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Xinbin Zhao

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3 papers

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EAAI Journal 2026 Journal Article

Channel Clustering-based Attention Network for interpretable hard landing prediction

Hao Zhang
Huabo Sun
Yu Liu
Xinbin Zhao
Xu Li
Jiaxing Shang
Linjiang Zheng

Hard landing incidents are common flight safety events during the landing phase and are of significant concern in the aviation industry. Recent hard landing prediction methods tend to overemphasize temporal features while overlooking the landing process along the altitude dimension, importantly, altitude-based alignment enables more practical interpretability. Additionally, they often fail to capture dependencies between contributing factors and offer limited interpretability under fixed time windows. To address the above issues, we propose a Channel Clustering based Attention Network, termed CCAN, to predict hard landing incidents and identify their potential causes. Specifically, we resample and interpolate different flight parameters along the altitude dimension to align the landing process across different flights into a common reference frame for the subsequent interpretability. Subsequently, we design a channel clustering module that groups flight parameters into distinct clusters based on a predefined assignment threshold. Then, we employ graph attention network (GAT) to capture the dependencies between different flight parameters within and across clusters. To further reveal the interactions between flight parameters throughout the landing process, we incorporate attention mechanism into Gated Recurrent Units (GRUs) to extract informative temporal features. We conducted experiments on a real-world quick access recorders (QAR) dataset with 44, 729 Airbus A321 flights. Experimental results demonstrate that CCAN outperforms the baseline models in hard landing predictions and offers practical interpretability for hard landings by visualizing the dependencies between flight parameters and their interactions under the altitude reference during the landing process.

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EAAI Journal 2025 Journal Article

A Dual Two-Stage Attention-based Model for interpretable hard landing prediction from flight data

Jiaxing Shang
Xiaoquan Li
Ruixiang Zhang
Linjiang Zheng
Xu Li
Riquan Zhang
Xinbin Zhao
Fan Li

Hard landings are a significant safety concern in aviation, with potential consequences ranging from poor passenger experiences to serious injuries or fatalities. Predicting and explaining hard landing events are equally important for enhancing flight safety, the former makes it possible to give proactive warnings, while the latter helps pilots identify the reasons and refine their skills. However, existing studies generally lack a comprehensive consideration for the fine-grained characteristics of flight data containing both inter-temporal and inter-parametric relationships, resulting in suboptimal prediction performance. In addition, most of existing approaches aim at improving the prediction performance but fail to provide interpretability for the causes of hard landing. To address the above problems, we propose DUTSAM, a DUal Two-Stage Attention-based interpretable Model for hard landing prediction from quick access recorder (QAR) data. The model consists of dual parallel modules, each of which combines a convolutional feature encoder and a two-stage attention mechanism. The two encoders capture fine-grained characteristics by encoding multivariate data from temporal domain and parametric domain respectively. After that, the dual two-stage attention mechanism captures the inter-temporal and inter-parametric correlations in reverse order to predict hard landing and provide interpretation from both temporal and parametric perspectives. Experimental results on a real QAR dataset with 37, 920 flights show that DUTSAM achieves better prediction performance compared with other state-of-the-art baselines in terms of Precision, Recall, and F1-score. Additionally, case study demonstrates that DUTSAM can uncover key flight parameters and moments strongly correlated to the hard landing events.

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AAAI Conference 2020 Conference Paper

Hypergraph Label Propagation Network

Yubo Zhang
Nan Wang
Yufeng Chen
Changqing Zou
Hai Wan
Xinbin Zhao
Yue Gao

In recent years, with the explosion of information on the Internet, there has been a large amount of data produced, and analyzing these data is useful and has been widely employed in real world applications. Since data labeling is costly, lots of research has focused on how to efﬁciently label data through semi-supervised learning. Among the methods, graph and hypergraph based label propagation algorithms have been a widely used method. However, traditional hypergraph learning methods may suffer from their high computational cost. In this paper, we propose a Hypergraph Label Propagation Network (HLPN) which combines hypergraphbased label propagation and deep neural networks in order to optimize the feature embedding for optimal hypergraph learning through an end-to-end architecture. The proposed method is more effective and also efﬁcient for data labeling compared with traditional hypergraph learning methods. We verify the effectiveness of our proposed HLPN method on a real-world microblog dataset gathered from Sina Weibo. Experiments demonstrate that the proposed method can signiﬁcantly outperform the state-of-the-art methods and alternative approaches.

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