Sorting out the requirements for intelligent functions is the prerequisite and foundation of the top-level design for the development of intelligent ships. In light of the development of inland intelligent ships for 2030, 2035, and 2050, based on the analysis of the division of intelligent ship functional modules by international representative classification societies and relevant research institutions, eight necessary functional modules have been proposed: intelligent navigation, intelligent hull, intelligent engine room, intelligent energy efficiency management, intelligent cargo management, intelligent integration platform, remote control, and autonomous operation. Taking the technical realization of each functional module as the goal, this paper analyzes the status quo and development trend of related intelligent technologies and their feasibility and applicability when applied to each functional module. At the same time, it clarifies the composition of specific functional elements of each functional module, puts forward the stage goals of China’s inland intelligent ship development and the specific functional requirements of different modules under each stage, and provides reference for the Chinese government to subsequently formulate the top-level design development planning and implementation path of inland waterway intelligent ships.

In this paper, an intelligent water shooting robot system for situations of carrier shake and target movement is designed, which uses a 2 DOF (degree of freedom) robot as an actuator, a photoelectric camera to detect and track the desired target, and a gyroscope to keep the robot’s body stable when it is mounted on the motion carriers. Particularly, for the accurate shooting of the designed system, an online tuning model of the water jet landing point based on the back-propagation algorithm was proposed. The model has two stages. In the first stage, the polyfit function of Matlab is used to fit a model that satisfies the law of jet motion in ideal conditions without interference. In the second stage, the model uses the back-propagation algorithm to update the parameters online according to the visual feedback of the landing point position. The model established by this method can dynamically eliminate the interference of external factors and realize precise on-target shooting. The simulation results show that the model can dynamically adjust the parameters according to the state relationship between the landing point and the desired target, which keeps the predicted pitch angle error within 0.1°. In the test on the actual platform, when the landing point is 0.5 m away from the position of the desired target, the model only needs 0.3 s to adjust the water jet to hit the target. Compared to the state-of-the-art method, GA-BP (genetic algorithm-back-propagation), the proposed method’s predicted pitch angle error is within 0.1 degree with 1/4 model parameters, while costing 1/7 forward propagation time and 1/200 back-propagation calculation time.

Traditional cameras inevitably suffer from motion blur when facing high-speed moving objects. Event cameras, as high temporal resolution bionic cameras, record intensity changes in an asynchronous manner, and their recorded h... | Find, read and cite all the research you need on Tech Science Press

Intelligent jet systems are widely used in various fields, including firefighting, marine operations, and underwater exploration. Accurate extraction and prediction of jet trajectories are essential for optimizing their performance, but challenges arise due to environmental factors such as climate, wind direction, and suction efficiency. To address these issues, we introduce two novel jet segmentation datasets, Libary and SegQinhu, which cover both indoor and outdoor environments under varying weather conditions and temporal intervals. These datasets present significant challenges, including occlusions and strong light reflections, making them ideal for evaluating jet trajectory segmentation methods. Through empirical evaluation of several state-of-the-art (SOTA) techniques on these datasets, we observe that general methods struggle with highly imbalanced pixel distributions in jet trajectory images. To overcome this, we propose a data-driven pipeline for jet trajectory extraction and segmentation. At its core is MobileHDC, a new baseline model that leverages the MobileNetV2 architecture and integrates dilated convolutions to enhance the receptive field without increasing computational cost. Additionally, we introduce a parallel convolutional block and a decoder to fuse multi-level features, enabling a better capture of contextual information and improving the continuity and accuracy of jet segmentation. The experimental results show that our method outperforms existing SOTA techniques on both jet-specific datasets, highlighting the effectiveness of our approach.