A Directed Graph Model of Ecological Alcohol Systems Incorporating Spatiotemporal Effects

COMPSTAT 2008 ◽  
2008 ◽  
pp. 179-190
Author(s):  
Edward J. Wegman ◽  
Yasmin H. Said
Keyword(s):  
Directed Graph ◽  
Graph Model

scholarly journals A Graph-Based Optimal On-Ramp Merging of Connected Vehicles on the Highway

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 290
Author(s):  
Yanjun Shi ◽  
Zhiheng Yuan ◽  
Hao Yu ◽  
Yijia Guo ◽  
Yuhan Qi
Keyword(s):  
Fuel Consumption ◽  
Directed Graph ◽  
Shortest Path ◽  
Graph Model ◽  
Connected Vehicles ◽  
Shortest Path Algorithm ◽  
Promising Alternative ◽  
Traffic Efficiency ◽  
Total Fuel Consumption ◽  
Merge Point

Connected and automated vehicles (CAVs) are a very promising alternative for reducing fuel consumption and improving traffic efficiency when vehicles merge at on-ramps. In this study, we propose a graph-based method to coordinate CAVs to merge at the highway ramp. First, the optimized vehicles were divided into groups to pass the merging point. Then we built a directed graph model for each group of vehicles, where each path of the graph corresponds to one of all possible merging sequences. The improved shortest path algorithm is proposed to find the optimal merging sequence for minimizing total fuel consumption. The results of the simulation showed that the proposed graph-based method reduced fuel consumption and ensured high traffic efficiency; moreover, the vehicles can form a platoon after passing the merge point.

Research on the Process of Conceptual Design for Hoisting Machinery Based on Directed Graph Model

2013 ◽  
Vol 706-708 ◽  
pp. 2114-2118
Author(s):  
Bi Qiong Li ◽  
Zhi Tao Hu ◽  
Ying Liao
Keyword(s):  
Conceptual Design ◽  
Directed Graph ◽  
Graph Model ◽  
Mapping Method ◽  
Design Knowledge ◽  
Prototype System ◽  
Crucial Part

The schemes generating process of conceptual design is the most crucial part of the process of conceptual design, it is better for understanding the designer's intent process, transferring and accumulating design knowledge with this process, in order to analyze the schemes generating process of conceptual design effectively, this paper takes conceptual design for crane hoisting mechanism for an example, which describes the schemes generating process by using zigzag mapping method and proposes the principle of conceptual design based on directed graph model ,with using metamodeling technology, directed graph model can be applied to prototype system developed by GME, this prototype system provides a simple tools for the process of conceptual design and records the schemes generating process and relevant design knowledge. The zigzag mapping method and GME tools is used in the process of conceptual design for crane hoisting mechanism in this paper, which verifies effectiveness of the zigzag mapping method and GME tools, and has a certain practical value.

scholarly journals Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

2020 ◽  
Vol 34 (01) ◽  
pp. 549-556 ◽  
Author(s):  
Tian Bian ◽  
Xi Xiao ◽  
Tingyang Xu ◽  
Peilin Zhao ◽  
Wenbing Huang ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Directed Graph ◽  
Graph Model ◽  
Top Down ◽  
Learning Methods ◽  
Convolutional Networks ◽  
Rumor Propagation ◽  
New Information ◽  
Rumor Detection

Social media has been developing rapidly in public due to its nature of spreading new information, which leads to rumors being circulated. Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge. Therefore, some deep learning methods are applied to discover rumors through the way they spread, such as Recursive Neural Network (RvNN) and so on. However, these deep learning methods only take into account the patterns of deep propagation but ignore the structures of wide dispersion in rumor detection. Actually, propagation and dispersion are two crucial characteristics of rumors. In this paper, we propose a novel bi-directional graph model, named Bi-Directional Graph Convolutional Networks (Bi-GCN), to explore both characteristics by operating on both top-down and bottom-up propagation of rumors. It leverages a GCN with a top-down directed graph of rumor spreading to learn the patterns of rumor propagation; and a GCN with an opposite directed graph of rumor diffusion to capture the structures of rumor dispersion. Moreover, the information from source post is involved in each layer of GCN to enhance the influences from the roots of rumors. Encouraging empirical results on several benchmarks confirm the superiority of the proposed method over the state-of-the-art approaches.

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