2,805 research outputs found
Maritime Anomaly Detection using Autoencoders and OPTICS-OF
Anomaly detection is an important task in many domains such as maritime where it is used to detect, for example, unsafe, unexpected or criminal behaviour. This thesis studies the use of deep autoencoders for anomaly detection on high dimensional data in an unsupervised manner. The study is performed on a benchmark data set and a real-life AIS (Automatic Tracking System) data set containing actual ship trajectories. The ships’ trajectories in the AIS data set are a form of time-series data, and therefore recurrent layers are used in an autoencoder to allow the model to capture temporal dependencies in the data.
An autoencoder is a neural network architecture where an encoder network produces an encoding and decoder network takes the encoding intending to produce the original input. An encoding is a compressed fixed-sized vector presentation of the original input. Since the encoding is used by the decoder to construct the original input, the model learns during the training process to store essential information of the input sequence to the encoding.
Autoencoders can be used to detect anomalies using reconstruction error by assuming that a trained autoencoder is able to reconstruct non-anomalius data points more accurately than anomalous data points, and therefore data points with high reconstruction error can be considered anomalies. In addition to reconstruction error, the autoencoders produce encodings. The research of this thesis studies the possibility of calculating an outlier score for the encodings and combining the score with resconstruction error to form a combined outlier score. OPTICS-OF (Ordering Points to Identify the ClusteringStructure with Outlier Factors) is a density based anomaly detection technique which can be used to calculate outlier scores for the encodings. The outlier score of OPTICS-OF for a data point is based on how isolated it is within its neighbourhood.
The proposed method is evaluated on a benchmark Musk data set for which anomalies are known. A data set with labelled anomalies provides a setting for analyzing the performance of the method and its properties. The method is then put to the test on the AIS data set where it is used to find new anomalies in the data set from two derived distinct feature sets. The AIS data set contains one known anomaly which is presented both as an example of a maritime anomaly and for which more detailed analysis of the produced outlier scores are presented.
The results of the study show potential for the proposed combined score method, and the analysis identifies multiple areas for further research. Deep autoencoders are successfully used to find new anomalies from the AIS data set which show actual behaviour deviating from normal ship movement
Imitating Human Responses via a Dual-Process Model Approach
Human-autonomous system teaming is becoming more prevalent in the Air Force and in society. Often, the concept of a shared mental model is discussed as a means to enhance collaborative work arrangements between a human and an autonomous system. The idea being that when the models are aligned, the team is more productive due to an increase in trust, predictability, and apparent understanding. This research presents the Dual-Process Model using multivariate normal probability density functions (DPM-MN), which is a cognitive architecture algorithm based on the psychological dual-process theory. The dual-process theory proposes a bipartite decision-making process in people. It labels the intuitive mode as “System 1” and the reflective mode as “System 2”. The current research suggests by leveraging an agent which forms decisions based on a dual-process model, an agent in a human-machine team can maintain a better shared mental model with the user. Evaluation of DPM-MN in a game called Space Navigator shows that DPM-MN presents a successful dual-process theory motivated model
Trajectory Clustering and an Application to Airspace Monitoring
This paper presents a framework aimed at monitoring the behavior of aircraft
in a given airspace. Nominal trajectories are determined and learned using data
driven methods. Standard procedures are used by air traffic controllers (ATC)
to guide aircraft, ensure the safety of the airspace, and to maximize the
runway occupancy. Even though standard procedures are used by ATC, the control
of the aircraft remains with the pilots, leading to a large variability in the
flight patterns observed. Two methods to identify typical operations and their
variability from recorded radar tracks are presented. This knowledge base is
then used to monitor the conformance of current operations against operations
previously identified as standard. A tool called AirTrajectoryMiner is
presented, aiming at monitoring the instantaneous health of the airspace, in
real time. The airspace is "healthy" when all aircraft are flying according to
the nominal procedures. A measure of complexity is introduced, measuring the
conformance of current flight to nominal flight patterns. When an aircraft does
not conform, the complexity increases as more attention from ATC is required to
ensure a safe separation between aircraft.Comment: 15 pages, 20 figure
Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities
With the increasing amount of spatial-temporal~(ST) ocean data, numerous
spatial-temporal data mining (STDM) studies have been conducted to address
various oceanic issues, e.g., climate forecasting and disaster warning.
