Hasil untuk "Naval architecture. Shipbuilding. Marine engineering"

Weibo Rao, Qianning Hu, Gang Chen

The ocean archives massive, stable remote sensing datasets, and leveraging these data to achieve intelligent real-time recognition of marine organisms has become a core task in the field of marine remote sensing. However, in complex seabed environments, marine monitoring equipment is often constrained by limited computing power—this creates an urgent demand among oceanographers for detection algorithms with low computational complexity, which can be widely deployed on low-cost, simple marine remote sensing devices. To address this demand, this study proposes a deep learning-based algorithm for lightweight seabed organism detection efficiently (LSOD). This algorithm integrates Mamba and YOLO principles to enable efficient lightweight benthic organism detection. For LSOD’s neck, the original concatenation modules are improved, which efficiently aggregates feature layer information across backbone stages for cross-scale fusion. To further reduce the computational requirements of LSOD, a new detection head module based on group normalization and shared convolution operations is designed. These improvements maintain a reasonable computational load while enhancing the precision of the object detection network. EUDD tests indicate LSOD’s performance: the detection precision achieves 90.6% (sea cucumbers), 91.6% (sea urchins), and 93.5% (scallops). Comparisons with mainstream models confirm its superiority in detecting benthic organisms. This work is expected to provide new insights and approaches for intelligent remote sensing and analysis in marine ranches.

Daichi Narita, Shinsuke Iwasaki

Although subject to annual fluctuations, sea ice in the Sea of Okhotsk has decreased to a maximum extent at a rate of approximately 3.4% per decade since the 1970s. Thus far, few studies have focused on projections of sea ice in the Sea of Okhotsk. This study focused on sea ice in the Sea of Okhotsk and examined its past and future characteristics using a climate projection dataset termed the Future Ocean Regional Projection dataset. Historical sea ice areas have been reported to be larger than satellite observations, and some data contain biases of approximately double the actual value. Therefore, a simple bias correction was performed based on the ratio of historical to satellite observation sea-ice areas, and the bias was corrected. Furthermore, we performed future projections using two bias-corrected scenarios (RCP2.6 and RCP8.5). Results revealed that for the future analysis period of 2006–2100, sea ice loss would be approximately 12.3 (10² km²/year) under RCP2.6 and approximately 37.3 (10² km²/year) under RCP8.5, indicating that under both scenarios, there would be almost no sea ice in the southern Sea of Okhotsk between 2071 and 2100. The results of this study provide useful information for researchers to predict sea ice in related physical fields.

Shuaibing Luo, Xiaoxue Wang, Lifu Zhang et al.

Since the onset of the Cenozoic, the South China Sea has experienced complex plate interactions including peripheral plate collisions, the demise of the Paleo-South China Sea, and the subsequent opening of the modern basin. These processes produced three major types of sedimentary basins: extensional, strike-slip, and compressional. The Beikang Basin represents a typical extensional continental-margin rift basin that preserves the stratigraphic and sedimentary record of the transition from syn-rift to post-rift stages. Subsidence happened mainly during the post-rift stage. Five structural styles exist: extensional, compressional-inversion, strike-slip–extensional, magmatic, and diapiric. While the first three are fault-related, the last two are mainly controlled by the volcanic phases. Using a seismic-facies-to-sedimentary-system workflow, we delineate a tectono-stratigraphic framework, comprising five seismic facies, seven lithofacies, and eight depositional facies. This framework indicates that the Beikang Basin evolved through four major tectonic stages including initial rifting, inherited rifting, climax rifting, and post-rift thermal subsidence. Each stage has primary control on sediment supply and accommodation development. Our findings refine the basin’s tectono-sedimentary evolution and improve predictions for sediment distribution and hydrocarbon exploration in the underexplored Beikang Basin.

