Modeling urban growth by cellular automata
International audience; The structural development of human settlements can be characterized as a complex highly feedbacketed process. The assumption that this process is governed by rather few fundamental laws stimulated a considerable research in the field of urban growth during the last decade. Aiming at the comprehension of the basic underlying dynamics different approaches from the field of self-organizing systems have been proposed. In this paper we present a "cellular model" of urban growth dynamics based on the work of White, Engelen and Uljee. As a starting point this model throws some light on the mechanism of urban growth. But even more important, it raises a lot of questions con…
Condition classification of fibre ropes during cyclic bend over sheave testing using machine learning
Fibre ropes have been shown to be a viable alternative to steel wire rope for offshore lifting operations. Visual inspection remains a common method of fibre rope condition monitoring and has the potential to be further automated by machine learning. This would provide a valuable aid to current inspection frameworks to make more accurate decisions on recertification or retirement of fibre ropes in operational use. Three different machine learning algorithms: decision tree, random forest and support vector machine are compared to classical statistical approaches such as logistic regression, k-nearest neighbours and Naïve-Bayes for condition classification for fibre ropes under cyclic-bend-ov…