Hasil untuk "Ocean engineering"

Al Muttakin, Saikat Mondal, Chanchal K. Roy

Replication packages are crucial for enabling transparency, validation, and reuse in software engineering (SE) research. While artifact sharing is now a standard practice and even expected at premier SE venues such as ICSE, the practical usability of these replication packages remain underexplored. In particular, there is a marked lack of studies that comprehensively examine the executability and reproducibility of replication packages in SE research. In this paper, we aim to fill this gap by evaluating 100 replication packages published in ICSE proceedings over the past decade (2015 - 2024). We assess the (1) executability of the replication packages, (2) efforts and modifications required to execute them, (3) challenges that prevent executability, and (4) reproducibility of the original findings for those that are executable. We spent approximately 650 person-hours in total to execute the artifacts and reproduce the study findings. Our analysis shows that only 40 of the 100 evaluated artifacts were fully executable. Among these, 32.5% ran without any modification. However, even executable artifacts required varying levels of effort: 17.5% required low effort, while 82.5% required moderate to high effort to execute successfully. We identified five common types of modifications and 13 challenges that lead to execution failure, encompassing environmental, documentation, and structural issues. Among the executable artifacts, only 35% (14 out of 40) reproduced the original results. These findings highlight a notable gap between artifact availability, executability, and reproducibility. Our study proposes three actionable guidelines to improve the preparation, documentation, and review of research artifacts, thereby strengthening the rigor and sustainability of open science practices in SE research.

Pavel Perezhogin, Alistair Adcroft, Laure Zanna

Global ocean models exhibit biases in the mean state and variability, particularly at coarse resolution, where mesoscale eddies are unresolved. To address these biases, parameterization coefficients are typically tuned ad hoc. Here, we formulate parameter tuning as a calibration problem using Ensemble Kalman Inversion (EKI). We optimize parameters of a neural network parameterization of mesoscale eddies in two idealized ocean models at coarse resolution. The calibrated parameterization reduces errors in the time-averaged fluid interfaces and their variability by approximately a factor of two compared to the unparameterized model or the offline-trained parameterization. The EKI method is robust to noise in time-averaged statistics arising from chaotic ocean dynamics. Furthermore, we propose an efficient calibration protocol that bypasses integration to statistical equilibrium by carefully choosing an initial condition. These results demonstrate that systematic calibration can substantially improve coarse-resolution ocean simulations and provide a practical pathway for reducing biases in global ocean models.

Zheng Cheng, Wenri Wang, Guang-Yong Chen et al.

Underwater image enhancement techniques typically rely on multiscale feature extraction to restore images degraded by light absorption and scattering. This article challenges that dominant paradigm by demonstrating that a meticulously designed single-scale architecture can achieve highly comparable performance to multiscale counterparts, while significantly reducing model complexity. We propose the single-scale decomposition network (SSD-Net), an innovative framework that explores the full potential of single-scale representations. SSD-Net introduces an asymmetric pipeline to decouple the input into a scene-intrinsic clean layer and a medium-induced degradation layer. This is achieved through two core synergistic modules: first, the parallel feature decomposition block, which utilizes a sparse Transformer and CNNs for dual-branch feature disentanglement, and second, the bidirectional feature communication block, which enables cross-layer residual interactions for mutual refinement. This design preserves decomposition independence while establishing dynamic information pathways, maximizing the efficacy of single-scale features. Compared to state-of-the-art multiscale approaches, SSD-Net achieves superior enhancement quality with substantially fewer parameters and computations.

Xue Li, Ziang Wang, Xueyu Chen et al.

