Title: Carbon Intensity Assessment of a Bulk Carrier Operating in Different Sea State Conditions 🔑 Keywords: #shipoperation; #GHGemissions; #operationalcarbonintensityindicator; #shippropulsion system; #inverseFORM 🔗 paper link: https://lnkd.in/dd-AByV7 📜 Abstract：This work uses the environmental contour line approach to estimate the long-term extremes of carbon emission generated by a bulk carrier operating in different sea state conditions, utilising short-term analyses of the ship propulsion energy efficiency as a function of hull resistance in calm water due to appendages, aerodynamic resistance, and added wave resistance, resulting in the required permanent delivered power and the one induced by the waves. The analysis accounts for the ship’s main characteristics, operational profile based on mission conditions, and wave climatic data. All sources of inherent uncertainties are accounted for through the variability in the 3 h extreme value in any sea state in the long term, and the inverse first-order reliability method (IFORM) is employed in predicting the extreme operational carbon intensity indicator (CII). This study develops proper wave scatter diagrams as a function of the route description. The CII measures the energy efficiency of the installed propulsion system, accounting for the ship’s operational characteristics, such as the annual fuel consumption with corresponding CO2 factors, annual distance travelled, and capacity. The present study is limited to one operation route but can be extended to any other possible voyage or sea area. The estimated CII defined from the complete probabilistic characterisation of the sea state conditions conditional to the short-term maximum response is a rational approach that can be used for optimising the ship’s main characteristics, propulsion system, operational profile, and chosen route to achieve the best ship performance and energy efficiency.

💡 #Newpaper2024 #@National Key Laboratory of Science and Technology on Underwater Acoustic Antagonizing #@Shanghai Marine Electronic Equipment Research Institute #Shanghai Jiao Tong University 🌊 Title: A Low-Cost and High-Precision Underwater Integrated Navigation System 🔑 Keywords:#MEMS IMU; #Kalmanfilter #underwaterintegratednavigationsystem 🔗 paper link: https://lnkd.in/guZ3ds7T 📜 Abstract：The traditional underwater integrated navigation system is based on an optical fiber gyroscope and Doppler Velocity Log, which is high-precision but also expensive, heavy, bulky and difficult to adapt to the development requirements of AUV swarm, intelligence and miniaturization. This paper proposes a low-cost, light-weight, small-volume and low-computation underwater integrated navigation system based on MEMS IMU/DVL/USBL. First, according to the motion formula of AUV, a five-dimensional state equation of the system was established, whose dimension was far less than that of the traditional. Second, the main source of error was considered. As the velocity observation value of the system, the velocity measured by DVL eliminated the scale error and lever arm error. As the position observation value of the system, the position measured by USBL eliminated the lever arm error. Third, to solve the issue of inconsistent observation frequencies between DVL and USBL, a sequential filter was proposed to update the extended Kalman filter. Finally, through selecting the sensor equipment and conducting two lake experiments with total voyages of 5.02 km and 3.2 km, respectively, the correctness and practicality of the system were confirmed by the results. By comparing the output of the integrated navigation system and the data of RTK GPS, the average position error was 4.12 m, the maximum position error was 8.53 m, the average velocity error was 0.027 m/s and the average yaw error was 1.41°, whose precision is as high as that of an optical fiber gyroscope and Doppler Velocity Log integrated navigation system, but the price is less than half of that. The experimental results show that the proposed underwater integrated navigation system could realize the high-precision and long-term navigation of AUV in the designated area, which had great potential for both military and civilian applications.

💡 #Newpaper in 2024 #Shanghai Maritime University 🌊 Title: A Proactive-Reactive-Based Approach for Continuous Berth Allocation and Quay Crane Assignment Problems with Hybrid Uncertainty 🔑 Keywords: #berthallocation and #quaycraneassignment; #vesseldelay; proactive-reactive; local search; #geneticalgorithm 🔗 paper link: https://lnkd.in/g4f5KWsD 📜 Abstract： Port operations have been suffering from hybrid uncertainty, leading to various disruptions in efficiency and tenacity. However, these essential uncertain factors are often considered separately in literature during berth and quay crane assignments, leading to defective, even infeasible schedules. This paper addressed the integrated berth allocation and quay crane assignment problem (BACAP) with stochastic vessel delays under different conditions. A novel approach that combines both proactive and reactive strategies is proposed. First, a mixed-integer programming model is formulated for BACAP with quay crane maintenance activities under the ideal state of no delay. Then, for minor delays, buffer time is added to absorb the uncertainty of the arrival time of vessels. Thus, a robust optimization model for minimizing the total service time of vessels and maximizing the buffer time is developed. Considering that the schedule is infeasible when a vessel is seriously delayed, a reactive model is built to minimize adjustment costs. According to the characteristics of the problem, this article combined local search with the genetic algorithm and proposed an improved genetic algorithm (IGA). Numerical experiments validate the efficiency of the proposed algorithm with CPLEX and Squeaky Wheel Optimization (SWO) in different delay conditions and problem scales. An in-depth analysis presents some management insights on the coefficient setting, uncertainty, and buffer time.

