Image recognition, learning, and control in a cellular automata network

1991 ◽  
Author(s):  
Raghu Raghavan
Keyword(s):  
Cellular Automata ◽  
Image Recognition ◽  
Automata Network ◽  
And Control ◽  
Recognition Learning

Artificial Intelligence Applied to Natural Resources Management

2012 ◽  
pp. 1566-1582 ◽  
Author(s):  
Diana F. Adamatti ◽  
Marilton S. de Aguiar
Keyword(s):  
Artificial Intelligence ◽  
Data Analysis ◽  
Cellular Automata ◽  
Natural Resources ◽  
Natural Resources Management ◽  
Multi Agent Systems ◽  
Resources Management ◽  
Optimization And Control ◽  
Multi Agent ◽  
And Control

There are three computational challenges in natural resources management: data management and communication; data analysis; and optimization and control. The authors believe these three challenges can be dealt with Artificial Intelligence (AI) techniques, because they can manage dynamic activities in natural resources. There are several AI techniques such as Genetic Algorithms, Neural Networks, Multi-Agent Systems or Cellular Automata. In this chapter, the authors introduce some applications of Cellular Automata (CA) and Multi-Agent-Based Simulation (MABS) in natural resources management, because these are areas that the authors approach in their research and these areas can contribute to solve the three computational challenges. Specifically, the CA technique can face the challenge of data analysis because it can be extrapolated and new knowledge will be acquired from an area not known or experienced. Regarding the MABS technique, it can solve the challenge of optimization and control, because it works in an empiric way during the decision-making process, based on experiments and observations.

scholarly journals Motion Control System of Unmanned Railcars Based on Image Recognition

2019 ◽  
Vol 2 (1) ◽  
pp. 9
Author(s):  
Yuan-Wei Tseng ◽  
Tsung-Wui Hung ◽  
Chung-Long Pan ◽  
Rong-Ching Wu
Keyword(s):  
Computer Vision ◽  
Control System ◽  
Motion Control ◽  
Image Recognition ◽  
Electromagnetic Interference ◽  
Integrated System ◽  
Radio Communication ◽  
Motion Control System ◽  
Computer Vision Technology ◽  
And Control

The main purpose of this paper is to construct an autopilot system for unmanned railcars based on computer vision technology in a fixed luminous environment. Four graphic predefined signs of different colors and shapes serve as motion commands of acceleration, deceleration, reverse and stop for the motion control system of railcars based on image recognition. The predefined signs’ strong classifiers were trained based on Haar-like feature training and AdaBoosting from Open Source Computer Vision Library (OpenCV). Comprehensive system integrations such as hardware, device drives, protocols, an application program in Python and man machine interface have been properly done. The objectives of this research include: (1) Verifying the feasibility of graphic predefined signs serving as commands of a motion control system of railcars with computer vision through experiments; (2) Providing reliable solutions for motion control of unmanned railcars, based on image recognition at affordable cost. The experiment results successfully verify the proposed methodology and integrated system. In the main program, every predefined sign must be detected at least three times in consecutive images within 0.2 s before the system confirms the detection. This digital filter like feature can filter out false detections and make the correct rate of detections close to 100%. After detecting a predefined sign, it was observed that the system could generate new motion commands to drive the railcars within 0.3 s. Therefore, both real time performance and the precision of the system are good. Since the sensing and control devices of the proposed system consist of computer, camera and predefined signs only, both the implementation and maintenance costs are very low. In addition, the proposed system is immune to electromagnetic interference, so it is ideal to merge into popular radio Communication Based Train Control (CBTC) systems in railways to improve the safety of operations.

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