Abstract A fast-and-flexible method of ARIMA model optimal selection is suggested for univariate time series forecasting. The method allows obtaining as-highly-accurate-as-possible forecasts automatically. It is based on effectively finding lags by the autocorrelation function of a detrended time series, where the best-fitting polynomial trend is subtracted from the time series. The forecasting quality criteria are the root-mean-square error (RMSE) and the maximum absolute error (MaxAE) allowing to register information about the average inaccuracy and worst outlier. Thus, the ARIMA model optimal selection is performed by simultaneously minimizing RMSE and Max-AE, whereupon the minimum defines the best model. Otherwise, if the minimum does not exist, a combination of minimal-RMSE and minimal-MaxAE ARIMA models is used.

Abstract In the paper review of unmanned underwater vehicle (AUV) is presented. The description of main systems is depicted with focus on autonomous single vehicle as well as a swarm. As a consequence of development of AUV technology, research centers are focused on issues related to increasing the degree of their autonomy. Nowadays, mostly navigation and communication as well as high-efficient propeller systems are being developed. There are problems linking this issues. Their solutions includes development of new control laws containing algorithms to prevent collisions - for unmanned vehicles with elements of the underwater environment and for several underwater vehicles cooperating with each other in a swarm.

Abstract The article presents a simulation of a ship running aground. It introduces the analytical description, as well as the methodology of carrying out strength calculations when creating engineering tasks related to the topic. It shows the state of stresses and deformations of the hull of a modern minehunter after a collision with the bottom for two immersion depths. Research and development opportunities for future considerations are highlighted in the conclusions.

Abstract The presence of an outlier at the starting point of a univariate time series negatively influences the forecasting accuracy. The starting outlier is effectively removed only by making it equal to the second time point value. The forecasting accuracy is significantly improved after the removal. The favorable impact of the starting outlier removal on the time series forecasting accuracy is strong. It is the least favorable for time series with exponential rising. In the worst case of a time series, on average only 7 % to 11 % forecasts after the starting outlier removal are worse than they would be without the removal.

Abstract The continuous development of autonomous and unmanned technology is accelerating the adoption of unmanned vessels for various maritime operations. Despite the technological developments there is still a lack of clear regulatory and organizational frameworks for testing and exploiting the potential of unmanned surface vessels (USVs) in real-world maritime conditions. Such real-world testing becomes ever more complex when operating in multiple nations territorial waters. In May 2019 USV ‘Maxlimer’ crossed the North Sea from the United Kingdom to Belgium and back, carrying goods, to demonstrate the ability of unmanned surface vessels to interact with real marine traffic in an uncontrolled environment. The paper presents this mission in light of the current state of marine autonomy projects as well as the regulatory works conducted by various organizations worldwide.