Víctor Corchete

A study of the azimuthal variation in the surface wave fundamental-mode phase velocity is performed for the Philippine Sea Plate (PSP). This azimuthal variation has been anisotropically inverted for the PSP to determine the isotropic and anisotropic structure of this plate from 0 to 260 km. This azimuthal variation is due to anisotropy in the upper mantle. The crust is found in an isotropic structure, but the lithosphere and asthenosphere exhibit anisotropic structures. For the lithosphere, the main cause of anisotropy is the alignment of anisotropic crystals approximately parallel to the direction of seafloor spreading, and the fast axis of the seismic velocity is in the direction of ~163° of azimuth. For the asthenosphere, the seismic anisotropy can be derived from the lattice-preferred orientation (LPO) in response to the shear strains induced by mantle flow, and the fast axis of the seismic velocity is also the direction of ~163° of azimuth. This result suggests that a mantle flow pattern may occur in the asthenosphere and seems to be approximately parallel to the direction of seafloor spreading observed for the lithosphere. Finally, the changes in the parameter ξ with depth are studied to estimate the depth of the lithosphere–asthenosphere boundary (LAB), observing a clear change in this parameter at 80 km depth.

Md Saeed Siddik, Hao Li, Cor-Paul Bezemer

Context: Jupyter Notebook has emerged as a versatile tool that transforms how researchers, developers, and data scientists conduct and communicate their work. As the adoption of Jupyter notebooks continues to rise, so does the interest from the software engineering research community in improving the software engineering practices for Jupyter notebooks. Objective: The purpose of this study is to analyze trends, gaps, and methodologies used in software engineering research on Jupyter notebooks. Method: We selected 146 relevant publications from the DBLP Computer Science Bibliography up to the end of 2024, following established systematic literature review guidelines. We explored publication trends, categorized them based on software engineering topics, and reported findings based on those topics. Results: The most popular venues for publishing software engineering research on Jupyter notebooks are related to human-computer interaction instead of traditional software engineering venues. Researchers have addressed a wide range of software engineering topics on notebooks, such as code reuse, readability, and execution environment. Although reusability is one of the research topics for Jupyter notebooks, only 64 of the 146 studies can be reused based on their provided URLs. Additionally, most replication packages are not hosted on permanent repositories for long-term availability and adherence to open science principles. Conclusion: Solutions specific to notebooks for software engineering issues, including testing, refactoring, and documentation, are underexplored. Future research opportunities exist in automatic testing frameworks, refactoring clones between notebooks, and generating group documentation for coherent code cells.

Bianca Trinkenreich, Fabio Calefato, Geir Hanssen et al.

The adoption of Large Language Models (LLMs) is not only transforming software engineering (SE) practice but is also poised to fundamentally disrupt how research is conducted in the field. While perspectives on this transformation range from viewing LLMs as mere productivity tools to considering them revolutionary forces, we argue that the SE research community must proactively engage with and shape the integration of LLMs into research practices, emphasizing human agency in this transformation. As LLMs rapidly become integral to SE research - both as tools that support investigations and as subjects of study - a human-centric perspective is essential. Ensuring human oversight and interpretability is necessary for upholding scientific rigor, fostering ethical responsibility, and driving advancements in the field. Drawing from discussions at the 2nd Copenhagen Symposium on Human-Centered AI in SE, this position paper employs McLuhan's Tetrad of Media Laws to analyze the impact of LLMs on SE research. Through this theoretical lens, we examine how LLMs enhance research capabilities through accelerated ideation and automated processes, make some traditional research practices obsolete, retrieve valuable aspects of historical research approaches, and risk reversal effects when taken to extremes. Our analysis reveals opportunities for innovation and potential pitfalls that require careful consideration. We conclude with a call to action for the SE research community to proactively harness the benefits of LLMs while developing frameworks and guidelines to mitigate their risks, to ensure continued rigor and impact of research in an AI-augmented future.

Rubaiyat Tasnim Chowdhury, Nayan Bala, Ronojoy Roy et al.

