This paper presents a fuzzy method for the recognition of strings of fuzzy symbols containing substitution, deletion, and insertion errors. As a preliminary step, we propose a fuzzy automaton to calculate a similarity value between strings. The adequate selection of fuzzy operations for computing the transitions of the fuzzy automaton allows to obtain different string similarity definitions (including the Levenshtein distance). A deformed fuzzy automaton based on this fuzzy automaton is then introduced in order to handle strings of fuzzy symbols. The deformed fuzzy automaton enables the classification of such strings having an undetermined number of insertion, deletion and substitution errors. The selection of the parameters determining the deformed fuzzy automaton behavior would allow to implement recognizers adapted to different problems. This paper also presents algorithms that implement the deformed fuzzy automaton. Experimental results show good performance in correcting these kinds of errors.
This work introduces a new distributed history-based algorithm for deadlock detection and resolution under the single-unit request model. The algorithm has a communication cost of Ç´Ò ¡ ÐÓ Òµ messages for a deadlock cycle of Ò processes. This low cost is achieved by means of two mechanisms. On one hand, to reduce the number of instance initializations, a node compares the priorities between its immediate successor and all its predecessors, starting the algorithm only if an antagonistic conflict is given. On the other hand, each instance of the algorithm runs at a certain detection level and does not retransmit probes created by instances running at a lower detection level. An instance can switch to a higher level depending on the information received in the probes.
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