Institute of Navigation

Welcome to the Summer 2024 issue of NAVIGATION . A hot topic in our PNT research community these days is the development of techniques to counter jamming and spoofing of global navigation satellite systems. While much of this work is classified, some of it appears in the open literature. And in this

Nitrogen-vacancy (NV) color centers in diamond serve as promising atomic spin systems for measurement applications requiring high accuracy and sensitivity. A key challenge in NV-based quantum sensing is minimizing spin readout noise to approach the standard quantum limit (SQL). Based on a six-level model, this work analyze the dependence of NV-based quantum sensing performance, including spin state readout noise and signal-to-noise ratio (SNR), on controllable parameters such as the Purcell factor and excitation laser pulse characteristics. This study demonstrates that a shorter excitation pulse duration results in a higher saturation value of ground-state spin polarization, while the total time required for the polarization process remains constant. Additionally, the spin readout noise does not improve monotonically with decreasing excitation pulse duration; instead, it initially decreases and subsequently increases as the pulse duration varies. The spin readout noise reaches its optimal level when the pulse duration is 3 ns. Furthermore, no positive correlation exists between the signal-to-noise ratio (SNR) and the Purcell factor, and there is also an optimal value for SNR. When the pulse duration ranges from 1 ns to 40 ns, the variation in SNR is relatively insignificant. This research offers a novel perspective for enhancing the performance of quantum sensing based on diamond defects, such as nitrogen-vacancy (NV) centers.

This paper integrates the theoretical models of Transformer and BiGRU to construct the Transformer BiGRU Global Attention model, with the aim of enhancing the model’s ability to extract key information. Through the implementation of a cross-attention mechanism to amalgamate features and enhance feature representation, the model attains exact prediction of main engine fuel consumption for vessels. Compared to the Transformer and BiGRU models, our model achieves 86% higher prediction accuracy, enabling more accurate prediction of ship main engine fuel consumption. This furnishes data support for the purpose of comparison with original factory data, thereby facilitating the assessment of engine fault conditions.

Background: Steel box girders are widely employed in bridge engineering due to their excellent mechanical properties and construction convenience, yet their modular design still encounters bottlenecks such as knowledge reuse difficulties and information silos. This study proposes a BIM-driven framework based on knowledge graphs and data fusion. By constructing a professional knowledge graph comprising 85 core entity types and 150 semantic relationships (integrated with over 15,000 knowledge units), systematic management of design knowledge is achieved. The developed BIM reverse modeling technology improves parametric modeling efficiency by 30–40%, while the data fusion mechanism supports over 90% accuracy in design conflict detection. The intelligent decision-making system built upon this framework meets 75% of business scenario requirements while effectively assisting critical decisions such as module selection. Results demonstrate that this framework significantly enhances design collaboration efficiency and intelligence through knowledge structuring and deep data integration. Although some achievements were validated via simulation due to limited field measurement data, the approach demonstrates strong engineering applicability and provides novel technical pathways and methodological support for advancing digital transformation in bridge engineering.