Traditional rigid underwater vehicles pose risks to delicate marine ecosystems due to high-speed propellers and rigid hulls. This paper presents BactoBot, a low-cost, soft underwater robot designed for safe and gentle marine exploration. Inspired by the efficient flagellar propulsion of bacteria, BactoBot features 12 flexible, silicone-based arms arranged on a dodecahedral frame. Unlike high-cost research platforms, this prototype was fabricated using accessible DIY methods, including food-grade silicone molding, FDM 3D printing, and off-the-shelf DC motors. A novel multi-stage waterproofing protocol was developed to seal rotating shafts using a grease-filled chamber system, ensuring reliability at low cost. The robot was successfully tested in a controlled aquatic environment, demonstrating stable forward propulsion and turning maneuvers. With a total fabrication cost of approximately $355 USD, this project validates the feasibility of democratizing soft robotics for marine science in resource-constrained settings.

Mengqiu Xu, Kaixin Chen, Heng Guo et al.

Deep learning approaches for marine fog detection and forecasting have outperformed traditional methods, demonstrating significant scientific and practical importance. However, the limited availability of open-source datasets remains a major challenge. Existing datasets, often focused on a single region or satellite, restrict the ability to evaluate model performance across diverse conditions and hinder the exploration of intrinsic marine fog characteristics. To address these limitations, we introduce \textbf{MFogHub}, the first multi-regional and multi-satellite dataset to integrate annotated marine fog observations from 15 coastal fog-prone regions and six geostationary satellites, comprising over 68,000 high-resolution samples. By encompassing diverse regions and satellite perspectives, MFogHub facilitates rigorous evaluation of both detection and forecasting methods under varying conditions. Extensive experiments with 16 baseline models demonstrate that MFogHub can reveal generalization fluctuations due to regional and satellite discrepancy, while also serving as a valuable resource for the development of targeted and scalable fog prediction techniques. Through MFogHub, we aim to advance both the practical monitoring and scientific understanding of marine fog dynamics on a global scale. The dataset and code are at \href{https://github.com/kaka0910/MFogHub}{https://github.com/kaka0910/MFogHub}.

Xiaoling Han, Bin Lin, Nan Wu et al.

With the rapid growth of the global marine economy and flourishing maritime activities, the marine Internet of Things (IoT) is gaining unprecedented momentum. However, current marine equipment is deficient in data transmission efficiency and semantic comprehension. To address these issues, this paper proposes a novel End-to-End (E2E) coding scheme, namely the Turbo-based Deep Semantic Autoencoder (Turbo-DSA). The Turbo-DSA achieves joint source-channel coding at the semantic level through the E2E design of transmitter and receiver, while learning to adapt to environment changes. The semantic encoder and decoder are composed of transformer technology, which efficiently converts messages into semantic vectors. These vectors are dynamically adjusted during neural network training according to channel characteristics and background knowledge base. The Turbo structure further enhances the semantic vectors. Specifically, the channel encoder utilizes Turbo structure to separate semantic vectors, ensuring precise transmission of meaning, while the channel decoder employs Turbo iterative decoding to optimize the representation of semantic vectors. This deep integration of the transformer and Turbo structure is ensured by the design of the objective function, semantic extraction, and the entire training process. Compared with traditional Turbo coding techniques, the Turbo-DSA shows a faster convergence speed, thanks to its efficient processing of semantic vectors. Simulation results demonstrate that the Turbo-DSA surpasses existing benchmarks in key performance indicators, such as bilingual evaluation understudy scores and sentence similarity. This is particularly evident under low signal-to-noise ratio conditions, where it shows superior text semantic transmission efficiency and adaptability to variable marine channel environments.

