Genetic algorithm for shipping route estimation with long-range tracking data : automatic reconstruction of shipping routes based on the historical ship positions for maritime safety applications

Ship tracking systems allow Maritime Organizations that are concerned with the Safety at Sea to obtain information on the current location and route of merchant vessels. Thanks to Space technology in recent years the geographical coverage of the ship tracking platforms has increased significantly, from radar based near-shore traffic monitoring towards a worldwide picture of the maritime traffic situation. The long-range tracking systems currently in operations allow the storage of ship position data over many years: a valuable source of knowledge about the shipping routes between different ocean regions. The outcome of this Master project is a software prototype for the estimation of the most operated shipping route between any two geographical locations. The analysis is based on the historical ship positions acquired with long-range tracking systems. The proposed approach makes use of a Genetic Algorithm applied on a training set of relevant ship positions extracted from the long-term storage tracking database of the European Maritime Safety Agency (EMSA). The analysis of some representative shipping routes is presented and the quality of the results and their operational applications are assessed by a Maritime Safety expert.

Uncertainty in olfactory decision-making

Relationships between accuracy and speed of decision-making, or speed-accuracy tradeoffs (SAT), have been extensively studied. However, the range of SAT observed varies widely across studies for reasons that are unclear. Several explanations have been proposed, including motivation or incentive for speed vs. accuracy, species and modality but none of these hypotheses has been directly tested. An alternative explanation is that the different degrees of SAT are related to the nature of the task being performed. Here, we addressed this problem by comparing SAT in two odor-guided decision tasks that were identical except for the nature of the task uncertainty: an odor mixture categorization task, where the distinguishing information is reduced by making the stimuli more similar to each other; and an odor identification task in which the information is reduced by lowering the intensity over a range of three log steps. (...)