• Title/Summary/Keyword: Function Representation

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The Analysis and Simulation of Current Source Inverter-Induction Motor System Using Switching Function Representation (스위칭함수에 의한 전류형 인버어터 유도전동기 시스템의 해석 및 시뮬레이션)

  • 박민호;전태원;정승기
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.35 no.5
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    • pp.185-192
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    • 1986
  • This paper deals with the general mathmatical model of the CSI-fed induction motor system. The inverter is represented by a switching function matrix which is combined with the dg model of an induction motor. Complete system model is given by a set of differential equations in a closed form, which inherently includes the harmonic effects of the converter and the inverter. The model is valid in evaluating both the transient and steady state responses, while the more simple analytical model can be derived from the general model, in a steady state. With the model proposed, the digital simulation is carried out on a 10KVA CSI-induction motor system and the simulation results show good agreements with experimental ones.

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Neural Networks Based Modeling with Adaptive Selection of Hidden Layer's Node for Path Loss Model

  • Kang, Chang Ho;Cho, Seong Yun
    • Journal of Positioning, Navigation, and Timing
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    • v.8 no.4
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    • pp.193-200
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    • 2019
  • The auto-encoder network which is a good candidate to handle the modeling of the signal strength attenuation is designed for denoising and compensating the distortion of the received data. It provides a non-linear mapping function by iteratively learning the encoder and the decoder. The encoder is the non-linear mapping function, and the decoder demands accurate data reconstruction from the representation generated by the encoder. In addition, the adaptive network width which supports the automatic generation of new hidden nodes and pruning of inconsequential nodes is also implemented in the proposed algorithm for increasing the efficiency of the algorithm. Simulation results show that the proposed method can improve the neural network training surface to achieve the highest possible accuracy of the signal modeling compared with the conventional modeling method.

A Simultaneous Design of TSK - Linguistic Fuzzy Models with Uncertain Fuzzy Output

  • Kwak, Keun-Chang;Kim, Dong-Hwa
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.427-432
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    • 2005
  • This paper is concerned with a simultaneous design of TSK (Takagi-Sugeno-Kang)-linguistic fuzzy models with uncertain model output and the computationally efficient representation. For this purpose, we use the fundamental idea of linguistic models introduced by Pedrycz and develop their comprehensive design framework. The design process consists of several main phases such as (a) the automatic generation of the linguistic contexts by probabilistic distribution using CDF (conditional density function) and PDF (probability density function) (b) performing context-based fuzzy clustering preserving homogeneity based on the concept of fuzzy granulation (c) augment of bias term to compensate bias error (d) combination of TSK and linguistic context in the consequent part. Finally, we contrast the performance of the enhanced models with other fuzzy models for automobile MPG predication data and coagulant dosing process in a water purification plant.

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Power Exponential Distributions

  • Zheng, Shimin;Bae, Sejong;Bartolucci, Alfred A.;Singh, Karan P.
    • International Journal of Reliability and Applications
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    • v.4 no.3
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    • pp.97-111
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    • 2003
  • By applying Theorem 2.6.4 (Fang and Zhang, 1990, p.66) the dispersion matrix of a multivariate power exponential (MPE) distribution is derived. It is shown that the MPE and the gamma distributions are related and thus the MPE and chi-square distributions are related. By extending Fang and Xu's Theorem (1987) from the normal distribution to the Univariate Power Exponential (UPE) distribution an explicit expression is derived for calculating the probability of an UPE random variable over an interval. A representation of the characteristic function (c.f.) for an UPE distribution is given. Based on the MPE distribution the probability density functions of the generalized non-central chi-square, the generalized non-central t, and the generalized non-central F distributions are derived.

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The Performance Comparison for the Contention Resolution Policies of the Input-buffered Crosspoint Packet Switch

  • Paik, Jung-Hoon;Lim, Chae-Tak
    • Journal of Electrical Engineering and information Science
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    • v.3 no.1
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    • pp.28-35
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    • 1998
  • In this paper, an NxN input-buffered crosspoint packet switch which selects a Head of the Line, HOL, packet in contention randomly is analyzed with a new approach. The approach is based on both a Markov chain representation of the input buffer and the probability that a HOL packet is successfully served. The probability as a function of N is derived, and it makes it possible to express the average packet delay and the average number of packets in the buffer as a function of N. The contention resolution policy based on the occupancy of the input buffer is also presented and analyzed with this same approach and the relationship between two selection policies is analyzed in terms of the occupancy of the input buffer.

