1. A hierarchical knowledge graph embedding framework for link predictionShuang Liu, Chengwang Hou, Jiana Meng, Peng Chen, Simon Kolmanič, 2024, izvirni znanstveni članek Ključne besede: knowledge graph embedding, knowledge graph completion, negative sampling, link prediction
Objavljeno v DKUM: 04.02.2025; Ogledov: 0; Prenosov: 3
 Celotno besedilo (1,76 MB) |
2. CNN-Based Vessel Meeting Knowledge Discovery From AIS Vessel TrajectoriesPeng Chen, Shuang Liu, Niko Lukač, 2023, izvirni znanstveni članek Opis: How to extract a collection of trajectories for different vessels from the raw AIS data to discover vessel meeting knowledge is a heavily studied focus. Here, the AIS database is created based on the raw AIS data after parsing, noise reduction and dynamic Ramer-Douglas-Peucker compression. Potential encountering trajectory pairs will be recorded based on the candidate meeting vessel searching algorithm. To ensure consistent features extracted from the trajectories in the same time period, time alignment is also adopted. With statistical analysis of vessel trajectories, sailing segment labels will be added to the input feature. All motion features and sailing segment labels are combined as input to one trajectory similarity matching method based on convolutional neural network to recognize crossing, overtaking or head-on situations for each potential encountering vessel pair, which may lead to collision if false actions are adopted. Experiments on AIS data show that our method is effective in classifying vessel encounter situations to provide decision support for collision avoidance.
Ključne besede: AIS Data, CNN, Dynamic Rammer-Douglas-Peucker, knowledge discovery, maneuvering pattern, traffic pattern, trajectory
Objavljeno v DKUM: 19.03.2024; Ogledov: 538; Prenosov: 417
Celotno besedilo (3,84 MB)
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3. Research on vehicle re-identification algorithm based on fusion attention methodPeng Chen, Shuang Liu, Simon Kolmanič, 2023, izvirni znanstveni članek Opis: The specific task of vehicle re-identification is how to quickly and correctly match the same vehicle in different scenarios. In order to solve the problem of inter-class similarity and environmental interference in vehicle images in complex scenes, one fusion attention method is put forward based on the idea of obtaining the distinguishing features of details-the mechanism for the vehicle re-identification method. First, the vehicle image is preprocessed to restore the image's attributes better. Then, the processed image is sent to ResNet50 to extract the features of the second and third layers, respectively. Then, the feature fusion is carried out through the two-layer attention mechanism for a network model. This model can better focus on local detail features, and global features are constructed and named SDLAU-Reid. In the training process, a data augmentation strategy of random erasure is adopted to improve the robustness. The experimental results show that the mAP and rank-k indicators of the model on VeRi-776 and the VehicleID are better than the results of the existing vehicle re-identification algorithms, which verifies the algorithm's effectiveness.
Ključne besede: vehicle re-identification, attention mechanism, key-point, local feature, feature fusion
Objavljeno v DKUM: 06.02.2024; Ogledov: 348; Prenosov: 30
Celotno besedilo (5,28 MB)
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