In the framework of flow simulations in Discrete Fracture Networks, we consider the problem of identifying possible backbones, namely preferential channels in the network. Backbones can indeed be fruitfully used to an...
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In the framework of flow simulations in Discrete Fracture Networks, we consider the problem of identifying possible backbones, namely preferential channels in the network. Backbones can indeed be fruitfully used to analyze clogging or leakage, relevant for example in waste storage problems, or to reduce the computational cost of simulations. With a suitably trained Neural Network at hand, we use the Layer-wise Relevance Propagation as a feature selection method to detect the expected relevance of each fracture in a Discrete Fracture Network and thus identifying the backbone.
Frequent vegetation monitoring of agriculture crops during their phenological cycle helps farmers/agmnomist to perform their activities in efficient way to gain maximum yield and reduce the environmental noise caused ...
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ISBN:
(纸本)9781728136110
Frequent vegetation monitoring of agriculture crops during their phenological cycle helps farmers/agmnomist to perform their activities in efficient way to gain maximum yield and reduce the environmental noise caused by excessive use of chemicals. Various remote sensing platforms equipped with optical multispectral sensors such as satellite, airborne and unmanned air vehicles (UAVs) are being used in vegetation monitoring. Satellites equipped with multispectral sensors are popular due to their large coverage and temporal resolution. On the other hand, UAVs are preferred where more detailed imagery is needed while its expensive and time consuming if more frequent campaigns have to be performed. In this study, vineyard site is considered to assess the reliability of using satellite images for vegetation monitoring. Indeed, satellite imagery with decametric spatial resolution cannot describe the vegetation status at vine rows level due to the mixed nature of pixel, representing the cumulative effect of inter row terrain and vine rows. Therefore, a pixel refinement is needed to minimize this effect. In this work, a convolutional neural network (CNN) based approach is proposed to gain benefits from high resolution UAV images in order to reline the frequent moderate resolution satellite images over a vineyard.
Nowadays, applications of ultrasonic proximity sensors are limited to a post-processing of the acquired signals with a pipeline of filters and threshold comparators. This article proposes two different and novel proce...
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ISBN:
(纸本)9783030375997;9783030375980
Nowadays, applications of ultrasonic proximity sensors are limited to a post-processing of the acquired signals with a pipeline of filters and threshold comparators. This article proposes two different and novel processing methodologies, based on machine learning algorithms, that outperform classical approaches. Indeed, noisy signals and presence of thin or soundproofing objects are likely sources of false positive detections that can make traditional approaches useless and unreliable. In order to take advantage of correlations among the data, multiple parallel signals, coming from a cluster of ultrasonic sensors, have been exploited, producing a number of different features that allowed to achieve more accurate and precise predictions for object detection. Firstly, model-based learning as well as instance-based learning systems have been investigated for an independent time correlation analysis of the different signals. Particular attention has been given to the training and testing of the deep fully connected network that showed, since the beginning, more promising results. In the second part, a recurrent neural network, based on long short term memory cells, has been devised. As a result of its intrinsic nature, time correlations between successive samples are not more overlooked, further improving the overall prediction capability of the system. Finally, cutting edge training methodologies and strategies to find the different hyperparameters have been adopted in order to obtain the best results and performance from the available data.
Convolutional Neural Networks (CNNs) consistently proved state-of-the-art results in image Super-resolution (SR), representing an exceptional opportunity for the remote sensing field to extract further information and...
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Convolutional Neural Networks (CNNs) consistently proved state-of-the-art results in image Super-resolution (SR), representing an exceptional opportunity for the remote sensing field to extract further information and knowledge from captured data. However, most of the works published in the literature focused on the Single-image Super-resolution problem so far. At present, satellite-based remote sensing platforms offer huge data availability with high temporal resolution and low spatial resolution. In this context, the presented research proposes a novel residual attention model (RAMS) that efficiently tackles the Multi-image Super-resolution task, simultaneously exploiting spatial and temporal correlations to combine multiple images. We introduce the mechanism of visual feature attention with 3D convolutions in order to obtain an aware data fusion and information extraction of the multiple low-resolution images, transcending limitations of the local region of convolutional operations. Moreover, having multiple inputs with the same scene, our representation learning network makes extensive use of nestled residual connections to let flow redundant low-frequency signals and focus the computation on more important high-frequency components. Extensive experimentation and evaluations against other available solutions, either for Single or Multi-image Super-resolution, demonstrated that the proposed deep learning-based solution can be considered state-of-the-art for Multi-image Super-resolution for remote sensing applications.
Network representations of complex systems are limited to pairwise interactions, but real-world systems often involve higher-order interactions. This Perspective looks at the new physics emerging from attempts to char...
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Network representations of complex systems are limited to pairwise interactions, but real-world systems often involve higher-order interactions. This Perspective looks at the new physics emerging from attempts to characterize these interactions. Complex networks have become the main paradigm for modelling the dynamics of interacting systems. However, networks are intrinsically limited to describing pairwise interactions, whereas real-world systems are often characterized by higher-order interactions involving groups of three or more units. Higher-order structures, such as hypergraphs and simplicial complexes, are therefore a better tool to map the real organization of many social, biological and man-made systems. Here, we highlight recent evidence of collective behaviours induced by higher-order interactions, and we outline three key challenges for the physics of higher-order systems.
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