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Design of Genetic Algorithms-based Fuzzy Polynomial Neural Networks Using Symbolic Encoding (기호 코딩을 이용한 유전자 알고리즘 기반 퍼지 다항식 뉴럴네트워크의 설계)

  • Lee, In-Tae;Oh, Sung-Kwun;Choi, Jeoung-Nae
    • Proceedings of the KIEE Conference
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    • 2006.04a
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    • pp.270-272
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    • 2006
  • In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs) using symbolic coding for non-linear data. One of the major subject of genetic algorithms is representation of chromosomes. The proposed model optimized by the means genetic algorithms which used symbolic code to represent chromosomes. The proposed gFPNN used a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

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Characteristic Analysis of Single Phase SRM Using Fourier Series (퓨리에 급수를 이용한 단상 SRM 특성 해석)

  • Lee, Jong-Han;Lee, Eun-Woong;Kim, Yong-Heon
    • Proceedings of the KIEE Conference
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    • 2004.04a
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    • pp.80-82
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    • 2004
  • The single phase switched reluctance motor(SRM) is more simpler and robust in the structure than poly phase SRM. It has the merits that it reduces the switching elements and the energy density per volume is very high. Recently, it has been researched and developed in various types and starting method due to the technique of power electronics and the computer added design. This paper presents a analytical representation of the phase inductance of a single phase SRM, as function of position and current, taking into account the non-linearity of the magnetic circuit. the method is based on Fourier series expansion. Analytical expressions for the calculation of instantaneous phase inductance, flux linkage, coenergy and electromagnetic torque as a function of rotor position and winding currents are derived.

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Numerical Comparisons for the Null Distribution of the Bagai Statistic

  • Ha, Hyung-Tae
    • Communications for Statistical Applications and Methods
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    • v.19 no.2
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    • pp.267-276
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    • 2012
  • Bagai et al. (1989) proposed a distribution-free test for stochastic ordering in the competing risk model, and recently Murakami (2009) utilized a standard saddlepoint approximation to provide tail probabilities for the Bagai statistic under finite sample sizes. In the present paper, we consider the Gaussian-polynomial approximation proposed in Ha and Provost (2007) and compare it to the saddlepoint approximation in terms of approximating the percentiles of the Bagai statistic. We make numerical comparisons of these approximations for moderate sample sizes as was done in Murakami (2009). From the numerical results, it was observed that the Gaussianpolynomial approximation provides comparable or greater accuracy in the tail probabilities than the saddlepoint approximation. Unlike saddlepoint approximation, the Gaussian-polynomial approximation provides a simple explicit representation of the approximated density function. We also discuss the details of computations.

A NOTE ON CERTAIN LAPLACE TRANSFORMS FOR THE GENERALIZED HYPERGEOMETRIC FUNCTION 3F3

  • Kim, Insuk;Jun, Sungtae
    • The Pure and Applied Mathematics
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    • v.25 no.1
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    • pp.7-16
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    • 2018
  • The main objective of this paper is to demonstrate how one can obtain very quickly so far unknown Laplace transforms of rather general cases of the generalized hypergeometric function $_3F_3$ by employing generalizations of classical summation theorems for the series $_3F_2$ available in the literature. Several new as well known results obtained earlier by Kim et al. follow special cases of main findings.

Selection of a Probability Distribution for Modeling Labor Productivity during Overtime

  • Woo, Sung-Kwon
    • Korean Journal of Construction Engineering and Management
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    • v.6 no.1 s.23
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    • pp.49-57
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    • 2005
  • Construction labor productivity, which is the greatest source of variation in overall construction productivity, is the critical factor for determining the project performance in terms of time and cost, especially during scheduled overtime when extra time and cost are invested. The objective of this research is to select an appropriate type of probability distribution function representing the variability of daily labor productivity during overtime. Based on the results of statistical data analysis of labor performance during different weekly work hours, lognormal distribution is selected in order to take advantage of easiness of generating correlated random numbers. The selected lognormal distribution can be used for development of a simulation model in construction scheduling, cost analysis, and other applications areas where representation of the correlations between variables are essential.