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Chen Yin

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3 papers

2 author rows

EAAI Journal 2026 Journal Article

A fault evolution knowledge-driven adversarial meta-learning method for few-shot tool state recognition under variable working conditions

Chen Yin
Yining Dong
Jianliang He
Yulin Wang

The health status of cutting tools is vital to ensuring workpiece quality and operational safety. While most existing research on tool condition monitoring focuses on progressive wear, such as estimating wear volume or remaining useful life, the more critical and abrupt failure mode of tool breakage has received less attention. In real-world manufacturing, stringent safety protocols lead to a severe scarcity of breakage data, presenting a typical few-shot learning challenge for tool state recognition (TSR). To tackle this issue, we propose a novel fault evolution knowledge-driven adversarial meta-learning (FEK-AML) method for few-shot TSR, where fault evolution knowledge is creatively formulated and integrated into the proposed adversarial meta-learning method, resulting in a physics-guided learning framework. Specifically, a feature extraction network is first trained using domain adversarial training to learn domain-invariant features while capturing the fault evolution knowledge. Subsequently, a metric-based meta-learning network is designed to transfer this knowledge for effective TSR under few-shot conditions. Milling experiments are performed on cutting tools in healthy, worn, and broken states under various working conditions. A series of TSR tasks is constructed, with only one fault sample per class available in the target domain. Comparative results show that FEK-AML effectively mines fault evolution knowledge and outperforms existing approaches in recognizing tool states under extremely limited data conditions, confirming its potential for reliable deployment in CNC monitoring systems to achieve accurate and robust TSR.

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JBHI Journal 2026 Journal Article

ÆMMamba: An Efficient Medical Segmentation Model With Edge Enhancement

Xingbo Dong
Bowen Zhou
Chen Yin
Iman Yi Liao
Zhe Jin
Zhaozhao Xu
Bin Pu

Medical image segmentation is critical for disease diagnosis, treatment planning, and prognosis assessment, yet the complexity and diversity of medical images pose significant challenges to accurate segmentation. While Convolutional Neural Networks capture local features and Vision Transformers excel in the global context, both struggle with efficient long-range dependency modeling. Inspired by Mamba's State Space Modeling efficiency, we propose ÆMMamba, a novel multi-scale feature extraction framework built on the Mamba backbone network. ÆMMamba integrates several innovative modules: the Efficient Fusion Bridge (EFB) module, which employs a bidirectional state-space model and attention mechanisms to fuse multi-scale features; the Edge-Aware Module (EAM), which enhances low-level edge representation using Sobel-based edge extraction; and the Boundary Sensitive Decoder (BSD), which leverages inverse attention and residual convolutional layers to handle cross-level complex boundaries. ÆMMamba achieves state-of-the-art performance across 8 medical segmentation datasets. On polyp segmentation datasets (Kvasir, ClinicDB, ColonDB, EndoScene, ETIS), it records the highest mDice and mIoU scores, outperforming methods like MADGNet and Swin-UMamba, with a standout mDice of 72. 22 on ETIS, the most challenging dataset in this domain. For lung and breast segmentation, ÆMMamba surpasses competitors such as H2Former and SwinUnet, achieving Dice scores of 84. 24 on BUSI and 79. 83 on COVID-19 Lung. And on the LGG brain MRI dataset, ÆMMamba attains an mDice of 87. 25 and an mIoU of 79. 31, outperforming all compared methods.

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ICRA Conference 2019 Conference Paper

A Multi-Domain Feature Learning Method for Visual Place Recognition

Peng Yin 0001
Lingyun Xu
Xueqian Li
Chen Yin
Yingli Li
Rangaprasad Arun Srivatsan
Lu Li
Jianmin Ji

Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of environmental conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the environmental factors, leading to decreased accuracy decreases when environmental conditions change significantly, such as day versus night. To this end, we propose an end-to-end conditional visual place recognition method. Specifically, we introduce the multi-domain feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a feature detaching module to separate the environmental condition-related features from those that are not. The only label required within this feature learning pipeline is the environmental condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the feature robustness against variant environmental conditions.

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