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The advancement of intelligent technology has propelled the development of smart unmanned vessels into a new phase. To address the urgent demands of current smart ship development, this paper develops a comprehensive ship digital twin system based on a virtual-real mapping algorithm, focusing on the fundamental elements of digital twin model construction. Using the smart unmanned experimental ship Dolphin 1 as a prototype, a digital twin virtual model is proposed. This system leverages real-time internal and external data from the entire vessel to track its navigational status, performance indicators, sailing trends, and surrounding flow field information, offering coordinated "human-machine" navigation assistance. Based on historical data collected from the vessel's long-term navigation, a real-time precise prediction of the vessel's navigational state and hydrodynamic performance is conducted using physics-informed neural network algorithm. This establishes a self-learning iterative virtual-physical mapping model that enables autonomous updates and evolution. As the real navigation data of the vessel continuously update, the virtual model can more accurately simulate the vessel's state in real time. The proposed digital twin model has been tested through sea trials under real sea conditions, demonstrating its high accuracy, robustness, and potential for enhancing navigational safety and efficiency. This system marks a significant step forward in the integration of digital twin technology with maritime navigation, providing a valuable tool for the future development of smart shipping. [ABSTRACT FROM AUTHOR]

Nondestructive testing (NDT) is of paramount importance in ensuring the safe operation of equipment. Among various NDT techniques, ultrasonic NDT has garnered widespread attention due to its high sensitivity, fast speed, and accurate defect location. Photoacoustic NDT, a burgeoning field in ultrasonic NDT, is particularly attractive due to its immunity to electromagnetic interference. However, existing photoacoustic NDT systems suffer from inadequate excitation intensity and complex ultrasonic signal characteristics, impeding large-area NDT and accurate crack visualization. In this study, we present an all-fiber photoacoustic system for large-area NDT. To address the issues, we have developed a photoacoustic generator unit that can be optimized and controlled to generate stronger ultrasonic signals. Furthermore, we have employed mode decomposition to simplify the detected ultrasonic signals by mitigating the acoustic impedance mismatch-induced mode mixing problem in the system. As a result, the technology allows for large-area crack monitoring of up to 50*50 cm2 with an improved resolution of 1 mm. The present technology paves the way for high-resolution equipment crack monitoring with substantially enhanced accuracy in various environments. [ABSTRACT FROM AUTHOR]

Automated observation methods for monitoring river ice in high-latitude urban areas are crucial for resource utilization, risk assessment, and navigation. However, current research lacks actual-scale river ice classification, such as low-altitude surveys. This study established a dataset of river ice in the Songhua River near Harbin, Northeast China, using UAV aerial photography and applied the RININet semantic segmentation algorithm for precise classification of different ice types in low-altitude aerial remote sensing images. To address environmental challenges, a feature extraction method integrating channel and spatial attention mechanisms was adopted, along with an improved pyramid pool structure to enhance feature recognition. Additionally, a two-stage transfer learning method established an ice recognition database, overcoming issues like small data volume and high annotation costs. Comparative evaluation metrics demonstrated the high accuracy of the semantic segmentation framework. Furthermore, a method for estimating ice blockage risk was proposed, applicable to various urban river ice management tasks, with practical significance. [ABSTRACT FROM AUTHOR]

Although sensitizing traditional fluorescent dyes with thermally activated delayed fluorescence (TADF) exciplexes has been extensively studied, the device performance of solution‐processed organic light‐emitting diodes (OLEDs) still lags far behind in this regard. Herein, the novel exciplex with high up‐conversion efficiency composed of a triazatruxene‐based donor and a triazine‐based acceptor is employed as a TADF sensitizer to fluorescent dye in solution‐processed hyperfluorescent OLEDs. Benefiting from the small activation energy for triplet up‐conversion and less non‐radiative recombination of triplet excitons, the new TADF exciplex can up‐convert efficiently non‐radiative triplet excitons through reverse intersystem crossing. Besides, expeditious up‐conversion processes also shorten the residence time of triplet excitons, giving rise to significant suppression of detrimental Dexter energy transfer from the exciplex sensitizer to the dopant. By maximizing the triplet up‐conversion efficiency of exciplex sensitizers, the optimized OLEDs achieve a maximum external quantum efficiency (EQEmax) of 16.0%, which is the first report for the solution‐processed exciplex‐sensitized hyperfluorescent OLEDs with EQEmax exceeding 15%. These results validate that the up‐conversion efficiency of triplet excitons in the exciplexes strongly affects the performance of exciplex‐sensitized fluorescent devices, and the triazatruxene‐based molecules are promising donor candidates for constructing the exciplexes with high up‐conversion efficiency. [ABSTRACT FROM AUTHOR]