💡 #Newpaper2024 #UniversidadeUniversidade de Aveiro #@Lusofona University #@University of the Algarve 🌊 Title: Review of the Quantification of #AeolianSedimentTransport in Coastal Areas 🔑 Keywords: #coastaldune dynamics; #numericalmodels; #fieldmeasurement techniques; #vegetation and #fencing; #climatechange impacts 🔗 paper link: https://lnkd.in/gXY6Ggzh 📜 Abstract：Coastal dunes, formed and shaped by aeolian sediment transport, play a crucial role in ecosystem services and act as natural flood and coastal erosion defenses. This paper delves into theoretical equations and numerical models predicting sediment transport. Numerical models like cellular automata, XBeach-DUNA, the coastal dune model, and others are analyzed for their ability to simulate dune morphology, erosion processes, and vegetation impacts accurately. Evaluated are field observation and measurement techniques, such as sand traps, impact sensors, and optical sensors, for their precision in quantifying aeolian dynamics. Further examined is the effectiveness of vegetation and fencing in dune stabilization, noting species-specific responses and the influence of fence design on sediment accumulation. These tools offer insights into optimizing aeolian sediment management for coastal protection. By conducting a systematic review and connecting theoretical, empirical, and modeling findings, this study highlights the complex challenge of measuring and managing aeolian sediment transport and proposes integrated strategies for enhancing coastal dune resilience against the backdrop of climate change and erosion. This study’s objectives to bridge gaps in current understanding are met, highlighting the need for a multidisciplinary approach to coastal dune management and conservation, especially combining wind- and wave-driven processes.

💡 #Highcitedpaper #NorthwesternNorthwestern Polytechnical University 🌊 Title: A Lightweight Network Model Based on an Attention Mechanism for Ship-Radiated Noise Classification 🔑 Keywords: #underwateracoustictargetrecognition; #ship-radiated #noise; #deeplearning; #residualnetwork; #attentionmechanism; #delta-spectral and double-delta #spectralcoefficients 🔗 paper link: https://lnkd.in/gbssc3gk 📜 Abstract：Recently, deep learning has been widely used in ship-radiated noise classification. To improve classification efficiency, avoiding high computational costs is an important research direction in ship-radiated noise classification. We propose a lightweight squeeze and excitation residual network 10 (LW-SEResNet10). In ablation experiments of LW-SEResNet10, the use of ResNet10 instead of ResNet18 reduced 56.1% of parameters, while the accuracy is equivalent to ResNet18. The improved accuracy indicates that the ReLU6 enhanced the model stability, and an attention mechanism captured the channel dependence. The ReLU6 activation function does not introduce additional parameters, and the number of parameters introduced by the attention mechanism accounts for 0.2‰ of the model parameters. The 3D dynamic MFCC feature performs better than MFCC, Mel-spectrogram, 3D dynamic Mel-spectrogram, and CQT. Moreover, the LW-SEResNet10 model is also compared with ResNet and two classic lightweight models. The experimental results show that the proposed model achieves higher classification accuracy and is lightweight in terms of not only the model parameters, but also the time consumption. LW-SEResNet10 also outperforms the state-of-the-art model CRNN-9 by 3.1% and ResNet by 3.4% and has the same accuracy as AudioSet pretrained STM, which achieves the trade-off between accuracy and model efficiency.

💡 #Highcitedpaper #The Hong Kong Polytechnic University #Shanghai Jiao Tong University #Shanghai University 🌊 Title: Green Technology Adoption and Fleet Deployment for New and Aged Ships Considering Maritime Decarbonization 🔑 Keywords: #fleetdeploymentoptimization; #speedoptimization; #greentechnology adoption; #maritime #decarbonization 🔗 paper link: https://lnkd.in/gzvsYSfM 📜 Abstract：Maritime decarbonization and strict international regulations have forced liner companies to find new solutions for reducing fuel consumption and greenhouse gas emissions in recent years. Green technology is regarded as one of the most promising alternatives to achieve environmental benefits despite its high initial investment costs. Therefore, a scientific method is required to assess the possibility of green technology adoption for liner companies. This study formulates a mixed-integer nonlinear programming model to determine whether to retrofit their ship fleets with green technology and how to deploy ships while taking maritime decarbonization into account. To convert the nonlinear model into a linear model that can be solved directly by off-the-shelf solvers, several linearization techniques are applied in this study. Sensitivity analyses involving the influences of the initial investment cost, fuel consumption reduction rate of green technology, unit fuel cost, and fixed operating cost of a ship on operation decisions are conducted. Green technology may become more competitive when modern technology development makes it efficient and economical. As fuel and fixed operating costs increase, more ships retrofitted with green technology will be deployed on all shipping routes.