In practical applications of aerial object detection, real-time uncrewed aerial vehicle (UAV) imagery is often affected by noise, low light, and cloud occlusion, leading to poor image quality. The performance of mainstream UAV object detection algorithms tends to degrade when applied to such imagery, as these models are typically trained and evaluated on clean datasets. To address these challenges, we propose a robust YOLO-based network, YOLO-atrous feature pyramid (AFP), which integrates an AFP module. This allows the model to generalize effectively under various corrupted conditions, despite being trained only on clean data. First, we introduced AFP module, which employs atrous convolutions with varying dilation rates, and integrate it into the path aggregation network to expand the receptive field. This enhancement allows the model to better capture object-background relationships and reduce feature corruption caused by local pixel changes. Second, we propose a robust ResNet-spatial pyramid pooling fast (SPPF) as the backbone network, which retains strong feature extraction capabilities while having fewer residual connections compared to YOLO’s Darknet. This design effectively mitigates the impact of corrupted image features on subsequent feature extraction. To validate the effectiveness of our method, we constructed two new datasets based on the DOTAv1.0 dataset, named DOTA-HC and DOTA-HCloud. Experimental results demonstrate that on the DOTA-HC dataset, YOLO-AFP achieved an mean performance under corruption of 60.8% and an relative performance under corruption (rPC) of 80.3%, outperforming the best real-time detection model by 1.5% and 2%, respectively. On the DOTA-HCloud dataset, YOLO-AFP achieved an rPC of 88.5%, surpassing the top model by 1.1% .

Junde Chen, Wenzhao Li, Surendra Maharjan et al.

Hyperspectral image (HSI) band selection (BS) plays a crucial role in HSI dimensionality reduction, aiming to identify a representative subset of bands with minimal redundancy. However, conventional BS approaches primarily operate in the Euclidean domain, often overlooking the structural characteristics of pixels and spectral bands, such as spatial continuity and spectral dependencies. In addition, they handle each HSI as an integrated unit to harness implicit spatial information, disregarding spatial distribution variations across different homogeneous regions. To fully leverage structural information, this study introduces a novel BS method, termed the dual heterogeneous graph convolutional network with enhanced self-representation (ESR-HGCN), for HSI BS. The heterogeneous graph convolutional network (HGCN) and enhanced self-representation (ESR) serve as the two essential components of the proposed ESR-HGCN. To explore spatial features and the potential hidden interactions among spectral bands, we use the HGCN as the backbone network for heterogeneous graph-based HSI BS. Dual graphs at the pixel and band levels are separately constructed and integrated into the ESR module, where a sparsity constraint is enforced and the original Frobenius norm is replaced with <inline-formula><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula>- and <inline-formula><tex-math notation="LaTeX">$\ell _{2,1}$</tex-math></inline-formula>-norm regularizations to achieve robust BS. Meanwhile, dual graph convolution operations are performed to separately extract spatial and spectral features, thereby seamlessly integrating spectral, spatial, and geometric information, offering significant advantages for HSI BS. Finally, an effective optimization scheme is developed to refine the proposed approach. Experimental findings on representative HSI datasets highlight the superiority of ESR-HGCN over state-of-the-art methods.

Jie Li, Chenbo Yang, Chengyong Zhu et al.

Lodging, a major agricultural issue, significantly compromises the yield, stability, and quality of oilseed crops, particularly rapeseed (Brassica napus L.). Real-time monitoring and accurate assessment of lodging are critical for precise yield estimation and the development of lodging-resistant varieties. However, traditional methods for quantifying lodging rates, which rely on manual measurements of lodged plant proportions, are often labor-intensive and prone to inaccuracies, limiting their utility in large-scale breeding programs. This article provides an indirect method for lodging assessment by simplifying the lodging issue to the enumeration of upright plants. First, we use a deep learning model for plant counting from Unmanned aerial vehicle (UAV) imagery in plot level. A novel CMRNet model is developed for upright plants counting and localization, leveraging a hybrid CNN-Mamba backbone network. The model synergizes local feature extraction via CNN with the global modeling strengths of the Mamba state space model, yielding semantically rich features while significantly enhancing computational efficiency and inference speed. Then, we created a new Upright Rapeseed Center Point (URCP) dataset using high-altitude UAV remote sensing orthoimages, encompassing rapeseed fields at various maturity stages and lodging degrees. Training and validation of CMRNet on the URCP dataset yielded exceptional performance metrics, with mean absolute error (MAE) of 5.70, relative root mean square error (rrMSE) of 8.08, and coefficient of determination (R<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>) of 0.9220. These results significantly outperformed existing TasselNetV2, RapeNet, and RPNet models. The number of parameters in our model is only 7.94 M, which is lower than SOTA counting networks. In addition, we also verified the robustness on different rape materials in two years, 2023 and 2025, and the R<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> were all above 0.8, indicating that the model should cope with different field conditions.

