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Shihao Xu

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3 papers

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EAAI Journal 2025 Journal Article

Broad learning system-based model-free adaptive robust control for hypersonic morphing aircraft with appointed-time prescribed performance

Shihao Xu
Yingzi Guan
Changzhu Wei
Hechuan Xu

This paper proposes a model-free attitude tracking controller for hypersonic morphing aircraft with appointed-time prescribed performance. An error transformation methodology is developed, ensuring adherence to performance constraints irrespective of initial tracking conditions. Subsequently, an adaptive control scheme for the transformed system is devised, comprising two broad learning system-based adaptive neural network compensators and an adaptive robust integral of the signum of error (RISE) controller. The neural network compensators, featuring a node update strategy, are developed to compensate for uncertain system dynamics and unknown lumped disturbances. Subsequently, the adaptive RISE controller is integrated with the compensators to ensure precise tracking. The stability of the closed-loop system is analyzed by employing the Lyapunov’s direct method. Finally, the effectiveness and enhanced performance of the proposed control scheme are validated via numerical simulations.

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JBHI Journal 2024 Journal Article

EPIC: Emotion Perception by Spatio-Temporal Interaction Context of Gait

Haifeng Lu
Shihao Xu
Shipeng Zhao
Xiping Hu
Rong Ma
Bin Hu

Recently, psychophysiological computing has received considerable attention. Due to easy acquisition at a distance and less conscious initiation, gait-based emotion recognition is considered as a valuable research branch in the field of psychophysiological computing. However, most existing methods rarely explore the spatio-temporal context of gait, which limits the ability to capture the higher-order relationship between emotion and gait. In this paper, we utilize a range of research, including psychophysiological computing and artificial intelligence, to propose an integrated emotion perception framework called EPIC, which can find novel joint topology and generate thousands of synthetic gaits by spatio-temporal interaction context. First, we analyze the joint coupling among non-adjacent joints by calculating Phase Lag Index (PLI), which can discover the latent connection among body joints. Second, to synthesize more sophisticated and accurate gait sequences, we explore the effect of spatio-temporal constraints, and propose a new loss function that utilizes the Dynamic Time Warping (DTW) algorithm and pseudo-velocity curve to constrain the output of Gated Recurrent Units (GRU). Finally, Spatial Temporal Graph Convolution Networks (ST-GCN) is used to classify emotions using the generation and the real data. Experimental results demonstrate our approach achieves the accuracy of 89. 66%, and outperforms the state-of-the-art methods on Emotion-Gait dataset.

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IJCAI Conference 2021 Conference Paper

Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identification

Haocong Rao
Shihao Xu
Xiping Hu
Jun Cheng
Bin Hu

Skeleton-based person re-identification (Re-ID) is an emerging open topic providing great value for safety-critical applications. Existing methods typically extract hand-crafted features or model skeleton dynamics from the trajectory of body joints, while they rarely explore valuable relation information contained in body structure or motion. To fully explore body relations, we construct graphs to model human skeletons from different levels, and for the first time propose a Multi-level Graph encoding approach with Structural-Collaborative Relation learning (MG-SCR) to encode discriminative graph features for person Re-ID. Specifically, considering that structurally-connected body components are highly correlated in a skeleton, we first propose a multi-head structural relation layer to learn different relations of neighbor body-component nodes in graphs, which helps aggregate key correlative features for effective node representations. Second, inspired by the fact that body-component collaboration in walking usually carries recognizable patterns, we propose a cross-level collaborative relation layer to infer collaboration between different level components, so as to capture more discriminative skeleton graph features. Finally, to enhance graph dynamics encoding, we propose a novel self-supervised sparse sequential prediction task for model pre-training, which facilitates encoding high-level graph semantics for person Re-ID. MG-SCR outperforms state-of-the-art skeleton-based methods, and it achieves superior performance to many multi-modal methods that utilize extra RGB or depth features. Our codes are available at https: //github. com/Kali-Hac/MG-SCR.

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