Expanding License Plate Matching Capabilities with Secondary Self-Learning Algorithm

2016 ◽  
Vol 2594 (1) ◽  
pp. 51-60
Author(s):  
Stephanie R. Hargrove ◽  
Hyeonsup Lim ◽  
Lee D. Han
Keyword(s):  
Learning Algorithm ◽  
License Plate ◽  
Self Learning

An Online Self-Learning Algorithm for License Plate Matching

2013 ◽  
Vol 14 (4) ◽  
pp. 1806-1816 ◽  
Author(s):  
Francisco Moraes Oliveira-Neto ◽  
Lee D. Han ◽  
Myong Kee Jeong
Keyword(s):  
Learning Algorithm ◽  
License Plate ◽  
Self Learning

Operon Prediction Based On an Iterative Self-learning Algorithm*

2011 ◽  
Vol 38 (7) ◽  
pp. 642-651
Author(s):  
Wen-Qi Wu ◽  
Xiao-Bin ZHENG ◽  
Yong-Chu LIU ◽  
Kai TANG ◽  
Huai-Qiu ZHU
Keyword(s):  
Learning Algorithm ◽  
Operon Prediction ◽  
Self Learning

A Robotic Driver on Roller Dynamometer with Vehicle Performance Self Learning Algorithm

1991 ◽  
Author(s):  
Akinobu Moriyama ◽  
Isao Murase ◽  
Akira Shimozono ◽  
Tohru Takeuchi
Keyword(s):  
Learning Algorithm ◽  
Vehicle Performance ◽  
Self Learning

scholarly journals Reinforcement learning-based link adaptation in long delayed underwater acoustic channel

2019 ◽  
Vol 283 ◽  
pp. 07001 ◽  
Author(s):  
Jingxi Wang ◽  
Chau Yuen ◽  
Yong Liang Guan ◽  
Fengxiang Ge
Keyword(s):  
Reinforcement Learning ◽  
Learning Strategy ◽  
Learning Algorithm ◽  
Optimal Parameters ◽  
Underwater Acoustic Channel ◽  
Transfer Data ◽  
Data Packets ◽  
Significant Area ◽  
Channel Environment ◽  
Self Learning

In this paper, we apply reinforcement learning, a significant area of machine learning, to formulate an optimal self-learning strategy to interact in an unknown and dynamically variable underwater channel. The dynamic and volatile nature of the underwater channel environment makes it impossible to employ pre-knowledge. In order to select the optimal parameters to transfer data packets, by using reinforcement learning, this problem could be resolved, and better throughput could be achieved without any environmental pre-information. The slow sound velocity in an underwater scenario, means that the delay of transmitting packet acknowledgement back to sender from the receiver is material, deteriorating the convergence speed of the reinforcement learning algorithm. As reinforcement learning requires a timely acknowledgement feedback from the receiver, in this paper, we combine a juggling-like ARQ (Automatic Repeat Request) mechanism with reinforcement learning to minimize the long-delayed reward feedback problem. The simulation is accomplished by OPNET.

Sign in / Sign up

Export Citation Format

Share Document