4,832 research outputs found

    Stability prediction of muddy submarine channel slope based on sub-bottom profile acoustic images and transfer learning

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    This research addresses the challenging task of predicting the stability of muddy submarine channel slopes, crucial for ensuring safe port operations. Traditional methods falter due to the submerged nature of these channels, impacting navigation and infrastructure maintenance. The proposed approach integrates sub-bottom profile acoustic images and transfer learning to predict slope stability in Lianyungang Port. The study classifies slope stability into four categories: stable, creep, expansion, and unstable based on oscillation amplitude and sound intensity. Utilizing a sub-bottom profiler, acoustic imagery is collected, which is then enhanced through Gabor filtering. This process generates source data to pre-train Visual Geometry Group (VGG)16 neural network. This research further refines the model using targeted data, achieving a 97.92% prediction accuracy. When benchmarked against other models and methods, including VGG19, Inception-v3, Densenet201, Decision Tree (DT), Naive Bayes (NB), Support Vector Machine (SVM), and an unmodified VGG16, this approach exhibits superior accuracy. This model proves highly effective for real-time analysis of submarine channel slope dynamics, offering a significant advancement in marine safety and operational efficiency

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Glaciological history and structural evolution of the Shackleton Ice Shelf system, East Antarctica, over the past 60 years

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    The discovery of Antarctica's deepest subglacial trough beneath the Denman Glacier, combined with high rates of basal melt at the grounding line, has caused significant concern over its vulnerability to retreat. Recent attention has therefore been focusing on understanding the controls driving Denman Glacier's dynamic evolution. Here we consider the Shackleton system, comprised of the Shackleton Ice Shelf, Denman Glacier, and the adjacent Scott, Northcliff, Roscoe and Apfel glaciers, about which almost nothing is known. We widen the context of previously observed dynamic changes in the Denman Glacier to the wider region of the Shackleton system, with a multi-decadal time frame and an improved biannual temporal frequency of observations in the last 7 years (2015–2022). We integrate new satellite observations of ice structure and airborne radar data with changes in ice front position and ice flow velocities to investigate changes in the system. Over the 60-year period of observation we find significant rift propagation on the Shackleton Ice Shelf and Scott Glacier and notable structural changes in the floating shear margins between the ice shelf and the outlet glaciers, as well as features indicative of ice with elevated salt concentration and brine infiltration in regions of the system. Over the period 2017–2022 we observe a significant increase in ice flow speed (up to 50 %) on the floating part of Scott Glacier, coincident with small-scale calving and rift propagation close to the ice front. We do not observe any seasonal variation or significant change in ice flow speed across the rest of the Shackleton system. Given the potential vulnerability of the system to accelerating retreat into the overdeepened, potentially sediment-filled bedrock trough, an improved understanding of the glaciological, oceanographic and geological conditions in the Shackleton system are required to improve the certainty of numerical model predictions, and we identify a number of priorities for future research. With access to these remote coastal regions a major challenge, coordinated internationally collaborative efforts are required to quantify how much the Shackleton region is likely to contribute to sea level rise in the coming centuries.</p

    Monthly extended ocean predictions based on a convolutional neural network via the transfer learning method

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    Sea surface temperature anomalies (SSTAs) and sea surface height anomalies (SSHAs) are indispensable parts of scientific research, such as mesoscale eddy, current, ocean-atmosphere interaction and so on. Nowadays, extended-range predictions of ocean dynamics, especially in SSTA and SSHA, can provide daily prediction services in the range of 30 days, which bridges the gap between synoptic-scale weather forecasts and monthly average scale climate predictions. However, the forecast efficiency of extended range remains problematic. With the development of ocean reanalysis and satellite remote sensing products, large amounts datasets provide an unprecedented opportunity to use big data for the extended range prediction of ocean dynamics. In this study, a hybrid model, combing convolutional neural network (CNN) model with transfer learning (TL), was established to predict SSTA and SSHA at monthly scales, which makes full use of these data resources that arise from delayed gridding reanalysis products and real-time satellite remote sensing observations. The proposed model, where both ocean and atmosphere reanalysis datasets serve as the pretraining dataset and the satellite remote sensing observations are employed for fine-tuning based on the transfer learning (TL) method, can effectively capture the evolving spatial characteristics of SSTAs and SSHAs with low prediction errors over the 30 days range. When the forecast lead time is 30 days, the root means square errors for the SSTAs and SSHAs model results are 0.32°C and 0.027 m in the South China Sea, respectively, indicating that this model has not only satisfactory prediction performance but also offers great potential for practical operational applications in improving the skill of extended-range predictions

