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    Latest Research

    Perspective  |  2021-05-07

    SESAR: The Past, Present, and Future of European Air Traffic Management Research

    The Single European Sky ATM Research (SESAR) project is the technological pillar of the European Commission’s Single European Sky Initiative to modernize air traffic management (ATM). Here, we describe the process of establishing SESAR and the main parts of the project: the research and development (R&D) part, which is led by the SESAR Joint Undertaking; the deployment part, which is managed by the SESAR Deployment Manager; and the European ATM Master Plan, which collects and lays out both the R&D and deployment needs. The latest European ATM Master Plan was adopted just prior to the current pandemic. The huge loss in air traffic due to the pandemic, and the speed of the recovery of the aviation industry will require reprioritization, but the main elements that have been established—particularly those in support of the environment—remain valid.

    Tatjana Boli? ,   Paul Ravenhill  

    Article  |  2021-05-07

    Effect of Radial Porosity Oscillation on the Thermal Performance of Packed Bed Latent Heat Storage

    Owing to its high heat storage capacity and fast heat transfer rate, packed bed latent heat storage (LHS) is considered as a promising method to store thermal energy. In a packed bed, the wall effect can impact the packing arrangement of phase change material (PCM) capsules, inducing radial porosity oscillation. In this study, an actual-arrangement-based three-dimensional packed bed LHS model was built to consider the radial porosity oscillation. Its fluid flow and heat transfer were analyzed. With different cylindrical sub-surfaces intercepted along the radial direction in the packed bed, the corresponding relationships between the arrangement of capsules and porosity oscillation were identified. The oscillating distribution of radial porosity led to a non-uniform distribution of heat transfer fluid (HTF) velocity. As a result, radial temperature distributions and liquid fraction distributions of PCMs were further affected. The effects of different dimensionless parameters (e.g., tube-to-capsule diameter ratio, Reynolds number, and Stefan number) on the radial characteristics of HTF and PCMs were discussed. The results showed that different diameter ratios correspond to different radial porosity distributions. Further, with an increase in diameter ratio, HTF velocity varies significantly in the near wall region while the non-uniformity of HTF velocity in the center region will decrease. The Reynolds and Stefan numbers slightly impact the relative velocity distribution of the HTF—while higher Reynolds numbers can lead to a proportional improvement of velocity, an increase in Stefan number can promote heat storage of the packed bed LHS system.

    H. B. Liu ,   C. Y. Zhao  

    Perspectives  |  2021-05-17

    Miniaturized five fundamental issues about visual knowledge

    Yun-he Pan ,   panyh@zju.edu.cn  

    Perspectives  |  2021-05-17

    Visual knowledge: an attempt to explore machine creativity

    Yueting Zhuang ,   Siliang Tang   et al.

    Research Articles  |  2021-05-17

    Visual commonsense reasoning with directional visual connections

    To boost research into cognition-level visual understanding, i.e., making an accurate inference based on a thorough understanding of visual details, (VCR) has been proposed. Compared with traditional visual question answering which requires models to select correct answers, VCR requires models to select not only the correct answers, but also the correct rationales. Recent research into human cognition has indicated that brain function or cognition can be considered as a global and dynamic integration of local neuron connectivity, which is helpful in solving specific cognition tasks. Inspired by this idea, we propose a to achieve VCR by dynamically reorganizing the that is contextualized using the meaning of questions and answers and leveraging the directional information to enhance the reasoning ability. Specifically, we first develop a GraphVLAD module to capture to fully model visual content correlations. Then, a contextualization process is proposed to fuse sentence representations with visual neuron representations. Finally, based on the output of , we propose to infer answers and rationales, which includes a ReasonVLAD module. Experimental results on the VCR dataset and visualization analysis demonstrate the effectiveness of our method.

    Yahong Han ,   Aming Wu   et al.

    Research Articles  |  2021-05-17

    Unsupervised object detection with scene-adaptive concept learning

    Object detection is one of the hottest research directions in computer vision, has already made impressive progress in academia, and has many valuable applications in the industry. However, the mainstream detection methods still have two shortcomings: (1) even a model that is well trained using large amounts of data still cannot generally be used across different kinds of scenes; (2) once a model is deployed, it cannot autonomously evolve along with the accumulated unlabeled scene data. To address these problems, and inspired by theory, we propose a novel scene-adaptive evolution algorithm that can decrease the impact of scene changes through the concept of object groups. We first extract a large number of object proposals from unlabeled data through a pre-trained detection model. Second, we build the dictionary of object concepts by clustering the proposals, in which each cluster center represents an object prototype. Third, we look into the relations between different clusters and the object information of different groups, and propose a graph-based group information propagation strategy to determine the category of an object concept, which can effectively distinguish positive and negative proposals. With these pseudo labels, we can easily fine-tune the pre-trained model. The effectiveness of the proposed method is verified by performing different experiments, and the significant improvements are achieved.

    Shiliang Pu ,   Wei Zhao   et al.