CAAI Transactions on Intelligence Technology最新文献

The Unmanned Aerial Vehicle (UAV) air combat trajectory prediction algorithm facilitates strategic pre-planning by predicting UAV flight trajectories with high accuracy, thus mitigating risks and securing advantages in intricate aerial scenarios. This study tackles the prevalent limitations of existing datasets, which are often restricted in scale and scenario diversity, by introducing a novel UAV air combat trajectory prediction methodology predicated on QCNet. Firstly, a robust UAV air combat dynamics model is developed to synthesise air combat trajectories, forming the basis for a comprehensive trajectory prediction dataset. Subsequently, a specialised trajectory prediction framework utilising QCNet is devised, followed by rigorous algorithm training. The parameter impact analysis is conducted to assess the influence of critical algorithm parameters on efficiency. The results of the parameter impact analysis experiment indicate that augmenting the number of encoder layers and the decoder's recurrent steps generally enhances performance, albeit an excessive increment in recurrent steps may inversely affect efficiency. Finally, the proposed algorithm is evaluated compared with other traditional time-series prediction algorithms and shows better performance. The effectiveness experiment indicates that the proposed algorithm can predict the flight trajectories of UAVs and provide corresponding probabilities under different manoeuvres.

Epilepsy is a neurological disorder characterised by recurrent seizures due to abnormal neuronal discharges. Seizure detection via EEG signals has progressed, but two main challenges are still encountered. First, EEG data can be distorted by physiological factors and external variables, resulting in noisy brain networks. Static adjacency matrices are typically used in current mainstream methods, which neglect the need for dynamic updates and feature refinement. The second challenge stems from the strong reliance on long-range dependencies through self-attention in current methods, which can introduce redundant noise and increase computational complexity, especially in long-duration data. To address these challenges, the Attention-based Adaptive Graph ProbSparse Hybrid Network (AA-GPHN) is proposed. Brain network structures are dynamically optimised using variational inference and the information bottleneck principle, refining the adjacency matrix for improved epilepsy classification. A Linear Graph Convolutional Network (LGCN) is incorporated to focus on first-order neighbours, minimising the aggregation of distant information. Furthermore, a ProbSparse attention-based Informer (PAT) is introduced to adaptively filter long-range dependencies, enhancing efficiency. A joint optimisation loss function is applied to improve robustness in noisy environments. Experimental results on both patient-specific and cross-subject datasets demonstrate that AA-GPHN outperforms existing methods in seizure detection, showing superior effectiveness and generalisation.

Point cloud processing plays a crucial role in tasks such as point cloud classification, partial segmentation and semantic segmentation. However, existing processing frameworks are constrained by several challenges, such as recognising features in irregular and complex spatial structures, large attention parameter volumes and limitations in generalisation across different scenes. We propose a geometry transformer (PointGeo) method for addressing these concerns through point cloud analysis. This method utilises a geometry transformation network to process point cloud data, effectively capturing both local and global features and enhancing the modelling capability for irregular structures. We extensively test this method on multiple datasets, including ModelNet and ScanObjectNN for point cloud classification tasks, ShapeNet for point cloud partial segmentation tasks and S3DIS and SemanticKITTI for point cloud semantic segmentation tasks. Experimental results show that our approach delivers outstanding performance across all tasks, validating its effectiveness and generalisation capability in handling point cloud data.

In the field of infrared small target detection (ISTD), the ability to detect targets in dim environments is critical, as it improves the performance of target recognition in nighttime and harsh weather conditions. The blurry contour, small size and sparse distribution of infrared small targets increase the difficulty of identifying such targets in cluttered backgrounds. Existing methodologies fall short of satisfying the requisites for the detection and categorisation of infrared small targets. To address these challenges and to enhance the precision of small object detection and classification, this paper introduces an innovative approach called location refinement and adjacent feature fusion YOLO (LA-YOLO), which enhances feature extraction by integrating a multi-head self-attention mechanism (MSA). We have improved the feature fusion method to merge adjacent features, to enhance information utilisation in the path aggregation network (PAN). Lastly, we introduce supervision on the target centre points in the detection network. Empirical results on publicly available datasets demonstrate that LA-YOLO achieves an impressive average precision (AP) of 92.46% on IST-A and a mean average precision (mAP) of 84.82% on FLIR. The results surpass those of contemporary state-of-the-art detectors, striking a balance between precision and speed. LA-YOLO emerges as a viable and efficacious solution for ISTD, making a substantial contribution to the progression of infrared imagery analysis. The code is available at https://github.com/liusjo/LA-YOLO.

Due to their exceptional programmability, DNA molecules are widely employed in the design of molecular circuits for applications such as DNA computing, DNA storage and cancer diagnosis and treatment. The quality of DNA sequences directly determines the reliability of these molecular circuits. However, existing DNA encoding algorithms suffer from limitations such as reliance on Hamming distance and conflicts among multiple objectives, resulting in insufficient stability of the generated sequences. To address these issues, this paper proposes a thermodynamics-based multi-objective evolutionary optimisation algorithm (TEMOA). The core innovations of the proposed algorithm are as follows: First, a thermodynamics-based DNA encoding modelling strategy (TDEMS) is introduced, which simplifies the encoding process and significantly improves the sequence quality by incorporating thermodynamic stability constraints. Second, two diversity optimisation strategies—the diversity assessment strategy (DAS) and the front equalisation nondominated sorting (FENS) strategy—are designed to enhance the algorithm's global search capability. Finally, a flexible fitness function design is incorporated to accommodate diverse user requirements. Experimental results demonstrate that TEMOA is more effective than state-of-the-art methods on challenging multi-objective optimisation problems, whereas the DNA sequences generated by TEMOA exhibit greater reliability compared to those produced by traditional DNA encoding algorithms.

