Hasil untuk "Ocean engineering"

Viola-Joanna Stamer, Panagiotis Agrafiotis, Behnood Rasti et al.

Accurate ocean mapping is essential for applications such as bathymetry estimation, seabed characterization, marine litter detection, and ecosystem monitoring. However, ocean remote sensing (RS) remains constrained by limited labeled data and by the reduced transferability of models pre-trained mainly on land-dominated Earth observation imagery. In this paper, we propose OceanMAE, an ocean-specific masked autoencoder that extends standard MAE pre-training by integrating multispectral Sentinel-2 observations with physically meaningful ocean descriptors during self-supervised learning. By incorporating these auxiliary ocean features, OceanMAE is designed to learn more informative and ocean-aware latent representations from large- scale unlabeled data. To transfer these representations to downstream applications, we further employ a modified UNet-based framework for marine segmentation and bathymetry estimation. Pre-trained on the Hydro dataset, OceanMAE is evaluated on MADOS and MARIDA for marine pollutant and debris segmentation, and on MagicBathyNet for bathymetry regression. The experiments show that OceanMAE yields the strongest gains on marine segmentation, while bathymetry benefits are competitive and task-dependent. In addition, an ablation against a standard MAE on MARIDA indicates that incorporating auxiliary ocean descriptors during pre-training improves downstream segmentation quality. These findings highlight the value of physically informed and domain-aligned self-supervised pre- training for ocean RS. Code and weights are publicly available at https://git.tu-berlin.de/joanna.stamer/SSLORS2.

Haimo Joehri

This paper gives an overview about aspects of mechanical engineering of undulators. It is based mainly on two types that are used in the SwissFEL facility. The U15 Undulator is an example of an in-vacuum type and the UE38 is an APPLE-X type. It describes the frame, the adjustment of the magnets with flexible keepers and the adjustment of the whole device with eccentric movers.

Ana Arauzo

This lecture presents an overview of the basic concepts and fundamentals of Engineering Materials within the framework of accelerator applications. After a short introduction, main concepts relative to the structure of matter are reviewed, like crystalline structures, defects and dislocations, phase diagrams and transformations. The microscopic description is correlated with physical properties of materials, focusing in metallurgical aspects like deformation and strengthening. Main groups of materials are addressed and described, namely, metals and alloys, ceramics, polymers, composite materials, and advanced materials, where brush-strokes of tangible applications in particle accelerators and detectors are given. Deterioration aspects of materials are also presented, like corrosion in metals and degradation in plastics.

E. Frajka-Williams, F. Landerer, T. Lee

\textit{In situ} observations of transbasin deep ocean transports at $26^\circ$N show variability on monthly to decadal timescales (2004--2015). Satellite-based estimates of ocean bottom pressure from the Gravity Recovery and Climate Experiment (GRACE) satellites were previously used to estimate interannual variability of deep ocean transports at $26^\circ$N. Here, we use GRACE ocean bottom pressure, reanalysis winds and \textit{in situ} transport estimates at $26^\circ$N to diagnose the large-scale response of the deep ocean circulation to wind-forcing. We find that deep ocean transports -- including those associated with a reversal of the Atlantic meridional overturning circulation in 2009/10 and 2010/11 -- are part of a large-scale response to wind stress curl over the intergyre-gyre region. Wind-forcing dominates deep ocean circulation variability on monthly timescales, but interannual fluctuations in the residual \textit{in situ} transports (after removing the wind-effect) are also captured by GRACE bottom pressure measurements. On decadal timescales, uncertainty in regional trends in GRACE ocean bottom pressure preclude investigation of decadal-timescale transport trends.

Peng Li, Jinlei Mu, Zhanzhi Wang et al.

To identify the evolution of the vortices hidden in the wake behind a shaft-less pump-jet propulsor, the delayed detached eddy simulation is performed on a computational domain comprising 22.8 million cells to analyze the wake evolution behind a shaftless pump-jet propulsor under various uniform freestream conditions. Convergence analysis and validation against available data confirm the reliability of the numerical approach. Vortex structures are visualized by using the Q-criterion iso-surfaces, while instantaneous and phase-locked averaged flow variables are employed to elucidate the detailed wake dynamics. The numerical results reveal a novel wake evolution mechanism characterized by two wake transition points: Before the first wake transition point, duct-induced vortices rapidly dissipate, leaving the wake dominated by blade/stator trailing vortices and the hub vortex. Between the two wake transition points, the wake is primarily governed by the scythe-shaped vortices (evolved from earlier vortex structures) and the persistent hub vortex. Beyond the second wake transition point, the legs of the scythe-shaped vortices merge with each other and detach from the knee region, triggering instability and fragmenting into smaller-scale structures, which eventually turn into the far-field wake. This study provides valuable insights for the hydrodynamic optimization of the shaftless pump-jet propulsor.

