Strong Neel ordering and luminescence correlation in a two-dimensional antiferromagnet

Magneto-optical effect has been widely used in light modulation, optical sensing and information storage. Recently discovered two-dimensional (2D) van der Waals layered magnets are considered as promising platforms for investigating novel magneto-optical phenomena and devices, due to the long-range magnetic ordering down to atomically-thin thickness, rich species and tunable properties. However, majority 2D antiferromagnets suffer from low luminescence efficiency which hinders their magneto-optical investigations and applications. Here, we uncover strong light-magnetic ordering interactions in 2D antiferromagnetic MnPS3 utilizing a newly-emerged near-infrared photoluminescence (PL) mode far below its intrinsic bandgap. This ingap PL mode shows strong correlation with the Neel ordering and persists down to monolayer thickness. Combining the DFT, STEM and XPS, we illustrate the origin of the PL mode and its correlation with Neel ordering, which can be attributed to the oxygen ion-mediated states. Moreover, the PL strength can be further tuned and enhanced using ultraviolet-ozone treatment. Our studies offer an effective approach to investigate light-magnetic ordering interactions in 2D antiferromagnetic semiconductors

A Self-Organized Reciprocal Decision Approach for Sensing Coverage with Multi-UAV Swarms

This paper tackles the problem of sensing coverage for multiple Unmanned Aerial Vehicles (UAVs) with an approach that takes into account the reciprocal between neighboring UAVs to reduce the oscillation of their trajectories. The proposed reciprocal decision approach, which is performed in three steps, is self-organized, distributed and autonomous. First, in contrast to the traditional method modeled and optimized in configuration space, the sensing coverage problem is directly presented as an optimal reciprocal coverage velocity (ORCV) in velocity space that is concise and effective. Second, the ORCV is determined by adjusting the action velocity out of weak coverage velocity relative to neighboring UAVs to demonstrate that the ORCV supports a collision-avoiding assembly. Third, a corresponding random probability method is proposed for determining the optimal velocity in the ORCV. The results from the simulation indicate that the proposed method has a high coverage rate, rapid convergence rate and low deadweight loss. In addition, for up to 103-size UAVs, the proposed method has excellent scalability and collision-avoiding ability

A Distributed Task Scheduling Method Based on Conflict Prediction for Ad Hoc UAV Swarms

UAV swarms have attracted great attention, and are expected to be used in scenarios, such as search and rescue, that require many urgent jobs to be completed in a minimum time by multiple vehicles. For complex missions with tight constraints, careful assigning tasks is inseparable from the scheduling of these tasks, and multi-task distributed scheduling (MTDS) is required. The Performance Impact (PI) algorithm is an excellent solution for MTDS, but it suffers from the suboptimal solution caused by the heuristics for local task selection, and the deadlock problem that it may fall into an infinite cycle of exchanging the same task. In this paper, we improve the PI algorithm by integrating a new task-removal strategy and a conflict prediction mechanism into the task-removal phase and the task-inclusion phase, respectively. Specifically, the task-removal strategy results in better exploration of the inclusion of more tasks than the original PI by freeing up more space in the local scheduler, improving the suboptimal solution caused by the heuristics for local task selection, as done in PI. In addition, we design a conflict prediction mechanism that simulates adjacent vehicles performing inclusion operations as the criteria for local task inclusion. Therefore, it can reduce the deadlock ratio and iteration times of the MTDS algorithm. Furthermore, by combining the protocol stack with the physical transmission model, an ad-hoc network simulation platform is constructed, which is closer to the real-world network, and serves as the supporting environment for testing the MTDS algorithms. Based on the constructed ad-hoc network simulation platform, we demonstrate the advantage of the proposed algorithm over the original PI algorithm through Monte Carlo simulation of search and rescue tasks. The results show that the proposed algorithm can reduce the average time cost, increase the total allocation number under most random distributions of vehicles-tasks, and significantly reduce the deadlock ratio and the number of iteration rounds

A Distributed Task Scheduling Method Based on Conflict Prediction for Ad Hoc UAV Swarms

