scholarly journals Detecting Braess Routes: An Algorithm Accounting for Queuing Delays With an Extended Graph

Author(s):  
Mikhail Burov ◽  
Can Kizilkale ◽  
Alexander Kurzhanskiy ◽  
Murat Arcak
Keyword(s):  
2015 ◽  
Vol 193 ◽  
pp. 61-79 ◽  
Author(s):  
Ergun Akleman ◽  
Jianer Chen ◽  
Jonathan L. Gross

2021 ◽  
Author(s):  
Ali Mortazavi ◽  
Bakytzhan Osserbay

Abstract The stability graph method of stope design is one of the most widely used methods of stability assessments of stopes in underground polymetallic mines. The primary objective of this work is to introduce a new stability chart, which includes all relevant case histories, and to exclude parameters with uncertainties in the determination of stability number. The modified stability number was used to achieve this goal, and the Extended Mathews database was recalculated and compared with the new stability graph. In this study, a new refined Consolidated stability graph was developed by excluding the entry mining methods data from the Extended graph data, and only the non-entry methods data was used. The applicability of the proposed Consolidated stability chart was demonstrated by an open stope example. The stability for each stope surface was evaluated by a probabilistic approach employing a logistic regression model and the developed Consolidated stability chart. Comparing the stability analysis results with that of other published works of the same example shows that the determined Consolidated chart, in which the entry-method data is excluded, produces a more conservative and safer design. In conclusion, the size and quality of the dataset dictate the reliability of this approach.


Author(s):  
Ding Li ◽  
Scott Dick

AbstractGraph-based algorithms are known to be effective approaches to semi-supervised learning. However, there has been relatively little work on extending these algorithms to the multi-label classification case. We derive an extension of the Manifold Regularization algorithm to multi-label classification, which is significantly simpler than the general Vector Manifold Regularization approach. We then augment our algorithm with a weighting strategy to allow differential influence on a model between instances having ground-truth vs. induced labels. Experiments on four benchmark multi-label data sets show that the resulting algorithm performs better overall compared to the existing semi-supervised multi-label classification algorithms at various levels of label sparsity. Comparisons with state-of-the-art supervised multi-label approaches (which of course are fully labeled) also show that our algorithm outperforms all of them even with a substantial number of unlabeled examples.


2002 ◽  
Vol 18 (Suppl 2) ◽  
pp. S182-S191 ◽  
Author(s):  
P. Pipenbacher ◽  
A. Schliep ◽  
S. Schneckener ◽  
A. Schonhuth ◽  
D. Schomburg ◽  
...  

Author(s):  
Timo Krotzky ◽  
Thomas Fober ◽  
Eyke Hullermeier ◽  
Gerhard Klebe

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