A spatially constrained clustering program for river valley segment delineation from GIS digital river networks

2008 ◽  
Vol 23 (5) ◽  
pp. 638-649 ◽  
Author(s):  
T.O. Brenden ◽  
L. Wang ◽  
P.W. Seelbach ◽  
R.D. Clark ◽  
M.J. Wiley ◽  
...  
Keyword(s):  
River Valley ◽  
River Networks ◽  
Constrained Clustering ◽  
Valley Segment ◽  
Spatially Constrained Clustering

Optimal distributed interconnectivity of multi-robot systems by spatially-constrained clustering

2017 ◽  
Vol 25 (2) ◽  
pp. 96-113 ◽  
Author(s):  
Matin Macktoobian ◽  
Mahdi Aliyari Sh
Keyword(s):  
Clustering Algorithm ◽  
Task Assignment ◽  
Constrained Clustering ◽  
Loosely Coupled ◽  
Robot Systems ◽  
Multi Agent ◽  
Probabilistic Proof ◽  
Data Passing ◽  
Spatially Constrained Clustering ◽  
Multi Robot

A spatially-constrained clustering algorithm is presented in this paper. This algorithm is a distributed clustering approach to fine-tune the optimal distances between agents of the system to strengthen the data passing among them using a set of spatial constraints. In fact, this method will increase interconnectivity among agents and clusters, leading to improvement of the overall communicative functionality of the multi-robot system. This strategy will lead to the establishment of loosely-coupled connections among the clusters. These implicit interconnections will mobilize the clusters to receive and transmit information within the multi-agent system. In other words, this algorithm classifies each agent into the clusters with the lowest cost of local communication with its peers. This research demonstrates that the presented decentralized method will actually boost the communicative agility of the swarm by probabilistic proof of the acquired optimality. Hence, the common assumption regarding the full-knowledge of the agents’ primary locations has been fully relaxed compared to former methods. Consequently, the algorithm’s reliability and efficiency is confirmed. Furthermore, the method’s efficacy in passing information will improve the functionality of higher-level swarm operations, such as task assignment and swarm flocking. Analytical investigations and simulated accomplishments, corresponding to highly-populated swarms, prove the claimed efficiency and coherence.

scholarly journals A River Valley Segment Classification of Michigan Streams Based on Fish and Physical Attributes

2008 ◽  
Vol 137 (6) ◽  
pp. 1621-1636 ◽  
Author(s):  
Travis O. Brenden ◽  
Lizhu Wang ◽  
Paul W. Seelbach
Keyword(s):  
River Valley ◽  
Physical Attributes ◽  
Valley Segment

scholarly journals Spatially constrained clustering of ecological networks

2014 ◽  
Vol 5 (8) ◽  
pp. 771-779 ◽  
Author(s):  
Vincent Miele ◽  
Franck Picard ◽  
Stéphane Dray
Keyword(s):  
Ecological Networks ◽  
Constrained Clustering ◽  
Spatially Constrained Clustering
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