Discrimination Model of QAR High-Severity Events Using Machine Learning

Li, Junchen; Zhang, Haigang; Yang, Jinfeng

doi:10.1007/978-3-030-36204-1_36

Cited by 1 publication

(1 citation statement)

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“…(3) RF [37] sets the number of n_estimators to 50; (4) The closest neighbor of KNN is 10; (5) SVM uses the poly kernel function; (6) The number of GBDT n_estimators is 500; and (7) The solver of LR is lbfgs, and the class_weight is balanced; (8) NB and MLP use default parameters. Table 7 and Figure 7 present the comparison results.…”

Section: ) Anomaly Detection Model Performance Comparison and Analysismentioning

confidence: 99%

Data-Driven Long-Landing Event Detection and Interpretability Analysis in Civil Aviation

Yang

Ren

et al. 2022

IEEE Access

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Various animals exhibit accurate navigation using environment cues. The Earth's magnetic field has been proved a reliable information source in long-distance fauna migration. Inspired by animal navigation, this work proposes a bionic and data-driven approach for long-distance underwater navigation. The proposed approach uses measured geomagnetic data for the navigation, and requires no GPS systems or geographical maps. Particularly, we construct and train a Temporal Attentionbased Long Short-Term Memory (TA-LSTM) network to predict the heading angle during the navigation. To mitigate the impact of geomagnetic anomalies, we develop the mechanism to detect and quantify the anomalies based on Maximum Likelihood Estimation. We integrate the developed mechanism with the TA-LSTM, and calibrate the predicted heading angles to gain resistance against geomagnetic anomalies. Using the retrieved data from the WMM model, we conduct numerical simulations with diversified navigation conditions to test our approach. The simulation results demonstrate a resilience navigation against geomagnetic anomalies by our approach, along with precision and stability of the underwater navigation in single and multiple destination missions.

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Section: ) Anomaly Detection Model Performance Comparison and Analysismentioning

confidence: 99%