Hasil untuk "Naval architecture. Shipbuilding. Marine engineering"

Uroš Puc, Andreja Abina, Edvin Salvi et al.

Biofouling on ship hulls significantly increases hydrodynamic drag, fuel consumption, and greenhouse gas emissions, while also facilitating the spread of invasive species in regional and global waters, thereby threatening marine biodiversity. To address these environmental and economic issues, we developed an innovative robotic platform for in-water hull cleaning. The platform utilizes a cavitation-based cleaning module that removes biofouling while minimizing hull surface damage and preventing the spread of detached particles into the marine environment. This paper describes the design, operation, and testing of a developed robotic cleaning system prototype. Emphasis is placed on integrating components and sensors for continuous monitoring of key seawater parameters (temperature, salinity, turbidity, dissolved oxygen, chlorophyll-a, etc.) before, during, and after underwater cleaning. Results from real-sea trials show the platform’s effectiveness in removing biofouling and its minimal environmental impact, confirming its potential as a sustainable solution for in-water hull cleaning.

Guanhao Zhang, Jijian Lian, Jinliang Zhang et al.

This study presents a series of hydrodynamic experiments on a novel pontoon-type offshore floating photovoltaic (OFPV) structure, designed to improve wave attenuation performance and platform stability in marine environments. Using a 1:14 Froude-scaled physical model capable of representing different connector stiffness levels, nine structural configurations were tested, covering four array scales, three stiffness levels, and two floater sizes. Experiments were conducted under regular wave conditions, with structural responses measured at three representative positions: wave-facing front (T1), mid-array (T2), and leeward side (T3). Recorded parameters included surge acceleration, heave acceleration, pitch angle, and heave displacement. Results show that increasing array scale consistently reduced motion amplitudes at all positions, with heave acceleration at T3 substantially decreased compared with the smallest array. Enhancing connector stiffness significantly suppressed dynamic motions, particularly downstream, while larger floaters notably reduced heave responses under short-period waves. Despite variations in magnitude, response trends with respect to wave period remained broadly consistent across configurations. These findings provide quantitative evidence and engineering guidance for optimizing array configuration, connector stiffness, and floater dimensions to enhance the hydrodynamic performance and operational reliability of large-scale offshore FPV platforms.

Edwin Meriaux, Shuo Wen, David Widhalm et al.

Marine environments present significant challenges for perception and autonomy due to dynamic surfaces, limited visibility, and complex interactions between aerial, surface, and submerged sensing modalities. This paper introduces the Aerial Marine Perception Dataset (AMP2026), a multi-platform marine robotics dataset collected across multiple field deployments designed to support research in two primary areas: multi-view tracking and marine environment mapping. The dataset includes synchronized data from aerial drones, boat-mounted cameras, and submerged robotic platforms, along with associated localization and telemetry information. The goal of this work is to provide a publicly available dataset enabling research in marine perception and multi-robot observation scenarios. This paper describes the data collection methodology, sensor configurations, dataset organization, and intended research tasks supported by the dataset.

Ishaan Gakhar, Laven Srivastava, Sankarshanaa Sagaram et al.

Semantic segmentation in marine environments is crucial for the autonomous navigation of unmanned surface vessels (USVs) and coastal Earth Observation events such as oil spills. However, existing methods, often relying on deep CNNs and transformer-based architectures, face challenges in deployment due to their high computational costs and resource-intensive nature. These limitations hinder the practicality of real-time, low-cost applications in real-world marine settings. To address this, we propose LEMMA, a lightweight semantic segmentation model designed specifically for accurate remote sensing segmentation under resource constraints. The proposed architecture leverages Laplacian Pyramids to enhance edge recognition, a critical component for effective feature extraction in complex marine environments for disaster response, environmental surveillance, and coastal monitoring. By integrating edge information early in the feature extraction process, LEMMA eliminates the need for computationally expensive feature map computations in deeper network layers, drastically reducing model size, complexity and inference time. LEMMA demonstrates state-of-the-art performance across datasets captured from diverse platforms while reducing trainable parameters and computational requirements by up to 71x, GFLOPs by up to 88.5\%, and inference time by up to 84.65\%, as compared to existing models. Experimental results highlight its effectiveness and real-world applicability, including 93.42\% IoU on the Oil Spill dataset and 98.97\% mIoU on Mastr1325.

Langxiong Gan, Qihao Yang, Yi Xu et al.

