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Studying of operation balance in single-phase induction motors is an issue of interest due to the need for reducing the power consumption and increasing the motors’ life. The paper focuses on improving the motor performance by balancing the stator phase operation for the most common-used connection diagrams of single-phase capacitor-run induction motors (SPCRIMs) and three-phase induction motors (TPIMs) operating from single-phase supply (SPS). Therefore, a mathematical model is used to balance the motor operation by varying the frequency supply voltage. Characteristics of balancing parameters are investigated, various methods of motor balancing are presented and comparisons were done among these balancing methods.
We consider n observations from the GARCH-type model: Z = UY, where U and Y are independent random variables. We aim to estimate density function Y where Y have a weighted distribution. We determine a sharp upper bound of the associated mean integrated square error. We also make use of the measure of expected true evidence, so as to determine when model leads to a crisis and causes data to be lost.
Automatic text summarization involves reducing a text document or a
larger corpus of multiple documents to a short set of sentences or paragraphs that
convey the main meaning of the text. In this paper, we discuss about
multi-document summarization that differs from the single one in which the
issues of compression, speed, redundancy and passage selection are critical in
the formation of useful summaries. Since the number and variety of online
medical news make them difficult for experts in the medical field to
read all of the medical news, an automatic multi-document summarization can be
useful for easy study of information on the web. Hence we propose a new approach
based on machine learning meta-learner algorithm called AdaBoost that is used
for summarization. We treat a document as a set of sentences, and the
learning algorithm must learn to classify as positive or negative examples of
sentences based on the score of the sentences. For this learning task, we
apply AdaBoost meta-learning algorithm where a C4.5 decision tree has been
chosen as the base learner. In our experiment, we use
In this paper, homotopy analysis method (HAM) and Padé approximant will be considered for finding analytical solution of three-dimensional viscous flow near an infinite rotating disk which is a well-known classical problem in fluid mechanics. The solution is compared to the numerical (fourth-order Runge-Kutta) solution and the convergence of the obtained series solution is carefully analyzed. The results illustrate that HAM-Padé is an appropriate method in solving the systems of nonlinear equations.