    Designs of Blackness

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    Across more than two centuries Afro-America has created a huge and dazzling variety of literary self-expression. Designs of Blackness provides less a narrative literary history than, precisely, a series of mappings—each literary-critical and comparative while at the same time offering cultural and historical context. This carefully re-edited version of the 1998 publication opens with an estimation of earliest African American voice in the names of Phillis Wheatley and her contemporaries. It then takes up the huge span of autobiography from Frederick Douglass through to Maya Angelou. "Harlem on My Mind," which follows, sets out the literary contours of America’s premier black city. Womanism, Alice Walker’s presiding term, is given full due in an analysis of fiction from Harriet E. Wilson to Toni Morrison. Richard Wright is approached not as some regulation "realist" but as a more inward, at times near-surreal, author. Decadology has its risks but the 1940s has rarely been approached as a unique era of war and peace and especially in African American texts. Beat Generation work usually adheres to Ginsberg and Kerouac, but black Beat writing invites its own chapter in the names of Amiri Baraka, Ted Joans and Bob Kaufman. The 1960s has long become a mythic change-decade, and in few greater respects than as a black theatre both of the stage and politics. In Leon Forrest African America had a figure of the postmodern turn: his work is explored in its own right and for how it takes its place in the context of other reflexive black fiction. "African American Fictions of Passing" unpacks the whole deceptive trope of "race" in writing from Williams Wells Brown through to Charles Johnson. The two newly added chapters pursue African American literary achievement into the Obama-Trump century, fiction from Octavia Butler to Darryl Pinkney, poetry from Rita Dove to Kevin Young

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

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    Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

    Integrated Geophysical Analysis of Passive Continental Margins: Insights into the Crustal Structure of the Namibian Margin from Magnetotelluric, Gravity, and Seismic Data

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    Passive continental margin research amalgamates the investigation of many broad topics, such as the emergence of oceanic crust, lithospheric stress patterns and plume-lithosphere interaction, reservoir potential, methane cycle, and general global geodynamics. Central tasks in this field of research are geophysical investigations of the structure, composition, and dynamic of the passive margin crust and upper mantle. A key practice to improve geophysical models and their interpretation, is the integrated analysis of multiple data, or the integration of complementary models and data. In this thesis, I compare four different inversion results based on data from the Namibian passive continental margin. These are a) a single method MT inversion; b) constrained inversion of MT data, cross-gradient coupled with a fixed structural density model; c) cross-gradient coupled joint inversion of MT and satellite gravity data; d) constrained inversion of MT data, cross-gradient coupled with a fixed gradient velocity model. To bridge the formal analysis of geophysical models with geological interpretations, I define a link between the physical parameter models and geological units. Therefore, the results from the joint MT and gravity inversion (c) are correlated through a user-unbiased clustering analysis. This clustering analysis results in a distinct difference in the signature of the transitional crust south of- and along the supposed hot-spot track Walvis Ridge. I ascribe this contrast to an increase in magmatic activity above the volcanic center along Walvis Ridge. Furthermore, the analysis helps to clearly identify areas of interlayered massive, and weathered volcanic flows, which are usually only identified in reflection seismic studies as seaward dipping reflectors. Lastly, the clustering helps to differentiate two types of sediment cover. Namely, one of near-shore, thick, clastic sediments, and one of further offshore located, more biogenic, marine sediments

    Image Diversification via Deep Learning based Generative Models

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    Machine learning driven pattern recognition from imagery such as object detection has been prevalenting among society due to the high demand for autonomy and the recent remarkable advances in such technology. The machine learning technologies acquire the abstraction of the existing data and enable inference of the pattern of the future inputs. However, such technologies require a sheer amount of images as a training dataset which well covers the distribution of the future inputs in order to predict the proper patterns whereas it is impracticable to prepare enough variety of images in many cases. To address this problem, this thesis pursues to discover the method to diversify image datasets for fully enabling the capability of machine learning driven applications. Focusing on the plausible image synthesis ability of generative models, we investigate a number of approaches to expand the variety of the output images using image-to-image translation, mixup and diffusion models along with the technique to enable a computation and training dataset efficient diffusion approach. First, we propose the combined use of unpaired image-to-image translation and mixup for data augmentation on limited non-visible imagery. Second, we propose diffusion image-to-image translation that generates greater quality images than other previous adversarial training based translation methods. Third, we propose a patch-wise and discrete conditional training of diffusion method enabling the reduction of the computation and the robustness on small training datasets. Subsequently, we discuss a remaining open challenge about evaluation and the direction of future work. Lastly, we make an overall conclusion after stating social impact of this research field

    Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

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    The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure
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