The Port State Control (PSC) inspection of liquefied natural gas (LNG) carriers is crucial in maritime transportation. PSC inspection requires rapid and accurate identification of defects with limited resources, necessitating professional knowledge and efficient technical methods. Knowledge distillation, as a model lightweighting approach in the field of artificial intelligence, offers the possibility of enhancing the responsiveness of LNG carrier PSC inspections. In this study, a knowledge distillation method is introduced, namely, the multilayer dynamic multi-teacher weighted knowledge distillation (MDMD) model. This model fuses multilayer soft labels from multi-teacher models by extracting intermediate feature soft labels and minimizing intermediate feature knowledge fusion. It also employs a comprehensive dynamic weight allocation scheme that combines global loss weight allocation with label weight allocation based on the inner product, enabling dynamic weight allocation across multiple teachers. The experimental results show that the MDMD model achieves a 90.6% accuracy rate in named entity recognition, which is 6.3% greater than that of the direct training method. In addition, under the same experimental conditions, the proposed model achieves a prediction speed that is approximately 64% faster than that of traditional models while reducing the number of model parameters by approximately 55%. To efficiently assist in PSC inspections, an LNG carrier PSC inspection knowledge graph is constructed on the basis of the recognition results to quickly and effectively support knowledge queries and assist PSC personnel in making decisions at inspection sites.

Yu Cai, Jingyi Yang, Jun Song

This article introduces a mesoscale eddy detection algorithm that employs orthogonal transformations of flow field data, and subsequently, for simplicity, it is abbreviated as the OG algorithm. By implementing orthogonal geometric transformations on sea surface flow field data and examining the geometric properties of the transformed data, the study establishes criteria for the identification of mesoscale eddies based on these geometric attributes. The research utilizes sea surface flow field data sourced from the Copernicus Marine Environment Monitoring Service and validates the proposed algorithm through experimental comparisons with the traditional Velocity Geometry-based algorithm (VG algorithm). The findings indicate that the OG algorithm exhibits superior accuracy and computational precision in the detection of mesoscale eddies and in the calculation of each eddy’s center when juxtaposed with the VG algorithm. Additionally, the OG algorithm not only excels in identifying standard eddies but also shows promising applicability in the detection of eccentric and dual-core eddies. Mesoscale eddies play a crucial role in ocean dynamics and significantly influence ocean circulation, heat transport, and ecosystems. Therefore, the development of a more efficient and precise mesoscale eddy detection algorithm holds substantial importance for advancing research in ocean dynamics and climate forecasting.

Zlatko Boko, Ivica Skoko, Zaloa Sanchez Varela et al.

This literature review provides a structured quantitative analysis of existing research on the application of machine learning models (MLMs) and multi-criteria decision-making methods (MCDM) in the context of port state control (PSC). The aim of the study is to capture current research trends, identify thematic priorities, and demonstrate how these analytical tools have been used to support decision-making and risk assessment in the maritime domain. Rather than evaluating the effectiveness of individual models, the study focuses on the distribution and frequency of their use and provides insights into the development of methodological approaches in this area. Although several studies suggest that the integration of MLMs and MCDM techniques can improve the objectivity and efficiency of PSC inspections, this report does not provide a comparative assessment of their performance. Instead, it lays the groundwork for future qualitative studies that will assess the practical benefits and challenges of such integration. The findings suggest a fragmented but growing research interest in data-driven approaches to PSC and highlight the potential of advanced analytics to support maritime safety and regulatory compliance.

Chi Zhang, Zengan Deng

The three-spot seahorse (<i>Hippocampus trimaculatus</i>) is an economically important marine species in the northern South China Sea (NSCS). However, due to overfishing and marine environmental changes, its wild populations have been gradually depleted. To investigate the transport and settlement mechanisms of <i>H. trimaculatus</i> larvae in the NSCS, a physical–biological coupled model was developed based on the ocean model CROCO and the biological model Ichthyop for the period 2016–2018. The results indicate that the transport and settlement processes of larvae are primarily regulated by the combined influence of the South China Sea Warm Current, coastal upwelling, and Kuroshio intrusion. The larvae predominantly undergo short distance (0–300 km) and mid-short distance (300–600 km) transport, exhibiting significant spatial aggregation along coastal waters, particularly in the Gulf of Tonkin, the Pearl River Estuary, Shantou, Xiamen, and the western coast of Taiwan. Furthermore, extreme weather events, such as typhoons, significantly enhance larval settlement success rates. Notably, Typhoon Hato in August 2017 increased settlement success by 12.2%. This study elucidates the transport and settlement mechanisms of <i>H. trimaculatus</i> larvae, providing a scientific foundation for the conservation and management of its populations in the NSCS.

