Hasil untuk "Ocean engineering"

Michael J. Bianco, P. Gerstoft, James Traer et al.

Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of techniques, which are often based in statistics, for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in four acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, and environmental sounds in everyday scenes.

Fabrice Ardhuin, Alejandro Orfila

Wind-generated waves dominate sea surface motions for periods shorter than 300 seconds. Waves are of interest for many applications ranging from navigation safety to ocean and coastal engineering. Waves also define air-sea fluxes and have important interactions with surface currents, upper ocean turbulence, and sea ice. Given the general focus of this book, we emphasize here the successes of wave forecasting methods, starting with a review of basic principles and how wave energy and momentum are modeled. In particular, we discuss the connection between wave modeling and remote sensing, and opportunities for joint measurements of currents and waves. A more detailed account of wave research and applications to geosciences can be found in Ardhuin (2018). theory able to study the propagation of waves from deep to shallow waters including all the physics of the wave transformation phenomenon (all dispersion ranges).

H. Jung, Giyoung Shin, Hojung Kwak et al.

Human society has become increasingly reliant on plastic because it allows for convenient and sanitary living. However, recycling rates are currently low, which means that the majority of plastic waste ends up in landfills or the ocean. Increasing recycling and upcycling rates is a critical strategy for addressing the issues caused by plastic pollution, but there are several technical limitations to overcome. This article reviews advancements in polymer technology that aim to improve the efficiency of recycling and upcycling plastic waste. In food packaging, natural polymers with excellent gas barrier properties and self-cleaning abilities have been introduced as environmentally friendly alternatives to existing materials and to reduce food-derived contamination. Upcycling and valorization approaches have emerged to transform plastic waste into high-value-added products. Recent advancements in the development of recyclable high-performance plastics include the design of super engineering thermoplastics and engineering chemical bonds of thermosets to make them recyclable and biodegradable. Further research is needed to develop more cost-effective and scalable technologies to address the plastic pollution problem through sustainable recycling and upcycling.

Shuyun Yuan, Ying Li, Fangwen Bao et al.

Basic monitoring of the marine environment is crucial for the early warning and assessment of marine hydrometeorological conditions, climate change, and ecosystem disasters. In recent years, many marine environmental monitoring platforms have been established, such as offshore platforms, ships, or sensors placed on specially designed buoys or submerged marine structures. These platforms typically use a variety of sensors to provide high-quality observations, while they are limited by low spatial resolution and high cost during data acquisition. Satellite remote sensing allows monitoring over a larger ocean area; however, it is susceptible to cloud contamination and atmospheric effects that subject the results to large uncertainties. Unmanned vehicles have become more widely used as platforms in marine science and ocean engineering in recent years due to their ease of deployment, mobility, and the low cost involved in data acquisition. Researchers can acquire data according to their schedules and convenience, offering significant improvements over those obtained by traditional platforms. This study presents the state-of-the-art research on available unmanned vehicle observation platforms, including unmanned aerial vehicles (UAVs), underwater gliders (UGs), unmanned surface vehicles (USVs), and unmanned ships (USs), for marine environmental monitoring, and compares them with satellite remote sensing. The recent applications in marine environments have focused on marine biochemical and ecosystem features, marine physical features, marine pollution, and marine aerosols monitoring, and their integration with other products are also analysed. Additionally, the prospects of future ocean observation systems combining unmanned vehicle platforms (UVPs), global and regional autonomous platform networks, and remote sensing data are discussed.

Shengnan Tang, Yong Zhu, Shouqi Yuan

Hydraulic axial piston pump is broadly-used in aerospace, ocean engineering and construction machinery since it is the vital component of fluid power systems. In the light of the undiscoverability of its fault and the potential serious losses, it is valuable and challenging to complete the fault identification of a hydraulic pump accurately and effectively. Owing to the limitations of shallow machine learning methods in the intelligent fault diagnosis, more attention has been paid to deep learning methods. Hyperparameter plays an important role in a deep learning model. Although some manual tuning methods may represent good results in some cases, it is hard to reproduce due to the differences of datasets and other factors. Hence, Bayesian optimization (BO) algorithm is adopted to automatically select the hyperparameters. Firstly, the time-frequency images of vibration signals by continuous wavelet transform are taken as input data. Secondly, by setting some hyperparameters, a preliminary convolutional neural network (CNN) model is established. Thirdly, by identifying the range of each hyperparameter, BO based on Gaussian process is employed to construct an adaptive CNN model named CNN-BO. The performance of CNN-BO is verified by comparing with traditional LeNet 5 and improved LeNet 5 with manual optimization. The results indicate that CNN-BO can accomplish the intelligent fault diagnosis of a hydraulic pump accurately.

