Hasil untuk "Ocean engineering"

Peng Liu, Changzheng Yang, Jin Gao

IntroductionIn the contemporary digital communication environment, online emergencies often trigger cognitive biases among audiences, affecting the health of public opinion ecosystems and potentially threatening social stability. While existing research has largely focused on the manifestations and consequences of cognitive biases, the formation mechanisms, particularly the role of contextual factors in the online environment, remain understudied. This study examines how field situational heuristics influence cognitive biases in online emergencies through the mediating pathways of adaptive expectations and implicit attributions.MethodsThis research integrates field theory and heuristic information processing theory to construct a theoretical framework. Using anchoring heuristics, representativeness heuristics, and availability heuristics as independent variables, cognitive bias as the dependent variable, and adaptive expectations and implicit attributions as mediating variables, data were collected through questionnaires and analyzed using structural equation modeling with AMOS 22.0 statistical software.ResultsThe findings reveal that: (1) in online emergencies, anchoring heuristics, representativeness heuristics, and availability heuristics exert a significantly positive influence on cognitive biases, mediated by adaptive expectations and implicit attributions; (2) representativeness heuristics have the greatest impact on cognitive biases, followed by availability heuristics, and finally anchoring heuristics; (3) the effect of contextual heuristics on cognitive biases exhibits significant demographic differences both between and within groups.DiscussionThe findings provide theoretical insights for improving online public opinion governance and enhancing audience media literacy. The study highlights the importance of understanding how situational heuristics shape cognitive outcomes in digital communication environments and offers practical implications for managing information dissemination during online emergencies.

Xuran Men, Jinlong He, Bo Jiao et al.

With the rapid development and accelerated utilization of marine resources, multi-body floating systems have become extensively used in practical applications. This study examines the coupled motions of a side-by-side anchoring system for five fishing vessels in a harbor using ANSYS-AQWA. The system is connected by hawsers and equipped with fenders to reduce collisions between the vessels. It is designed to operate in the sheltered wind-wave combined environment within Ningbo Zhoushan Port, China. Considering the diverse types and quantities of fishing vessels in the anchorage area, this paper proposes a mixed arrangement of three large-scale fishing vessels in the middle and two small-scale vessels on both sides. The time-domain analysis is performed on this system under the combined effects of wind and waves, calculating the motion responses of the five fishing vessels along with the mechanical loads at the hawsers, fenders, and moorings. The results indicate that the maximum loads on these mechanical components remain well within the safe working limits, ensuring reliable operation. In addition, the impact of varying wind-wave angles on the coupled motions of the fishing vessel system are studied. As the wind-wave angle increases, the surge motion of the fishing vessels gradually decreases, while the sway motion intensifies. The forces on the hawsers, fenders, and mooring system exhibit distinct characteristics at different angles.

Zhaoxuan Lu, Lyuchao Liao, Xingang Xie et al.

In recent years, climate change and marine pollution have significantly degraded coral reefs, highlighting the urgent need for automated coral detection to monitor marine ecosystems. However, underwater coral detection presents unique challenges, including low image contrast, complex coral structures, and dense coral growth, which limit the effectiveness of general object detection algorithms. To address these challenges, we propose SCoralDet, a soft coral detection model based on the YOLO architecture. First, we introduce a Multi-Path Fusion Block (MPFB) to capture coral features across multiple scales, enhancing the model’s robustness to uneven lighting and image blurring. We further improve inference efficiency by applying reparameterization. Second, we integrate lightweight components such as GSConv and VoV-GSCSP to reduce computational overhead without sacrificing performance. Additionally, we develop an Adaptive Power Transformation label assignment strategy, which dynamically adjusts anchor alignment metrics. By incorporating soft labels and soft central region loss, our model is guided to prioritize high-quality, well-aligned predictions. We evaluate SCoralDet on the Soft-Coral dataset, achieving an inference latency of 9.52 ms and an mAP50 of 81.9. This surpasses the performance of YOLOv5 (79.9), YOLOv6 (79.4), YOLOv8 (79.5), YOLOv9 (78.3), and YOLOv10 (79.5). These results demonstrate the effectiveness and practicality of SCoralDet in underwater coral detection tasks.

