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Dongsheng Yang

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5 papers

2 author rows

EAAI Journal 2026 Journal Article

A conditional diffusion vision transformer model via data augmentation for few-shot fault diagnosis

Beijia Zhao
Dongsheng Yang
Jiayue Sun
Yanhong Luo
Zhong Luo
Xin Wang

The scarcity of labeled training data degrades the performance of accurate fault diagnosis models, highlighting the critical need for research in few-shot fault diagnosis (FSFD). Despite being a predominant FSFD solution, current data augmentation-based methods still suffer from distribution mismatch between generated and real data, as well as insufficient hierarchical diversity, particularly in fault severities. To overcome these limitations, the conditional diffusion vision transformer model (CDViT) is proposed for FSFD. CDViT leverages a dual-constrained denoising diffusion probabilistic model to accurately model the underlying distribution of real fault data. Subsequently, a fault severity attention module is designed to effectively extract fault severity features by capturing local–global hierarchical characteristics. Additionally, a fault refinement classifier is employed to better capture fault severity, improving diagnostic performance. The effectiveness of CDViT has been validated through multiple comparative experiments conducted on three datasets, demonstrating superior performance compared to mainstream methods in FSFD.

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NeurIPS Conference 2025 Conference Paper

Learned Prefix Caching for Efficient LLM Inference

Dongsheng Yang
Austin Li
Kai Li
Wyatt Lloyd

Prefix caching is a key technique for reducing Large Language Model (LLM) inference costs. However, the prevalent least-recently-used (LRU) eviction algorithm has a large gap to the optimal algorithm. This paper introduces LPC, the first learned method to perform LLM prefix cache eviction. LPC leverages conversational content analysis to provide predictive guidance for eviction, determining which conversations are likely to continue. These insights, combined with last access timestamps, inform more effective cache management. Extensive evaluations across three real-world datasets demonstrate that LPC achieves 18-47% reductions in required cache sizes for equivalent hit ratios and has an 11% improvement in LLM prefilling throughput in an emulated environment.

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ICRA Conference 2025 Conference Paper

Learning Based MPC for Autonomous Driving Using a Low Dimensional Residual Model

Yaoyu Li
Chaosheng Huang
Dongsheng Yang
Wenbo Liu
Jun Li

In this paper, a learning based Model Predictive Control (MPC) using a low dimensional residual model is proposed for autonomous driving. One of the critical challenge in autonomous driving is the complexity of vehicle dynamics, which impedes the formulation of accurate vehicle model. Inaccurate vehicle model can significantly impact the performance of MPC controller. To address this issue, this paper decomposes the nominal vehicle model into invariable and variable elements. The accuracy of invariable elements are ensured by calibration, while the deviations in the variable elements are learned by a low-dimensional residual model. The features of residual model are selected as the physical variables most correlated with nominal model errors. Physical constraints among these features are formulated to explicitly define the valid region within the feature space. The formulated model and constraints are incorporated into the MPC framework and validated through both simulation and real vehicle experiments. The results indicate that the proposed method significantly enhances the model accuracy and controller performance.

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EAAI Journal 2023 Journal Article

Convolutional sparse filter with data and mechanism fusion: A few-shot fault diagnosis method for power transformer

Jia Qin
Dongsheng Yang
Nan Wang
Xueqing Ni

In actual industrial scenarios, fault data is rare and fault labels are difficult to obtain, which brings many obstacles for fault diagnosis. For this situation, this research proposes a novel semi-supervised convolutional sparse filter with local mechanism similarity regularization (CSF-LMSR) to construct a more reliable few-shot diagnosis method for power transformer. First, a sparse filter with local mechanism similarity regularization term (SF-LMSR) is designed as a more interpretable unsupervised feature extractor with prior knowledge. This unsupervised process enables the model to extract satisfactory features from the whole dataset even with a lower proportion of labeled data. Second, SF-LMSR is combined with convolutional neural network (CNN) by a novel coupling mode of kernel replacement, which enhances the learning ability of CNN. This classification model still adopts supervised learning training, but the demand for labeled fault data is greatly reduced, which reduces the burden of labels. The effectiveness of the proposed method is verified using real power transformer dissolved gas analysis (DGA) datasets. It can be seen from experimental results that the proposed method does provide a new perspective for transformer fault diagnosis, and it is a successful attempt for the power industry few-shot diagnosis problem.

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EAAI Journal 2023 Journal Article

Research on real-time detection method of rail corrugation based on improved ShuffleNet V2

Hongjuan Yang
Jiaxin Liu
Guiming Mei
Dongsheng Yang
Xingqiao Deng
Chao Duan

Rail corrugation is a common wear mechanism of high-speed railways and subways, which can cause derailment and running noise. However, rail corrugation only has slight texture change on the rail surface, so it is difficult to detect accurately by traditional detection methods. In this paper, a real-time detection method of rail corrugation based on machine vision and a convolutional neural network is proposed, which effectively improves the accuracy and efficiency of rail corrugation detection. Combined with the gray features of each part of the image, a rail surface segmentation method based on the gray maximum value of the sliding window is also proposed. Moreover, the obtained rail surface image is clearer and the feature information of the rail surface can be completely retained, compared with the adaptive threshold segmentation and edge detection segmentation. ShuffleNet V2, a lightweight convolutional neural network, was selected as the corrugation detection model. The squeeze-and-excitation module was integrated into its basic unit to improve its channel attention, and the activation function was re-selected to make the detection have better real-time performance and accuracy. Through experimental verification, the average detection time of a single image of the improved model is 4. 01ms, and the detection accuracy is 2. 78% higher than that of the unimproved ShuffleNet V2. The research results will be beneficial to the development of the intelligent real-time detection of rail corrugation.

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