233 research outputs found
Brain Dynamic Information Flow Estimation Based on EEG and Diffusion MRI: A Proof-of-principle Study and Application in Stroke
No full textIn the hemiparetic stroke, functional recovery of paretic limb may occur with the reorganization of neural networks in the brain. Electroencephalography (EEG), with an excellent temporal resolution, can be used to reveal functional changes in the brain following a stroke. This study assessed a novel multimodal brain imaging technique namely Variational Bayesian Multimodal Encephalography (VBMEG), which combines EEG, anatomical MRI and diffusion weighted imaging (DWI), to estimation brain dynamic information flow and its changes following a stroke. EEG data were acquired from individuals suffering from a stroke as well as able-bodied participants while electrical stimuli were delivered sequentially at their index finger in the left and right hand, respectively. The locations of active sources related to this stimulus were precisely identified, resulting in high Variance Accounted For (VAF above 80%). An accurate estimation of dynamic information flow between sources was achieved in this study, showing a high VAF (above 88%) in the cross-validation test. The estimated dynamic information flow was compared between chronic hemiparetic stroke and able-bodied individuals, using matrices lateralization index and activation complexity. The results demonstrate the feasibility of VBMEG method in revealing the changes of information flow in the brain after stroke. This study verified the VBMEG method as an advanced computational approach to track the dynamic information flow in the brain following a stroke. This may lead to the development of a quantitative tool for monitoring functional changes of the cortical neural networks after a unilateral brain injury and therefore facilitate the research into, and the practice of stroke rehabilitation.Mechanical Engineerin
A Distributed Task Scheduling Method Based on Conflict Prediction for Ad Hoc UAV Swarms
No full textUAV 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 Data-driven System-level Health State Prognostics Method for Large-scale Spacecraft Systems
No full text
A Study on Undergraduate English Program Modes in China
No full textWhile English major education is of significant importance in China, there is a lack of comprehensive research that provides a broader perspective, encompassing a thorough exploration and comparison of the diverse program modes employed across different undergraduate English programs in China. The focus of this study is to (1) discern the overarching characteristics of undergraduate English programs in China; (2) identify undergraduate English program modes in China; and (3) delve into the relationship between the identified modes and university discipline evaluation rankings. The dataset includes undergraduate English program handbooks from 50 universities in China and information on 2942 courses extracted from these handbooks. The findings suggest that English programs in universities and colleges in China exhibit a predominantly application-oriented approach. In addition, three modes were identified: Literature and Linguistics, Balanced, and High English Skills. The High English Skill mode was found to be linked with a lower ranking compared to the High Literature and Linguistics mode. The study concludes by offering implications for the design of a future English program based on the insights gained from the analysis
A Self-Organized Reciprocal Decision Approach for Sensing Coverage with Multi-UAV Swarms
No full textThis 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
No full textDistributed 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
No full textScheduling 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
Data-Driven Health Assessment in a Flight Control System under Uncertain Conditions
No full textPHM 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
A novel approach for modeling neural responses to joint perturbations using the NARMAX method and a hierarchical neural network
No full textThe human nervous system is an ensemble of connected neuronal networks. Modeling and system identification of the human nervous system helps us understand how the brain processes sensory input and controls responses at the systems level. This study aims to propose an advanced approach based on a hierarchical neural network and non-linear system identification method to model neural activity in the nervous system in response to an external somatosensory input. The proposed approach incorporates basic concepts of Non-linear AutoRegressive Moving Average Model with eXogenous input (NARMAX) and neural network to acknowledge non-linear closed-loop neural interactions. Different from the commonly used polynomial NARMAX method, the proposed approach replaced the polynomial non-linear terms with a hierarchical neural network. The hierarchical neural network is built based on known neuroanatomical connections and corresponding transmission delays in neural pathways. The proposed method is applied to an experimental dataset, where cortical activities from ten young able-bodied individuals are extracted from electroencephalographic signals while applying mechanical perturbations to their wrist joint. The results yielded by the proposed method were compared with those obtained by the polynomial NARMAX and Volterra methods, evaluated by the variance accounted for (VAF). Both the proposed and polynomial NARMAX methods yielded much better modeling results than the Volterra model. Furthermore, the proposed method modeled cortical responded with a mean VAF of 69.35% for a three-step ahead prediction, which is significantly better than the VAF from a polynomial NARMAX model (mean VAF 47.09%). This study provides a novel approach for precise modeling of cortical responses to sensory input. The results indicate that the incorporation of knowledge of neuroanatomical connections in building a realistic model greatly improves the performance of system identification of the human nervous system.Biomechatronics & Human-Machine Contro
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