Xiaojie Shi

Zhaoqi Leng, Mingxing Tan, Chenxi Liu et al.

Cross-entropy loss and focal loss are the most common choices when training deep neural networks for classification problems. Generally speaking, however, a good loss function can take on much more flexible forms, and should be tailored for different tasks and datasets. Motivated by how functions can be approximated via Taylor expansion, we propose a simple framework, named PolyLoss, to view and design loss functions as a linear combination of polynomial functions. Our PolyLoss allows the importance of different polynomial bases to be easily adjusted depending on the targeting tasks and datasets, while naturally subsuming the aforementioned cross-entropy loss and focal loss as special cases. Extensive experimental results show that the optimal choice within the PolyLoss is indeed dependent on the task and dataset. Simply by introducing one extra hyperparameter and adding one line of code, our Poly-1 formulation outperforms the cross-entropy loss and focal loss on 2D image classification, instance segmentation, object detection, and 3D object detection tasks, sometimes by a large margin.

Leyou Zhou, Mucheng Li, Xiaojie Shi et al.

Broadband oscillations in wind farms have been widely reported in recent years. Past studies have examined various types of oscillations in wind farms, relating small-signal stability to control settings, operating conditions, and electrical parameters. However, most analyses are performed on aggregated single-unit models, which may deviate from the true behavior, leading to misleading stability assessments. To investigate how aggregation affects stability conclusions, this paper develops detailed single-, two-, and three-unit doubly-fed induction generator (DFIG) models and their aggregated counterparts. Then, a D-decomposition-related ray-extrapolation method is proposed to characterize the small-signal stability region of nonlinear DFIG models in the parameter space, delineating stability boundaries under numerous parameter combinations. The study reveals that aggregated models stability regions within the parameter planes of control settings and operating conditions differ from those of granular models in terms of basic shape, critical modes, and evolution patterns, posing a risk of misjudging stability margins.

Xiaojie Shi, Yijun Shao

Recently, we have witnessed the explosive growth of images with complex information and content. In order to effectively and precisely retrieve desired images from a large-scale image database with low time-consuming, we propose the multiple feature fusion image retrieval algorithm based on the texture feature and rough set theory in this paper. In contrast to the conventional approaches that only use the single feature or standard, we fuse the different features with operation of normalization. The rough set theory will assist us to enhance the robustness of retrieval system when facing with incomplete data warehouse. To enhance the texture extraction paradigm, we use the wavelet Gabor function that holds better robustness. In addition, from the perspectives of the internal and external normalization, we re-organize extracted feature with the better combination. The numerical experiment has verified general feasibility of our methodology. We enhance the overall accuracy compared with the other state-of-the-art algorithms.