Compared with typical ST data (e.g., traffic data), ST ocean data is more
complicated with some unique characteristics, e.g., diverse regionality and
high sparsity. These characteristics make it difficult to design and train STDM
models. Unfortunately, an overview of these studies is still missing, hindering
computer scientists to identify the research issues in ocean while discouraging
researchers in ocean science from applying advanced STDM techniques. To remedy
this situation, we provide a comprehensive survey to summarize existing STDM
studies in ocean. Concretely, we first summarize the widely-used ST ocean
datasets and identify their unique characteristics. Then, typical ST ocean data
quality enhancement techniques are discussed. Next, we classify existing STDM
studies for ocean into four types of tasks, i.e., prediction, event detection,
pattern mining, and anomaly detection, and elaborate the techniques for these
tasks. Finally, promising research opportunities are highlighted. This survey
will help scientists from the fields of both computer science and ocean science
have a better understanding of the fundamental concepts, key techniques, and
open challenges of STDM in ocean
Workflow to detect ship encounters at sea with GIS support
Dissertation presented as the partial requirement for obtaining a Master's degree in Geographic Information Systems and ScienceAccording to the United Nations, more than 80% of the global trade is currently transported by
sea. The Portuguese EEZ has a very extensive area with high maritime traffic, among which illicit
activities may occur. This work aims to contribute to the official control of illegal transshipment
actions by studying and proposing a new way of detecting encounters between ships.
Ships with specific characteristics use an Automatic Identification System (AIS) on board which
transmits a signal via radio frequencies, allowing shore stations to receive static and dynamic
data from the ship. Thus, there is an increase in maritime situational awareness and,
consequently, in the safety of navigation.
The methodology of this dissertation employs monthly and daily AIS data in the study area, which
is located in southern mainland Portugal.
A bibliometric and content analysis was performed in order to assess the state of the art
concerning geospatial analysis models of maritime traffic, based on AIS data, and focus on
anomalous behaviour detection.
Maritime traffic density maps were created with the support of a GIS (QGIS software), which
allowed to characterize the maritime traffic in the study area and, subsequently, to pattern the
locations where ship encounters occur. The algorithm to detect ship-to-ship meetings at sea was
developed using a rule-based methodology.
After analysis and discussion of results, it was found that the areas where the possibility of ship
encounters at sea is greatest are away from the main shipping lanes, but close to areas with
fishing vessels.
The study findings and workflow are useful for decision making by the competent authorities for
patrolling the maritime areas, focusing on the detection of illegal transhipment actions.Segundo as Nações Unidas, mais de 80% do comércio global é, atualmente, transportado por
via marítima. A ZEE portuguesa tem uma área muito extensa, com tráfego marítimo elevado,
entre o qual podem ocorrer atividades ilícitas. Este trabalho pretende contribuir para o controlo
oficial de ações de transbordo ilegal, estudando e propondo uma nova forma de deteção de
encontros entre navios.
Os navios com determinadas características, utilizam a bordo um Automatic Identification System
(AIS) que transmite sinal através de frequências rádio, permitindo que estações em terra
recebam dados estáticos e dinâmicos do navio. Deste modo, verifica-se um aumento do
conhecimento situacional marítimo e, consequentemente, da segurança da navegação.
Foi realizada uma análise bibliométrica e de conteúdo a fim de avaliar o estado da arte referente
a modelos de análise geoespacial do tráfego marítimo, com base em dados AIS, e foco na
deteção de comportamentos anómalos.
Na metodologia desta dissertação, são utilizados dados AIS mensais e diários na área de estudo,
situada a sul de Portugal Continental.
Foram criados mapas de densidade de tráfego marítimo com o apoio de um SIG (software QGIS),
o que permitiu caracterizar o tráfego marítimo na área de estudo e, posteriormente, padronizar
os locais onde ocorrem encontros entre navios. O algoritmo para detetar encontros entre navios
no mar foi desenvolvido através de uma metodologia baseada em regras.
Após análise e discussão de resultados, constatou-se que as áreas onde a possibilidade de
ocorrer encontros de navios no mar é maior, encontram-se afastadas dos corredores principais
de navegação, mas próximas de zonas com embarcações de pesca.
Os resultados do estudo e o workflow desenvolvidos são úteis à tomada de decisão pelas
autoridades competentes por patrulhar as áreas marítimas, com incidência na deteção de ações
de transbordo ilegal
Intelligent Deep Fusion Network for Anomaly Identification in Maritime Transportation Systems
This paper introduces a novel deep learning architecture for identifying outliers in the context of intelligent transportation systems. The use of a convolutional neural network with decomposition is explored to find abnormal behavior in maritime data. The set of maritime data is first decomposed into similar clusters containing homogeneous data, and then a convolutional neural network is used for each data cluster. Different models are trained (one per cluster), and each model is learned from highly correlated data. Finally, the results of the models are merged using a simple but efficient fusion strategy. To verify the performance of the proposed framework, intensive experiments were conducted on marine data. The results show the superiority of the proposed framework compared to the baseline solutions in terms of several accuracy metrics.acceptedVersio
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