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Yangyang Wang

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4 papers

1 author row

EAAI Journal 2025 Journal Article

Semi-supervised segmentation model for crack detection based on mutual consistency constraint and boundary loss

Tianxiang Shi
Yangyang Wang
Yu Fang
Yongqiang Zhang

Detecting and measuring cracks are crucial for ensuring the safety of civil infrastructures. Traditional fully supervised methods require an amount of high-precision labeled data, making them time-consuming to deploy. In this paper, a semi-supervised learning network model designed for crack segmentation is proposed to address this issue, which incorporates a mutual consistency constraint and a boundary loss function. The mutual consistency constraint enables the model to utilize information from unlabeled data, thereby improving its performance and efficacy. Meanwhile, the boundary loss function enhances the model's ability to predict images when the background pixels outnumber the crack pixels. To comprehensively evaluate the model performance, a new dataset featuring various environmental interferences is constructed. The model optimal hyperparameters and architecture are determined through the experiments and an ablation study. To highlight the advantages of the proposed network model, its prediction results are compared with other segmentation models. It is demonstrated that the proposed model delivers high-precision and robust results while requiring less labeled data.

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AAAI Conference 2025 Conference Paper

Unveiling Multi-View Anomaly Detection: Intra-view Decoupling and Inter-view Fusion

Kai Mao
Yiyang Lian
Yangyang Wang
Meiqin Liu
Nanning Zheng
Ping Wei

Anomaly detection has garnered significant attention for its extensive industrial application value. Most existing methods focus on single-view scenarios and fail to detect anomalies hidden in blind spots, leaving a gap in addressing the demands of multi-view detection in practical applications. Ensemble of multiple single-view models is a typical way to tackle the multi-view situation, but it overlooks the correlations between different views. In this paper, we propose a novel multi-view anomaly detection framework, Intra-view Decoupling and Inter-view Fusion (IDIF), to explore correlations among views. Our method contains three key components: 1) a proposed Consistency Bottleneck module extracting the common features of different views through information compression and mutual information maximization; 2) an Implicit Voxel Construction module fusing features of different views with prior knowledge represented in the form of voxels; and 3) a View-wise Dropout training strategy enabling the model to learn how to cope with missing views during test. The proposed IDIF achieves state-of-the-art performance on three datasets. Extensive ablation studies also demonstrate the superiority of our methods.

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AAAI Conference 2022 Short Paper

A Multi-Factor Classification Framework for Completing Users’ Fuzzy Queries (Student Abstract)

Yaning Zhang
Liangqing Wu
Yangyang Wang
Jia Wang
Xiaoguang Yu
Shuangyong Song
Youzheng Wu
Xiaodong He

Intent identification is the key technology in dialogue system. However, not all online queries are clear or complete. To identify users’ intents from those fuzzy queries accurately, this paper proposes a multi-factor classification framework on the query level. Experimental results on our online serving system JIMI demonstrate the effectiveness of our proposed framework.

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EAAI Journal 2020 Journal Article

Long short-term memory neural network with weight amplification and its application into gear remaining useful life prediction

Sheng Xiang
Yi Qin
Caichao Zhu
Yangyang Wang
Haizhou Chen

As an important component of industrial equipment, once gears have failures, they may cause serious catastrophes. Thus, the prediction of gear remaining life is of great significance. The health indicator of gears is first generated by fusing time-domain and frequency-domain features of gears vibration signals via the isometric mapping algorithm. Then a new type of long-short-term memory neural network with weight amplification (LSTMP-A) is proposed for accurately predicting gear remaining life. Compared with traditional LSTMs, LSTMP-A amplifies the input weights and the recurrent weights of the hidden layer to different degrees by the attention mechanism according to the contribution degree of the corresponding data, and a projection layer is added into the network. With LSTMP-A, we can predict the health characteristics of gears based on historical fusion features. With the monitoring data of a gear life cycle test, the comparative experiments show that the proposed gear remaining life prediction method has higher prediction accuracy than the conventional prediction methods.

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