Xinghu Yu

Yipeng Yang, Yiqiao Tang, Hao Zhang et al.

Conventional multirotors suffer from a rapid collapse of attainable wrench space (AWS) under abrupt total rotor failures, rendering full 6-DOF recovery physically impossible. This paper addresses passive fault-tolerant flight of a biaxial-tilt overactuated hexacopter (BTO) under abrupt total rotor failures that are a priori unknown to the controller. The control design and analysis focus on representative abrupt rotor-failure cases for which the post-failure system remains fully actuated, while no explicit fault detection, isolation, or fault-mode switching is assumed. First, we extend the inscribed-sphere metric of the AWS by incorporating the transient-wrench-jump term, enabling quantitative feasibility assessment under up to three simultaneous rotor failures and benchmarking against uniaxial-tilt and coplanar hexacopters. Second, we develop two computationally efficient passive schemes without relying on fault detection or online optimization. One scheme operates at the controller layer by combining a high-order fully actuated (HOFA) controller with a linear extended state observer (LESO) for lumped-disturbance rejection. The other scheme operates at the allocator layer by using model-reference adaptive control allocation with momentum-based wrench estimation to compensate for control-allocation biases. Simulations and flight experiments validate stable hovering and 6-DOF trajectory tracking under single and multiple rotor failures. Further systematic comparisons confirm that the BTO provides larger recovery margins than uniaxial-tilt and coplanar designs. Additional onboard-sensor-only experiments, including indoor tracking under wind disturbance, outdoor tracking under extreme conditions, narrow-frame traversal, and contact-based aerial writing, further validate the robustness of the proposed framework in complex operational environments.

Yuanduo Hong, Huihui Pan, Weichao Sun et al.

Vision transformers (ViTs) encoding an image as a sequence of patches bring new paradigms for semantic segmentation.We present an efficient framework of representation separation in local-patch level and global-region level for semantic segmentation with ViTs. It is targeted for the peculiar over-smoothness of ViTs in semantic segmentation, and therefore differs from current popular paradigms of context modeling and most existing related methods reinforcing the advantage of attention. We first deliver the decoupled two-pathway network in which another pathway enhances and passes down local-patch discrepancy complementary to global representations of transformers. We then propose the spatially adaptive separation module to obtain more separate deep representations and the discriminative cross-attention which yields more discriminative region representations through novel auxiliary supervisions. The proposed methods achieve some impressive results: 1) incorporated with large-scale plain ViTs, our methods achieve new state-of-the-art performances on five widely used benchmarks; 2) using masked pre-trained plain ViTs, we achieve 68.9% mIoU on Pascal Context, setting a new record; 3) pyramid ViTs integrated with the decoupled two-pathway network even surpass the well-designed high-resolution ViTs on Cityscapes; 4) the improved representations by our framework have favorable transferability in images with natural corruptions. The codes will be released publicly.

Dongxu Lei, Xiaotian Lin, Xinghu Yu et al.

In a multitude of industrial fields, a key objective entails optimising resource management whilst satisfying user requirements. Resource management by industrial practitioners can result in a passive transfer of user loads across resource providers, a phenomenon whose accurate characterisation is both challenging and crucial. This research reveals the existence of user clusters, which capture macro-level user transfer patterns amid resource variation. We then propose CLUSTER, an interpretable hierarchical Bayesian nonparametric model capable of automating cluster identification, and thereby predicting user transfer in response to resource variation. Furthermore, CLUSTER facilitates uncertainty quantification for further reliable decision-making. Our method enables privacy protection by functioning independently of personally identifiable information. Experiments with simulated and real-world data from the communications industry reveal a pronounced alignment between prediction results and empirical observations across a spectrum of resource management scenarios. This research establishes a solid groundwork for advancing resource management strategy development.