Variable-Time-Domain Online Neighboring Optimal Trajectory Modification for Hypersonic Interceptors

The predicted impact point (PIP) of hypersonic interception changes continually; therefore the midcourse guidance law must have the ability of online trajectory optimization. In this paper, an online trajectory generation algorithm is designed based on neighboring optimal control (NOC) theory and improved indirect Radau pseudospectral method (IRPM). A trajectory optimization model is designed according to the features of operations in near space. Two-point boundary value problems (TPBVPs) are obtained based on NOC theory. The second-order linear form of transversality conditions is deduced backward to express the modifications of terminal states, costates, and flight time in terms of current state errors and terminal constraints modifications. By treating the current states and the optimal costates modifications as initial constraints and perturbations, the feedback control variables are obtained based on improved IRPM and nominal trajectory information. The simulation results show that when the changes of terminal constraints are not relatively large, this method can generate a modified trajectory effectively with high precision of terminal modifications. The design concept can provide a reference for the design of the online trajectory generation system of hypersonic vehicles

Complex Formation Control of Large-Scale Intelligent Autonomous Vehicles

A new formation framework of large-scale intelligent autonomous vehicles is developed, which can realize complex formations while reducing data exchange. Using the proposed hierarchy formation method and the automatic dividing algorithm, vehicles are automatically divided into leaders and followers by exchanging information via wireless network at initial time. Then, leaders form formation geometric shape by global formation information and followers track their own virtual leaders to form line formation by local information. The formation control laws of leaders and followers are designed based on consensus algorithms. Moreover, collision-avoiding problems are considered and solved using artificial potential functions. Finally, a simulation example that consists of 25 vehicles shows the effectiveness of theory

Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation

Medical imaging has witnessed remarkable progress but usually requires a large amount of high-quality annotated data which is time-consuming and costly to obtain. To alleviate this burden, semi-supervised learning has garnered attention as a potential solution. In this paper, we present Meta-Learning for Bootstrapping Medical Image Segmentation (MLB-Seg), a novel method for tackling the challenge of semi-supervised medical image segmentation. Specifically, our approach first involves training a segmentation model on a small set of clean labeled images to generate initial labels for unlabeled data. To further optimize this bootstrapping process, we introduce a per-pixel weight mapping system that dynamically assigns weights to both the initialized labels and the model's own predictions. These weights are determined using a meta-process that prioritizes pixels with loss gradient directions closer to those of clean data, which is based on a small set of precisely annotated images. To facilitate the meta-learning process, we additionally introduce a consistency-based Pseudo Label Enhancement (PLE) scheme that improves the quality of the model's own predictions by ensembling predictions from various augmented versions of the same input. In order to improve the quality of the weight maps obtained through multiple augmentations of a single input, we introduce a mean teacher into the PLE scheme. This method helps to reduce noise in the weight maps and stabilize its generation process. Our extensive experimental results on public atrial and prostate segmentation datasets demonstrate that our proposed method achieves state-of-the-art results under semi-supervision. Our code is available at https://github.com/aijinrjinr/MLB-Seg.Comment: Accepted to MICCAI 2023. Code is publicly available at https://github.com/aijinrjinr/MLB-Se

Multi-UAVs Formation Autonomous Control Method Based on RQPSO-FSM-DMPC

For various threats in the enemy defense area, in order to achieve covert penetration and implement effective combat against enemy, the unmanned aerial vehicles formation needs to be reconfigured in the process of penetration; the mutual collision avoidance problems and communication constraint problems among the formation also need to be considered. By establishing the virtual-leader formation model, this paper puts forward distributed model predictive control and finite state machine formation manager. Combined with distributed cooperative strategy establishing the formation reconfiguration cost function, this paper proposes that adopting the revised quantum-behaved particle swarm algorithm solves the cost function, and it is compared with the result which is solved by particle swarm algorithm. Simulation result shows that this algorithm can control multiple UAVs formation autonomous reconfiguration effectively and achieve covert penetration safely

Effects of fundamental movement skills on health-related quality of life in Chinese school-age children: the mediating role of physical fitness level

BackgroundThe primary purpose of this study is to analyze the relationship between school-age children’s fundamental movement skills (FMS), physical fitness levels, and the health-related quality of life (HRQoL); To explore the mediating role of physical fitness levels between school-age children’s FMS and HRQoL.MethodsIn the cross-sectional survey in 2021, 334 school-age children aged 6–10 (8.20 ± 1.16) were recruited from primary schools in Jinhua City, Zhejiang Province, China. Test of Gross Motor Development 2 (TGMD-2), National Standards for Students’ Physical Health, and Pediatric Quality of Life Inventory TM Version 4.0 (PedsQL™ 4.0) were used to investigate the FMS, physical fitness level, and HRQoL of school-age children. Hierarchical regression was used to analyze the relationship among FMS, physical fitness levels, and HRQoL. Bootstrap is used to evaluate the mediating role of physical fitness levels in the relationship between FMS and HRQoL.ResultsThe higher the FMS and physical fitness, the higher the school-age children’s HRQoL, physical functioning, social functioning, and school functioning (r = 0.244–0.301, p < 0.01). In addition, developing children’s FMS promotes physical fitness levels (r = 0.358, p < 0.01). The regression analysis results of controlling gender, age, and body mass index z (BMI-z) scores showed that FMS significantly positively predicted the physical functioning (β = 0.319, p < 0.01), social functioning (β = 0.425, p < 0.01), and school functioning (β = 0.333, p < 0.01) of school-age children. When the physical fitness level enters the regression equation, the absolute value of the regression coefficient of FMS decreases. However, it can still significantly predict the physical functioning (β = 0.211, p < 0.01) and school functioning (β = 0.142, p < 0.05) of school-age children. Simple intermediary analysis shows that physical fitness level plays an intermediary role between FMS, physical functioning (indirect effect = 0.089 [95% Confidence interval (CI) = 0.015,0.195]), and school functioning (indirect effect = 0.065 [95% CI = 0.007,0.150]).ConclusionThis study shows that physical fitness levels mediate the relationship between FMS and HRQoL. Encouraging the development of FMS and promoting physical fitness levels of school-age children can effectively improve the HRQoL of school-age children

Brain atlas fusion from high-thickness diagnostic magnetic resonance images by learning-based super-resolution

It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.e., with high inter-slice thickness), the problem of atlas fusion has not been addressed yet. In this paper, we intend to fuse the brain atlas from the high-thickness diagnostic MR images that are prevalent for clinical routines. The main idea of our works is to extend the conventional groupwise registration by incorporating a novel super-resolution strategy. The contribution of the proposed super-resolution framework is two-fold. First, each high-thickness subject image is reconstructed to be isotropic by the patch-based sparsity learning. Then, the reconstructed isotropic image is enhanced for better quality through the random-forest-based regression model. In this way, the images obtained by the super-resolution strategy can be fused together by applying the groupwise registration method to construct the required atlas. Our experiments have shown that the proposed framework can effectively solve the problem of atlas fusion from the low-quality brain MR images