Xiaoqiong Bao, Zhi-Hua Hu, Yanling Huang

A fleet of drones is considered in the routing problems with an offshore drone base station, considering the simultaneous movements of drones and ships. A model, entitled meeting model, between a drone and a moving ship is devised, and an extended model is developed based on the vehicle routing problem model. A genetic algorithm based on a sequential insert heuristic (SIH) is designed to solve the model as a holistic framework with two strategies to determine the sequential assignments of ships to drones, namely, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="normal">D</mi><mi mathvariant="normal">r</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">n</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">B</mi><mi mathvariant="normal">y</mi><mi mathvariant="normal">D</mi><mi mathvariant="normal">r</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">n</mi><mi mathvariant="normal">e</mi><mo>,</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">h</mi><mi mathvariant="normal">i</mi><mi mathvariant="normal">p</mi><mi mathvariant="normal">B</mi><mi mathvariant="normal">y</mi><mi mathvariant="normal">S</mi><mi mathvariant="normal">h</mi><mi mathvariant="normal">i</mi><mi mathvariant="normal">p</mi></mrow></semantics></math></inline-formula> strategies. The proposed models and solution algorithms are demonstrated and verified by experiments. Numerical studies show that the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="normal">D</mi><mi mathvariant="normal">r</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">n</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">B</mi><mi mathvariant="normal">y</mi><mi mathvariant="normal">D</mi><mi mathvariant="normal">r</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">n</mi><mi mathvariant="normal">e</mi></mrow></semantics></math></inline-formula> strategy can overperform the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">h</mi><mi mathvariant="normal">i</mi><mi mathvariant="normal">p</mi><mi mathvariant="normal">B</mi><mi mathvariant="normal">y</mi><mi mathvariant="normal">S</mi><mi mathvariant="normal">h</mi><mi mathvariant="normal">i</mi><mi mathvariant="normal">p</mi></mrow></semantics></math></inline-formula> strategy regarding traveling distances. In addition, when considering the simultaneous movement of the ship and drone, improving the drone flying speeds can reduce the flying time of drones rather than optimizing the ship’s moving speed. The managerial implications and possible extensions are discussed based on modeling and experimental studies.

Pengyue Wang, Mingyang Pan, Zongying Liu et al.

Ship trajectory prediction plays a key role in the early warning and safety of maritime traffic. It is a necessary assistant tool that can forecast a ship’s trajectory in a certain period to prevent ship collision. However, highly precise prediction of long-term ship trajectories is still a challenge. This study proposes a ship trajectory prediction model called ShipTrack-TVAE, which is based on a Variational Autoencoder (SocialVAE) and Transformer architecture. It aims to address ship trajectory prediction tasks in complex waterways. To enable the model to avoid potential collision risks, this study designs a collision avoidance mechanism, which comprehensively incorporates safety constraints related to the distance between ships into the loss function. The experimental results show that on the Qiongzhou Strait ship AIS dataset, the Average Displacement Error (ADE) of ShipTrack-TVAE improved by 21.85% compared to the current state-of-the-art trajectory prediction model, SocialVAE, while the Final Displacement Error (FDE) improved by 17.83%. The experimental results demonstrate that the ShipTrack-TVAE model can effectively improve the prediction accuracy of short-term, medium-term, and long-term ship trajectories. It has excellent performance and provides a certain reference value for advancing unmanned ship collision avoidance.

Lei Wang, Ling-Zhi Chen, Bo Peng et al.

Cage aquaculture makes it easier to produce high-quality aquatic products and allows full use of water resources. 3Therefore, cage aquaculture development is highly valued globally. However, the current digitalization level of cage aquaculture is low, and the farming risks are high. Research and development of digital management of the fish population in cages are greatly desired. Real-time monitoring of the activity status of the fish population and changes in the fish population size in cages is a pressing issue that needs to be addressed. This paper proposes an improved network called CC-YOLOv5 by embedding CoordConv modules to replace the original ConV convolution modules in the network, which improves the model’s generalization capability. By using two-stage detection logic, the target detection accuracy is enhanced to realize prediction of the number of fish populations. OpenCV is then used to measure fish tail lengths to establish growth curves of the fish and to predict the output of the fish population in the cages. Experimental results demonstrate that the mean average precision (mAP) of the improved algorithm increases by 14.9% compared to the original YOLOv5, reaching 95.4%. This research provides an effective solution to promote the intelligentization of cage aquaculture processes. It also lays the foundation for AI (Artificial Intelligence) applications in other aquaculture scenarios.