UAV swarms have attracted great attention, and are expected to be used in scenarios, such as search and rescue, that require many urgent jobs to be completed in a minimum time by multiple vehicles. For complex missions with tight constraints, careful assigning tasks is inseparable from the scheduling of these tasks, and multi-task distributed scheduling (MTDS) is required. The Performance Impact (PI) algorithm is an excellent solution for MTDS, but it suffers from the suboptimal solution caused by the heuristics for local task selection, and the deadlock problem that it may fall into an infinite cycle of exchanging the same task. In this paper, we improve the PI algorithm by integrating a new task-removal strategy and a conflict prediction mechanism into the task-removal phase and the task-inclusion phase, respectively. Specifically, the task-removal strategy results in better exploration of the inclusion of more tasks than the original PI by freeing up more space in the local scheduler, improving the suboptimal solution caused by the heuristics for local task selection, as done in PI. In addition, we design a conflict prediction mechanism that simulates adjacent vehicles performing inclusion operations as the criteria for local task inclusion. Therefore, it can reduce the deadlock ratio and iteration times of the MTDS algorithm. Furthermore, by combining the protocol stack with the physical transmission model, an ad-hoc network simulation platform is constructed, which is closer to the real-world network, and serves as the supporting environment for testing the MTDS algorithms. Based on the constructed ad-hoc network simulation platform, we demonstrate the advantage of the proposed algorithm over the original PI algorithm through Monte Carlo simulation of search and rescue tasks. The results show that the proposed algorithm can reduce the average time cost, increase the total allocation number under most random distributions of vehicles-tasks, and significantly reduce the deadlock ratio and the number of iteration rounds

A Self-Organized Reciprocal Decision Approach for Sensing Coverage with Multi-UAV Swarms

This paper tackles the problem of sensing coverage for multiple Unmanned Aerial Vehicles (UAVs) with an approach that takes into account the reciprocal between neighboring UAVs to reduce the oscillation of their trajectories. The proposed reciprocal decision approach, which is performed in three steps, is self-organized, distributed and autonomous. First, in contrast to the traditional method modeled and optimized in configuration space, the sensing coverage problem is directly presented as an optimal reciprocal coverage velocity (ORCV) in velocity space that is concise and effective. Second, the ORCV is determined by adjusting the action velocity out of weak coverage velocity relative to neighboring UAVs to demonstrate that the ORCV supports a collision-avoiding assembly. Third, a corresponding random probability method is proposed for determining the optimal velocity in the ORCV. The results from the simulation indicate that the proposed method has a high coverage rate, rapid convergence rate and low deadweight loss. In addition, for up to 103-size UAVs, the proposed method has excellent scalability and collision-avoiding ability

A Distributed Task Rescheduling Method for UAV Swarms Using Local Task Reordering and Deadlock-Free Task Exchange

Distributed task scheduling is an ongoing concern in the field of multi-vehicles, especially in recent years; UAV swarm performing complex tasks endows it with new characteristics, such as self-organization, scalability, reconfigurability, etc. This requires the swarm to have distributed rescheduling capability to dynamically include as many unassigned tasks or new tasks as possible, while satisfying tight time constraints. As one of the most advanced rescheduling methods, the Performance Impact (PI)-MaxAss algorithm provides an important reference for this paper. However, its task exchange-based strategy faces the deadlock problem, and the task rescheduling method should not be limited to this. To this end, a new distributed rescheduling method is proposed for UAV swarms, which combines the local task reordering strategy and the improved task exchange strategy. On the one hand, based on the analysis of the fact that the scheduler is unreasonable for individuals, this paper proposes a local task reordering strategy denoted as PI-Reorder, which simply adds the reordering strategy to the recursive inclusion phase of the PI-MinAvg algorithm, so that unassigned tasks or new tasks can be included without relying on the task exchange. On the other hand, from the phenomenon that two or more vehicles occasionally get caught in an infinite cycle of exchanging the same tasks, the deadlock problem of PI-MaxAss is analyzed, which is then solved by introducing a deadlock-free task exchange strategy, where some defined counters are used to detect and isolate the deadlocks. Then, a rescue scenario is used to demonstrate the performance of the proposed methods, PI-Hybrid compared with PI-MaxAss. Monte Carlo simulation results show that, compared with PI-MaxAss, this method can not only increase the number of allocations to varying degrees, but also reduce the average waiting time, while ensuring deadlock avoidance. The methods can be used not only for the secondary optimization of the existing task exchange scheduling algorithms to escape local optima, but also for task reconfiguration of swarm tasks after adding or removing tasks

