The open maritime traffic analysis dataset

Ships traverse the world’s oceans for a diverse range of reasons, including the bulk transportation of goods and resources, carriage of people, exploration and fishing. The size of the oceans and the fact that they connect a multitude of different countries provide challenges in ensuring the safety of vessels at sea and the prevention of illegal activities. To assist with the tracking of ships at sea, the International Maritime Organisation stipulates the use of the Automatic Identification System (AIS) on board ships. The AIS system periodically broadcasts details of a ship’s position, speed and heading, along with other parameters corresponding to the ship’s type, size and set destination. The availability of AIS data has led to a large effort to develop automated systems which could identify and be used to prevent undesirable incidents at sea. For example, detecting when ships are in danger of colliding, running aground, engaged in illegal activity, traveling at unsafe speeds, or otherwise attempting manoeuvres that exceed their physical capabilities. Despite this interest, there is a lack of a publicly available ‘standard’ dataset that can be used to benchmark different approaches. As such, each new approach to automated maritime activity modelling is tested using a different dataset to previous work, making the comparison of technique efficacy problematic. In this paper a new public dataset of shipping tracks is introduced, containing data for four vessel types: cargo, tanker, fishing and passenger. Each track corresponds to a leg of a vessel’s journey within an area of interest located around the west coast of Australia. The tracks in the dataset have been validated according to a set of rules, consisting of journeys at minimum 10 hours long, with no missing data. The tracks cover a three-year period (2018 to 2020) and are further categorised by month, allowing for the analysis of seasonal variations in shipping. The intention of releasing this dataset is to allow researchers developing methods for maritime behaviour analysis and classification to compare their techniques on a standard set of data. As an example of how this dataset can be used, we use it to build a model of ‘expected’ behaviour trained on data for three vessel categories: cargo, tanker, and passenger vessels, using a convolutional autoencoder architecture. We then demonstrate how this model of ship behaviour can be used to test new data that was not used to build the model to determine whether a track fits the model or is an anomaly. Specifically, we verify that the behaviour of fishing vessels, whose movement patterns are quite different to those of the other three vessel types, is classified as an anomaly when presented to the trained model

Access to Psychological Therapies - DCAQ in NHS Lothian: Phase 2 Report

EdinburghThe purpose of this document is to report on phase two of the Demand, Capacity, Activity & Queue (DCAQ) work carried out with Midlothian Psychological Therapies Service and East Lothian Psychological Therapies Service between April 2011 and March 2012. The overall project was broken down into two phases and this report is a summary of the work completed in phase two. The phase one report can be accessed at the following web address; http://www.qihub.scot.nhs.uk/media/220541/nhs%20lothian%20dcaq%20phase%201%20report%20vfinal2.doc The phase two report has two main purposes: To provide feedback on the work completed in phase two and to outline the additional service improvement opportunities that might be explored for each service participating; To provide a learning resource for other services interested in applying DCAQ.sch_occpub3131pu

Analysing Intelligence, Surveillance and Reconnaissance

We investigate our novel and new technique for analysing Intelligence, Surveillance and Reconnaissance (ISR) in military engagements. This is a small part of the work that has been carried out at the Defence Science and Technology Organisation (DSTO) and the Western Australian Centre of Excellence in Industrial Optimisation (WACEIO) to assess the value of ISR systems when the friendly operational commander is conducting Manoeuvre Warfare, which requires the friendly force that is relatively small and mobile be advantageously positioned in space and time to disrupt the strength and will to fight of the enemy force [2, 3]. Mathematical models of the ISR operations are developed for a generic engagement between the friendly and enemy forces, and then demonstrated using a maritime battle that necessitates the collection of information on the dispositions of the enemy scouts and their threats by a satellite (Option 1), an Unmanned Aerial Vehicle (UAV) (Option 2) or both of these ISR systems (Option 3) prior to commencing hostilities. For the parametric choices that define these options, the results show that Option 3 is the best, Option 1 is the second best and Option 2 is the third best. Furthermore, the results show that our technique will assist with gaining a deeper understanding of how the ISR operations impact on the operational commander's objective