Huu Phu Nguyen, Chien Ming Wang

With the world population projected to reach 10 billion by 2050, the demand for food and energy is expected to increase significantly [...]

Lei Zhang, Andriy Miranskyy

A flaky test yields inconsistent results upon repetition, posing a significant challenge to software developers. An extensive study of their presence and characteristics has been done in classical computer software but not quantum computer software. In this paper, we outline challenges and potential solutions for the automated detection of flaky tests in bug reports of quantum software. We aim to raise awareness of flakiness in quantum software and encourage the software engineering community to work collaboratively to solve this emerging challenge.

Jose-Carlos Gamazo-Real, Raul Torres Fernandez, Adrian Murillo Armas

The large increase in the number of Internet of Things (IoT) devices have revolutionised the way data is processed, which added to the current trend from cloud to edge computing has resulted in the need for efficient and reliable data processing near the data sources using energy-efficient devices. Two methods based on low-cost edge-IoT architectures are proposed to implement lightweight Machine Learning (ML) models that estimate indoor environmental quality (IEQ) parameters, such as Artificial Neural Networks of Multilayer Perceptron type. Their implementation is based on centralised and distributed parallel IoT architectures, connected via wireless, which share commercial off-the-self modules for data acquisition and sensing, such as sensors for temperature, humidity, illuminance, CO2, and other gases. The centralised method uses a Graphics Processing Unit and the Message Queuing Telemetry Transport protocol, but the distributed method utilises low performance ARM-based devices and the Message Passing Interface protocol. Although multiple IEQ parameters are measured, the training and testing of ML models is accomplished with experiments focused on small temperature and illuminance datasets to reduce data processing load, obtained from sudden spikes, square profiles and sawteeth test cases. The results show a high estimation performance with F-score and Accuracy values close to 0.95, and an almost theorical Speedup with a reduction in power consumption close to 37% in the distributed parallel approach. In addition, similar or slightly better performance is achieved compared to equivalent IoT architectures from related research, but error reduction of 35 to 76% is accomplished with an adequate balance between performance and energy efficiency.

Arturo Silva-Campillo, J.C. Suárez-Bermejo, M.A. Herreros-Sierra

The aim of the work is to evaluate different design alternatives to obtain criteria for the selection of the most effective design by fatigue strength assessment of the local cut-out as a result of the connection between the longitudinal or ordinary stiffener and the transverse web frame (longi-web) in the side hull structure (upper wing torsional box), very important area due to its high stress concentration, of a container vessel, one of the most important ships in terms of its influence on the world economy. Structural solutions and design guidelines are established, by means of numerical models validated by experimental tests, which allow alternative designs to be obtained that improve their fatigue behaviour. Standard cut-out geometries are studied under the presence of different variables (radius of curvature, longitudinal spacing, longitudinal stiffener cross-section, and flange arrangement) that are evaluated to determine their effect in the structural assessment (fatigue damage, stress concentration, and fracture mechanics) and the weight comparison between alternatives.

