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Jiawei Yang

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6 papers

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EAAI Journal 2026 Journal Article

A fractional order-multimodal densely connected convolutional network approach for wind turbine yaw system abnormal noise diagnosis under small sample

Tao Li
Jiawei Yang
Xiaoting Wu
Yanan Chen
Rongjun Ding
Caichun He
Jun Yang

Abnormal noise faults in the yaw system are one of the frequently encountered faults in the wind turbines, posing a serious threat to the safe and stable operation of the wind turbine. It is also the primary source of noise in the wind power system, significantly impacting the residents nearby. This paper proposes a fractional order-multimodal densely connected convolutional network (FO-MDESNET) approach for wind turbine yaw system abnormal noise diagnosis under small sample. The approach creates tri-modal input signals in time-domain, frequency-domain, and acoustic spectrogram to analyze yaw abnormal noise features. It overcomes the limitation of extracting features from single-modal input signals under small sample. The utilization of key features in small sample is further enhanced by the densely connected convolutional network (DenseNet), boosting its generalization ability. Mitigating gradient vanishing during computation and reducing overfitting risk through iterative computation of the DenseNet is optimized by momentum fractional order. This approach improves diagnosis accuracy of yaw abnormal noise faults under acoustic signals, surpassing traditional fault diagnosis approaches’ performance especially with fewer samples. This approach can lay an important foundation for the early acoustic-based fault diagnosis of key components and the entire system, as well as for the intelligent operation and maintenance of the wind power network.

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AAAI Conference 2026 Conference Paper

SEAP: Sparse Expert Activation Pruning Unlocks the Brainpower of Large Language Models

Xun Liang
Hanyu Wang
Huayi Lai
Simin Niu
Shichao Song
Jiawei Yang
Jihao Zhao
Feiyu Xiong

Pruning is a promising approach to reduce the high inference cost of large language models (LLMs), but it often comes at the expense of performance. Motivated by the "functional localization" theory in neuroscience, we hypothesize that LLMs contain task-specific expert activation paths, where specific subsets of neurons are co-activated for particular tasks. This structure allows selective activation to preserve task performance while improving inference efficiency. We introduce Sparse Expert Activation Pruning (SEAP), a training-free pruning method for large language models. SEAP identifies task-relevant activation paths by analyzing the clustering patterns of hidden states and neuron activations on a multi-task calibration dataset. Cross-task transfer evaluations confirm the existence of such expert activation structures. SEAP constructs task-aware pruning masks by leveraging a task-expert calibration dataset, which provides representative samples across diverse tasks to reveal their activation signatures. It then employs a lightweight task router to dynamically select relevant computation paths based on the input task. This design significantly reduces inference cost without compromising accuracy. Experimental results show that SEAP retains model performance with only a 1.5% drop on most tasks at 20% sparsity, and at 50% sparsity, it surpasses strong pruning baselines such as WandA and FLAP by over 20%. These results highlight SEAP as a scalable and effective solution for efficient LLM inference.

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EAAI Journal 2025 Journal Article

An efficient anchor-free model for ore particle size detection

Kanghui Zhang
Qingkai Wang
Guobin Zou
Jiawei Yang
Tao Song
Yang Liu
Daoxi Liu

Accurate detection of ore size is crucial in mineral processing, directly impacting equipment efficiency and product quality. However, traditional anchor-based models often struggle with the irregular shapes and varying scales of ore particles, resulting in limited performance. To overcome these challenges, an anchor-free detection framework was proposed. It incorporates a cross-stage partial bottleneck and a spatial pyramid pooling cross-stage partial connections (SPPCSCP-DualConv), both enhanced with dual convolution, to improve feature extraction and multi-scale fusion. In the backbone, the dual convolution module combines group convolution with heterogeneous convolution to improve feature diversity. The SPPCSCP-DualConv module further enhances feature representation in complex backgrounds. Additionally, a simplified path aggregation network (simPANet) feature fusion module is employed in the neck to refine the integration of multi-scale features. The proposed model was trained using a combination of binary cross-entropy, complete intersection over union (IoU), and distribution focal loss to optimize detection accuracy. The proposed model achieved a mean average precision of 86. 80 % at an IoU threshold of. 5 and 78. 50 % across IoU thresholds from. 5 to. 95, surpassing existing methods while maintaining a lightweight architecture with only 10. 10 million parameters and 89. 45 giga floating point operations per second. Ablation studies confirmed the effectiveness of the simPANet and SPPCSPC-DualConv modules in enhancing feature representation. Generalization tests across mining sites with similar distributions demonstrated strong performance, although limitations remain for exceptionally large ore blocks due to dataset bias. The proposed model significantly improved the accuracy and efficiency of ore particle size detection, providing reliable real-time insights to improve grinding control and mineral processing operations.

