Search Results (2,199)

The advantages of hybrid models for time series forecasting have received significant attention, and several studies focus on and test their application in the seakeeping of ship motions. A hybrid model integrating an LSTM encoder and Transformer decoder (LT) is introduced to overcome the limitation of individual LSTM and Transformer: initially, the seakeeping response of the KCS ship was simulated by ANSYS-AQWA considering the sea state 3 and 4 simultaneously and established a dataset; secondly, three standalone baseline models (LSTM, Transformer, and TCN), and two hybrid models, LT and LT, with PSO-optimized hyperparameters (P-LT) were constructed and trained to forecast the seakeeping performance of ships with multiple steps of 30, 60 and 90; finally, the comparison between solo and hybrid models was made by different steps on RMSE, MAE and NRMSE evaluations to prove the advancement of LT and P-LT models. The P-LT hybrid model achieved consistent accuracy improvements compared with the best-performing individual models across different ship motions. Notably, RMSE reductions were observed at all prediction horizons (30, 60, and 90 steps), with maximum improvements reaching 13.54% for rolling, 11.83% for pitching, and 12.87% for heaving motions. This study provides both theoretical and practical support to ship motion prediction and demonstrates the potential of the proposed study as an effective engineering product for enhancing safety in ship operation. Full article

In the construction of large cruise ships, the restricted deck space and dense obstacles create a strongly coupled problem between path planning and sequence optimization during prefabricated cabin boarding operations, significantly impairing overall installation efficiency. To coordinately optimize the boarding sequence of multiple cabins and minimize operational conflicts, this study proposes a dual-layer coordinated planning methodology. The lower layer generates feasible paths satisfying kinematic and contour-based obstacle avoidance constraints through optimal control theory, while the upper layer introduces a dynamic priority evaluation mechanism based on grid mapping and an “enclosure factor”, combined with a reverse planning strategy to dynamically adjust the cabin boarding sequence. Through iterative feedback between path feasibility and sequence efficiency, the proposed method effectively resolves the strong coupling between sequencing and path planning. Case validation demonstrates that the proposed approach significantly reduces total installation time compared to conventional sequence planning methods, proving its effectiveness and practical value in enhancing the efficiency of coordinated multi-cabin installation. Full article

Maritime UAV operations face challenges in real-time ship detection. Complex ocean backgrounds, drastic scale variations, and prevalent distant small targets create difficulties. We propose MCB-RT-DETR, a real-time detection transformer enhanced by multi-component boosting. This method builds upon the RT-DETR architecture. It significantly improves detection under wave interference, lighting changes, and scale differences. Key innovations address these challenges. An Orthogonal Channel Attention (Ortho) mechanism preserves high-frequency edge details in the backbone network. Receptive Field Attention Convolution (RFAConv) enhances robustness against background clutter. A Small Object Detail Enhancement Pyramid (SOD-EPN) strengthens small-target representation. SOD-EPN combines SPDConv with multi-scale CSP-OmniKernel transformations. The neck network integrates ultra-lightweight DySample upsampling. This enables content-aware sampling for precise multi-scale localization. The method maintains high computational efficiency. Experiments on the SeaDronesSee dataset show significant improvements. MCB-RT-DETR achieves 82.9% mAP@0.5 and 49.7% mAP@0.5:0.95. These correspond to improvements of 4.5% and 3.4% relative to the baseline model. Inference speed maintains 50 FPS for real-time processing. The outstanding performance in cross-dataset tests further validates the algorithm’s strong generalization capability on DIOR remote sensing images and VisDrone2019 aerial scenes. The method provides a reliable visual perception solution for autonomous maritime UAV operations. Full article

Ship lock zones represent bottlenecks and a particular challenge for authorities managing vessel traffic. Traditionally, the control strategy of such systems has relied heavily on the subjective judgment, experience, and tacit knowledge of ship lock operators. To address the inherent uncertainty and imprecision associated with these subjective assessments, fuzzy logic and fuzzy set theory have been adopted as appropriate mathematical frameworks. In this work, the control strategy and the Fuzzy Decision Support System (FDSS) of a single-chamber ship lock designed for two vessels on a two-way waterway are analyzed and modeled. The input data is generated based on a synthesized dataset reflecting the annual schedule of vessel arrivals. The software is based on proposals and suggestions of experienced ship lock operators, and it is further validated through vessel traffic simulations. Moreover, the development of an appropriate Supervisory Control and Data Acquisition (SCADA) system integrated with a Programmable Logic Controller (PLC) is detailed, providing the necessary infrastructure for real-time deployment of the fuzzy control algorithm. The proposed control system represents an original contribution and offers practical applications both as a decision-support tool for real-time lock management and as a training platform for novice or less experienced operators. Full article

