1,514 research outputs found

    Deeper and Wider Networks for Performance Metrics Prediction in Communication Networks

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    In today's era, users have increasingly high expectations regarding the performance and efficiency of communication networks. Network operators aspire to achieve efficient network planning, operation, and optimization through Digital Twin Networks (DTN). The effectiveness of DTN heavily relies on the network model, with graph neural networks (GNN) playing a crucial role in network modeling. However, existing network modeling methods still lack a comprehensive understanding of communication networks. In this paper, we propose DWNet (Deeper and Wider Networks), a heterogeneous graph neural network modeling method based on data-driven approaches that aims to address end-to-end latency and jitter prediction in network models. This method stands out due to two distinctive features: firstly, it introduces deeper levels of state participation in the message passing process; secondly, it extensively integrates relevant features during the feature fusion process. Through experimental validation and evaluation, our model achieves higher prediction accuracy compared to previous research achievements, particularly when dealing with unseen network topologies during model training. Our model not only provides more accurate predictions but also demonstrates stronger generalization capabilities across diverse topological structures

    Occurrence and Control of Soybean Aphid, Aphis glycines Matsumura

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    The soybean aphid, Aphis glycines Matsumura, is one of the most important pests of soybean. The A. glycines outbreak in 1998 followed another aphid outbreak after 1989, which caused enormous economic losses. The aphid infested areas exceeded 200 thousand mu, and the soybean yields decreased by 20%. Among aphid infested areas, 78 thousand mu were severely infested with a yield loss of 46%. More than 3,000 mu had no yield at all.Originating text in Chinese.Citation: Wu, Xiaobing, Ni, Wenjun, Liu, Peijing. (1999). Occurrence and Control of Soybean Aphid, Aphis glycines Matsumura. How Peasants Can Increase Wealth, 6, 20

    Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention

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    Deep neural networks, including recurrent networks, have been successfully applied to human activity recognition. Unfortunately, the final representation learned by recurrent networks might encode some noise (irrelevant signal components, unimportant sensor modalities, etc.). Besides, it is difficult to interpret the recurrent networks to gain insight into the models' behavior. To address these issues, we propose two attention models for human activity recognition: temporal attention and sensor attention. These two mechanisms adaptively focus on important signals and sensor modalities. To further improve the understandability and mean F1 score, we add continuity constraints, considering that continuous sensor signals are more robust than discrete ones. We evaluate the approaches on three datasets and obtain state-of-the-art results. Furthermore, qualitative analysis shows that the attention learned by the models agree well with human intuition.Comment: 8 pages. published in The International Symposium on Wearable Computers (ISWC) 201

    Reduction of Amine Emissions from an Aqueous Amine Carbon Dioxide Capture System Using Charged Colloidal Gas Aphrons

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    The present invention includes a system and process to reduce amine mist emissions (or MEA) from carbon capture systems using colloidal gas aphrons (CGA), and includes a method for separating and recovering an amine solvent (e.g., in the form of entrained droplets/mist and/or fine aerosol particles) from a carbon dioxide scrubbed flue gas stream exiting a carbon capture system (e.g., oil-fired power plants, coal-fired power plants, and/or natural gas combined cycle plants)
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