Derivative functions in specific with complex module gives the development of induction motor at start position. in describing the torque of induction motor at position 1,2and 3 a vector control with exponential systems in liouvilles principle gives equality of defining stages in elementary calculation carrying start torque with function of z is derived with positive nth root convection which required the exp(z) as given in Euler's method. Conversation of singularities from 2 and 3 stages complex powers with non-zero sections in logarithmic properties of poles and zeros. initiation of logarithmic complex numbers with function accordance in formulating increases poles and zeros version at tst and tmax in induction motor by carrying slip range to running mode. Considering liouvilles vector a mathematical solution is derived with geometrical structures. A wavelet function of trapezoidal, sinusoidal is calculated in running condition along with stand still position of induction motor. Variable frequency of drives in exponentials with algebraic gives 20% of energy saving in semiconductor drives at industry applications .
Frequent itemsets mining with differential privacy refers to the problem of mining all frequent itemsets whose supports are above a given threshold in a given transactional dataset, with the constraint that the mined results should not break the privacy of any single transaction. Current solutions for this problem cannot well balance efficiency, privacy, and data utility over large-scale data. Toward this end, we propose an efficient, differential private frequent itemsets mining algorithm over large-scale data. Based on the ideas of sampling and transaction truncation using length constraints, our algorithm reduces the computation intensity, reduces mining sensitivity, and thus improves data utility given a fixed privacy budget. Experimental results show that our algorithm achieves better performance than prior approaches on multiple datasets. INDEX TERMS Frequent itemsets mining, differential privacy, sampling, transaction truncation, string matching.
Transmission line is a main portion of power system owing to its capacity of increasing power in a power grid. Nonetheless, due to increasing complexity, faulty detection in power line has been always a potential issue. Parallel incomplete journey transmission lines (PIJTL) frequently subject a variety of technical issues in the view of power system protection. This study presents artificial neural networks (ANN) based inter circuit fault classification of PIJTL using MATLAB Software. Although different approaches have been addressed for ordinary shunt faults in PIJTL, nonetheless, determining the inter circuit faults in PIJTL hasn't been focused so far. When fault occurs in the PIJTL current waveforms are distorted due to transients and its pattern changes according to the fault type in the line. The ANN approach finds the inter circuit faults by means of currents. ANN takes a reduced set of feature inputs, i.e., the fundamental components of six phase currents of the two parallel lines at source of parallel incomplete journey only. The result performed that proposed ANN is capability of right tripping action then type of fault at high speed as a result can be applied in practical application. The main feature of ANN is that it acceptably estimates finds the inter circuit faults and also ordinary shunt faults, thus making it more accurate and reliable when compared to other approaches. Several fault case studies have conformed the effectiveness of ANN technique. Further, fuzzy based inter circuit fault locator and classifier for PIJTL we can design.
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