Shalini Chakraborty, Sebastian Baltes

The IT industry provides supportive pathways such as returnship programs, coding boot camps, and buddy systems for women re-entering their job after a career break. Academia, however, offers limited opportunities to motivate women to return. We propose a diverse multicultural research project investigating the challenges faced by women with software engineering (SE) backgrounds re-entering academia or related research roles after a career break. Career disruptions due to pregnancy, immigration status, or lack of flexible work options can significantly impact women's career progress, creating barriers for returning as lecturers, professors, or senior researchers. Although many companies promote gender diversity policies, such measures are less prominent and often under-recognized within academic institutions. Our goal is to explore the specific challenges women encounter when re-entering academic roles compared to industry roles; to understand the institutional perspective, including a comparative analysis of existing policies and opportunities in different countries for women to return to the field; and finally, to provide recommendations that support transparent hiring practices. The research project will be carried out in multiple universities and in multiple countries to capture the diverse challenges and policies that vary by location.

Yara AlaaEldin, Enrico Simetti, Francesca Odone

Developing a robust and effective obstacle detection and tracking system for Unmanned Surface Vehicle (USV) at marine environments is a challenging task. Research efforts have been made in this area during the past years by GRAAL lab at the university of Genova that resulted in a methodology for detecting and tracking obstacles on the image plane and, then, locating them in the 3D LiDAR point cloud. In this work, we continue on the developed system by, firstly, evaluating its performance on recently published marine datasets. Then, we integrate the different blocks of the system on ROS platform where we could test it in real-time on synchronized LiDAR and camera data collected in various marine conditions available in the MIT marine datasets. We present a thorough experimental analysis of the results obtained using two approaches; one that uses sensor fusion between the camera and LiDAR to detect and track the obstacles and the other uses only the LiDAR point cloud for the detection and tracking. In the end, we propose a hybrid approach that merges the advantages of both approaches to build an informative obstacles map of the surrounding environment to the USV.

Mia Mohammad Imran, Tarannum Shaila Zaman

Large Language Models (LLMs) are increasingly used in empirical software engineering (ESE) to automate or assist annotation tasks such as labeling commits, issues, and qualitative artifacts. Yet the reliability and reproducibility of such annotations remain underexplored. Existing studies often lack standardized measures for reliability, calibration, and drift, and frequently omit essential configuration details. We argue that LLM-based annotation should be treated as a measurement process rather than a purely automated activity. In this position paper, we outline the \textbf{Operationalization for LLM-based Annotation Framework (OLAF)}, a conceptual framework that organizes key constructs: \textit{reliability, calibration, drift, consensus, aggregation}, and \textit{transparency}. The paper aims to motivate methodological discussion and future empirical work toward more transparent and reproducible LLM-based annotation in software engineering research.

Liyuan Chen, Shuoling Liu, Jiangpeng Yan et al.

The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have demonstrated promising performance in tasks ranging from financial report summarization to sentiment-aware forecasting, many financial applications remain constrained by unique domain requirements such as multimodal reasoning, regulatory compliance, and data privacy. These challenges have spurred the emergence of financial foundation models (FFMs): a new class of models explicitly designed for finance. This survey presents a comprehensive overview of FFMs, with a taxonomy spanning three key modalities: financial language foundation models (FinLFMs), financial time-series foundation models (FinTSFMs), and financial visual-language foundation models (FinVLFMs). We review their architectures, training methodologies, datasets, and real-world applications. Furthermore, we identify critical challenges in data availability, algorithmic scalability, and infrastructure constraints and offer insights into future research opportunities. We hope this survey can serve as both a comprehensive reference for understanding FFMs and a practical roadmap for future innovation.

Daicheng Peng, Fei Cheng, Hao Xu et al.

