Real Time Autonomous Expert Systems in Network Management

1989 ◽  
Vol 5 (3) ◽  
pp. 205-228 ◽  
Author(s):  
Rodney M. Goodman ◽  
John Miller ◽  
Padhraic Smyth ◽  
Hayes Latin
Keyword(s):  
Expert Systems ◽  
Network Management ◽  
Real Time ◽  
Time Autonomous

The design of an intelligent real‐time autonomous vehicle, TAIWANiTS‐1

2007 ◽  
Vol 30 (5) ◽  
pp. 829-842 ◽  
Author(s):  
Bing‐Fei Wu ◽  
Chao‐Jung Chen ◽  
Hsin‐Han Chiang ◽  
Hsin‐Yuan Peng ◽  
Jau‐Woei Perng ◽  
...  
Keyword(s):  
Real Time ◽  
Autonomous Vehicle ◽  
Time Autonomous

Rule-based expert systems for network management and operations: an introduction

IEEE Network ◽  
1988 ◽  
Vol 2 (5) ◽  
pp. 7-21 ◽  
Author(s):  
R.N. Cronk ◽  
P.H. Callahan ◽  
L. Bernstein
Keyword(s):  
Expert Systems ◽  
Network Management ◽  
Rule Based

scholarly journals Fast Drivable Areas Estimation with Multi-Task Learning for Real-Time Autonomous Driving Assistant

2021 ◽  
Vol 11 (22) ◽  
pp. 10713
Author(s):  
Dong-Gyu Lee
Keyword(s):  
Real Time ◽  
Computational Efficiency ◽  
Autonomous Driving ◽  
Learning Approaches ◽  
Practical Applications ◽  
Task Learning ◽  
Lane Line ◽  
High Level ◽  
Time Autonomous

Autonomous driving is a safety-critical application that requires a high-level understanding of computer vision with real-time inference. In this study, we focus on the computational efficiency of an important factor by improving the running time and performing multiple tasks simultaneously for practical applications. We propose a fast and accurate multi-task learning-based architecture for joint segmentation of drivable area, lane line, and classification of the scene. An encoder-decoder architecture efficiently handles input frames through shared representation. A comprehensive understanding of the driving environment is improved by generalization and regularization from different tasks. The proposed method learns end-to-end through multi-task learning on a very challenging Berkeley Deep Drive dataset and shows its robustness for three tasks in autonomous driving. Experimental results show that the proposed method outperforms other multi-task learning approaches in both speed and accuracy. The computational efficiency of the method was over 93.81 fps at inference, enabling execution in real-time.

Expert Systems Methodology for Real-time Process Control

1987 ◽  
Vol 20 (5) ◽  
pp. 279-286 ◽  
Author(s):  
R.L. Moore ◽  
L.B. Hawkinson ◽  
M. Levin ◽  
A.G. Hofmann ◽  
B.L. Matthews ◽  
...  
Keyword(s):  
Process Control ◽  
Expert Systems ◽  
Real Time ◽  
Time Process ◽  
Systems Methodology

Real-time autonomous multi resolution visual surveys based on seafloor scene complexity

2016 ◽  
Author(s):  
Yuto Otsuki ◽  
Blair Thornton ◽  
Toshihiro Maki ◽  
Yuya Nishida ◽  
Adrian Bodenmann ◽  
...  
Keyword(s):  
Real Time ◽  
Time Autonomous
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