1,720,972 research outputs found
Real-time critical marine infrastructure multi-sensor surveillance via a constrained stochastic coverage algorithm
No full textIn recent years, monitoring and protecting marine infrastructure have become increasingly critical. Surface
marine vessels can provide valuable support in monitoring structures such as offshore wind farms, data cables,
and pipelines. Employing surface vessels with ever-increasing autonomous capabilities allows for increased op-
eration efficiency and strategic advantages. In critical infrastructure monitoring, the area of interest is known in
advance, and the aim is to detect anomalies. This paper focuses on developing a guidance, navigation, and control
framework suitable for a MASS and tailored for critical infrastructure monitoring missions. The primary goals are
developing a stochastic-based coverage algorithm to ensure the surveillance of an area of interest and a real-time
compliant shadow vessels monitoring system that provides situational awareness of the above-water surrounding
environment, detecting threats and unexpected targets over time. The navigation in the operational environment
is ensured by an appropriate proprioceptive sensing layer. The hypotheses and the methodologies are shown and
explained in detail, together with the preliminary results reporting the first integration. The results are obtained
via computer simulations applied to a wind farm critical infrastructure scenario; additional experimental tests are
carried out in indoor and outdoor controlled environments to assess the proposed navigation and control systems
capability, involving a marine autonomous surface ships test platform available in the university laboratory. The
preliminary results demonstrate the ability of the systems to cooperate in the proposed architecture, monitor an
Area Of Interest, detect threats, effectively manoeuvre the vessel in real-time, and estimate its state. Such a
framework can be further enhanced by extending the perception capabilities for the underwater domain, integrat-
ing multiple control logic to allow for more efficient surveillance strategies, or extending the vessel capabilities
beyond surveillance missions, adding capabilities like target chasin
LiDAR target detection and classification for ship situational awareness: A hybrid learning approach
No full textIn recent years, LiDARs have been used to enhance situational awareness of autonomous vehicles, including in the marine domain, driven by the need for reliable detections in Marine Autonomous Surface Ships and Unmanned Surface Vehicles. Detecting obstacles and targets within point clouds is generally handled by a fully unsupervised learning framework. While effective and simple, this approach cannot classify targets. This paper presents a combined unsupervised/supervised approach for detecting and classifying marine targets and obstacles. The unsupervised detection framework is maintained by incorporating a lightweight supervised module capable of classifying detection outputs without disrupting the workflow. Rather than training on the entire point cloud, the proposed method focuses on selected target features, reducing model size and information exchange. Specifically, a Random Forest Classifier is trained on features extracted from the point-cloud dataset. The acquisition of an ad-hoc training dataset and its statistical analysis are presented to identify key features. The selection, training, and validation processes are outlined. Finally, the supervised model is integrated into a state-of-the-art unsupervised LiDAR detection pipeline and tested in a real scenario. The results demonstrate the hybrid framework's effectiveness and compliance with real-time constraints
Multi-obstacle detection and tracking algorithms for the marine environment based on unsupervised learning
No full textNowadays, more than in the past, research is focused on the study and development of enabling technologies to achieve the goal of autonomous navigation. The investigations cover all industrial fields: automotive, aerospace, and maritime. The first level to be developed to achieve the goal of autonomous vehicles is obstacle detection and consecutively object tracking in real-time. The most important results were obtained in the automotive sector, where the available financial resources are more significant than the others. Indeed, several methods based on trained neural networks are used to detect the obstacles. Usually, the neural networks are trained by using large datasets of LiDAR point clouds and images. Unfortunately, it is impossible to emulate this approach in the marine sector because there are no large datasets for training a neural network. For such a reason, this paper aims to present multi-object tracking based on unsupervised learning. The proposed method is tailored for the challenging marine environment, and it is suitable for the detection and tracking of both fixed and moving obstacles. This paper presents a low-computational method for multi-object tracking based on unsupervised learning, and it is discussed and analysed step by step, indicating the pros and weaknesses. Moreover, the tracking has been tested on both experimental LiDAR point clouds and virtual LiDAR point clouds created employing a tailored virtual scenario. The results are reported and discussed
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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