Lucia Porlan-Ferrando, J. David Nuñez-Gonzalez, Alain Ulazia Manterola et al.

Accurate prediction of extreme waves, particularly the maximum wave height and the ratio between the maximum and significant wave heights of individual waves, is crucial for maritime safety and the resilience of offshore infrastructure. This study employs machine learning (ML) techniques such as linear regression modeling (LM), support vector regression (SVR), long short-term memory (LSTM), and gated recurrent units (GRU) to develop predictive models based on historical data (1990–2024) obtained from a buoy at a specific oceanic location. The results show that the SVR model provides the highest accuracy in predicting the maximum wave height (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>H</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></semantics></math></inline-formula>), achieving a coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>) of 0.9006 and mean squared error (MSE) of 0.0185. For estimation of the ratio between maximum and significant wave heights (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>H</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub><mo>/</mo><msub><mi>H</mi><mi>s</mi></msub></mrow></semantics></math></inline-formula>), the SVR and LM models exhibit comparable performance, with MSE values of 0.0229. These findings have significant implications for improving early warning systems, optimizing the structural design of offshore infrastructure, and enhancing the efficiency of energy extraction under changing climate conditions.

Fangrun Yao, Dahai Gao, Xinghong Yan

The archeospores produced from the blades of Pyropia yezoensis could develop into new blades, which is of significant both in cultivation and research. Nevertheless, the molecular mechanisms behind the formation and release of archeospores remained unclear. In this study, two strains of P. yezoensis with similar genetic backgrounds and opposite abilities for archeospores formation were used for genomic and transcriptomic analysis. Based on whole-genome resequencing, a total of 54,439 SNPs and 12,922 InDels were detected. Specifically, 211 SNPs and 8 InDels in coding regions could introduce codon change or frameshift mutation, resulted in sequence variations of corresponding encoded proteins. Furthermore, a total of 2888 differentially expressed genes (DEGs) between two strains were identified based on transcriptomic analysis, and 68 DEGs shared SNPs or InDels according to resequencing analysis, which may be associated with the processes related to the formation of archeospores. This study integrates genomic approaches to identify candidate genes and loci related to archeospores formation in P. yezoensis, laying a foundation for elucidating the regulatory mechanisms of archeospores formation and release.

Lianhui Liang, Shuai Yuan, Yixuan Zeng et al.

Hyperspectral image classification (HSIC) is a crucial task in remote sensing. In existing HSIC architectures, convolutional neural networks excel at capturing local information through regional feature representations, while transformers are adept at establishing long-range dependencies with the self-attention mechanism. However, these methods still encounter challenges of imbalanced global&#x2013;local feature explorations and boundary feature extractions. To address these issues, this study proposes the cross-stage attention edge enhancement and Fourier-wavelet transformer integrated network (CAEEFT-Net), which effectively balances global context modeling with local detail preservation and boundary feature extraction for HSIC tasks. Specifically, for spatial feature refinement, three key modules are designed: the cross-stage attention module to enable the interaction of features across different stages, thereby strengthening the model&#x2019;s feature representation ability, the global&#x2013;local attention module to jointly enhance global and local features, and the pyramid-stripe attention module to capture discriminative edge features. For spectral feature extraction, this article proposes a spectral Fourier-wavelet transformer to integrate the strengths of both global frequency-domain patterns and local token-level features. Experimental results on three benchmark datasets demonstrate that CAEEFT-Net achieved superior performance compared to state-of-the-art methods, validating the effectiveness of the proposed CAEEFT-Net model for HSIC.

Dongho Jeon, Haemin Song, Younghoon Bae et al.

Delayed ettringite formation (DEF) poses a serious durability risk in precast concrete elements, particularly when exposed to high-temperature curing and cyclic environmental conditions. Although DEF mechanisms have been reported, the combined influence of steam curing and cyclic exposure, as well as the mitigating role of ground granulated blast-furnace slag (GGBFS), remains insufficiently clarified. This study evaluated Portland cement mortars incorporating 0, 20, and 40 % GGBFS subjected to steam curing at up to 90 °C followed by cyclic dry–wet exposure. Mortars without GGBFS exhibited visible surface cracking and up to 0.16 % expansion after 196 days, whereas GGBFS mixtures remained crack-free with expansions below 0.025 %. Microstructural analyses revealed that DEF suppression was associated with reductions in the SO3/Al2O3 molar ratio (1.41→0.88), Na2Oeq (1.32→1.04), pozzolanic consumption of Ca(OH)2, and microstructural densification. In addition, the S40–90C mixture retained over 98 % of its 28-day strength at 196 days, in contrast to the ∼8 % strength loss observed in the control. These findings clarify the mechanisms by which GGBFS mitigates DEF and provide practical guidance, suggesting that incorporating ≥ 20 % GGBFS is an effective strategy for durable precast high-early-strength mortars.

