A generic decision support architecture for maritime operations

Grasso, Raffaele; Cococcioni, Marco; Rixen, M.; Trees, Charles C.; Baldacci, Alberto

doi:10.1504/ijidss.2010.037094

Cited by 10 publications

(13 citation statements)

References 14 publications

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“…A first architecture based on fuzzy systems coupled with web-based geographical information systems (GISs) for data management and dissemination was proposed in Grasso and Giannecchini (2006). Since then, the system has been incrementally updated (Grasso 2009;Grasso et al 2010b) up to the latest version presented in Grasso et al (2011) and tested during REP10 as reported in the present work. Improvements with respect to previous works include fusion of METOC predictions from different centres and production of stochastic forecasts as well as efficient and explicit use of the predicted uncertainty through the unscented transform (UT) to improve performance of the decision makers.…”

Section: Related Work On Environmental Dsssmentioning

confidence: 99%

“…The fuzzy/Bayesian DSS will be described in the following subsections, mainly reviewing the concepts outlined in Grasso et al (2010b) and Grasso et al (2011), with additional details, in particular those specific to the REP10 experiment.…”

Section: System Descriptionmentioning

confidence: 99%

“…2. The core of the system (Grasso et al 2010b) is a risk calculator based on a hybrid fuzzy/Bayesian inference engine (Ross 2010). This block calculates the risk of performing an action, selected from a pre-defined list of actions, under given environmental conditions described by a set of METOC variables predicted by the SE model.…”

Section: The Fuzzy/bayesian Dssmentioning

confidence: 99%

“…As originally proposed in Grasso et al (2010b), the system chooses, according to the classical Bayesian decision theory (Duda et al 2000), the action having the minimum conditional risk:…”

Section: Action Selection and Decision Confidencementioning

confidence: 99%

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A maritime decision support system to assess risk in the presence of environmental uncertainties: the REP10 experiment

et al. 2012

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The aim of this work is to report on an activity carried out during the 2010 Recognized Environmental Picture experiment, held in the Ligurian Sea during summer 2010. The activity was the first at-sea test of the recently developed decision support system (DSS) for operation planning, which had previously been tested in an artificial experiment. The DSS assesses the impact of both environmental conditions (meteorological and oceanographic) and nonenvironmental conditions (such as traffic density maps) on people and assets involved in the operation and helps in deciding a course of action that allows safer operation. More precisely, the environmental variables (such as wind speed, current speed and significant wave height) taken as input by the DSS are the ones forecasted by a super-ensemble model, which fuses the forecasts provided by multiple forecasting centres. The uncertainties associated with the DSS's inputs (generally due to disagreement between forecasts) are propagated through the DSS's output by using the unscented transform. In this way, the system is not only able to provide a traffic light map (run/not run the operation), but also to specify the confidence level associated with each action. This feature was tested on a particular type of operation with underwater gliders: the glider surfacing for data transmission. It is also shown how the availability of a glider path prediction tool provides surfacing options along the predicted path. The applicability to different operations is demonstrated by applying the same system to support diver operations.

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Section: Related Work On Environmental Dsssmentioning

confidence: 99%

Section: System Descriptionmentioning

confidence: 99%

Section: The Fuzzy/bayesian Dssmentioning

confidence: 99%

“…As originally proposed in Grasso et al (2010b), the system chooses, according to the classical Bayesian decision theory (Duda et al 2000), the action having the minimum conditional risk:…”

Section: Action Selection and Decision Confidencementioning

confidence: 99%

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A maritime decision support system to assess risk in the presence of environmental uncertainties: the REP10 experiment

et al. 2012

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“…[21][22] [23] [24]) is assumed to be available, together with the presence of some fixed observing stations (harbor control towers, coast guard station, etc. ).…”

Section: Introductionmentioning

confidence: 99%

Adaptive sampling using fleets of underwater gliders in the presence of fixed buoys using a constrained clustering algorithm

Cococcioni

Lazzerini

Lermusiaux

2015

OCEANS 2015 - Genova

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Abstract-This paper presents a novel way to approach the problem of how to adaptively sample the ocean using fleets of underwater gliders. The technique is particularly suited for those situations where the covariance of the field to sample is unknown or unreliable but some information on the variance is known. The proposed algorithm, which is a variant of the well-known fuzzy C-means clustering algorithm, is able to exploit the presence of non-maneuverable assets, such as fixed buoys. We modified the fuzzy C-means optimization problem statement by including additional constraints. Then we provided an algorithmic solution to the new, constrained problem.

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A decision support system for optimal deployment of sonobuoy networks based on sea current forecasts and multi-objective evolutionary optimization

Grasso¹,

Cococcioni²,

Mourre³

et al. 2013

Expert Systems with Applications

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A generic decision support architecture for maritime operations

Cited by 10 publications

References 14 publications

A maritime decision support system to assess risk in the presence of environmental uncertainties: the REP10 experiment

A maritime decision support system to assess risk in the presence of environmental uncertainties: the REP10 experiment

Adaptive sampling using fleets of underwater gliders in the presence of fixed buoys using a constrained clustering algorithm

A decision support system for optimal deployment of sonobuoy networks based on sea current forecasts and multi-objective evolutionary optimization

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