Precise surveys are indispensable in coastal engineering projects. The extensive presence of sand in the coastal area leads to significant attenuation of seismic waves within unsaturated loose sediments. As a result, it becomes challenging for seismic waves to penetrate the weathered zone and reach the desired depth with significant amount of energy. In this study, the application of three-dimensional (3D) cross-well elastic reverse time migration (RTM) imaging based on multi-wave and multi-component techniques in coastal engineering exploration is explored. Accurate decomposition of vector compressional (P) and shear (S) waves is achieved through two wavefield decoupling algorithms without any amplitude and phase distortion. Additionally, compressional wave pressure components are obtained, which facilitates subsequent independent imaging. This study discusses and analyzes the imaging results of four imaging strategies under cross-correlation imaging conditions in RTM imaging. The analysis leads to the conclusion that scalarizing vector wavefields imaging yields superior imaging of P- and S-waves. Furthermore, the imaging results obtained through this approach are of great physical significance. In order to validate the efficacy of this method in 3D geological structure imaging in coastal areas, RTM imaging experiments were performed on two representative models. The results indicate that the proposed 3D elastic wave imaging method effectively generates accurate 3D cross-well imaging of P- and S-waves. This method utilizes the multi-wave and multi-component elastic wave RTM imaging technique to effectively leverage the Earth’s elastic medium without increasing costs. It provides valuable information about the distribution of subsurface rock layers, interfaces, and other structures in coastal engineering projects. Importantly, this can be achieved without resorting to extensive excavation or drilling operations. This method addresses the limitations of current cross-well imaging techniques, thereby providing abundant and accurate geological and geophysical information for the analysis and interpretation of 3D geological structures in coastal engineering projects. It has important theoretical and practical significance in real-world production, as well as for the study of geological structures in coastal engineering.

Guangxi SUN, Hui CAO, Ziwei ZHANG et al.

ObjectiveIn order to monitor the health status of a centrifugal pump in real time, this study proposes a model for the real-time identification of the degradation state of centrifugal pumps. MethodsFirst, based on the operating parameters and degradation mechanism of the centrifugal pump, a combined weighting model using a combination of subjective and objective weights is used to calculate the combined weights, then a health index during the degradation process of the centrifugal pump is constructed. Second, based on the existing pump degradation data, a degradation identification model based on the genetic algorithm-group method of data handling (GA-GMDH) algorithm is proposed.ResultsThe reliability of the GA-GMDH monitoring model is relatively high, with a root mean square error of 0.029216 between the output values of the health index and the actual values. Based on the model's output results, the accuracy of degradation state identification is 93.333%. ConclusionThe results of this study can provide valuable references for the health monitoring and maintenance operation management of centrifugal pumps.

YANG Bo, LIU Bingqiang, CHEN Yijun, WU Shaocong, SHU Hongchun, HAN Yiming

In order to enhance the generation efficiency of wave energy converter (WEC) arrays, an optimization method for three-tether WEC array based on an improved honey badger algorithm is proposed. First, to overcome the shortcomings of the primal honey badger algorithm (HBA), such as slow convergence speed and low convergence accuracy, three improvement strategies are introduced, i.e., good point set initialization, chaos mechanism, and honey badger population mutation. Then, three wave farms including 2-buoy, 10-buoy, and 20-buoy are tested to verify the advancement and effectiveness of the improved honey badger algorithm (IHBA). The simulation results of the 2-buoy array demonstrate that there are multiple groups of optimal solutions in WEC array optimization. Furthermore, IHBA, HBA, genetic algorithm, and particle swarm optimization can find these optimal solutions at different speeds. Nevertheless, with increasing size of the WEC array, three comparative algorithms fall into local optima solutions. On the contrary, IHBA still exhibits a strong optimization ability and can seek global optima solutions. Finally, the q-factor values obtained by IHBA in 10-buoy and 20-buoy arrays reach 1.059 and 0.968, respectively, which are dramatically larger than those of other algorithms.

Zaid Shaker Abuhamour, Ahmed Ismail Ahmed Hafez

<p>Covid-19 pandemic stands as a monumental disaster in human history, reshaping the global landscape and profoundly impacting various sectors of human life, most notably the world economy. The pandemic's repercussions were felt acutely, resulting in significant disruptions marked by the variation of demand and supply and the effect on the supply chain. Furthermore, the conflict arising from Russia's invasion of Ukraine instigated a new global economic crisis, particularly in Europe. This war exerted a substantial toll on global economic growth, further compounded by the lingering consequences of the Covid-19 pandemic.</p>

