Researchers are aiming to "teach" a computer to recognize the sounds of resident killer whales in order to develop a warning system for preventing ships from fatally striking endangered orcas off British Columbia's coast. Steven Bergner, a computing science research associate at Simon Fraser University's Big Data Hub, said he is collecting and managing a database of sounds picked up 24 hours a day by a network of hydrophones in the Salish Sea. Marine biologists will identify the sounds of different species of whales, including humpbacks and transients, and differentiate the acoustics from other noise such as waves and boats, he said. Machine learning or artificial intelligence would help detect the presence of orcas through patterns in the data. "That (information) goes through another system that then decides whether there should be a warning that ultimately reaches the vessel pilots," Bergner said.
Ioannis Psarros, chief commercial officer of Signal Maritime Services discusses the limitations of AI for tanker chartering At Signal we are passionate about technology and the significant assistance it can bring to shipping professionals, but we don’t believe that algorithms can replace good intuitive shipbroking. Despite what you may have read in the media, AI …
Neptune Lines PCTCs feature METIS Ship Connect – the automated data acquisition solution whose accuracy is approved by Lloyds Register. The METIS platform uses a network of Wireless Intelligent Collectors to harvest machinery, navigation and operational data regardless of equipment supplier. Its ship performance analysis also integrates AIS data, data fetched from vessels' daily / arrival / departure regular reporting and weather forecasts to provide services such as automated noon reporting, analysis of technical and operational domains and weather-related reporting. Outputs include live dashboards showing the condition of main engines, diesel generators, ballast water treatment systems and other machinery as well as power and fuel consumption. However, the METIS platform also allows Neptune Lines management to visualize KPIs such as power vs speed under the full ship speed range and in all weathers using machine learning models and run'what if' routing scenarios to weigh up consequences for fuel and arrival times.
My childhood friend Marco was born and raised like me, in the Italian maritime city of Monfalcone. Fifty miles away from Venice, at the very North tip of the Mediterranean, he works in the city shipyard. Marco is a descendant of a long history of artisans whose lineage can be traced back to Venetian shipbuilders in the Middle Ages. Unlike his ancestors, much of Marco's work relies on his manual skills, augmented by today's digital aids. Paraphrasing an old Industry 4.0 joke, I once told Marco how the super-automated shipyard of the future "…will only need two employees: a guard dog, and you, hired to feed the dog."
A robotic cargo vessel has passed through the Panama Canal for the first time. The uncrewed ship, an Overlord Unmanned Surface Vessel (USV) of the US Navy, made a 4700 nautical mile (8700 kilometre) journey including passage from the Atlantic to the Pacific almost entirely without human assistance. Pentagon spokesman Josh Frey says the vessel was in autonomous mode for over 97 per cent of the trip's length. A remote crew assisted when needed.
A robotic cargo vessel has passed through the Panama Canal for the first time. The ship, an Overlord uncrewed surface vessel belonging to the US Navy, made a 4700-nautical-mile (8700-kilometre) journey including passage from the Atlantic to the Pacific almost entirely without human assistance. Pentagon spokesperson Josh Frey says the vessel was in autonomous mode for more than 97 per cent of the trip's length. A remote crew assisted when needed. The US Navy has two of the 59-metre Overlord vessels, modified from crewed fast transport ships.
The i4 Insight Platform allows shipowners, operators and charterers to access insights on performance and fuel consumption across all ships in their fleet. The addition of GreenSteam's advanced machine learning technology means that platform users will have a more accurate picture of the leading contributors to excessive fuel consumption as well as access to actionable recommendations on how to optimise fleet performance. "Given the sheer volume of performance data available, machine learning is essential to help make sense of complex factors impacting vessel performance to help ensure operational efficiency," a press release from Lloyd's Register stated. GreenSteam was one of the first companies to apply machine learning to vessel performance data and its system can analyse data from thousands of vessels, continually learning, adapting and updating what it knows about each vessel. Shaun Gray, executive chairman of GreenSteam, commented: "An in-depth, data-driven approach to understanding and acting on fuel consumption has never been more necessary for the industry. GreenSteam's machine learning technology uses real ship performance data to provide owners and operators with actionable advice. Unlike traditional analytic approaches that fail to use and model 90% of performance data, by using machine learning, GreenSteam includes all ship performance data in its models to deliver insights other standard methodologies just cannot see."
The paper presents a method to validate and refine the ship's route during the voyage. The method is based on computing several characteristic coefficients that represent and measure route properties. Thru the analysis of the values of these coefficient, one can analyse the overall route quality and detect possibly dangerous discrepancies between the actual route and the planned route.The paper describes the proposed characteristic coefficients, the process of route refinement and the method for prediction and validation of the route's future changes.
Sometime next April, a 50-foot-long autonomous ship will shake loose the digital bonds of its human controllers, scan the horizon with radar, and set a course westward across the Atlantic. The Mayflower Autonomous Ship won't be taking commands from a human captain like the first Mayflower did during its crossing back in 1620. Instead it will get orders from an "AI captain" built by programmers at IBM. The Mayflower's computing system processes data from 30 onboard sensors and six cameras to help the ship sail across the ocean, obey shipping rules (like how to pass other ships at sea), and control electrical and mechanical systems like the engine and rudder. There won't be anyone on board if something goes wrong, although it does have to send a daily report to a human operator back in the UK.
Recent advances in time series classification have largely focused on methods that either employ deep learning or utilize other machine learning models for feature extraction. Though successful, their power often comes at the requirement of computational complexity. In this paper, we introduce GeoStat representations for time series. GeoStat representations are based off of a generalization of recent methods for trajectory classification, and summarize the information of a time series in terms of comprehensive statistics of (possibly windowed) distributions of easy to compute differential geometric quantities, requiring no dynamic time warping. The features used are intuitive and require minimal parameter tuning. We perform an exhaustive evaluation of GeoStat on a number of real datasets, showing that simple KNN and SVM classifiers trained on these representations exhibit surprising performance relative to modern single model methods requiring significant computational power, achieving state of the art results in many cases. In particular, we show that this methodology achieves good performance on a challenging dataset involving the classification of fishing vessels, where our methods achieve good performance relative to the state of the art despite only having access to approximately two percent of the dataset used in training and evaluating this state of the art.