1,720,996 research outputs found
Unsupervised extraction of maritime patterns of life from Automatic Identification System data
No full textThis paper presents an unsupervised approach to extract maritime Patterns of Life (PoL) from historical Automatic Identification System (AIS) data based on a low-dimensional synthetic representation of ship routes. Recent advances in long-term vessel motion modeling through Ornstein-Uhlenbeck mean-reverting stochastic processes allow to encode knowledge about maritime traffic via a compact graph-based model where waypoints are graph vertices and the connections between them, i.e., the navigational legs, are graph edges. The resulting directed graph ultimately leads to the detection and statistical characterization of recurrent maritime traffic patterns. To demonstrate its effectiveness and applicability to real-world case studies, the proposed methodology has been tested on two extensive AIS datasets, collected in the areas of two operational trials of EU-H2020's MARISA (Maritime Integrated Surveillance Awareness) project
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
Proprietà elettriche di compositi conduttivi
No full textL’attività di ricerca riguarda la modellazione delle proprietà elettriche dei materiali dispersivi, con lo scopo di predire l’efficienza di schermatura alle onde elettromagnetiche di schermi realizzati con tali materiali. L’attività è stata condotta in parziale collaborazione con ricercatori stranieri del mondo industriale.
L’approccio di modellazione sviluppato può essere applicato a materiali compositi contenenti fibre conduttive. L’interesse per tali materiali compositi è motivato dal fatto che l’aggiunta di fibre, comportando una diminuzione della resistività elettrica, apre numerose opportunità applicative.
Per utilizzare questi compositi per la schermatura elettromagnetica occorre conoscere e stimare le proprietà elettriche modificate, ovvero la permittività complessa effettiva del materiale composito, in funzione della concentrazione dell’additivo conduttivo. Di rilevante interesse sono i materiali da costruzione, quali il calcestruzzo, la cui bassa conducibilità elettrica ed efficienza di schermatura possono essere aumentate sensibilmente mediante additivi conduttivi, ottenendo così una soluzione pratica di costo limitato impiegabile quando non siano richieste elevate prestazioni schermanti
Random Finite Set Tracking for Anomaly Detection in the Presence of Clutter
No full textIn this paper, a sequential Bayesian framework is proposed to address the task of joint anomaly detection and tracking for surveillance applications in the presence of clutter. This is achieved by modeling the anomaly as a switching unknown control input which goes into action by modifying the expected dynamics of a target and ceases its activity (becomes nonexistent) under nominal behavior. Random Finite Sets (RFS) make it possible to represent the switching nature of the object anomalous behavior and derive a hybrid Bernoulli filter (HBF) that sequentially updates the joint posterior density of a Bernoulli RFS for the unknown velocity input and the object kinematic state. In addition, the proposed HBF has been customized for maritime anomaly detection by using a piecewise Ornstein-Uhlenbeck (OU) stochastic process as dynamic model of vessels. We illustrate the effectiveness of the proposed filter, implemented in Gaussian-mixture form, and compare its performance in a maritime surveillance example with the Interacting Multiple Model Probabilistic Data Association Filter (IMM - PDAF) for different levels of clutter
Prediction of Vessel Trajectories from AIS Data Via Sequence-To-Sequence Recurrent Neural Networks
No full textIn this paper, we address the problem of predicting vessel trajectories based on Automatic Identification System (AIS) data. The goal is to learn the predictive distribution of maritime traffic patterns using historical data during the training phase, in order to be able to forecast future target trajectory samples online on the basis of both the extracted knowledge and the available observation sequence. We explore neural sequence-to-sequence models based on the Long Short-Term Memory (LSTM) encoder-decoder architecture to effectively capture long-term temporal dependencies of sequential AIS data and increase the overall predictive power. The experimental evaluation on a real-world AIS dataset demonstrates the effectiveness of sequence-to-sequence recurrent neural networks (RNNs) for vessel trajectory prediction and shows their potential benefits compared to model-based methods
Anomaly Detection and Tracking Based on Mean-Reverting Processes with Unknown Parameters
No full textPiecewise mean-reverting stochastic processes have been recently proposed and validated as an effective model for long-term object prediction. In this paper, we exploit the Ornstein-Uhlenbeck (OU) dynamic model to represent an anomaly as any deviation of the long-run mean velocity from the nominal condition. This amounts to modeling the anomaly as an unknown switching control input that can affect the dynamics of the object. Under this model, the problem of joint anomaly detection and tracking can be addressed within the Bayesian random set framework by means of a hybrid Bernoulli filter (HBF) that sequentially estimates a Bernoulli random set (empty under nominal behavior) for the unknown long-run mean velocity, and a random vector for the kinematic state of the object. An additional challenge is represented by the fact that two extra parameters, i.e. the reversion rate and the noise covariance of the underlying OU process, need to be specified for Bayes-optimal prediction. We propose a multiple-model adaptive filter (MMA-HBF) for anomaly detection, tracking and simultaneous estimation of the OU unknown parameters. The effectiveness of these tools is demonstrated on a simulated maritime scenario
Bayesian Filtering for Dynamic Anomaly Detection and Tracking
No full textThis paper presents a Bayesian approach for sequential detection of anomalies in the motion of a target and joint tracking. The anomaly is modeled as a binary (on/off) switching unknown control input that goes into action (begins to exist, or “switches on”) thereby modifying the object dynamics; and by ceasing its activity (becoming non-existent, or “switching off”) returns the dynamics to nominal. The developed Bayesian framework brings together Random Finite Set (RFS) theory to represent the switching nature of the anomaly, and optimal Joint Input and State Estimation (JISE) to sequentially update a hybrid state that incorporates a random vector for the kinematic state and a Bernoulli RFS for the unknown control input. In addition, a closed-form solution, the Gaussian-mixture hybrid Bernoulli filter (GM-HBF), has been developed to provide a customized solution for dynamic anomaly detection in the maritime domain characterized by linear Gaussian target dynamics. Based on the Ornstein-Uhlenbeck (OU) dynamic model for vessels where the evolution of the object velocity is governed by a piecewise mean-reverting stochastic process, the anomaly can be represented by a change in the long-run mean velocity (i.e., the unknown control input) from the nominal condition that forces the vessel to deviate from its standard route. We demonstrate the effectiveness of the proposed GM-HBF in both simulated and real-world maritime surveillance applications and test its performance in the face of false measurements, detection uncertainty, and sensor data gaps
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