Yajun Wang

Horacio Silva-Saravia, Yajun Wang, Hector Pulgar-Painemal et al.

This paper describes a novel approach to analyze and control systems with multi-mode oscillation problems. Traditional single dominant mode analysis fails to provide effective control actions when several modes have similar low damping ratios. This work addresses this problem by considering all modes in the formulation of the system kinetic oscillation energy. The integral of energy over time defines the total action as a measure of dynamic performance, and its sensitivity allows comparing the performance of different actuators/locations in the system to select the most effective one to damp the oscillation energy. Time domain simulations in the IEEE 9-bus system and IEEE 39-bus system verify the findings obtained by the oscillation energy based analysis. Applications of the proposed method in control and system planning are discussed.

Yonghao Liu, Yajun Wang, Chunli Guo et al.

Graph few-shot learning has attracted increasing attention due to its ability to rapidly adapt models to new tasks with only limited labeled nodes. Despite the remarkable progress made by existing graph few-shot learning methods, several key limitations remain. First, most current approaches rely on predefined and unified graph filters (e.g., low-pass or high-pass filters) to globally enhance or suppress node frequency signals. Such fixed spectral operations fail to account for the heterogeneity of local topological structures inherent in real-world graphs. Moreover, these methods often assume that the support and query sets are drawn from the same distribution. However, under few-shot conditions, the limited labeled data in the support set may not sufficiently capture the complex distribution of the query set, leading to suboptimal generalization. To address these challenges, we propose GRACE, a novel Graph few-shot leaRning framework that integrates Adaptive spectrum experts with Cross-sEt distribution calibration techniques. Theoretically, the proposed approach enhances model generalization by adapting to both local structural variations and cross-set distribution calibration. Empirically, GRACE consistently outperforms state-of-the-art baselines across a wide range of experimental settings. Our code can be found here.