ZHANG Cheng, KUANG Yu, CHEN Wenxing, ZHENG Yang

In order to enable a multi-energy coupling integrated energy system (IES) to meet the needs of load diversity in low-carbon economic operation, a bi-level optimal configuration method for low-carbon economic operation of multi-energy coupling IES in different charging modes of electric vehicles (EVs) is proposed. First, an IES including cold-thermal-electric-gas coupling is established. Then, in the day-to-day operation stage, factors such as hierarchical carbon trading mechanism and different charging modes of EVs are considered to achieve the lowest daily scheduling cost. In the configuration planning stage, based on the daily operation cost, the equipment capacity is configured with the lowest equipment investment cost and annual operation cost. Finally, Cplex is used to solve the above two-stage objective functions and obtain the optimal configuration scheme and scheduling results through mutual iteration. The results show that the charging method considering the remaining charge of EVs and carbon trading mechanism can significantly reduce carbon emissions and operating costs of the system. The proposed configuration approach can well realize low-carbon economic operation of the multi-energy coupling IES.

James S. Wheaton, Daniel R. Herber

Traditional requirements engineering tools do not readily access the SysML-defined system architecture model, often resulting in ad-hoc duplication of model elements that lacks the connectivity and expressive detail possible in a SysML-defined model. Further integration of requirements engineering activities with MBSE contributes to the Authoritative Source of Truth while facilitating deep access to system architecture model elements for V&V activities. We explore the application of MBSE to requirements engineering by extending the Model-Based Structured Requirement SysML Profile to comply with the INCOSE Guide to Writing Requirements while conforming to the ISO/IEC/IEEE 29148 standard requirement statement patterns. Rules, Characteristics, and Attributes were defined in SysML according to the Guide to facilitate requirements definition, verification & validation. The resulting SysML Profile was applied in two system architecture models at NASA Jet Propulsion Laboratory, allowing us to assess its applicability and value in real-world project environments. Initial results indicate that INCOSE-derived Model-Based Structured Requirements may rapidly improve requirement expression quality while complementing the NASA Systems Engineering Handbook checklist and guidance, but typical requirement management activities still have challenges related to automation and support in the system architecture modeling software.

Konstantin I. Matveev

Semi-submersible vehicles keep most of their hulls underwater while maintaining a small platform above the water surface. These craft can find use for both naval operations and civil transportation due to special properties, including the low above-water hull profile, reduced wave drag in some speed regimes, and potentially better seaworthiness. However, hydrodynamics of these marine craft is not well studied. In this work, computational modeling is undertaken to explore steady hydrodynamic characteristics of several semi-submersible hull variations in a range of speeds in deep-water and finite-depth conditions. The validation and verification study is conducted using experimental data obtained with a Suboff model in a near-surface regime. Parametric simulations are performed for this hull and two others generated by modifying the original Suboff geometry to produce narrow and wide hull shapes with similar volumes. The computational results indicate that the narrow hull excels in deep water, having lower drag and experiencing lower downward suction force and smaller longitudinal moment. However, in shallow-water operations, the narrow hull exhibits noticeably larger resistance than other hulls with the same displacement due to smaller gap between the hull and sea floor. Main hydrodynamic characteristics of the studied hulls and illustrations of wave patterns are presented in the paper. These findings can be useful for designers of semi-submersible vehicles.

Claudio Di Sipio, Riccardo Rubei, Juri Di Rocco et al.

Software engineering (SE) activities have been revolutionized by the advent of pre-trained models (PTMs), defined as large machine learning (ML) models that can be fine-tuned to perform specific SE tasks. However, users with limited expertise may need help to select the appropriate model for their current task. To tackle the issue, the Hugging Face (HF) platform simplifies the use of PTMs by collecting, storing, and curating several models. Nevertheless, the platform currently lacks a comprehensive categorization of PTMs designed specifically for SE, i.e., the existing tags are more suited to generic ML categories. This paper introduces an approach to address this gap by enabling the automatic classification of PTMs for SE tasks. First, we utilize a public dump of HF to extract PTMs information, including model documentation and associated tags. Then, we employ a semi-automated method to identify SE tasks and their corresponding PTMs from existing literature. The approach involves creating an initial mapping between HF tags and specific SE tasks, using a similarity-based strategy to identify PTMs with relevant tags. The evaluation shows that model cards are informative enough to classify PTMs considering the pipeline tag. Moreover, we provide a mapping between SE tasks and stored PTMs by relying on model names.