An identification method of LBL underwater positioning systematic error with optimal selection criterion.

Unlike the space targets such as satellites whose observation distance is about the order of 100 km, the measurement distance of long baseline (LBL) underwater positioning system is much shorter. If the noncoincidence between the multiple beacons and the target center is not considered, this systematic error will cause a little larger positioning error. Therefore, aiming at the situation that multiple beacons are installed outside the target and the distance between the beacons and the target center is significant, a multi-beacon positioning model of the LBL underwater system is constructed in this paper.

Few-shot RUL prediction for engines based on CNN-GRU model.

In the realm of prognosticating the remaining useful life (RUL) of pivotal components, such as aircraft engines, a prevalent challenge persists where the available historical life data often proves insufficient. This insufficiency engenders obstacles such as impediments in performance degradation feature extraction, inadequacies in capturing temporal relationships comprehensively, and diminished predictive accuracy. To address this issue, a 1D CNN-GRU prediction model for few-shot conditions is proposed in this paper.

Measurement sensitivity analysis and on-orbit calibration of systematic errors for a narrow field-of-view camera.

Narrow field-of-view (FOV) cameras enable long-range observations and have been often used in deep space exploration missions. To solve the problem of systematic error calibration for a narrow FOV camera, the sensitivity of the camera systematic errors to the angle between the stars is analyzed theoretically, based on a measurement system for observing the angle between stars. In addition, the systematic errors for a narrow FOV camera are classified into "Non-attitude Errors" and "Attitude Errors".

Fault Detection Based on Multi-Dimensional KDE and Jensen-Shannon Divergence.

Weak fault signals, high coupling data, and unknown faults commonly exist in fault diagnosis systems, causing low detection and identification performance of fault diagnosis methods based on T2 statistics or cross entropy. This paper proposes a new fault diagnosis method based on optimal bandwidth kernel density estimation (KDE) and Jensen-Shannon (JS) divergence distribution for improved fault detection performance. KDE addresses weak signal and coupling fault detection, and JS divergence addresses unknown fault detection.