Emergency Flight Control Based on the Fan Wing

FanWing has been taken to the visual field because of its performance combination of fixed-wing aircraft and helicopter. Its flight mode is currently limited mainly by a remote control, while the research of automated flight control is on the rise. The fan wing could offer lift, thrust, and the additional pitch moment for longitudinal control. At the same time, the roll moment and the yaw moment can be generated by the differential rotation of the cross-flow fan to realize the lateral control. It provides the possibility for its emergency flight control when the aerodynamic control becomes inefficient at a low speed. The difficulties in designing the emergency control system in both the longitudinal and lateral controls are analyzed. And it emphasizes the importance of selecting its center of gravity and the emergency control method of longitudinal control in engineering. The simulation results show that as an emergency flight control system, fan wing control is feasible. The study of the fan wing control will provide a reference solution for its further engineering applications.

For RF Signal-Based UAV States Recognition, Is Pre-processing Still Important At The Era Of Deep Learning?

Unmanned Aerial Vehicles (UAVs, also called drones) have been widely deployed in our living environments for a range of applications such as healthcare, agriculture, and logistics. Despite their unprecedented advantages, the increased number of UAVs and their growing threats demand high-performance management and emergency control strategies. To accurately detect a UAV's working state including hovering and flying, data collection from Radio Frequency (RF) signals is a key step of these strategies and has thus attracted significant research interest. Deep neural networks (DNNs) have been applied for UAV state detection and shown promising potentials. While existing work mostly focuses on improving the DNN structures, we discover that RF signals' pre-processing before sending them to the classification model is as important as improving the DNN structures. Experiments on a dataset show that, after applying proposed pre-processing methods, the 10-time average accuracy is improved from 46.8% to 91.9%, achieving nearly 50% gain comparing with the benchmark work using the same DNN structure. This work also outperforms the state-of-the-art CNN models, confirming the great potentials of data pre-processing for RF-based UAV state detection.

For RF Signal-Based UAV States Recognition, Is Pre-processing Still Important At The Era Of Deep Learning?

Unmanned Aerial Vehicles (UAVs, also called drones) have been widely deployed in our living environments for a range of applications such as healthcare, agriculture, and logistics. Despite their unprecedented advantages, the increased number of UAVs and their growing threats demand high-performance management and emergency control strategies. To accurately detect a UAV's working state including hovering and flying, data collection from Radio Frequency (RF) signals is a key step of these strategies and has thus attracted significant research interest. Deep neural networks (DNNs) have been applied for UAV state detection and shown promising potentials. While existing work mostly focuses on improving the DNN structures, we discover that RF signals' pre-processing before sending them to the classification model is as important as improving the DNN structures. Experiments on a dataset show that, after applying proposed pre-processing methods, the 10-time average accuracy is improved from 46.8% to 91.9%, achieving nearly 50% gain comparing with the benchmark work using the same DNN structure. This work also outperforms the state-of-the-art CNN models, confirming the great potentials of data pre-processing for RF-based UAV state detection.

Subperiosteal Orbital Hematoma From SVC Occlusion During Cardiac Surgery: To See or Not to See

We present a case in which the superior vena cava (SVC) cannula was inadvertently clamped for a short while during cardiopulmonary bypass, completely occluding SVC drainage. This resulted in a rarely seen complication – bilateral subperiosteal orbital hematomas causing orbital compartment syndrome. Other instances of intentional SVC occlusion include during the creation of a bidirectional cavo-pulmonary shunt and for emergency control of bleeding during thoracic surgery.