scholarly journals Animation of Flocks Flying in Line Formations

2011 ◽  
Vol 18 (1) ◽  
pp. 91-105 ◽  
Author(s):  
Marina Klotsman ◽  
Ayellet Tal
Keyword(s):  
Energy Savings ◽  
Data Driven ◽  
Rule Based ◽  
Data Driven Approach ◽  
Initiation Stage

We provide a biologically motivated technique for modeling and animating bird flocks in flight, which produces plausible and realistic-looking flock animations. While most previous approaches have focused on animating cluster formations, this article introduces a technique for animating flocks that fly in certain patterns, so-called line formations. We distinguish between the behavior of such flocks during initiation and their behavior during steady flight. Our simulation of the initiation stage is rule-based and incorporates an artificial bird model. Our simulation of the steady-flight stage combines a data-driven approach and an energy-savings model.

scholarly journals Reducing the Energy Budget in WSN Using Time Series Models

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Felicia Engmann ◽  
Ferdinand Apietu Katsriku ◽  
Jamal-Deen Abdulai ◽  
Kofi Sarpong Adu-Manu
Keyword(s):  
Data Transmission ◽  
Energy Savings ◽  
Data Driven ◽  
Wireless Sensor ◽  
Time Series Models ◽  
Duty Cycling ◽  
Data Driven Approach ◽  
Save Energy ◽  
The Cost ◽  
Prediction Approach

Energy conservation is critical in the design of wireless sensor networks since it determines its lifetime. Reducing the frequency of transmission is one way of reducing the cost, but it must not tamper with the reliability of the data received at the sink. In this paper, duty cycling and data-driven approaches have been used together to influence the prediction approach used in reducing data transmission. While duty cycling ensures nodes that are inactive for longer periods to save energy, the data-driven approach ensures features of the data that are used in predicting the data that the network needs during such inactive periods. Using the grey series model, a modified rolling GM(1,1) is proposed to improve the prediction accuracy of the model. Simulations suggest a 150% energy savings while not compromising on the reliability of the data received.

Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

2012 ◽  
Author(s):  
Michael Ghil ◽  
Mickael D. Chekroun ◽  
Dmitri Kondrashov ◽  
Michael K. Tippett ◽  
Andrew Robertson ◽  
...  
Keyword(s):  
Dynamic Models ◽  
Data Driven ◽  
Stochastic Dynamic ◽  
Extended Range ◽  
Data Driven Approach ◽  
Low Dimensional
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