Decentralized UAV Swarm Scheduling with Constrained Task Exploration Balance

Scheduling is one of the key technologies used in unmanned aerial vehicle (UAV) swarms. Scheduling determines whether a task can be completed and when the task is complete. The distributed method is a fast way to realize swarm scheduling. It has no central node and UAVs can freely join or leave it, thus making it more robust and flexible. However, the two most representative methods, the Consensus-Based Bundle Algorithm (CBBA) and the Performance Impact (PI) algorithm, pursue the minimum cost impact of tasks, which have optimization limitations and are easily cause task conflicts. In this paper, a new concept called “task consideration” is proposed to quantify the impact of tasks on scheduling and the regression of the task itself, balancing the exploration of the UAV for the minimum-impact task and the regression of neighboring tasks to improve the optimization and convergence of scheduling. In addition, the conflict resolution rules are modified to fit the proposed method, and the exploration of tasks is increased by a new removal method to further improve the optimization. Finally, through extensive Monte Carlo experiments, compared with CBBA and PI, the proposed method is shown to perform better in terms of task allocation and total travel time, and with the increase in the number of average UAV tasks, the number of iterations is less and the convergence is faster

Aircraft Landing Gear Retraction/Extension System Fault Diagnosis with 1-D Dilated Convolutional Neural Network

The faults of the landing gear retraction/extension(R/E) system can result in the deterioration of an aircraft’s maneuvering conditions; how to identify the faults of the landing gear R/E system has become a key issue for ensuring aircraft take-off and landing safety. In this paper, we aim to solve this problem by proposing the 1-D dilated convolutional neural network (1-DDCNN). Aiming at developing the limited feature information extraction and inaccurate diagnosis of the traditional 1-DCNN with a single feature, the 1-DDCNN selects multiple feature parameters to realize feature integration. The performance of the 1-DDCNN in feature extraction is explored. Importantly, using padding dilated convolution to multiply the receptive field of the convolution kernel, the 1-DDCNN can completely retain the feature information in the original signal. Experimental results demonstrated that the proposed method has high accuracy and robustness, which provides a novel idea for feature extraction and fault diagnosis of the landing gear R/E system

Data-Driven Health Assessment in a Flight Control System under Uncertain Conditions

PHM technology plays an increasingly significant role in modern aviation condition-based maintenance. As an important part of prognostics and health management (PHM), a health assessment can effectively estimate the health status of a system and provide support for maintenance decision making. However, in actual conditions, various uncertain factors will amplify assessment errors and cause large fluctuations in assessment results. In this paper, uncertain factors are incorporated into flight control system health assessment modeling. First, four uncertain factors of health assessment characteristic parameters are quantified and described by the extended λ-PDF method to acquire their probability distribution function. Secondly, a Monte Carlo simulation (MCS) is used to simulate a flight control system health assessment process with uncertain factors. Thirdly, the probability distribution of the output health index is solved by the maximum entropy principle. Finally, the proposed model was verified with actual flight data. The comparison between assessment results with and without uncertain factors shows that a health assessment conducted under uncertain conditions can reduce the impact of the uncertainty of outliers on the assessment results and make the assessment results more stable; therefore, the false alarm rate can be reduced

Simultaneous and Continuous Estimation of Shoulder and Elbow Kinematics from Surface EMG Signals

In this paper, we present a simultaneous and continuous kinematics estimation method for multiple DoFs across shoulder and elbow joint. Although simultaneous and continuous kinematics estimation from surface electromyography (EMG) is a feasible way to achieve natural and intuitive human-machine interaction, few works investigated multi-DoF estimation across the significant joints of upper limb, shoulder and elbow joints. This paper evaluates the feasibility to estimate 4-DoF kinematics at shoulder and elbow during coordinated arm movements. Considering the potential applications of this method in exoskeleton, prosthetics and other arm rehabilitation techniques, the estimation performance is presented with different muscle activity decomposition and learning strategies. Principle component analysis (PCA) and independent component analysis (ICA) are respectively employed for EMG mode decomposition with artificial neural network (ANN) for learning the electromechanical association. Four joint angles across shoulder and elbow are simultaneously and continuously estimated from EMG in four coordinated arm movements. By using ICA (PCA) and single ANN, the average estimation accuracy 91.12% (90.23%) is obtained in 70-s intra-cross validation and 87.00% (86.30%) is obtained in 2-min inter-cross validation. This result suggests it is feasible and effective to use ICA (PCA) with single ANN for multi-joint kinematics estimation in variant application conditions