Yao Li, Yujie Zhang, Hongwei Li

The task of hyperspectral image completion generally involves random missing entries completion, stripes inpainting, and cloud removal, which can enhance the accuracy of subsequent image analysis. Recently, tensor completion has been presented for image recovery. Owing to the framelet basis redundancy, the tensor rank of the extended tensor via feature extraction is smaller, which can characterize the correlation between any two modes of the tensor more accurately. In this work, the fully connected tensor network decomposition has been suggested to depict the low-rankness of the extended tensor with feature extraction. The process of feature extraction via framelet transform reduces the need for fewer principal components to depict the low-rankness of the underlying tensor. Moreover, total variation is incorporated into the proposed completion model to capture the spatial smoothness of the underlying tensor via minimizing the sum of the gradients across the image. To solve the large-scale resulting model, the augmented Lagrange multiplier-based proximal alternating minimization algorithm has been proposed. To accelerate the optimization algorithm, the leverage score sampling and fast Fourier transform have been introduced. Numerical results on several types of hyperspectral image completion problem demonstrate that the proposed method performs better than the compared methods in data completion.

Byung-Ju Sohn, Jihoon Ryu, Sang-Wook Yeh et al.

Abstract Understanding the relationship between tropical heavy rainfall and large-scale circulation provides valuable insights for improving the climate models. Here we use Gaussian Mixture Model to identify two distinct types of heavy rainfall over the tropical Pacific, “strong deep convection” and “moderately strong deep convection,” using satellite-borne precipitation radar measurements. They differ in two typical climatological deep convection-related rainfall modes between the western and eastern Pacific regions. The occurrence frequency of moderately strong deep convection is significantly different between the western and eastern Pacific, potentially linked to the Walker circulation. The enhanced Walker circulation appears to weaken the local Hadley circulation, thereby reducing strong deep convective activity in the eastern Pacific. This increases moderately heavy rainfall and decreases diabatic heating, which can affect global climate. We propose incorporating the close link between large-scale Walker circulation and mesoscale heavy convective rainfall into the current climate models.

Chenyu WANG, Likun PENG, Jiabao CHEN et al.

To respond to the endurance bottleneck faced by unmanned undersea vehicles(UUVs) in missions such as ocean observation and resource exploration, this paper studied the hydrodynamic performance optimization of a novel foldable solar wing. To balance computational efficiency and optimization accuracy, a parametric model of the wing was established in CAESES software with variables including wing point coordinates, rounding factors of wing edges, wing gaps, and gaps between the wing and the hull. Innovatively, a hybrid optimization framework combining Sobol global sampling and the non-dominatedsorting genetic algorithm II(NSGA-II) optimization algorithm was constructed. Firstly, the Sobol algorithm was used to generate 80 sample points within the threshold space of each variable to fully explore the design space, followed by multi-generation optimization through NSGA-II. To avoid the accuracy degradation of traditional surrogate models, a coupled computational process integrating high-precision hydrodynamic solutions and optimization algorithms was established, enabling automatic co-simulation between CAESES and STAR-CCM + software. Hydrodynamic analyses were conducted on UUVs equipped with wings of different shapes to explore the impact of different parameter combinations on total drag. The optimization results indicate that a certain height difference between the two wing sections protruding from the hull is beneficial for reducing total drag. Flow field analysis shows that the optimized shape effectively suppresses energy dissipation caused by turbulence. The proposed technical route of parametric modeling, intelligent optimization, and high-precision verification not only reduces the straight-line drag of the UUV with a new configuration but also provides a methodological reference for the optimization of complex appendages, possessing significant engineering value for improving the energy utilization efficiency of underwater equipment.

Jiangnan Zhang, Hai Wang, Fengjuan Cui et al.

The establishment of ship trajectory prediction is critical in analyzing trajectory data. It serves as a critical reference point for identifying abnormal behavior and potential collision risks for ships. Accurate and real-time ship trajectory prediction is essential during navigation. Since the timing of automatic identification system (AIS) data is irregular, traditional methods usually use time calibration to simulate the data of uniform sequencing before analysis. Inevitably, this increases the chances of error and time delays. To address this issue, we propose a time-aware LSTM (T-LSTM) single-ship trajectory model combined with the generative adversarial network (GAN) to predict multiple ship trajectories. These analysis methods are capable of directly analyzing AIS data and have demonstrated better performance in both single-ship and multi-ship trajectories. Our experimental results show that the proposed method achieves high accuracy and can meet the practical navigation requirements of ships.

Hui Jin, Hui Jin, Yong Min Lao et al.