Existing occluded person re-identification methods employ hard or soft partition strategies to explore fine-grained information. However, the hard partition strategy which extracts region-level features may impair the semantic connectivity of correlated human body parts. A pose-guided soft partition establishes correlations among human keypoints, while the generated pixel-level embeddings may lose the surrounding semantic information. In this paper, we propose a keypoint-guided feature partition (KGFP) method that consists of a feature extractor, a hard partition branch, and a soft partition branch. Specifically, we adopt a vision transformer and a pose estimator to extract features and keypoint information. In the hard partition branch, we partition features into distinct groups and classify them into nonoccluded, semi-occluded, and occluded features to obtain region-level features and filter out occlusions. Furthermore, we design a dissimilarity loss to reduce the similarity between semi-occluded and occluded features. In the soft partition branch, we introduce a graph attention network and consider global and keypoint embeddings as nodes of a graph to discover interrelationships. Additionally, we formulate image alignment as a graph matching problem and propose a feature alignment-based graph to reduce position misalignment. Extensive experiments demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods on Occluded-DukeMTMC, Markt1501, and DukeMTMC-reID.

Point of interest (POI) recommendation analyses user preferences through historical check-in data. However, existing POI recommendation methods often overlook the influence of weather information and face the challenge of sparse historical data for individual users. To address these issues, this paper proposes a new paradigm, namely temporal-weather-aware transition pattern for POI recommendation (TWTransNet). This paradigm is designed to capture user transition patterns under different times and weather conditions. Additionally, we introduce the construction of a user-POI interaction graph to alleviate the problem of sparse historical data for individual users. Furthermore, when predicting user interests by aggregating graph information, some POIs may not be suitable for visitation under current weather conditions. To account for this, we propose an attention mechanism to filter POI neighbours when aggregating information from the graph, considering the impact of weather and time. Empirical results on two real-world datasets demonstrate the superior performance of our proposed method, showing a substantial improvement of 6.91%–23.31% in terms of prediction accuracy.

Point cloud upsampling is an essential yet challenging task in various 3D computer vision and graphics applications. Existing methods often struggle with limitations such as the generation of outliers or shrinkage artifacts. Additionally, these methods usually ignore the overall spatial structure of point clouds, leading to suboptimal results. To tackle these challenges, we propose a novel framework that enhances geometric spatial consistency in upsampled point clouds through a dual-supervision mechanism and enables the generation of high-fidelity results with precise geometric structures. Specifically, we first design a tailored feature extractor that iteratively extracts the comprehensive and distinctive features by integrating both fine-grained local geometric details and global structure information. Then, our network predicts the point-to-point distances and Chamfer distances of upsampled points to accurately capture the spatial relation within them. To enhance spatial consistency, we formulate a joint loss function that enables our model to perceive the spatial relations between points by indirect and direct supervision. This ensures the precise alignment between upsampled points and ground truth during training. Furthermore, we propose a coordinate reconstruction to generate more high-quality upsampled points iteratively. We conduct extensive experiments across multiple benchmark datasets and downstream tasks. The results comprehensively demonstrate that our method achieves state-of-the-art performance and exhibits superior generalisation capabilities.

Accurate medical image segmentation plays a crucial role in improving the precision of computer-aided diagnosis. However, complex boundary shapes, low contrast and blurred anatomical structures make fine-grained segmentation a challenging task. Variational Bayesian inference quantifies uncertainty through probability distributions and can construct robust probabilistic models for the boundaries of ambiguous organs and tissues. In this paper, we apply variational Bayesian inference to medical image segmentation and propose variational attention to model the uncertainty of low-contrast and blurry tissue and organ boundaries. This enhances the model's ability to perceive segmentation boundaries, improving robustness and segmentation accuracy. Variational attention first estimates the parameters of the probability distribution of latent representations based on input features. Then, it samples latent representations from the learnt distribution to generate attention weights that optimise the interaction between global features and ambiguous boundaries. We integrate variational attention into the U-Net model by replacing its skip connections, constructing a multi-scale variational attention segmentation model (V-UNet). Experiments on the ISBI 2012 and MoNuSeg 2018 datasets show that our method achieves Dice scores of 95.89% and 82.18%, respectively. Moreover, we integrate V-UNet into the Mask R-CNN framework by replacing the FPN feature extraction head and propose a two-stage segmentation method. Compared to the original Mask R-CNN, our method improves the Dice score by 0.81%, mAP by 8.06% and F1 score by 0.51%.

Density peaks clustering (DPC) is a density-based clustering algorithm that identifies cluster centres by constructing decision graphs and allocates noncluster centres. Although DPC does not require specifying cluster numbers in advance, the local density is affected by the distribution of data points. Meanwhile, allocating noncluster centres is likely to result in continuous errors. Hence, a novel DPC based on weighted density estimating and multicluster merging (DPC-WDMM) is proposed. Firstly, a novel local density is defined by the nearest neighbour relationship. Then, to avoid incorrect selection of cluster centres, data points with relatively high local density are all marked within the local range. Finally, using these data points to represent microclusters for merging, the final clustering results can be obtained. The performance of this novel algorithm has been demonstrated through the experimental results on several datasets.