Yuqi Cheng, Jia Li, Xiaochen Xiu et al.

Non-steroidal anti-inflammatory drugs (NSAIDs) have received increasing attention owing to their ubiquitous occurrence in environmental water systems and adverse effects. In order to monitor trace levels of NSAIDs from complex water samples, development of facile and efficient sample pretreatment is of great significance. Herein, a nitrogen-rich covalent organic polymer containing phenyl, triazine and amine groups was fabricated via solvent-free copolymerization. Then, utilizing nitrogen-rich covalent organic polymer as adsorbent for solid phase extraction cartridges, the pretreatment method was combined with high-performance liquid chromatography-diode array detection to quantify five representative NSAIDs (ketoprofen, carprofen, flurbiprofen, diclofenac and mefenamic acid) in environmental water samples. Under the optimal extraction conditions (adsorbent amount: 40 mg; NaCl concentration: 0%; pH 6; extraction time: 20min; eluent solvent: 4 mL of formic acid/acetonitrile (5%, v/v)), the proposed method provided low detection limits (0.06–0.2 μg L-1), wide linear ranges (0.2–100 μg L-1) with correlation coefficients (0.9991–0.9997) and acceptable precision (RSDs, 6.6–8.5% for intra-day, 7.2–9.5% for inter-day). The practical application of the method was confirmed through the successful determination of NSAIDs in tap water, surface water, and sewage. The recoveries in these samples at the four NSAIDs concentration levels ranged from 81.3% to 109.8%, with the RSDs lower than 7.8%.

Yang Zhao, Mengqian Lu, Deliang Chen et al.

This study focuses on changes in the Tibetan Plateau vortices (TPVs) by using ERA5 reanalysis, covering the summers from 1979 to 2022 within the Tibetan Plateau (TP) region. These TPVs were identified using a geopotential height analysis. We discovered that the central-western TP had the most TPV activity and observed a clear decreasing trend in both the intensity and frequency of the TPVs in this region. This decrease was also accompanied by a decline in the strength of the associated vertical upward motion. To better understand this change, we employed the quasi-geostrophic omega equation. This allowed us to examine the dynamic, diabatic, and topographic factors contributing to the vertical motion during different phases of TPV activity in this region. Our results indicate that the main reason behind the weakened TPVs is the diminishing upper-level jet stream, which exerts dynamic forcing on the system. In the later stage, we observed that intensive moisture transport induces heightened diabatic vertical motion. However, this effect is not potent enough to counterbalance the diminishing dynamic influence. Therefore, our findings suggest a significant shift in TPV activity, transitioning from a dynamic-dominated regime to a latent heating-dominated diabatic regime. This new insight enhances our understanding of the complex mechanisms that influence TPV behavior.

Jinghua Xu, Linxuan Wang, Mingyu Gao et al.

Abstract This paper proposes a deformation evolution and perceptual prediction methodology for additive manufacturing of lightweight composite driven by hybrid digital twins (HDT). In order to improve manufacturing quality of irregular lightweight composite through boosting conceptual design in aeronautic and aerospace engineering, the HDT meaning hybridization of physical and digital domains, including deformation and energy efficiency can be built, where the essential parameters can be perceptually predicted in advance, by virtue of the fusion of physical sensors and digital information. The long short term memory (LSTM) can be employed to void vanishing gradient problem and improve predicting precision via Recurrent Neural Networks, thereby laying a foundation for the HDT. The diverse manufacturing requirements of different regions are integrated into the parameters designing phase by attaching region weights confirmed via empiricism and in-service simulation. The effects of slicing strategy and external support structures on manufacturing quality are considered from the perspective of improving dimensional accuracy. The manufacturing efficiency and comprehensive costs are accounted as consideration factors, which are perceptually predicted via LSTM. The designed manufacturing parameters through HDT were virtually examined by evaluating the deformation and equivalent stress distributions of fabricated lightweight component with composite material through AM process simulation. The physical experiments were conducted to verify the HDT-based pre-designing and optimization method of manufacturing parameters via fused deposition modeling (FDM). The energy consumption of actual manufacturing process was measured via digital power meter and applied to evaluate accuracy of perceptual prediction outcomes. The dimensional accuracy and distortion distribution of the manufactured lightweight prototype made with composite material were measured through the coordinate measuring machine (CMM) and 3D optical scanner. The proposed method demonstrates effectiveness in improving manufacturing quality and accurately predicting energy consumption, which have been verified with a three-way solenoid valve element, in which the maximum deformation was reduced by 39.78% and the mean absolute percentage error for perceptual prediction was 3.76%.

