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Learning Search Algorithms: An Educational View

Janota M., Šimák V., Hrbček J.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2014

Vol. 8 no. 4

565--570

Artificial intelligence methods find their practical usage in many applications including maritime industry. The paper concentrates on the methods of uninformed and informed search, potentially usable in solving of complex problems based on the state space representation. The problem of introducing the search algorithms to newcomers has its technical and psychological dimensions. The authors show how it is possible to cope with both of them through design and use of specialized authoring systems. A typical example of searching a path through the maze is used to demonstrate how to test, observe and compare properties of various search strategies. Performance of search methods is evaluated based on the common criteria.

Comparison of different approaches to predict air pollution inside the tunnel tube

Hrbček J., Šimák V., Spalek J., Nemec D.

Archives of Transport System Telematics

2013

Vol. 6, iss. 4

9--13

This paper deals with models of air pollution inside a tunnel tube. Various gases are emitted by combustion engines. They consist largely of oxides of nitrogen, carbon monoxide, steam and particles (opacity). Since each tunnel is unique, the design of the model must be realized for the particular road tunnel. Data measured in the control center of the tunnel is used to create various models and to realize them in the program environment MATLAB. It is possible to describe this system by description of the equations of physical dependences. These physical dependencies are between the speeds, piston effect, traffic density, quantity produced pollution, etc. Next we plan to show how fuzzy sets can be used to represent a real system. Finally, we are going to describe this system by the parametric model. For the purpose of parametric identification it is interesting to describe the sought process using input-output relations. The general procedure for estimation of the process model consists of several steps: determination of the model structure, estimation of parameters and verification of the model.