Malte Pedersen, Marianne Nyegaard, Thomas B. Moeslund

The Giant Sunfish (<i>Mola alexandrini</i>) has unique patterns on its body, which allow for individual identification. By continuously gathering and matching images, it is possible to monitor and track individuals across location and time. However, matching images manually is a tedious and time-consuming task. To automate the process, we propose a pipeline based on finding and matching keypoints between image pairs. We evaluate our pipeline with four different keypoint descriptors, namely ORB, SIFT, RootSIFT, and SuperPoint, and demonstrate that the number of matching keypoints between a pair of images is a strong indicator for the likelihood that they contain the same individual. The best results are obtained with RootSIFT, which achieves an mAP of 75.91% on our test dataset (TinyMola+) without training or fine-tuning any parts of the pipeline. Furthermore, we show that the pipeline generalizes to other domains, such as re-identification of seals and cows. Lastly, we discuss the impracticality of a ranking-based output for real-life tasks and propose an alternative approach by viewing re-identification as a binary classification. We show that the pipeline can be easily modified with minimal fine-tuning to provide a binary output with a precision of 98% and recall of 44% on the TinyMola+ dataset, which basically eliminates the need for time-consuming manual verification on nearly half the dataset.

Tao Xia, Hang Li, Haitao Yu et al.

The wireless charging system (WCS) is widely employed to solve the problem of underwater charging of autonomous underwater vehicles (AUVs). However, the AUV is prone to misalignment caused by the tidal currents, which directly leads to fluctuations in transmission efficiency and output power. For this reason, a circular-arc-type (CA-type) magnetic coupler with strong misalignment tolerance was proposed in this article. Compared with the ring-type magnetic coupler, the proposed magnetic coupler had better magnetic field convergence and lower weight. Further, the effect of dimensional parameters on the CA-type magnetic coupler performance was analyzed by ANSYS Maxwell, with which the parameters of the magnetic coupler were optimized, and its coupling coefficient could finally reach 0.671. To analyze the influence of misalignment on the CA-type magnetic coupler, EE-type and UI-type magnetic cores are compared. Within the same range of rotation misalignment [−10°, 10°] and axial misalignment [−30, 30 mm], the CA-type magnetic core has stronger misalignment adaptability, and it can achieve a stable output power of 575 W and DC-DC efficiency of 92.51% when rotational misalignment occurs. A WCS experimental prototype is built based on one of the magnetic couplers and its experimental results verify the correctness of the theoretical analysis and simulation results.

Bianca Matos de Barros, Douglas Galimberti Barbosa, Cristiano Lima Hackmann

Marine litter poses significant threats to marine and coastal environments, with its impacts ever-growing. Remote sensing provides an advantageous supplement to traditional mitigation techniques, such as local cleaning operations and trawl net surveys, due to its capabilities for extensive coverage and frequent observation. In this study, we used Radiative Transfer Model (RTM) simulated data and data from the Multispectral Instrument (MSI) of the Sentinel-2 mission in combination with machine learning algorithms. Our aim was to study the spectral behavior of marine plastic pollution and evaluate the applicability of RTMs within this research area. The results from the exploratory analysis and unsupervised classification using the KMeans algorithm indicate that the spectral behavior of pollutants is influenced by factors such as the type of polymer and pixel coverage percentage. The findings also reveal spectral characteristics and trends of association and differentiation among elements. The applied methodology is strongly dependent on the data, and if reapplied in new, more diverse, and detailed datasets, it can potentially generate even better results. These insights can guide future research in remote sensing applications for detecting marine plastic pollution.

Talya ten Brink

Climate change substantially impacts ecological systems. Marine species are shifting their distribution because of climate change towards colder waters, potentially compromising the benefits of currently established Marine Protected Areas (MPAs). Therefore, we demonstrate how three case study regions will be impacted by warming ocean waters to prepare stakeholders to understand how the fisheries around the MPAs is predicted to change. We chose the case studies to focus on large scale MPAs in i) a cold, polar region, ii) a tropical region near the equator, and iii) a tropical region farther from the equator. We quantify the biological impacts of shifts in species distribution due to climate change for fishing communities that depend on the Palau National Marine Sanctuary, the Great Barrier Reef Marine National Park Zone, and the North Bering Sea Research Area MPAs. We find that fisheries sectors will be impacted differently in different regions and show that all three regions can be supported by this methodology for decision making that joins sector income and species diversity.