Alexandra González, Xavier Franch, Silverio Martínez-Fernández

Integrating Artificial Intelligence into Software Engineering (SE) requires having a curated collection of models suited to SE tasks. With millions of models hosted on Hugging Face (HF) and new ones continuously being created, it is infeasible to identify SE models without a dedicated catalogue. To address this gap, we present SEMODS: an SE-focused dataset of 3,427 models extracted from HF, combining automated collection with rigorous validation through manual annotation and large language model assistance. Our dataset links models to SE tasks and activities from the software development lifecycle, offering a standardized representation of their evaluation results, and supporting multiple applications such as data analysis, model discovery, benchmarking, and model adaptation.

Mairieli Wessel, Daniel Feitosa, Sangeeth Kochanthara

Rising publication pressure and the routine use of generative AI tools are reshaping how software engineering research is produced, assessed, and taught. While these developments promise efficiency, they also raise concerns about skill degradation, responsibility, and trust in scholarly outputs. This vision paper employs Design Fiction as a methodological lens to examine how such concerns might materialise if current practices persist. Drawing on themes reported in a recent community survey, we construct a speculative artifact situated in a near future research setting. The fiction is used as an analytical device rather than a forecast, enabling reflection on how automated assistance might impede domain knowledge competence, verification, and mentoring practices. By presenting an intentionally unsettling scenario, the paper invites discussion on how the software engineering research community in the future will define proficiency, allocate responsibility, and support learning.

Golnoush Abaei, Mojtaba Shahin, Maria Spichkova

The inclusion of internship courses in Software Engineering (SE) programs is essential for closing knowledge gaps and improving graduates' readiness for the software industry. Our study focuses on year-long internships at RMIT University (Melbourne, Australia), which offers in-depth industry engagement. We analysed how the course evolved over the last 10 years to incorporate students' needs and summarised the lessons learned that can be helpful for other educators supporting internship courses. Our qualitative analysis of internship data based on 91 reports during 2023-2024 identified three challenge themes the students faced, and which courses were found by students to be particularly beneficial during their internships. On this basis, we proposed recommendations for educators and companies to help interns overcome challenges and maximise their learning experience.

Massimiliano Di Penta, Kelly Blincoe, Marsha Chechik et al.

As software engineering conferences grow in size, rising costs and outdated formats are creating barriers to participation for many researchers. These barriers threaten the inclusivity and global diversity that have contributed to the success of the SE community. Based on survey data, we identify concrete actions the ACM Special Interest Group on Software Engineering (SIGSOFT) can take to address these challenges, including improving transparency around conference funding, experimenting with hybrid poster presentations, and expanding outreach to underrepresented regions. By implementing these changes, SIGSOFT can help ensure the software engineering community remains accessible and welcoming.

R. Ni

Fragmentation of bubbles and droplets in turbulence produces a dispersed phase spanning a broad range of scales, encompassing everything from droplets in nanoemulsions to centimeter-sized bubbles entrained in breaking waves. Along with deformation, fragmentation plays a crucial role in enhancing interfacial area, with far-reaching implications across various industries, including food, pharmaceuticals, and ocean engineering. However, understanding and modeling these processes are challenging due to the complexity of anisotropic and inhomogeneous turbulence typically involved, the unknown residence time in regions with different turbulence intensities, and difficulties arising from the density and viscosity ratios. Despite these challenges, recent advances have provided new insights into the underlying physics of deformation and fragmentation in turbulence. This review summarizes existing works in various fields, highlighting key results and uncertainties, and examining the impact on turbulence modulation, drag reduction, and heat and mass transfer. Expected final online publication date for the Annual Review of Fluid Mechanics, Volume 56 is January 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Chenjie Zhao, Ryan Wen Liu, Jingxiang Qu et al.