Global container shipping faces increasing pressure to reduce fuel consumption and greenhouse gas (GHG) emissions while still meeting strict port schedules under highly uncertain terminal operations and met-ocean conditions. However, most existing voyage-planning approaches either ignore real port operation variability or treat fuel optimization and just-in-time (JIT) arrival as separate problems, limiting their applicability in actual operations. This study presents a data-driven just-in-time voyage optimization framework that integrates port-side uncertainty and marine environmental dynamics into the routing process. A dwell-time prediction model based on Gradient Boosting was developed using port throughput and meteorological–oceanographic variables, achieving a validation accuracy of R² = 0.84 and providing a data-driven required time of arrival (RTA) estimate. A Transformer encoder model was constructed to forecast fuel consumption from multivariate navigation and environmental data, and the model achieved a segment-level predictive performance with an R² value of approximately 0.99. These predictive modules were embedded into a Deep Q-Network (DQN) routing model capable of optimizing headings and speed profiles under spatially varying ocean conditions. Experiments were conducted on three container-carrier routes in which the historical AIS trajectories served as operational benchmark routes. Compared with these AIS-based baselines, the optimized routes reduced fuel consumption and CO₂ emissions by approximately 26% to 69%, while driving the JIT arrival deviation close to zero. The proposed framework provides a unified approach that links port operations, fuel dynamics, and ocean-aware route planning, offering practical benefits for smart and autonomous ship navigation. Full article

The alignment of the propulsion shafting system is crucial to ensuring the safe and efficient operation of ships. As ships grow in size and engine output increases, the complexity of propulsion systems also escalates, making precise alignment more challenging. Traditional methods often neglect hull deformation caused by varying operational conditions, which can lead to uneven bearing loads, excessive vibrations, and potential bearing failures. This study addresses these challenges by analyzing the effects of hull deformation on bearing reaction forces in a large crude oil tanker. Shaft alignment analysis was conducted under six different loading conditions, ranging from dry docking to fully loaded states. The results indicated that hull deformation significantly alters the distribution of bearing loads along the propulsion shaft. Initial alignment, without considering hull deflection, showed satisfactory results, but when hull deformation was included, notable deviations in bearing loads emerged. These deviations pose risks of bearing overloads or underloads, which could accelerate wear or cause failure. To mitigate these risks, this study proposes an optimized bearing offset configuration, adjusting intermediate shaft bearings to maintain balanced loads across all conditions. The findings demonstrate that incorporating hull deformation data into shaft alignment improves the system’s reliability and safety, providing a foundation for better alignment practices for large vessels in varied operational conditions. Full article

This paper presents a two-stage stochastic programming model for the joint optimization of fleet deployment and sailing speed in liner shipping under fuel price volatility and carbon tax uncertainty. The integrated framework addresses strategic fleet planning by determining optimal fleet composition in the first stage, while the second stage optimizes operational decisions, including vessel assignment to routes and sailing speeds on individual voyage legs, after observing stochastic parameter realizations. The model incorporates nonlinear fuel consumption functions that are approximated using piecewise linearization techniques, with the resulting formulation being solved using the Sample Average Approximation (SAA) method. To enhance computational tractability, we employ big-M methods to linearize mixed-integer terms and introduce auxiliary variables to handle nonlinear relationships in both the objective function and constraints. The proposed model provides shipping companies with a comprehensive decision-support tool that effectively captures the complex interdependencies between long-term strategic fleet planning and short-term operational speed optimization. Numerical experiments demonstrate the model’s effectiveness in generating optimal solutions that balance economic objectives with environmental considerations under uncertain market conditions, highlighting its practical value for resilient shipping operations in volatile fuel and carbon pricing environments. Full article