Jing Ling, Hongsheng Zhang, Rui Liu et al.

Accurate and timely monitoring of functional urban impervious surfaces (FUISs), such as ports, roads, and buildings, is essential yet challenging for complex coastal cities due to their cloudy weather and diverse land surfaces. Synthetic aperture radar (SAR) provides unique all-weather observation capabilities for prompt and regular urban mapping. However, SAR scattering information is limited to distinguish impervious surfaces with similar scattering responses but different functions. This study develops a scattering&#x2013;compactness fusion network (SCFN), which integrates SAR polarimetric scattering and object compactness characteristics for enhanced FUIS recognition. Central to our approach is the scattering object compactness index, which is specifically designed to capture the distinct spatial patterns and compactness of scattering objects and complement their intrinsic scattering signatures. The dual-branch SCFN concurrently extracts and fuses object-scale scattering and compactness features using tailored network architectures. Experiments on <italic>L</italic>-band and <italic>C</italic>-band fully polarimetric ALOS-2 and GF-3 data in Hong Kong, as well as <italic>L</italic>-band dual-polarized ALOS-2 data, are undertaken to verify SCFN&#x0027;s effectiveness, achieving up to 8&#x0025; improvement in the overall FUIS classification accuracy over baselines. The transferability of SCFN is further validated using fully polarimetric ALOS-2 data in Shenzhen, where consistent performance improvements are observed. The successful application of SCFN in both coastal cities highlights the potential of joint scattering&#x2013;compactness modeling for advanced SAR-based urban mapping and its robustness across different urban landscapes.

Benjamin Decardi-Nelson, Abdulelah S. Alshehri, Akshay Ajagekar et al.

This article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.

Walter Dellisanti, Walter Dellisanti, Jeffery T. H. Chung et al.

Scleractinian corals are increasingly subjected to local stressors combined with global changes. In subtropical areas, corals exhibit metabolic plasticity and resilience in response to variability and extremes in local temperature, salinity, and light; however, the physiological mechanisms by which corals acclimate or adapt to these changing conditions remain disputed. We assessed the physiological status of the coral Platygyra carnosa during a two-year in situ monitoring survey. To obtain metabolic rates (respiration and photosynthesis), photochemical efficiency (Fv / Fm), and biocalcification measurements, non-invasive techniques such as underwater respirometry, Pulse Amplitude Modulated (PAM) fluorometry, total alkalinity measurements, and digital photography were used. Our findings show clear seasonality in water quality parameters, which affected coral health. Elevated temperatures during the summer were below the maximum monthly mean &lt; 31°C) but reduced the energetic productivity of corals (-44% relative to winter). Fluctuations in salinity (25–38 ppt) and pH (7.65–8.44) were linked to rainfall and reduced calcification rates. The conditions during the spring were favorable for coral metabolism and calcification (+20% relative to summer). Overall, our research demonstrates that the metabolic plasticity of P. carnosa in response to shifts in seawater quality allows this species to survive ongoing environmental change. Our in situ observations provide fundamental insights into coral response mechanisms under changing environmental conditions and contribute to projections of coral health under future scenarios of global change.