Frances E. Rigby, Nikku Madhusudhan

Recent studies have suggested the possibility of Hycean worlds, characterised by deep liquid water oceans beneath H$_2$-rich atmospheres. These planets significantly widen the range of planetary properties over which habitable conditions could exist. We conduct internal structure modelling of Hycean worlds to investigate the range of interior compositions, ocean depths and atmospheric mass fractions possible. Our investigation explicitly considers habitable oceans, where the surface conditions are limited to those that can support potential life. The ocean depths depend on the surface gravity and temperature, confirming previous studies, and span 10s to $\sim$1000 km for Hycean conditions, reaching ocean base pressures up to $\sim$6$\times$10$^4$ bar before transitioning to high-pressure ice. We explore in detail test cases of five Hycean candidates, placing constraints on their possible ocean depths and interior compositions based on their bulk properties. We report limits on their atmospheric mass fractions admissible for Hycean conditions, as well as those allowed for other possible interior compositions. For the Hycean conditions considered, across these candidates we find the admissible mass fractions of the H/He envelopes to be $\lesssim$10$^{-3}$. At the other extreme, the maximum H/He mass fractions allowed for these planets can be up to $\sim$4-8$\%$, representing purely rocky interiors with no H$_2$O layer. These results highlight the diverse conditions possible among these planets and demonstrate their potential to host habitable conditions under vastly different circumstances to the Earth. Upcoming JWST observations of candidate Hycean worlds will allow for improved constraints on the nature of their atmospheres and interiors.

Md. Toukir Ahmed, Ocean Monjur, Mohammed Kamruzzaman

Hyperspectral imaging (HSI) has recently emerged as a promising tool for many agricultural applications; however, the technology cannot be directly used in a real-time system due to the extensive time needed to process large volumes of data. Consequently, the development of a simple, compact, and cost-effective imaging system is not possible with the current HSI systems. Therefore, the overall goal of this study was to reconstruct hyperspectral images from RGB images through deep learning for agricultural applications. Specifically, this study used Hyperspectral Convolutional Neural Network - Dense (HSCNN-D) to reconstruct hyperspectral images from RGB images for predicting soluble solid content (SSC) in sweet potatoes. The algorithm accurately reconstructed the hyperspectral images from RGB images, with the resulting spectra closely matching the ground-truth. The partial least squares regression (PLSR) model based on reconstructed spectra outperformed the model using the full spectral range, demonstrating its potential for SSC prediction in sweet potatoes. These findings highlight the potential of deep learning-based hyperspectral image reconstruction as a low-cost, efficient tool for various agricultural uses.

Jun Wu, Jingke Zeng, Hao Tang et al.

Navigable tunnels serve as an effective method to connect upstream and downstream navigation structures in mountainous regions with sharp bends. The towing resistance of ships in navigable tunnels, a key technical indicator for towing equipment development, demands focused research. Utilizing the innovative top towing method for tunnels, this study develops a physical model for towed navigable tunnels, conducts ship model tests, and measures and calculates the total resistance of ships towing through navigation under various conditions. Through resistance test results, it analyzes factors influencing the total resistance of ship navigation. The findings reveal: (1) regarding towing speed, at speeds exceeding 1.5 m/s, resistance spikes by 100 kN to 560 kN; (2) concerning water depth, at depths lower than 5.5 m, the impact on a ship navigation’s total resistance is pronounced, reaching 5 to 13 times that of calm water; (3) in terms of flow velocity, at velocities over 2 m/s, the impact on a ship navigation’s total resistance is substantial, amounting to 1.5 to 2 times the resistance at a flow velocity of 1.5 m/s; (4) in comparative analyses, the total resistance of ships towing through navigation in narrow tunnels is significantly higher than calculations based on existing formulas, increasing by 7 to 138 times.

Xiuchuan He, Hui Xiong, Jianming Qi et al.

