Accurate modeling of ship performance is crucial for the shipping industry to optimize fuel consumption and subsequently reduce emissions. However, predicting the speed-power relation in real-world conditions remains a challenge. In this study, we used in-service monitoring data from multiple vessels with different hull shapes to compare the accuracy of data-driven machine learning (ML) algorithms to traditional methods for assessing ship performance. Our analysis consists of two main parts: (1) a comparison of sea trial curves with calm-water curves fitted on operational data, and (2) a benchmark of multiple added wave resistance theories with an ML-based approach. Our results showed that a simple neural network outperformed established semi-empirical formulas following first principles. The neural network only required operational data as input, while the traditional methods required extensive ship particulars that are often unavailable. These findings suggest that data-driven algorithms may be more effective for predicting ship performance in practical applications.

Distributional shift, or the mismatch between training and deployment data, is a significant obstacle to the usage of machine learning in high-stakes industrial applications, such as autonomous driving and medicine. This creates a need to be able to assess how robustly ML models generalize as well as the quality of their uncertainty estimates. Standard ML baseline datasets do not allow these properties to be assessed, as the training, validation and test data are often identically distributed. Recently, a range of dedicated benchmarks have appeared, featuring both distributionally matched and shifted data. Among these benchmarks, the Shifts dataset stands out in terms of the diversity of tasks as well as the data modalities it features. While most of the benchmarks are heavily dominated by 2D image classification tasks, Shifts contains tabular weather forecasting, machine translation, and vehicle motion prediction tasks. This enables the robustness properties of models to be assessed on a diverse set of industrial-scale tasks and either universal or directly applicable task-specific conclusions to be reached. In this paper, we extend the Shifts Dataset with two datasets sourced from industrial, high-risk applications of high societal importance. Specifically, we consider the tasks of segmentation of white matter Multiple Sclerosis lesions in 3D magnetic resonance brain images and the estimation of power consumption in marine cargo vessels. Both tasks feature ubiquitous distributional shifts and a strict safety requirement due to the high cost of errors. These new datasets will allow researchers to further explore robust generalization and uncertainty estimation in new situations. In this work, we provide a description of the dataset and baseline results for both tasks.

The hydrodynamic performance of a sea-going ship varies over its lifespan due to factors like marine fouling and the condition of the anti-fouling paint system. In order to accurately estimate the power demand and fuel consumption for a planned voyage, it is important to assess the hydrodynamic performance of the ship. The current work uses machine-learning (ML) methods to estimate the hydrodynamic performance of a ship using the onboard recorded in-service data. Three ML methods, NL-PCR, NL-PLSR and probabilistic ANN, are calibrated using the data from two sister ships. The calibrated models are used to extract the varying trend in ship's hydrodynamic performance over time and predict the change in performance through several propeller and hull cleaning events. The predicted change in performance is compared with the corresponding values estimated using the fouling friction coefficient ($\Delta C_F$). The ML methods are found to be performing well while modelling the hydrodynamic state variables of the ships with probabilistic ANN model performing the best, but the results from NL-PCR and NL-PLSR are not far behind, indicating that it may be possible to use simple methods to solve such problems with the help of domain knowledge.

Shipyards that build large and complex ships are dependent on accurate project management. New techniques such as artificial intelligence and machine learning make it possible to design more efficient processes by making use of historical data. That is exactly what the Floor2Plan tool developed by the Dutch company Floorganise does, making artificial intelligence a key element of the optimisation process in shipbuilding. 'The earlier you can identify and resolve risks, the less impact they will have on the entire process. That's the core idea behind the Floor2Plan tool,' explains Ronald de Vries of Floorganise.

In this blog, we will show our approach to classifying images of ship using supervised models. We use a dataset obtained from Kaggle in order to perform our analyses. We discuss various data preprocesses we went through in order to reduce the dimensionality of the data, and to feed our models the best inputs possible. Ship or vessel detection has a wide range of applications, in the areas of maritime safety, fisheries management, marine pollution, defence and maritime security, protection from piracy, illegal migration, etc. Keeping this in mind, a Governmental Maritime and Coastguard Agency is planning to deploy a computer vision based automated system to identify ship type only from the images taken by the survey boats. You have been hired as a consultant to build an efficient model for this project.

Thousands of historical images from across Ireland are being brought to life in color for the first time, thanks to a new AI-led photo project. Combining digital technology with painstaking historical research, professors John Breslin and Sarah-Anne Buckley at the National University of Ireland, Galway, have been able to turn photos, originally shot in black in white, into rich color images. It includes portraits of key figures like Oscar Wilde and poet W.B. Yeats, as well as defining moments in history, like the Titanic setting sail from the Belfast shipyard where it was constructed. Yet, some of the most compelling photos depict everyday scenes -- people herding pigs, spinning wool or packed onto the back of horse-drawn carts. And while poverty is evident in pictures of barefoot villagers crowding around for a photo, or of Dublin's working-class tenement buildings, there are also well-to-do family shots and depictions of upper-class pastimes like fox hunting.

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."

Daewoo Shipbuilding & Marine Engineering is the first global shipbuilding industry with artificial intelligence for hot processing A robot system that combines technology is applied. Daewoo Shipbuilding & Marine Engineering (CEO Lee Seong-Geun) has developed an artificial intelligent hot processing robot'Goknuri' that can produce high-quality products even with low-skilled people using standardized big data and artificial intelligence technology while improving the working environment and applied it to the field It was revealed on the 20th. The newly developed robot'Goknuri' contributes to maintaining high quality by standardizing work contents while storing and utilizing the know-how and performance of existing workers as data. In addition, the accumulated data can be used for the construction of other ships using artificial intelligence technology in the future. In addition, it is possible to dramatically improve the working environment of workers who have been exposed to noise and musculoskeletal diseases.

Daewoo Shipbuilding Company, one of the largest shipbuilders in the world, has experienced great deal of trouble with the planning and scheduling of its production process. To solve the problems, from 1991 to 1993, Korea Advanced Institute of Science and Technology (KAIST) and Daewoo jointly conducted the Daewoo Shipbuilding Scheduling (das) Project. To integrate the scheduling expert systems for shipbuilding, we used a hierarchical scheduling architecture. To automate the dynamic spatial layout of objects in various areas of the shipyard, we developed spatial scheduling expert systems. For reliable estimation of person-hour requirements, we implemented the neural network-based person-hour estimator.

The Anglo-Saxon vessel found in the Sutton Hoo burial mound in Suffolk 80 years ago will sail again as experts look to rebuild the ship from digital 3D models. Dated back to the early 7th century, the 90 foot (27 metre) -long vessel is oft dubbed a'ghost ship' thanks to its manner of preservation. In the mound -- thought the resting place of King Rædwald -- only the impression of the ship and its iron rivets remained, the timber having long rotted away. Nevertheless, a team of archaeologists and shipbuilders have succeeded in creating a three-dimensional digital mock-up of the vessel to allow it to be reconstructed. Expert hope that recreating a full-size, fully-operational version of the ship will help shine light on how the Anglo-Saxons began England's tradition of seafaring.