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Aimed at the problem of generating a smooth, safe, and dynamically feasible continuous-time trajectory for unmanned aerial vehicles (UAV) in complex environments, a trajectory planning algorithm is proposed to minimize a multi-objective function based on safe flight corridors. The safe flight corridor represented by a collection of convex polyhedra is built based on the initial discrete waypoints generated by the improved rapidly-exploring random tree(RRT), namely the RRT* algorithm. The safety objective function is established according to the constraints of limiting the trajectory inside safe flight corridors. In combination with the flight smoothness, dynamic characteristics, and time performance, a multi-objective function is built. The gradient-based convex optimization algorithm is used to derive the continuous-time trajectory expressed as a piece-wise polynomial by optimizing the position, velocity, acceleration of waypoints, and time allocation. The effectiveness and performance of the proposed algorithm is tested and compared under complex environments such as the coal mine. The test results demonstrate that the proposed algorithm has a better comprehensive performance in comparison with existing algorithms.

As an effective physical field feature to perceive ferromagnetic targets, magnetic anomaly is widely used in covert marine surveillance tasks. However, its practical usability is affected by the complex marine magnetic noise interference, making robust magnetic anomaly detection (MAD) quite a challenging task. Recently, learning-based detectors have been widely studied for the discrimination of magnetic anomaly signal and achieve superior performance than traditional rule-based detectors. Nevertheless, learning-based detectors require abundant data for model parameter training, which are difficult to access in practical marine applications. In practice, target magnetic anomaly data are usually expensive to acquire, while rich marine magnetic noise data are readily available. Thus, there is an urgent need to develop effective models to learn discriminative features from the abundant marine magnetic noise data for newly appearing target anomaly detection. Motivated by this, in this paper we formulate MAD as a single-edge detection problem and develop a self-supervised marine noise learning approach for target anomaly classification. Specifically, a sparse autoencoder network is designed to model the marine noise and restore basis geomagnetic field from the collected noisy magnetic data. Subsequently, reconstruction error of the network is used as a statistical decision criterion to discriminate target magnetic anomaly from cluttered noise. Finally, we verify the effectiveness of the proposed approach on real sea trial data and compare it with seven state-of-the-art MAD methods on four numerical indexes. Experimental results indicate that it achieves a detection accuracy of 93.61% and has a running time of 21.06 s on the test dataset, showing superior MAD performance over its counterparts. [ABSTRACT FROM AUTHOR]

Background: High mechanical shear stress (HMSS) generated by blood pumps during mechanical circulatory support induces blood damage (or function alteration) not only of blood cell components but also of plasma proteins. Methods: In the present study, fresh, healthy human blood was used to prime a blood circuit assisted by a CentriMag centrifugal pump at a flow rate of 4.5 L/min under three pump pressure heads (75, 150, and 350 mm Hg) for 4 h. Blood samples were collected for analyses of plasma‐free hemoglobin (PFH), von Willebrand factor (VWF) degradation and platelet glycoprotein (GP) IIb/IIIa receptor shedding. Results: The extent of all investigated aspects of blood damage increased with increasing cross‐pump pressure and duration. Loss of high‐molecular‐weight multimers (HMWM)‐VWF in Loop 2 and Loop 3 significantly increased after 2 h. PFH, loss of HMWM‐VWF, and platelet GPIIb/IIIa receptor shedding showed a good linear correlation with mean shear stress corresponding to the three pump pressure heads. Conclusions: HMSS could damage red blood cells, cause pathological VWF degradation, and induce platelet activation and platelet receptor shedding. Different blood components can be damaged to different degrees by HMSS; VWF and VWF‐enhanced platelet activation may be more susceptible to HMSS. [ABSTRACT FROM AUTHOR]

This study presents an innovative approach to attitude control in microsatellites by utilizing a spherical magnetohydrodynamic attitude control device (MHD-SACD) for three-axis attitude adjustments. A novel method is developed to quantify the output torque generated by this device, analyzing the relationship between torque, voltage, and average fluid velocity. Utilizing the energy conservation principle, an equivalent coupling circuit for the MHD-SACD is derived to assess its power dynamics. This leads to the formulation of the electromagnetic torque equation. Additionally, simulations are conducted to calculate friction torque and resultant output torque. The findings provide a qualitative assessment of the device's torque characteristics, serving as a reference for future design optimization and setting the stage for subsequent experimental investigations. This study introduces a groundbreaking perspective to the domain of microsatellite attitude control. [ABSTRACT FROM AUTHOR]