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Chenwei Tang

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5 papers

2 author rows

EAAI Journal 2026 Journal Article

Physics informed Dual-Layer Bidirectional Gated Recurrent Unit for Nuclear-Grade Electric Gate Valves Fault Prognostics

Jie Liu
Mian Zhang
Chenwei Tang
Jiancheng Lv
Yanping Huang
Yanshan Li
Chenhui Li

Nuclear-grade electric gate valves (NEGVs) are mission-critical components in nuclear power plants, characterized by widespread deployment yet prone to high failure rates. Sticking faults pose the most significant risk, often triggering unscheduled plant shutdowns and potentially resulting in severe safety incidents. While accurate fault prediction is crucial for plants safety, current prognostic investigations for NEGVs facing challenges: (1) Inadequate actual operational data, (2) Suboptimal feature selection, (3) Limited prediction accuracy. To overcome these limitations, this study introduces an integrated prognostic framework combining physics informed data augmentation (DA) with optimized feature selection and a Dual-Layer Bidirectional Gated Recurrent Unit (DL-BiGRU) architecture. The proposed DA method capitalizes on ‘segmented wave’ patterns in operating current during sticking faults to effectively describe the degradation trend. Feature selection is enhanced through a random weighting method that simultaneously evaluates feature monotonicity, correlation, and robustness. Case study validation using actual operational data demonstrates the proposed model architecture’s superior predictive capability than other deep learning models, establishing a reasonable strategy in NEGVs degradation trend prediction.

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AAAI Conference 2026 Conference Paper

S3Net: Spatiotemporally Separated Sparse Network for Neuromorphic Vision Processing

Ping He
Rong Xiao
Wanying Xu
Chenwei Tang
Shudong Huang
Huajin Tang

Dynamic Vision Sensor (DVS) asynchronously records sparse events triggered by changes in pixel intensity, offering high temporal resolution and low latency. Existing frame-based methods process event data densely, violating its inherent sparsity and introducing computational redundancy. While asynchronous models preserve the event stream's native format, they often neglect spatial information, compromising their adaptability and efficiency. To address these limitations, we propose a Spatiotemporally Separated Sparse Network (S3Net) for efficient event stream encoding and learning. Specifically, we employ a learnable sparse encoding scheme to construct a voxel-structured representation that effectively extracts spatiotemporal relationships among event data. After that, we propose a dual-branch architecture to capture localized spatial dependencies and dynamic temporal patterns of event data. By explicitly decoupling spatial and temporal modeling, S3Net enables end-to-end asynchronous processing of variable-length event sequences, achieving both strong representational capacity and high computational efficiency. Experimental results on six event-based datasets demonstrate that S3Net achieves state-of-the-art performance. Compared to frame-based methods, it significantly reduces computational overhead and model complexity, while also outperforming existing asynchronous approaches in inference speed without compromising accuracy. Extensive experiments across six event-based datasets show that S3Net establishes new state-of-the-art performance. Our method reduces computational costs by 35% and model parameters by 27% compared to frame-based approaches, while delivering 1.58× faster inference than existing point-based methods at comparable accuracy levels.

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IJCAI Conference 2025 Conference Paper

PALA: Class-imbalanced Graph Domain Adaptation via Prototype-anchored Learning and Alignment

Xin Ma
Yifan Wang
Siyu Yi
Wei Ju
Bei Wu
Ziyue Qiao
Chenwei Tang
Jiancheng Lv

Graph domain adaptation is a key subfield of graph transfer learning that aims to bridge domain gaps by transferring knowledge from a label-rich source graph to an unlabeled target graph. However, most existing methods assume balanced labels in the source graph, which often fails in practice and leads to biased knowledge transfer. To address this, in this paper, we propose a prototype-anchored learning and alignment framework for class-imbalanced graph domain adaptation. Specifically, we incorporate pointwise node mutual information into the graph encoder to capture high-order topological proximity and learn generalized node representations. Leveraging this, we then introduce categorical prototypes with adversarial proto-instances for prototype-anchored learning and recalibration to represent the source graph under an imbalanced class distribution. Finally, we introduce a weighted prototype contrastive adaptation strategy that aligns target pseudo-labels with source prototypes to handle class imbalance during adaptation. Extensive experiments show that our PALA outperforms the state-of-the-art methods. Our code is available at https: //github. com/maxin88scu/PALA.

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ECAI Conference 2024 Conference Paper

BOB-YOLO: Balancing Optimization Binarized YOLO via Module-Wise Latency

Xinyu Liu 0028
Wenqiang Zhou
Zhendong Yu
Jiaming Yang
Tao Wang 0053
Chenwei Tang
Jiancheng Lv 0001

When it comes to object detection tasks, YOLO stands out for its impressive speed and efficiency. Nonetheless, deploying YOLO on resource-constrained devices remains a challenge due to its substantial model size and memory requirements. The direct application of conventional binary quantization strategies to YOLO can result in significant accuracy degradation. A prevalent solution is to introduce floating-point shortcuts. However, the increased computational demand and parameter complexity associated with these shortcuts limit their practical deployment on hardware platforms for optimal acceleration. To solve this problem, we propose a binary neural network (BNN) for object detection called BOB-YOLO to achieve a balanced performance in terms of computational speed, model size, and detection accuracy. Our BOB-YOLO fully leverages module-wise latency (MWL) to supervise the latency of floating-point shortcut branches by that of 1-bit trunk branches. This supervision maximizes the information carried by the floating-point data flow in shortcuts while maintaining latency within the limits set by the 1-bit convolution branch, thereby improving parallel computational efficiency. We also introduce the Roofline Model to address these limitations by considering both computational complexity and parameter compression, ensuring high computational intensity. Additionally, we propose a performance evaluation metric Pd, which provides an intuitive description of the trade-off between speed and accuracy, aligning closely with the practical requirements of binary quantization strategies. Extensive experiments on the VOC and COCO datasets demonstrate the significant advantages of our method over state-of-the-art BNN methods.

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AAAI Conference 2024 Conference Paper

Zero-Shot Aerial Object Detection with Visual Description Regularization

Zhengqing Zang
Chenyu Lin
Chenwei Tang
Tao Wang
Jiancheng Lv

Existing object detection models are mainly trained on large-scale labeled datasets. However, annotating data for novel aerial object classes is expensive since it is time-consuming and may require expert knowledge. Thus, it is desirable to study label-efficient object detection methods on aerial images. In this work, we propose a zero-shot method for aerial object detection named visual Description Regularization, or DescReg. Concretely, we identify the weak semantic-visual correlation of the aerial objects and aim to address the challenge with prior descriptions of their visual appearance. Instead of directly encoding the descriptions into class embedding space which suffers from the representation gap problem, we propose to infuse the prior inter-class visual similarity conveyed in the descriptions into the embedding learning. The infusion process is accomplished with a newly designed similarity-aware triplet loss which incorporates structured regularization on the representation space. We conduct extensive experiments with three challenging aerial object detection datasets, including DIOR, xView, and DOTA. The results demonstrate that DescReg significantly outperforms the state-of-the-art ZSD methods with complex projection designs and generative frameworks, e.g., DescReg outperforms best reported ZSD method on DIOR by 4.5 mAP on unseen classes and 8.1 in HM. We further show the generalizability of DescReg by integrating it into generative ZSD methods as well as varying the detection architecture. Codes will be released at https://github.com/zq-zang/DescReg.

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