A GMPLS-based traffic engineering weighting scheme for reducing the routing overheads

2011 ◽  
Author(s):  
Shengwei Meng ◽  
Min Zhang ◽  
Lifang Zhang ◽  
Yongli Zhao ◽  
Hongxiang Wang ◽  
...  
Keyword(s):  
Traffic Engineering ◽  
Weighting Scheme

INTRODUCTION TO RISK AND SAFETY OF AIR NAVIGATION

2020 ◽  
Author(s):  
Fedja Netjasov
Keyword(s):  
Doctoral Students ◽  
Traffic Engineering ◽  
International Standards ◽  
Air Transport ◽  
Civil Aviation ◽  
Undergraduate Studies ◽  
Recommended Practices ◽  
The University ◽  
Navigation Safety

"Introduction to Risk and Safety of Air Navigation" is an authorized script compiled on the basis of the curriculum of the course "Introduction to Risk and Safety of Air Navigation" which is taught in undergraduate studies at the University of Belgrade - Faculty of Transport and Traffic Engineering. The scripts are primarily intended for students of undergraduate (bachelor) studies at the Department of Air Transport and Traffic at the University of Belgrade - Faculty of Transport and Traffic Engineering. Scripts can be useful to both master's and doctoral students at the University of Belgrade - Faculty of Transport and Traffic Engineering, especially those who have not completed undergraduate studies at the Department of Air Transport and Traffic. They can also be useful to air transport and aeronautical engineers in order to expand and update knowledge in the field of air navigation safety. The material presented in these scripts relates mainly to civil aviation and is largely based on international standards, recommended practices, regulations and documents which deal with issues related to air navigation safety. As these standards, regulations and documents are subject to frequent changes and alterations, users of these scripts are advised to also use the original (updated) documents, which are listed in the references, in order to take into account any changes that have occurred after the release of the scripts.

Knowledge Transfer with Weighted Adversarial Network for Cold-Start Store Site Recommendation

2021 ◽  
Vol 15 (3) ◽  
pp. 1-27
Author(s):  
Yan Liu ◽  
Bin Guo ◽  
Daqing Zhang ◽  
Djamal Zeghlache ◽  
Jingmin Chen ◽  
...  
Keyword(s):  
Large Scale ◽  
Cold Start ◽  
Weighting Scheme ◽  
Training Data ◽  
Chain Store ◽  
Useful Knowledge ◽  
Adversarial Network ◽  
Brick And Mortar ◽  
New City ◽  
Learning Machine

Store site recommendation aims to predict the value of the store at candidate locations and then recommend the optimal location to the company for placing a new brick-and-mortar store. Most existing studies focus on learning machine learning or deep learning models based on large-scale training data of existing chain stores in the same city. However, the expansion of chain enterprises in new cities suffers from data scarcity issues, and these models do not work in the new city where no chain store has been placed (i.e., cold-start problem). In this article, we propose a unified approach for cold-start store site recommendation, Weighted Adversarial Network with Transferability weighting scheme (WANT), to transfer knowledge learned from a data-rich source city to a target city with no labeled data. In particular, to promote positive transfer, we develop a discriminator to diminish distribution discrepancy between source city and target city with different data distributions, which plays the minimax game with the feature extractor to learn transferable representations across cities by adversarial learning. In addition, to further reduce the risk of negative transfer, we design a transferability weighting scheme to quantify the transferability of examples in source city and reweight the contribution of relevant source examples to transfer useful knowledge. We validate WANT using a real-world dataset, and experimental results demonstrate the effectiveness of our proposed model over several state-of-the-art baseline models.

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