Wenbin CHANG, Mingren MU, Haipeng JIA, Yunquan ZHANG, Sijia ZHANG

Image filtering algorithms have wide applications in such fields as machine learning, image processing, and image recognition.They play an important role in reducing &quot;salt and pepper&quot; noise, image binarization, edge recognition, and feature extraction. Although common image filtering algorithms are implemented in the OpenCV open source library, a significant gap in performance exists compared with other platforms on the Android platform. With the rapid development of embedded platforms, the performance requirements for filtering algorithms on embedded platforms have become increasingly high in practical applications. Therefore, starting with filtering algorithms with wide application scenarios, such as morphological filtering, box filtering, threshold filtering, compression filtering, and arithmetic filtering, a series of high-performance image filtering algorithms designed for the Android platform based on OpenGL ES are developed and implemented. OpenGL ES calculation shaders are used to accelerate the algorithm in parallel, using texture objects for memory optimization, and in-depth optimization in image boundary processing, image data types, and data communication is conducted. This approach resulted in better performance. The optimized image filtering algorithm is compared with the corresponding algorithm in the open-source OpenCV library. The experimental results show that the overall performance of the image filtering algorithm based on the Android platform using the OpenGL ES interface is significantly better than the performances of the relevant algorithms in the OpenCV library. The larger the image size, the more obvious the computational advantage. The maximum performance improvement is 110.018 times that of the corresponding algorithm in the OpenCV library.

Qianxia Lin, Yueping Guo, Jie Li et al.

Oxidative stress and abnormal glucose metabolism are the important physiological mechanisms in the occurrence and development of diabetes. Antioxidant peptides have been reported to attenuate diabetes complications by regulating levels of oxidative stress, but few studies have focused on peptides from marine bone collagen. In this study, we prepared the peptides with a molecular weight of less than 1 kD (HNCP) by enzymolysis and ultrafiltration derived from <i>Harpadon nehereus</i> bone collagen. Furthermore, the effects of HNCP on blood glucose, blood lipid, liver structure and function, oxidative stress, and glucose metabolism were studied using HE staining, kit detection, and Western blotting experiment in streptozocin-induced type 1 diabetes mice. After the 240 mg/kg HNCP treatment, the levels of blood glucose, triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C) in streptozotocin-induced diabetes mice decreased by 32.8%, 42.2%, and 43.2%, respectively, while the levels of serum insulin and hepatic glycogen increased by 142.0% and 96.4%, respectively. The antioxidant enzymes levels and liver function in the diabetic mice were markedly improved after HNCP intervention. In addition, the levels of nuclear factor E2-related factor 2 (Nrf2), glucokinase (GK), and phosphorylation of glycogen synthase kinase-3 (p-GSK3β) in the liver were markedly up-regulated after HNCP treatment, but the glucose-6-phosphatase (G6Pase) and phosphoenolpyruvate carboxykinase1 (PEPCK1) were down-regulated. In conclusion, HNCP could attenuate oxidative stress, reduce blood glucose, and improve glycolipid metabolism in streptozocin-induced type 1 diabetes mice.

Yixuan Sun, Elizabeth Cucuzzella, Steven Brus et al.

Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean. At the same time, ocean processes affect phenomena such as hurricanes and droughts. Parameters in the models that cannot be physically measured have a significant effect on the model output. For an idealized ocean model, we generated perturbed parameter ensemble data and trained surrogate neural network models. The neural surrogates accurately predicted the one-step forward dynamics, of which we then computed the parametric sensitivity.

Andy Zou, Long Phan, Sarah Chen et al.

In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs). We provide baselines and an initial analysis of RepE techniques, showing that they offer simple yet effective solutions for improving our understanding and control of large language models. We showcase how these methods can provide traction on a wide range of safety-relevant problems, including honesty, harmlessness, power-seeking, and more, demonstrating the promise of top-down transparency research. We hope that this work catalyzes further exploration of RepE and fosters advancements in the transparency and safety of AI systems.

Adam Larios, Mark R. Petersen, Collin Victor

We demonstrate a formulation of the Azouani-Olson-Titi (AOT) algorithm in the MPAS-Ocean implementation of the primitive equations of the ocean, presenting global ocean simulations with realistic coastlines and bathymetry. We observe an exponentially fast decay in the error before reaching a certain error level, which depends on the terms involved and whether the AOT feedback control term was handled implicitly or explicitly. A wide range of errors was observed for both schemes, with the implicit scheme typically exhibiting lower error levels, depending on the specific physical terms included in the model. Several factors seem to be contributing to this wide range, but the vertical mixing term is demonstrated to be an especially problematic term. This study provides insight into the promises and challenges of adapting the AOT algorithm to the setting of high-resolution, realistic ocean models.

Pengfei Sun, Haining Lu, Jianmin Yang et al.