Air pollution from ship operations can pose a significant challenge for coastal cities, particularly where ports are closely integrated into the urban fabric. This study examines the influence of ship docking on PM_2.5 concentrations in Chios, Greece, a medium size island city where the port directly borders densely populated neighbourhoods. Calibrated PurpleAir sensors were installed at urban and suburban sites to measure PM_2.5, with data analysed alongside ship call records and meteorological observations. An event-based concentration enhancement metric (%ΔC) was estimated to compare PM_2.5 during docking with the preceding 3 h background for 170 ship arrivals in February and August 2022. The results showed that under prevailing northerly winds in August, PM_2.5 at the downwind urban site increased on average by 5.0 µg m⁻³ (48%), whereas winter increments were smaller (6.1%) due to higher background variability. When both seasons and all wind directions were pooled, the urban site exhibited a mean enhancement of 1.7 µg m⁻³ (19%), while impacts at the suburban site remained minor (3%). Median-based uncertainty analysis confirmed robust enhancements under northerly winds only. Wind direction and wind speed were the primary controls on %ΔC, whereas ship engine power and time at berth had limited influence. The results suggest that ship-related PM_2.5 impacts are detectable but remain spatially and temporally limited in coastal urban environments, including medium-sized islands characterised by relatively low shipping activity. Full article

Turbocharged diesel engines are widely used for the propulsion and as the generators for powering auxiliary systems in marine applications. Many works were published on the development of diagnosis tools for the engines using data from simulation models or from experiments on a sophisticated engine test bench. However, the simulation data varies a lot with actual operational data, and the available sensor data on the actual vessel is much less compared to the data from test benches. Therefore, it is necessary to develop anomaly prediction and fault diagnosis models from limited data available from the engines. In this paper, an artificial intelligence (AI)-based anomaly detection model and machine learning (ML)-based fault diagnosis model were developed using the actual data acquired from a diesel engine of a cargo vessel. Unlike the previous works, the study uses operational, thermodynamic, and vibration data for the anomaly detection and fault diagnosis. The paper provides the overall architecture of the proposed predictive maintenance system including details on the sensorization of assets, data acquisition, edge computation, and AI model for anomaly prediction and ML algorithm for fault diagnosis. Faults with varying severity levels were induced in the subcomponents of the engine to validate the accuracy of the anomaly detection and fault diagnosis models. The unsupervised stacked autoencoder AI model predicts the engine anomalies with 87.6% accuracy. The balanced accuracy of supervised fault diagnosis model using Support Vector Machine algorithm is 99.7%. The proposed models are vital in marching towards sustainable shipping and have potential to deploy across various applications. Full article

Greenhouse gas (GHG) emissions from maritime transport account for nearly 3% of global totals, making the decarbonisation of this sector a critical priority. In response, the International Maritime Organization (IMO) adopted the GHG Strategy, targeting the full decarbonisation of international shipping by 2050, with interim milestones in 2030 and 2040. This study evaluates the greenhouse gas fuel intensity of three representative vessel types, an oil tanker, a container ship, and a bulk carrier, using one-year operational fuel consumption data in line with the Regulations of the IMO Net-Zero Framework. Both conventional fuels, including conventional marine fuels, and alternative options, encompassing liquefied natural gas (LNG), e-hydrogen, e-ammonia, e-methanol, and biodiesel, are assessed for compliance during 2028–2035. The findings reveal that conventional fuels are unable to meet future targets, resulting in significant compliance deficits and balancing costs of remedial units. LNG provides short-term benefits but is limited by methane slip. In contrast, e-hydrogen and e-ammonia enable long-term compliance and generate surplus units. E-methanol shows a partial potential, while biodiesel delivers only modest improvements. The results underscore the need for a transition toward near-zero-well-to-wake-emission fuels. This study contributes by combining life cycle assessments with regulatory compliance analysis, offering insights for policymakers and industry stakeholders. Full article

