Synchronous machine is an AC rotating machine whose speed under steady state condition is proportional to the frequency of the current in its armature. This paper presents an efficient neuro-fuzzy control of synchronous generator. It has been compared with fuzzy control and multilayer control. As evinced through the simulation results, neuro-fuzzy and type-2 are comparatively superior over the ...

In this paper, we study N-policy for a finite population Bernoulli feedback queueing model for machine repair problem with degraded failure. The running times of the machines between breakdowns have an exponential distribution. The repair times of the machines are independent and identically distributed random variables. If at any time a machine fails, it is sent to the repairman for repairing,...

This paper presents an on-line fuzzy tuning scheme for indirect field-orientation (IFO)-controlled induction machine drives. A fuzzy controller is used to regulate the speed, and another two fuzzy compensators are combined to correct detuning of field orientation. Since detuning effects of the IFO induction machine drive is minimized by the new fuzzy control scheme, the induction machine can ac...

While testing based on finite state machines has been extensively investigated in the literature, issues concerning testing based on extended finite state machines (EFSMs) have not been adequately addressed. Such a testing is complicated due to the fact that a transition in an EFSM transforms a large (sometime infinite) set of data in contrast to just one value in a finite state machine. Moreov...

Automata learning is a known technique to infer a finite state machine from a set of observations. In this paper, we revisit Angluin’s original algorithm from a categorical perspective. This abstract view on the main ingredients of the algorithm lays a uniform framework to derive algorithms for other types of automata. We show a straightforward generalization to Moore and Mealy machines, which ...

Phrasal segmentation models define a mapping from the words of a sentence to sequences of translatable phrases. We discuss the estimation of these models from large quantities of monolingual training text and describe their realization as weighted finite state transducers for incorporation into phrase-based statistical machine translation systems. Results are reported on the NIST Arabic-English...

This paper presents an efficient implementation of linearised lattice minimum Bayes-risk decoding using weighted finite state transducers. We introduce transducers to efficiently count lattice paths containing n-grams and use these to gather the required statistics. We show that these procedures can be implemented exactly through simple transformations of word sequences to sequences of n-grams....

We present our work on using statistical, corpusbased machine learning techniques to perform instantiated goal recognition — recognition of a goal schema and its parameter values. The recognizer is fast (linear in the number of goal schemas and observed actions) and is able to make partial predictions by optionally predicting individual parameter values for a goal schema. This allows it to make...