With the advancement of maritime unmanned aerial vehicles (UAVs) and deep learning technologies, the application of UAV-based object detection has become increasingly significant in the fields of maritime industry and ocean engineering. Endowed with intelligent sensing capabilities, the maritime UAVs enable effective and efficient maritime surveillance. To further promote the development of maritime UAV-based object detection, this paper provides a comprehensive review of challenges, relative methods, and UAV aerial datasets. Specifically, in this work, we first briefly summarize four challenges for object detection on maritime UAVs, i.e., object feature diversity, device limitation, maritime environment variability, and dataset scarcity. We then focus on computational methods to improve maritime UAV-based object detection performance in terms of scale-aware, small object detection, view-aware, rotated object detection, lightweight methods, and others. Next, we review the UAV aerial image/video datasets and propose a maritime UAV aerial dataset named MS2ship for ship detection. Furthermore, we conduct a series of experiments to present the performance evaluation and robustness analysis of object detection methods on maritime datasets. Eventually, we give the discussion and outlook on future works for maritime UAV-based object detection. The MS2ship dataset is available at \href{https://github.com/zcj234/MS2ship}{https://github.com/zcj234/MS2ship}.

Muhammad Ahmad, Francesco Mauro, Rana Aamir Raza et al.

Hyperspectral image (HSI) classification presents inherent challenges due to high spectral dimensionality, significant domain shifts, and limited availability of labeled data. To address these issues, we propose a novel Active Transfer Learning (ATL) framework built upon a spatial-spectral transformer (SST) backbone. The framework integrates multistage transfer learning with an uncertainty-diversity-driven active learning mechanism that strategically selects highly informative and diverse samples for annotation, thereby significantly reducing labeling costs and mitigating sample redundancy. A dynamic layer freezing strategy is introduced to enhance transferability and computational efficiency, enabling selective adaptation of model layers based on domain shift characteristics. Furthermore, we incorporate a self-calibrated attention mechanism that dynamically refines spatial and spectral weights during adaptation, guided by uncertainty-aware feedback. A diversity-promoting sampling strategy ensures broad spectral coverage among selected samples, preventing overfitting to specific classes. Extensive experiments on benchmark cross-domain HSI datasets demonstrate that the proposed SST–ATL framework achieves superior classification performance compared to conventional approaches.

Ning Wang, Jiaxuan Yin, Haoran Li et al.

Abstracts: The current widespread use of lithium-ion batteries (LIBs) in transportation and consumer electronics has spurred the pursuit of developing cathode materials with enhanced energy density, aiming to commercialize LIBs with improved performance. Mn-based Li-rich layered oxides, among the various types of cathode materials, possess outstanding merits such as high energy density, low cost, and environmentally friendly, which make them the most promising commercial cathode materials for LIBs. However, the low initial cycle efficiency, voltage and capacity attenuation, and phase transformation significantly slow down the process of commercial application. The essential origin of the above drawbacks is the redox reaction from the lattice oxygen in the initial uptake/release process. Based on the advanced characterizations and theoretical analysis, researchers have gained a deep understanding of the fundamental issues and subsequent solution strategies. Firstly, this present article provides a comprehensive review of the redox reaction mechanism involving lattice oxygen in liquid lithium-ion battery avenue, focusing on the perspective of electronic energy levels. Then, the article provides an in-depth analysis and summary of the relevant solution strategies, as well as a detailed overview of the application and challenges of Li-rich cathode materials in all-solid-state lithium-ion batteries (ASSLBs). The primary objective of this review is to offer targeted guidance for the development of Li-rich cathodes that are both highly efficient and safe, with a particular emphasis on their potential application in the future all-solid-state battery technology.

Jie Lian, Jiahao Shao, Hui Yu et al.

Tropical cyclones (TCs) are highly destructive weather phenomena that cause extensive human and economic losses in affected regions. Accurate prediction of tropical cyclone intensity (TCI) is crucial for disaster preparedness and mitigation. Traditional TCI forecasting methods fail to extract nonlinear features and suffer from high computation costs. In recent years, deep learning methods have been increasingly used to address this challenge. However, current approaches often underutilize meteorological variables and satellite cloud imagery, and fail to capture correlations between multimodal data. In this article, we propose TCIque, a sequence-to-sequence model specifically designed for TCI forecasting. TCIque is designed to integrate multimodal data and retrieve correlational features between them based on the Wide and Deep concept. The “Wide” component leverages domain knowledge to extract statistical features, while the “Deep” component captures nonlinear correlations and spatio-temporal dynamics based on self-attention mechanisms. This unique combination allows the model to fully utilize diverse data sources, such as meteorological variables, satellite imagery, and expert-driven features, ensuring robust feature fusion. Furthermore, a predictive encoder–decoder architecture associated with the self-attention mechanism is employed to address the challenge of long-term dependency decay. Experimental results demonstrate that the TCIque model outperforms existing methods, achieving more accurate performance in TCI prediction by 60.9%, 51.6%, 39.2%, and 1.8% compared to the best performance of baselines, which includes ConvLSTM, PredRNN, TC-Pred, SCSTque, SAF-Net, TCI-Net, Tint, and Pred_3d at 6h, 12h, 18h, and 24h forecast, respectively.