Quantitative Feedback Theory (QFT) enables the control system to guarantee stability and performance in the presence of plant uncertainty, thus offering a quantitative and less conservative framework for designing robust yet practical controllers. The presented work investigates a single-stage constraint optimization-based approach for synthesizing controllers for the ship roll stabilization. The typical QFT loop shaping is a manual two-stage procedure that demands a proficient understanding of loop-shaping principles on Nichols charts. The proposed procedure simplifies the QFT synthesis process by introducing a single-stage method that allows for concurrent synthesis of both the QFT controller and pre-filter. The present work considers the synthesis of fractional order controllers (using the FOMCON toolbox). The proposed method also enables the designer to pre-specify the controller architecture at the beginning of the design procedure. A comparative analysis with the controllers obtained using the QFT toolbox, Ziegler–Nichols, ${\mathcal{H}}_{\infty }$, IMC, and MPC have also been presented in the work. The implementation has been carried out for the ship roll stabilization, which is one of the critical problems in marine engineering, as it directly impacts the vessel safety, operational efficiency, and passenger comfort, wherein excessive roll can lead to reduced propulsion efficiency. The obtained results highlight that the proposed controller performs better than the benchmark controllers, and Monte Carlo simulations have also been included to support the results. Full article

Ship positional data are widely used for route inference, yet most existing studies rely on automatic identification system data, which contain irregular transmission intervals and limit the ability to capture vessel-specific operational habits and subtle route choices. This study addresses these limitations by proposing a methodology to infer customary routes using periodic 3 s ship position data collected through the Korean e-Navigation system based on long-term evolution maritime communication. The method comprises three main steps: constructing a sea-area grid with an associated weight map, determining data-driven importance and updating weights, and performing pathfinding. Domestic waters are divided into 100 m grids, and navigable and non-navigable areas are binarized to establish a framework for route exploration. Ship positional data are processed to extract inter-port trajectories, which are then classified by ship size and tidal time zone to account for navigational differences arising from vessel characteristics and tide-dependent accessibility. These trajectories are combined with spatial grids and transformed into a document–word structure, enabling the calculation of movement importance between grid cells using a modified term frequency–inverse document frequency measure. The resulting weights are applied to a pathfinding graph to derive routes that reflect vessel size and tidal conditions. The effectiveness of the proposed method is evaluated by computing cosine similarity between the inferred routes and actual trajectories. Full article

As critical assets for polar development and global strategy, nuclear-powered icebreakers necessitate rigorous safety research under extreme meteorological conditions. Evaluating their reliability under tornado loads is essential to ensure sustainable Arctic operations. This study employed numerical methods to solve tornado loads and assess the safety performance of an icebreaker subjected to tornado-induced loads. Tornado loads at varying azimuth angles were solved using a modified Ward-type simulator, while wave loads under tornado conditions were determined by a numerical wave model. The results demonstrated that the tornado applied the maximum wind load on the structure at a 0° azimuth angle. The total wind load was reduced by approximately 39% at a 60° azimuth angle. The tornado-induced moment on the ship exhibited a strongly nonlinear relationship with the azimuth angle. The maximum total moment occurred at a 15° azimuth angle, whereas the minimum total moment was observed at a 90° azimuth angle, where the hull experienced minimal wind loads. Full article

Improving ship energy efficiency has become a critical priority for reducing fuel consumption and meeting international decarbonization targets. In this study, eight major groups of energy efficiency improvement systems—including wind and solar energy technologies, hull and propeller modifications, air lubrication, green propulsion options, waste heat recovery, and engine power limitation—were evaluated against seven critical success factors. A hybrid neutrosophic fuzzy multi-criteria decision-making (MCDM) framework was employed to capture expert uncertainty and prioritize alternatives. Neutrosophic fuzzy sets were adopted because they more comprehensively represent uncertainty—simultaneously modeling truth, indeterminacy, and falsity, providing superior capability to address expert ambiguity compared with classical fuzzy, intuitionistic fuzzy, gray, or other uncertainty-handling frameworks. Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process (AHP) (TNF-AHP) was first applied to determine the relative importance of the criteria, highlighting fuel savings and cost-effectiveness as dominant factors with 38% weight. Subsequently, the Fuzzy Combined Compromise Solution (F-CoCoSo) method was used to rank the alternatives. Results indicate that solar energy systems and wind-assisted propulsion consistently rank highest (with 3.35 and 2.92 performance scores) across different scenarios, followed by green propulsion technologies, while waste heat recovery and engine power limitation show lower performance. These findings not only provide a structured assessment of current technological options, but also offer actionable guidance for shipowners, operators, and policymakers seeking to prioritize investments in sustainable maritime operations. Full article