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Ionospheric scintillation frequently occurs in equatorial, auroral and polar regions, posing a threat to the performance of the global navigation satellite system (GNSS). Thus, the detection of ionospheric scintillation is of great significance in regard to improving GNSS performance, especially when severe ionospheric scintillation occurs. Normal algorithms exhibit insensitivity in strong scintillation detection in that the natural phenomenon of strong scintillation appears only occasionally, and such samples account for a small proportion of the data in datasets relative to those for weak/moderate scintillation events. Aiming at improving the detection accuracy, we proposed a strategy combining an improved eXtreme Gradient Boosting (XGBoost) algorithm by using the synthetic minority, oversampling technique and edited nearest neighbor (SMOTE-ENN) resampling technique for detecting events imbalanced with respect to weak, medium and strong ionospheric scintillation. It outperformed the decision tree and random forest by 12% when using imbalanced training and validation data, for tree depths ranging from 1 to 30. For different degrees of imbalance in the training datasets, the testing accuracy of the improved XGBoost was about 4% to 5% higher than that of the decision tree and random forest. Meanwhile, the testing results for the improved method showed significant increases in evaluation indicators, while the recall value for strong scintillation events was relatively stable, above 90%, and the corresponding F1 scores were over 92%. When testing on datasets with different degrees of imbalance, there was a distinct increase of about 10% to 20% in the recall value and 6% to 11% in the F1 score for strong scintillation events, with the testing accuracy ranging from 90.42% to 96.04%.

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Using laser scanning techniques, scanning lens rotating around a laser diode and a circular laser trajectory was projected onto the surface of a weldment to detect it. Furthermore, one novel vision sensor based on the circular laser is developed. The three-dimensional (3D), circular laser based seam location sensor is investigated. On the basis of the light path system, 3D calculation algorithm is brought forward and used to locate the welded joint. After image denoise, filter, segmentation and thinning, characteristic points of the welded joint could be detected real time using a proposed vision sensor and confirmed by real experiments of butt welded joints with I groove and V groove, and lap, and fillet and ramp welded joints. The results show that circular laser based vision sensor can be used in seam locating and its calculation precision meets the requirement of seam tracking.