💡 #Highcitedpaper #AalborgAalborg University Malte von Benzon #@University of Southern Denmark 🌊 Title: An Open-Source Benchmark Simulator: Control of a BlueROV2 Underwater Robot 🔑 Keywords: #underwatervehicles; #BlueROV2; #modeling and #control; #benchmarking #controlalgorithms 🔗 paper link: https://lnkd.in/g2-CbTQc 📜 Abstract：This paper presents a simulation model environment for the popular and low-cost remotely operated vehicle (ROV) BlueROV2 implemented in Simulink™ which has been designed and experimentally validated for benchmark control algorithms for underwater vehicles. The BlueROV2 model is based on Fossen’s equations and includes a kinematic model of the vehicle, the hydrodynamics of vehicle and water interaction, a dynamic model of the thrusters, and, lastly, the gravitational/buoyant forces. The hydrodynamic parameters and thruster model have been validated in a test facility. The benchmark model also includes the ocean current, modeled as constant velocity. The tether connecting the ROV to the top-site facility has been modeled using the lumped mass method and is implemented as a force input to the ROV model. At last, to show the usefulness of the benchmark model, a case study is presented where a BlueROV2 is deployed to inspect an offshore monopile structure. The case study uses a sliding mode controller designed for the BlueROV2. The controller fulfills the design criteria defined for the case study by following the provided trajectory with a low error. It is concluded that the simulator establishes a benchmark for future control schemes for position control and trajectory tracking under the influence of environmental disturbances.

💡 #Highcitedpaper #Dalian Maritime University #@University College London (UCL) #China Ship Scientific Research Center #@Taihu Laboratory of Deepsea Technological Science 🌊 Title: Multi-Scale Object Detection Model for Autonomous Ship Navigation in Maritime Environment 🔑 Keywords: #autonomousships; #seasurface; #objectdetection; #computervision; #convolutionalneuralnetwork(CNN); #VarifocalNet 🔗 paper link: https://lnkd.in/gft2AzSC 📜 Abstract：Accurate detection of sea-surface objects is vital for the safe navigation of autonomous ships. With the continuous development of artificial intelligence, electro-optical (EO) sensors such as video cameras are used to supplement marine radar to improve the detection of objects that produce weak radar signals and small sizes. In this study, we propose an enhanced convolutional neural network (CNN) named VarifocalNet * that improves object detection in harsh maritime environments. Specifically, the feature representation and learning ability of the VarifocalNet model are improved by using a deformable convolution module, redesigning the loss function, introducing a soft non-maximum suppression algorithm, and incorporating multi-scale prediction methods. These strategies improve the accuracy and reliability of our CNN-based detection results under complex sea conditions, such as in turbulent waves, sea fog, and water reflection. Experimental results under different maritime conditions show that our method significantly outperforms similar methods (such as SSD, YOLOv3, RetinaNet, Faster R-CNN, Cascade R-CNN) in terms of the detection accuracy and robustness for small objects. The maritime obstacle detection results were obtained under harsh imaging conditions to demonstrate the performance of our network model.

💡 #NewResearch on #WaveEnergyConverter #University of New Brunswick #DEHLSEN ASSOCIATES LIMITED 🌊 Title: Comparison of Advanced Control Strategies Applied to a Multiple-Degrees-of-Freedom Wave Energy Converter: Nonlinear Model Predictive Controller versus Reinforcement Learning @Ali S. Haider, Kush Bubbar, Ph.D, P.Eng, @Alan McCall 🔑 Keywords: #energymaximizing control; #nonlinearmodel predictive control; #cyber–physicalmodeling; #waveenergyconverter; #reinforcementlearning; nonlinear #viscousdrag; non-ideal #powertakeoff 🔗 paper link: https://lnkd.in/gNAH9AKw 📜 Abstract：Achieving energy maximizing control of a Wave Energy Converter (WEC) not only needs a comprehensive dynamic model of the system—including nonlinear hydrodynamic effects and nonlinear characteristics of Power Take-Off (PTO)—but to treat the entire system using an integrated approach, i.e., as a cyber–physical system considering the WEC dynamics, control strategy, and communication interface. The resulting energy-maximizing optimization formulation leads to a non-quadratic and nonstandard cost function. This article compares the (1) Nonlinear Model Predictive Controller (NMPC) and (2) Reinforcement Learning (RL) techniques as applied to a class of multiple-degrees-of-freedom nonlinear WEC–PTO systems subjected to linear as well as nonlinear hydrodynamic conditions in simulation, using the WEC-Sim™ toolbox. The results show that with an optimal choice of RL agent and hyperparameters, as well as suitable training conditions, the RL algorithm is more robust under more stringent operating requirements, for which the NMPC algorithm fails to converge. Further, RL agents are computationally efficient on real-time target machines with a significantly reduced Task Execution Time (TET).