The seafloor soil is characterized by high water content, strong compressibility, and low shear strength. Deep-sea mining vehicles (DSMV) are prone to sinking when walking on the surface of the soil, which will cause significant reduction in traction performance. Therefore, it is necessary to study the sinkage performance. The track is usually considered the travelling mechanism of the DSMV, and the track plate is an important part of the movement system. The study of the interaction between the track plate and the soil is of great significance to the study of the DSMV’s sinkage performance. In this study, firstly, based on the in situ seafloor soil samples of 1000 m in a region of the South China Sea collected by a box sampler, the physical and mechanical parameters of soil were measured by indoor geotechnical instruments. Secondly, an elastoplastic soil numerical model similar to that of in situ soil was established. Based on coupled Eulerian-Lagrangian (CEL) method, a numerical model of the interaction between the track plate and soil was established. Considering the dynamic process, the structure of the track plate and the physical and mechanical properties of the soil, the numerical simulation were carried out under different conditions, such as different dynamic loading, the plate structural parameters and the soil physical and mechanical properties. It is found that the plate-sinkage curve were significantly influenced by these factors. The findings are as follows, firstly, with the increase in the pressure loading rate, the soil sinkage decreasing at the same pressure. On the other hand, with the increase in velocity, soil flow was accelerated, and the nonlinear relationship between resistance and velocity became more obvious; the L/B ratio of different track plates affects the variation law of the curve, and the maximum sinkage gradually decreases as the ratio of L/B increases; with the increase in the grouser height, the maximum sinkage gradually decreases, and the pressure-sinkage curve changes obviously with the grouser type; and different soil physical and mechanical properties affect the variation of pressure-sinkage curve. Innovatively, the heterogeneous soil stress distribution mode was obtained through the fitting function and Python secondary development. This study can provide a reference for studying the sinkage performance of the DSMV.

Claes Eskilsson, Johannes Palm, Pär Johannesson et al.

There are many uncertainties associated with the estimation of extreme loads acting on a wave energy converter (WEC). In this study we perform a sensitivity analysis of extreme loads acting on the Uppsala University (UU) WEC concept. The UU WEC consists of a bottom-mounted linear generator that is connected to a surface buoy with a taut mooring line. The maximum stroke length of the linear generator is enforced by end-stop springs. Initially, a Variation Mode and Effect Analysis (VMEA) was carried out in order to identify the largest input uncertainties. The system was then modelled in the time-domain solver WEC-SIM coupled to the dynamic mooring solver Moody. A sensitivity analysis was made by generating a surrogate model based on polynomial chaos expansions, which rapidly evaluates the maximum loads on the mooring line and the end-stops. The sensitivities are ranked using the Sobol index method. We investigated two sea states using equivalent regular waves (ERW) and irregular wave (IRW) trains. We found that the ERW approach significantly underestimate the maximum loads. Interestingly, the ERW predicted wave height and period as the most important parameters for the maximum mooring tension, whereas the tension in IRW was most sensitive to the drag coefficient of the surface buoy. The end-stop loads were most sensitive to the PTO damping coefficient.

Yuewei Wang, Lizhe Wang, Xiaodao Chen et al.

A marine oil spill is an environmental pollution incident that generally has the attributes of a high speed, widespread, and long duration. It seriously threatens the marine ecological environment and related industries. It is vital to determine the source of the oil leakage so that it may be stopped and related hazards can be reduced. Oil spill accidents in the sea are generally located in offshore and navigation channels. With the rapid development of remote-sensing techniques, oil leak extraction using remote-sensing data has played an essential role in oil spill research. This article proposes a Monte Carlo-based deep Q-transfer-learning network (DQTN) offshore oil leak detection method that uses remote-sensing data. Remote-sensing data are utilized to continuously monitor a marine oil spill on the surface. The estuarine and coastal ocean model is utilized to simulate a marine oil spill event. The deep-Q-network method with offline transferred knowledge is then utilized to determine the marine oil spill source location. In an experiment, based on the Bohai oil spill incident on June 2, 2011, the effectiveness of the remote-sensing-based DQTN marine oil spill search algorithm is verified. The accuracy of the targeted oil spill point is up to 98.97&#x0025;.