This paper investigates exact solutions of the potential Burgers equation with variable coefficients (PBEVCs), contributing to communication engineering, nonlinear acoustics, and fluid mechanics. It explores novel solutions using the improved modified extended tanh–function method (IMETFM), revealing diverse characteristics and providing deeper insights into nonlinear wave dynamics. The study also analyzes the long-term behavior of PBEVC under noise influence, highlighting insights into noise-perturbed systems’ robustness. Moreover, it establishes connections to practical applications like 5G optical fiber transmission and ocean wave oscillations, showcasing the wide applicability of the IMETFM approach. This work’s novelty lies in uncovering diverse solution characteristics and bridging theoretical understanding with real-world applications.

Dongyang Fu, Shangfeng Du, Yang Si et al.

In an increasingly globalized world, the intelligent extraction of maritime targets is crucial for both military defense and maritime traffic monitoring. The flexibility and cost-effectiveness of unmanned aerial vehicles (UAVs) in remote sensing make them invaluable tools for ship extraction. Therefore, this paper introduces a training-free, highly accurate, and stable method for ship extraction in UAV remote sensing images. First, we present the dynamic tracking matched filter (DTMF), which leverages the concept of time as a tuning factor to enhance the traditional matched filter (MF). This refinement gives DTMF superior adaptability and consistent detection performance across different time points. Next, the DTMF method is rigorously integrated into a recurrent neural network (RNN) framework using mathematical derivation and optimization principles. To further improve the convergence and robust of the RNN solution, we design an adaptive feedback recurrent neural network (AFRNN), which optimally solves the DTMF problem. Finally, we evaluate the performance of different methods based on ship extraction accuracy using specific evaluation metrics. The results show that the proposed methods achieve over 99% overall accuracy and KAPPA coefficients above 82% in various scenarios. This approach excels in complex scenes with multiple targets and background interference, delivering distinct and precise extraction results while minimizing errors. The efficacy of the DTMF method in extracting ship targets was validated through rigorous testing.

Meixiang Li, Mengge Yu, Jiali Liu et al.

Abstract The current research on the aerodynamic performance of the train running in rainy weather is primarily concerned with the trajectory of the raindrops and the aerodynamic variation of trains caused by raindrops. In fact, water film will generate on the train body when raindrops hit the train, which interacts with the flow field around the train, and would probably affect the aerodynamic performance of the train. In this paper, based on shear stress transport (SST) k-w turbulence model and Euler-Lagrange discrete phase model, the aerodynamic calculation model of a high-speed train under rainfall environment is established. The LWF (Lagrangian wall film) is used to simulate the water film distribution of the high-speed train under different rainfall intensities, and the aerodynamic performance of the train are studied. The calculation results show that raindrops will gather on the train surface and form water film under rainfall environment. With the extension of rainfall time, the thickness and coverage range of water film expand, and the maximum thickness of water film can reach 4.95 mm under the working conditions in this paper. The average thickness of water film on the train body increases with the rainfall intensity. When the rainfall intensity increases from 100 mm/h to 500 mm/h, the average water film thickness will increase by 3.26 times. The velocity of water film in the streamlined area of head car is larger than that in other areas, and the maximum velocity is 22.14 m/s. Compared with the rainless environment condition, the skin friction coefficient of the high-speed train increases and the average value will increase by 10.74% for a rainfall intensity of 500 mm/h. The positive pressure and resistance coefficient of the head car increase with the rainfall intensity. This research proposes a methodology to systematically analyze the generation of water film on the train surface and its influence on the train aerodynamic performance; the analysis can provide more practical results and can serve as a reference basis for the design and development of high-speed trains.

Anan Yaghmour, Saurabh Prasad, Melba M. Crawford

In geospatial image analysis, domain shifts caused by differences between datasets often undermine the performance of deep learning models due to their limited generalization ability. This issue is particularly pronounced in hyperspectral imagery, given the high dimensionality of the per-pixel reflectance vectors and the complexity of the resulting deep learning models. We introduce a semisupervised domain adaptation technique that improves on the adversarial discriminative framework, incorporating a novel multiclass discriminator to address low discriminability and negative transfer issues from which current approaches suffer. Significantly, our method addresses mode collapse by incorporating limited labeled data from the target domain for targeted guidance during adaptation. In addition, we integrate an attention mechanism that focuses on challenging spatial regions for the target mode. We tested our approach on three unique hyperspectral remote sensing datasets to demonstrate its efficacy in diverse conditions (e.g., cloud shadows, atmospheric variability, and terrain). This strategy improves discrimination and reduces negative transfer in domain adaptation for geospatial image analysis.

