Deadlock Avoidance remains to be a significant aspect of deadlock research. Modeling approaches based on Graph Theory provide effective strategy to solve this problem. We built avoidance model of directed graph and adjacent matrix via resource allocation graph, proposed three improved algorithm for deadlock avoidance and discussed the implementation of the program. The topology analyze of matri...

Path following and obstacle avoidances are used in heading control path replanning for unmanned surface vessels (USVs), which have attracted the interest of many researchers over years. This paper investigates path-following avoidance problems USVs. First, on basis current position desired path, an adaptive line-of-sight (ALOS) algorithm is to obtain angle, sideslip angle compensation considere...

In this paper, an automatic pre–crash collision avoidance strategy for cars is presented. It produces a collision avoidance manoeuvre, if feasible, or, otherwise, an emergency braking to reduce the energy at the impact. The manoeuvre is generated relying on the use of sliding mode control. The performances of the proposed collision avoidance system are tested on a scaled radio–controlled car an...

Self-avoidance, the tendency of neurites of the same cell to selectively avoid each other, is a property of both vertebrate and invertebrate neurons. In Drosophila, self-avoidance is mediated by a large family of cell recognition molecules of the immunoglobulin superfamily encoded, via alternative splicing, by the Dscam1 locus. Dscam1 promotes self-avoidance in dendrites, axons, and prospective...

|This paper provides an overview on evolutionary learning methods for the automated design and optimization of fuzzy logic controllers. In a genetic tuning process an evolutionary algorithm adjusts the membership functions or scaling factors of a prede ned fuzzy controller based on a performance index that speci es the desired control behavior. Genetic learning processes are concerned with the ...

This paper provides an overview on evolutionary learning methods for the automated design and optimization of fuzzy logic controllers. In a genetic tuning process an evolutionary algorithm adjusts the membership functions or scaling factors of a predefined fuzzy controller based on a performance index that specifies the desired control behavior. Genetic learning processes are concerned with the...