A RelA/SpoT homolog, HpRSH, was identified in Haematococcus pluvialis. HpRSH was found to catalyze Mg2+-dependent guanosine tetraphosphate (ppGpp) synthesis and Mn2+-dependent ppGpp hydrolysis, respectively. The transcription of HpRSH was significantly upregulated by environmental stresses, such as darkness, high light, nitrogen limitation, and salinity stress. The intracellular ppGpp level was also increased when exposed to these stresses. In addition, the classical initiator of stringent response, serine hydroxamate (SHX), was found to upregulate the transcription of HpRSH and increase the level of ppGpp. Moreover, stringent response induced by SHX or environmental stresses was proven to induce the accumulation of astaxanthin. These results indicated that stringent response regulatory system involved in the regulation of astaxanthin biosynthesis in H. pluvialis. Furthermore, stringent response was unable to induce astaxanthin accumulation under dark condition. This result implied that stringent response may regulate astaxanthin biosynthesis in a light-dependent manner.

Ronan Rialland, Charles Soussen, Rodolphe Marion et al.

Reflectance spectroscopy is a widely used technique for mineral identification and characterization. Since modern airborne and satellite-borne sensors yield an increasing number of hyperspectral data, it is crucial to develop unsupervised methods to retrieve relevant spectral features from reflectance spectra. Spectral deconvolution aims to decompose a reflectance spectrum as a sum of a continuum modeling its overall shape and some absorption features. We present a flexible and automatic method able to deal with various minerals. The approach is based on a physical model and allows us to include noise statistics. It consists of three successive steps: first, continuum pre-estimation based on nonlinear least-squares; second, pre-estimation of absorption features using a greedy algorithm; third, refinement of the continuum and absorption estimates. The procedure is first validated on synthetic spectra, including a sensitivity study to instrumental noise and a comparison to other approaches. Then, it is tested on various laboratory spectra. In most cases, absorption positions are recovered with an accuracy lower than 5 nm, enabling mineral identification. Finally, the proposed method is assessed using hyperspectral images of quarries acquired during a dedicated airborne campaign. Minerals such as calcite and gypsum are accurately identified based on their diagnostic absorption features, including when they are in a mixture. Small changes in the shape of the kaolinite doublet are also detected and could be related to crystallinity or mixtures with other minerals such as gibbsite. The potential of the method to produce mineral maps is also demonstrated.

Tianfu Zhang, Zhihao Wang, Ning Qiao et al.

Accurate estimation of the clutter covariance matrix for the cell under test (CUT) is a committed step in the spatial-temporal adaptive processing (STAP) algorithm. The unique nonstationary characteristic of signal for space-based early warning radar (SBEWR) leads to the spatial variation of training sample and the insufficient number of optional independent identically distributed (i.i.d.) training samples, which brings difficulties to training sample selection and covariance matrix estimation. To improve the estimation accuracy of clutter covariance matrix and the performance of STAP for SBEWR in a heterogeneous environment, a novel training sample selection and clutter covariance matrix estimation method is proposed. The method based on clutter subspace reconstruction and spectrum correction technology can improve the estimation accuracy of clutter covariance matrix in the case of nonstationary signals and heterogeneous environments. The clutter covariance matrix estimated by the proposed method is similar to the clutter covariance matrix of the CUT, and the performance of STAP is improved. The experimental results confirm the performance of the proposed method.

Jihoon Park, Sukkeun Kim, Geemoon Noh et al.

This study contains the process of developing a Mission Planning System (MPS) of an USV that can be applied in real situations and verifying them through HILS. In this study, we set the scenario of a single USV with limited operating time. Since the USV may not perform some missions due to the limited operating time, an objective function was defined to maximize the Mission Achievement Rate (MAR). We used a genetic algorithm to solve the problem model, and proposed a method using a 3-D population. The simulation showed that the probability of deriving the global optimal solution of the mission planning algorithm was 96.6% and the computation time was 1.6 s. Furthermore, USV showed it performs the mission according to the results of the MPS. We expect that the MPS developed in this study can be applied to the real environment where USV performs missions with limited time conditions.

Felipe Grijalva, Washington Ramos, Noel Perez et al.

With the growing ability to collect large volumes of volcano seismic data, the detection and labeling process of these records is increasingly challenging. Clearly, analyzing all available data through manual inspection is no longer a viable option. Supervised machine learning models might be considered to automatize the analysis of data acquired by <italic>in situ</italic> monitoring stations. However, the direct application of such algorithms is defiant, given the high complexity of waveforms and the scarce and often imbalanced amount of labeled data. In light of this and motivated by the wide success that generative adversarial networks (GANs) have seen at generating images, we present ESeismic-GAN, a GAN model to generate the magnitude frequency response of volcanic events. Our experiments demonstrate that ESeismic-GAN learns to generate the frequency components that characterize long-period and volcano-tectonic events from Cotopaxi volcano. We evaluate the performance of ESeismic-GAN during the training stage using Fr&#x00E9;chet distance, and, later on, we reconstruct the signals into time-domain to be finally evaluated with Frechet inception distance.