Open AccessEstimation of Power Spectral Density in SVPWM based Induction Motor Drives
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i1123.0789s219
Subject(s) - telugu , computer science , word (group theory) , consonant , speech recognition , pulse width modulation , set (abstract data type) , power (physics) , artificial intelligence , natural language processing , mathematics , physics , geometry , vowel , quantum mechanics , programming language
This paper is readied the product programming of the SVPWM and half of breed PWM basically based DTC of recognition engine manipulate for assessing the strength Spectral Density (PSD) and the overall consonant mutilation (THD) of the road flows. The PWM set of guidelines utilizes three beautiful PWM methodologies like traditional SVPWM, AZPWM3 and combination PWM for the evaluation of the vitality spectra and consonant spectra. In quality spectra appraisal the extents of the power accrued at express frequencies and inside the consonant spectra the problem band sizes at one among a type replacing frequencies are taken into consideration for the assessment. To confirm the PWM calculations, numerical activity is performed making use of MATLAB/simulink Telugu (తెలుగు) is one of the Dravidian languages which is morphologically rich. As in the other languages it too contains polysemous words which have different meanings in different contexts. There are several language models exist to solve the word sense disambiguation problem with respect to each language like English, Chinese, Hindi and Kannada etc. The proposed method gives a solution for the word sense disambiguation problem with the help of ngram technique which has given good results in many other languages. The methodology mentioned in this paper finds the co-occurrence words of target polysemous word and we call them as n-grams. A Telugu corpus sent as input for training phase to find n-gram joint probabilities. By considering these joint probabilities the target polysemous word will be assigned a correct sense in testing phase. We evaluate the proposed method on some polysemous Telugu nouns and verbs. The methodology proposed gives the F-measure 0.94 when tested on Telugu corpus collected from CIIL, various news papers and story books.The present methodology can give better results with increase in size of training corpus and in future we plan to evaluate it on all words not only nouns and verbs