In civil aviation industry, runway overrun is a typical landing safety incident concerned by both airlines and authorities. Among various contributing factors to the runway overrun incident, long landing plays an important role. However, existing studies for long landing prediction mainly depend on classic machine learning methods and handcrafted features. As a result, they usually require much expert knowledge and provide unsatisfactory results. To address these problems, this paper proposes an innovative deep sequence‐to‐sequence model which utilizes QAR (Quick Access Recorder) data for accurate long landing pre‐ diction. Specifically, to cope with the high heterogeneity of QAR dataset, a data pre‐processing procedure is first proposed which includes data cleaning, interpolation and normalization steps. Second, to avoid the noises incurred by too many QAR parameters and relieve the reliance on expert experience, the GBDT (gradient boosting decision trees) model is employed to choose the most relevant parameters as features. Then a CNN‐LSTM and TG‐attention encoder‐decoder architecture is proposed to accurately predict future aircraft ground speed and radio height sequences, based on which the touchdown distance can be finally calculated. Experimental results on a large QAR dataset with 44,176 A321 flights validate effectiveness of the proposed method.

Recent methods that project images into deep feature spaces to evaluate quality degradation have produced inefficient results due to biased mappings; i.e., these projections are not aligned with the perceptions of humans. In this paper, we develop a hyperdebiasing framework to address such bias in full-reference image quality assessment. First, we perform orthogonal Tucker decomposition on the top of feature tensors extracted by a feature extraction network to project features into a robust content-agnostic space and effectively eliminate the bias caused by subtle image perturbations. Second, we propose a hypernetwork in which the content-aware parameters are produced for reprojecting features in a deep subspace for quality prediction. By leveraging the content diversity of large-scale blind-reference datasets, the perception rule between image content and image quality is established. Third, a quality prediction network is proposed by combining debiased content-aware and content-agnostic features to predict the final image quality score. To demonstrate the efficacy of our proposed method, we conducted numerous experiments on comprehensive databases. The experimental results validate that our method achieves state-of-the-art performance in predicting image quality.

As social media communication develops, reliable multimedia quality evaluation indicators have become a prerequisite for enriching user experience services. In this paper, we propose a multiscale spatiotemporal pyramid attention (SPA) block for constructing a blind video quality assessment (VQA) method to evaluate the perceptual quality of videos. First, we extract motion information from the video frames at different temporal scales to form a feature pyramid, which provides a feature representation with multiple visual perceptions. Second, an SPA module, which can effectively extract multiscale spatiotemporal information at various temporal scales and develop a cross-scale dependency relationship, is proposed. Finally, the quality estimation process is completed by passing the extracted features obtained from a network of multiple stacked spatiotemporal pyramid blocks through a regression network to determine the perceived quality. The experimental results demonstrate that our method is on par with the state-of-the-art approaches. The source code necessary for conducting groundbreaking scientific research is accessible online https://github.com/Land5cape/SPBVQA.

Fast and accurate image retrieval is an important and challenging task in massive image data scenarios. As the core technology of image retrieval tasks, deep metric learning aims at learning effective embedding representations that possess two properties among data points: positive concentrated and negative separated. In this work, we propose a multilevel similarity-aware method based on deep local descriptors for deep metric learning. We take the rich interclass similarity relationship based on the deep local invariant descriptors from the data into account to optimize sampling strategies for mining informative samples. The method dynamically adjusts the margin between data points to better match the true similarity relationship between classes. Specifically, for images in a batch, we first obtain deep local descriptors and calculate the similarity matrix of the channel, pixel, and spatial levels. Then, depending on the calculated comprehensive similarity matrix, we propose a multilevel similarity-aware loss function through the deviation between pairwise distance and violate margin to make full use of informative samples. The experimental results demonstrate that our proposed method outperforms other state-of-the-art methods in terms of fine-grained image retrieval and clustering tasks.