A Manipulator-Assisted Multiple UAV Landing System for USV Subject to Disturbance

Marine waves significantly disturb the unmanned surface vehicle (USV) motion. An unmanned aerial vehicle (UAV) can hardly land on a USV that undergoes irregular motion. An oversized landing platform is usually necessary to guarantee the landing safety, which limits the number of UAVs that can be carried. We propose a landing system assisted by tether and robot manipulation. The system can land multiple UAVs without increasing the USV's size. An MPC controller stabilizes the end-effector and tracks the UAVs, and an adaptive estimator addresses the disturbance caused by the base motion. The working strategy of the system is designed to plan the motion of each device. We have validated the manipulator controller through simulations and well-controlled indoor experiments. During the field tests, the proposed system caught and placed the UAVs when the disturbed USV roll range was approximately 12 degrees

Exploration of sleep function connection and classification strategies based on sub-period sleep stages

BackgroundAs a medium for developing brain-computer interface systems, EEG signals are complex and difficult to identify due to their complexity, weakness, and differences between subjects. At present, most of the current research on sleep EEG signals are single-channel and dual-channel, ignoring the research on the relationship between different brain regions. Brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas.MethodsPhase-locked value (PLV) is used to construct a functional connection network. The connection network is used to analyze the connection mechanism and brain interaction in different sleep stages. Firstly, the entire EEG signal is divided into multiple sub-periods. Secondly, Phase-locked value is used for feature extraction on the sub-periods. Thirdly, the PLV of multiple sub-periods is used for feature fusion. Fourthly, the classification performance optimization strategy is used to discuss the impact of different frequency bands on sleep stage classification performance and to find the optimal frequency band. Finally, the brain function network is constructed by using the average value of the fusion features to analyze the interaction of brain regions in different frequency bands during sleep stages.ResultsThe experimental results have shown that when the number of sub-periods is 30, the α (8–13 Hz) frequency band has the best classification effect, The classification result after 10-fold cross-validation reaches 92.59%.ConclusionThe proposed algorithm has good sleep staging performance, which can effectively promote the development and application of an EEG sleep staging system

Cyclophilin A Restricts Influenza A Virus Replication through Degradation of the M1 Protein

Cyclophilin A (CypA) is a typical member of the cyclophilin family of peptidyl-prolyl isomerases and is involved in the replication of several viruses. Previous studies indicate that CypA interacts with influenza virus M1 protein and impairs the early stage of the viral replication. To further understand the molecular mechanism by which CypA impairs influenza virus replication, a 293T cell line depleted for endogenous CypA was established. The results indicated that CypA inhibited the initiation of virus replication. In addition, the infectivity of influenza virus increased in the absence of CypA. Further studies indicated that CypA had no effect on the stages of virus genome replication or transcription and also did not impair the nuclear export of the viral mRNA. However, CypA decreased the viral protein level. Additional studies indicated that CypA enhanced the degradation of M1 through the ubiquitin/proteasome-dependent pathway. Our results suggest that CypA restricts influenza virus replication through accelerating degradation of the M1 protein

Can GARCH-class models capture long memory in WTI crude oil markets?

This paper investigates the issue whether GARCH-type models can well capture the long memory widely existed in the volatility of WTI crude oil returns. In this frame, we model the volatility of spot and futures returns employing several GARCH-class models. Then, using two non-parametric methods, detrended fluctuation analysis (DFA) and rescaled range analysis (R/S), we compare the long memory properties of conditional volatility series obtained from GARCH-class models to that of actual volatility series. Our results show that GARCH-class models can well capture the long memory properties for the time scale larger than a year. However, for the time scale smaller than a year, the GARCH-class models are misspecified.Crude oil markets GARCH-class models Detrended fluctuation analysis Rescaled range analysis Long memory

Stiffness Design of Active Capture Claw-Type Docking Mechanism for Lunar Sample Return

The docking mechanism is the key system for realizing the lunar-orbit docking mission of two spacecraft, which needs to have both capture correction and connection hold functions. The different stiffness requirements between the capture correction process, where low stiffness is desired, and the connection hold process, where high stiffness is desired, pose a significant challenge to the design of the docking mechanism. In this paper, an active capture claw docking mechanism is designed. Under the constraints of being lightweight and having an envelope size, three sets of independent claw mechanisms are designed using the modular design idea to achieve the performance optimization and function integration of the docking mechanisms. The theoretical model of the collision dynamics between the active and passive docking mechanisms is established; the stiffness value range of the docking mechanism is determined, and the typical docking conditions are simulated and verified. The results show that the stiffness design in this paper can satisfy the requirements of the two docking processes. The active capture claw docking mechanism developed was applied to the lunar surface sample return mission successfully and played an important role in the lunar-orbit docking mission

Combined Estimation of Vehicle Dynamic State and Inertial Parameter for Electric Vehicles Based on Dual Central Difference Kalman Filter Method

Abstract Distributed drive electric vehicles (DDEVs) possess great advantages in the viewpoint of fuel consumption, environment protection and traffic mobility. Whereas the effects of inertial parameter variation in DDEV control system become much more pronounced due to the drastic reduction of vehicle weights and body size, and inertial parameter has seldom been tackled and systematically estimated. This paper presents a dual central difference Kalman filter (DCDKF) where two Kalman filters run in parallel to simultaneously estimate vehicle different dynamic states and inertial parameters, such as vehicle sideslip angle, vehicle mass, vehicle yaw moment of inertia, the distance from the front axle to centre of gravity. The proposed estimation method only integrates and utilizes real-time measurements of hub torque information and other in-vehicle sensors from standard DDEVs. The four-wheel nonlinear vehicle dynamics estimation model considering payload variations, Pacejka tire model, wheel and motor dynamics model is developed, the observability of the DCDKF observer is analysed and derived via Lie derivative and differential geometry theory. To address system nonlinearities in vehicle dynamics estimation, the DCDKF and dual extended Kalman filter (DEKF) are also investigated and compared. Simulation with various maneuvers are carried out to verify the effectiveness of the proposed method using Matlab/Simulink-Carsim®. The results show that the proposed DCDKF method can effectively estimate vehicle dynamic states and inertial parameters despite the existence of payload variations and variable driving conditions. This research provides a boot-strapping procedure which can performs optimal estimation to estimate simultaneously vehicle system state and inertial parameter with high accuracy and real-time ability