The nitrogen vacancy (NV) color center in diamonds is an electron spin that can measure magnetic fields with high sensitivity and resolution. Furthermore, the robustness of an NV-based quantum system should be improved for further application in other sensing methods and in the exploration of basic physics. In this work, the robustness of an NV magnetometer is improved by the double driving method. The sensitivity of the NV magnetometer was improved 2.1 times by strengthening the pumping power from 100 to 600 mW. In this process, thermal drift was introduced, which affects the measurement accuracy. The temperature drift of a diamond matrix was measured using an infrared camera, and the temperature change of a diamond host drifted to ∼80 K under high laser and microwave power. To address the drift of temperature owing to sensitivity improvement by pumping enhancement, the double driving method was introduced, to suppress the drift of the resonance frequency, to improve the robustness of a continuous-wave NV magnetometer. The magnetic noise density was improved from 10 to 1.2 nT/Hz1/2. This study checked the source of temperature noise in the process of measuring with the NV color centers and proposes a double driving measurement method to track the resonant frequency change due to environmental temperature drift and improve sensitivity. The findings of this study are useful in applying complex pulse protocols in high-level sensing applications based on solid-state spin.
A fast and accurate bauxite recognition method combining an attention module and a clustering algorithm is proposed in this paper. By introducing the K-means clustering algorithm into the YOLOv4 network and embedding the SE attention module, we calculate the corresponding anchor box value, enhance the feature learning ability of the network to bauxite, automatically learn the importance of different channel features, and improve the accuracy of bauxite target detection. In the experiment, 2189 bauxite photos were taken and screened as the target detection datasets, and the targets were divided into four categories: No. 55, No. 65, No. 70, and Nos. 72–73. By selecting the category volume balanced datasets, the optimal YOLOv4 network model was obtained after training 7000 times, so that the average accuracy of bauxite sorting reached 99%, and the reasoning speed was better than 0.05 s. Realizing the high-speed and high-precision sorting of bauxite greatly improves the mining efficiency and accuracy of the bauxite industry. At the same time, the model provides key technical support for the practical application of the same type of engineering.
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