Behzad Akbari, Ya-Jun Pan, Shiwei Liu et al.

Unmanned Surface Vehicles (USVs) in the ocean environment, considering various spatiotemporal factors such as ocean currents and other energy consumption factors. The paper uses Gaussian Process Motion Planning (GPMP2), a Bayesian optimization method that has shown promising results in continuous and nonlinear motion planning algorithms. The proposed work improves GPMP2 by incorporating a new spatiotemporal factor for tracking and predicting ocean currents using a spatiotemporal Bayesian inference. The algorithm is applied to the USV path planning and is shown to optimize for smoothness, obstacle avoidance, and ocean currents in a challenging environment. The work is relevant for practical applications in ocean scenarios where optimal path planning for USVs is essential for minimizing costs and optimizing performance.

Jie JW Wu

Software companies have widely used online A/B testing to evaluate the impact of a new technology by offering it to groups of users and comparing it against the unmodified product. However, running online A/B testing needs not only efforts in design, implementation, and stakeholders' approval to be served in production but also several weeks to collect the data in iterations. To address these issues, a recently emerging topic, called "Offline A/B Testing", is getting increasing attention, intending to conduct the offline evaluation of new technologies by estimating historical logged data. Although this approach is promising due to lower implementation effort, faster turnaround time, and no potential user harm, for it to be effectively prioritized as requirements in practice, several limitations need to be addressed, including its discrepancy with online A/B test results, and lack of systematic updates on varying data and parameters. In response, in this vision paper, I introduce AutoOffAB, an idea to automatically run variants of offline A/B testing against recent logging and update the offline evaluation results, which are used to make decisions on requirements more reliably and systematically.

Cyril Picard, Jürg Schiffmann, Faez Ahmed

Exploiting the recent advancements in artificial intelligence, showcased by ChatGPT and DALL-E, in real-world applications necessitates vast, domain-specific, and publicly accessible datasets. Unfortunately, the scarcity of such datasets poses a significant challenge for researchers aiming to apply these breakthroughs in engineering design. Synthetic datasets emerge as a viable alternative. However, practitioners are often uncertain about generating high-quality datasets that accurately represent real-world data and are suitable for the intended downstream applications. This study aims to fill this knowledge gap by proposing comprehensive guidelines for generating, annotating, and validating synthetic datasets. The trade-offs and methods associated with each of these aspects are elaborated upon. Further, the practical implications of these guidelines are illustrated through the creation of a turbo-compressors dataset. The study underscores the importance of thoughtful sampling methods to ensure the appropriate size, diversity, utility, and realism of a dataset. It also highlights that design diversity does not equate to performance diversity or realism. By employing test sets that represent uniform, real, or task-specific samples, the influence of sample size and sampling strategy is scrutinized. Overall, this paper offers valuable insights for researchers intending to create and publish synthetic datasets for engineering design, thereby paving the way for more effective applications of AI advancements in the field. The code and data for the dataset and methods are made publicly accessible at https://github.com/cyrilpic/radcomp .

Linying Zhao, Shunping Ji

Rich information in multitemporal satellite images can facilitate pixel-level land cover classification. However, what is the most suitable deep learning architecture for high-dimension spatio-temporal representation of remote sensing time series remains unclear. In this study, we theoretically analyzed the different mechanisms of the different deep learning structures, including the commonly used convolutional neural network (CNN), the high-dimension CNN [three-dimensional (3-D) CNN], the recurrent neural network, and the newest vision transformer (ViT), with regard to learning and representing the temporal information for spatio-temporal data. The performance of the different models was comprehensively evaluated on large-scale Sentinel-1 and Sentinel-2 time-series images covering the whole of Slovenia. First, the 3-D CNN, long short-term memory (LSTM), and ViT, which all have specific structures that preserve temporal information, can effectively extract the spatio-temporal information, with the 3-D CNN and ViT showing the best performance. Second, the performance of the 2-D CNN, in which the temporal information is collapsed, is lower than that of the 3-D CNN, LSTM, and ViT but outperforms the conventional methods. Thirdly,using both optical and synthetic aperture radar (SAR) images performs almost the same as using only optical images, indicating that the information that can be extracted from optical images is sufficient for land-cover classification. However, when optical images are unavailable, SAR imagescan provide satisfactorily classification results. Finally, the modern deep learning methods can effectively overcome the disadvantages in imaging conditions where parts of an image or images of some periods are missing. The testing data are available at <italic><uri>gpcv.whu.edu.cn&#x002F;data</uri></italic>.