Mahmoud I. Ibrahim, María J. Legaz, Adel A. Banawan et al.

In this paper, the aim is to optimise the hydrodynamic performance of the novel fin-ring horizontal axis hydrokinetic turbine (HAHK). The original unique fin-ring turbine is an unconventional marine current turbine that comprises seven concentric rings with 88 connecting cambered fins and a solid centre hub. To begin with, the hydrodynamic performance of the benchmark turbine is evaluated using CFD simulations and is validated against sea-test data available in the literature. Subsequently, three of the turbine design parameters, namely, the fins’ pitch angle, the fins’ camber length, and the fins’ aspect ratio, are optimised for maximum power generation. Further test simulations illustrated the existence of a laminar region of flow in the turbine flow field. The K-kL-ω transition-sensitive turbulence model is adopted to capture the influence of transition on the flow field with results compared against those of the fully turbulent K-ε turbulence model. A final fine-tuning in the turbine design is carried out by increasing the number of fins per ring in the outermost rings to further maximise the generated power. The turbine hydrodynamic performance is assessed by comparison against other conventional hydrokinetic turbines available in the literature. Very satisfactory results are obtained with an increase of about 35% in the turbine-generated C_P as compared to that of the benchmark turbine. The turbine performance compares very well with other conventional turbines, especially in terms of higher peak C_P values, wider operating TSR range, and less sensitivity to variations in the inflow current speeds.

Zirui Li, Stephan Husung, Haoze Wang

Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across independently developed system models. SysML v2 introduces enhanced structural modularity and formal semantics, offering a stronger foundation for interoperable modeling. Meanwhile, GPT-based Large Language Models (LLMs) provide new capabilities for assisting model understanding and integration. This paper proposes a structured, prompt-driven approach for LLM-assisted semantic alignment of SysML v2 models. The core contribution lies in the iterative development of an alignment approach and interaction prompts, incorporating model extraction, semantic matching, and verification. The approach leverages SysML v2 constructs such as alias, import, and metadata extensions to support traceable, soft alignment integration. It is demonstrated with a GPT-based LLM through an example of a measurement system. Benefits and limitations are discussed.

Jake Zappin, Trevor Stalnaker, Oscar Chaparro et al.

This position paper examines the substantial divide between academia and industry within quantum software engineering. For example, while academic research related to debugging and testing predominantly focuses on a limited subset of primarily quantum-specific issues, industry practitioners face a broader range of practical concerns, including software integration, compatibility, and real-world implementation hurdles. This disconnect mainly arises due to academia's limited access to industry practices and the often confidential, competitive nature of quantum development in commercial settings. As a result, academic advancements often fail to translate into actionable tools and methodologies that meet industry needs. By analyzing discussions within quantum developer forums, we identify key gaps in focus and resource availability that hinder progress on both sides. We propose collaborative efforts aimed at developing practical tools, methodologies, and best practices to bridge this divide, enabling academia to address the application-driven needs of industry and fostering a more aligned, sustainable ecosystem for quantum software development.

Martin Obaidi, Marc Herrmann, Jil Klünder et al.

Software development relies heavily on text-based communication, making sentiment analysis a valuable tool for understanding team dynamics and supporting trustworthy AI-driven analytics in requirements engineering. However, existing sentiment analysis tools often perform inconsistently across datasets from different platforms, due to variations in communication style and content. In this study, we analyze linguistic and statistical features of 10 developer communication datasets from five platforms and evaluate the performance of 14 sentiment analysis tools. Based on these results, we propose a mapping approach and questionnaire that recommends suitable sentiment analysis tools for new datasets, using their characteristic features as input. Our results show that dataset characteristics can be leveraged to improve tool selection, as platforms differ substantially in both linguistic and statistical properties. While transformer-based models such as SetFit and RoBERTa consistently achieve strong results, tool effectiveness remains context-dependent. Our approach supports researchers and practitioners in selecting trustworthy tools for sentiment analysis in software engineering, while highlighting the need for ongoing evaluation as communication contexts evolve.