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NeurIPS Conference 2024 Conference Paper

DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features

Letian Wang
Seung Wook Kim
Jiawei Yang
Cunjun Yu
Boris Ivanovic
Steven Waslander
Yue Wang
Sanja Fidler

We propose DistillNeRF, a self-supervised learning framework addressing the challenge of understanding 3D environments from limited 2D observations in outdoor autonomous driving scenes. Our method is a generalizable feedforward model that predicts a rich neural scene representation from sparse, single-frame multi-view camera inputs with limited view overlap, and is trained self-supervised with differentiable rendering to reconstruct RGB, depth, or feature images. Our first insight is to exploit per-scene optimized Neural Radiance Fields (NeRFs) by generating dense depth and virtual camera targets from them, which helps our model to learn enhanced 3D geometry from sparse non-overlapping image inputs. Second, to learn a semantically rich 3D representation, we propose distilling features from pre-trained 2D foundation models, such as CLIP or DINOv2, thereby enabling various downstream tasks without the need for costly 3D human annotations. To leverage these two insights, we introduce a novel model architecture with a two-stage lift-splat-shoot encoder and a parameterized sparse hierarchical voxel representation. Experimental results on the NuScenes and Waymo NOTR datasets demonstrate that DistillNeRF significantly outperforms existing comparable state-of-the-art self-supervised methods for scene reconstruction, novel view synthesis, and depth estimation; and it allows for competitive zero-shot 3D semantic occupancy prediction, as well as open-world scene understanding through distilled foundation model features. Demos and code will be available at https: //distillnerf. github. io/.

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NeurIPS Conference 2022 Conference Paper

TreeMoCo: Contrastive Neuron Morphology Representation Learning

Hanbo Chen
Jiawei Yang
Daniel Iascone
Lijuan Liu
Lei He
Hanchuan Peng
Jianhua Yao

Morphology of neuron trees is a key indicator to delineate neuronal cell-types, analyze brain development process, and evaluate pathological changes in neurological diseases. Traditional analysis mostly relies on heuristic features and visual inspections. A quantitative, informative, and comprehensive representation of neuron morphology is largely absent but desired. To fill this gap, in this work, we adopt a Tree-LSTM network to encode neuron morphology and introduce a self-supervised learning framework named TreeMoCo to learn features without the need for labels. We test TreeMoCo on 2403 high-quality 3D neuron reconstructions of mouse brains from three different public resources. Our results show that TreeMoCo is effective in both classifying major brain cell-types and identifying sub-types. To our best knowledge, TreeMoCo is the very first to explore learning the representation of neuron tree morphology with contrastive learning. It has a great potential to shed new light on quantitative neuron morphology analysis. Code is available at https: //github. com/TencentAILabHealthcare/NeuronRepresentation.

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AAAI Conference 2021 Conference Paper

Oral-3D: Reconstructing the 3D Structure of Oral Cavity from Panoramic X-ray

Weinan Song
Yuan Liang
Jiawei Yang
Kun Wang
Lei He

Panoramic X-ray (PX) provides a 2D picture of the patient’s mouth in a panoramic view to help dentists observe the invisible disease inside the gum. However, it provides limited 2D information compared with cone-beam computed tomography (CBCT), another dental imaging method that generates a 3D picture of the oral cavity but with more radiation dose and a higher price. Consequently, it is of great interest to reconstruct the 3D structure from a 2D X-ray image, which can greatly explore the application of X-ray imaging in dental surgeries. In this paper, we propose a framework, named Oral-3D, to reconstruct the 3D oral cavity from a single PX image and prior information of the dental arch. Specifically, we first train a generative model to learn the cross-dimension transformation from 2D to 3D. Then we restore the shape of the oral cavity with a deformation module with the dental arch curve, which can be obtained simply by taking a photo of the patient’s mouth. To be noted, Oral-3D can restore both the density of bony tissues and the curved mandible surface. Experimental results show that Oral-3D can efficiently and effectively reconstruct the 3D oral structure and show critical information in clinical applications, e. g. , tooth pulling and dental implants. To the best of our knowledge, we are the first to explore this domain transformation problem between these two imaging methods.

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