• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.99-118
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• 2021

In this paper, we introduce an extended form of the inverse power Lomax model via Marshall-Olkin approach. We call it the Marshall-Olkin inverse power Lomax (MOIPL) distribution. The four- parameter MOIPL distribution is very flexible which contains some former and new models. Vital properties of the MOIPL distribution are affirmed. Maximum likelihood estimators and approximate confidence intervals are considered under Type I censored samples. Maximum likelihood estimates are evaluated according to simulation study. Bayesian estimators as well as Bayesian credible intervals under symmetric loss function are obtained via Markov chain Monte Carlo (MCMC) approach. Finally, the flexibility of the new model is analyzed by means of two real data sets. It is found that the MOIPL model provides closer fits than some other models based on the selected criteria.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2188-2190
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• 2004

This paper presents an implementation of power system stabilizer using inverse dynamic neuro controller. Traditionally, mutilayer neural network is used for a universal approximator and applied to a system as a neuro-controller. In this case, at least two neural networks are used and continuous tuning of neuro-controller is required. Moreover, training of neural network is required considering all possible disturbances, which is impractical in real situation. In this paper, Taylor Model Based Inverse Dynamic Neuro Model (TMBIDNM) is introduced to avoid this problem. Inverse Dynamic Neuro Controller (IDNC) consists of TMBIDNM and Error Reduction Neuro Model (ERNM). Once the TMBIDNM is trained, it does not require retuning for cases with other types of disturbances. The controller is tested for one machine and infinite-bus power system for various operating conditions.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.3
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• pp.205-212
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• 2008

This paper describes the application of an inverse building model to a calibrated forward building model using EnergyPlus program. Typically, inverse models are trained using measured data. However, in this study, an inverse building model was trained using data generated by an EnergyPlus model for an actual office building. The EnergyPlus model was calibrated using field data for the building. A training data set for a month of July was generated from the EnergyPlus model to train the inverse model. Cooling load prediction of the trained inverse model was tested using another data set from the EnergyPlus model for a month of August. Predicted cooling loads showed good agreement with cooling loads from the EnergyPlus model with root-mean square errors of 4.11%. In addition, different control strategies with dynamic cooling setpoint variation were simulated using the inverse model. Peak cooling loads and daily cooling loads were compared for the dynamic simulation.

• Proceedings of the SAREK Conference
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• 2007.06a
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• pp.142-142
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• 2007

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2C
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• pp.191-199
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• 2006

In this paper, we propose a method to implement a predistorter of the $rho$-th order inverse filter structure to prevent signal distortion and spectral re-growth due to the high PAPR (peak-to-average ratio) of the OFDM signals and the non-linearity of high-power amplifiers. We model the memory-less non-linearity of the high-power amplifier with a polynomial model and utilize the inverse of the model, the $rho$-th order inverse filter, for the predistorter. Once the non-linearity is modeled with a polynomial, since we can determine the $rho$-th order inverse filter only with the coefficients of the polynomial, large memory is not required. To update the coefficients of the non-linear high-power amplifier model, we can use LMS or RLS algorithms. The convergence speed is high since the number of coefficients is small, and the computation is simple since manipulation of complex numbers is not necessary.

• The Transactions of the Korean Institute of Power Electronics
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• v.13 no.1
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• pp.1-8
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• 2008

A dynamic model for II-SEPIC(Isolated Inverse-SEPIC) is developed based on the state-space averaging method and its control characteristics are investigated in this paper. Equations for circuit design of II-SEPIC are derived through steady state analysis and the resulted circuit parameters are used in the consequent simulation and experiment works. A structure of control system is devised to obtain better control performance. In order to verify validity and effectiveness of the design equations and dynamic model derived, dynamic control responses of II-SEPIC system against line and load variation are illustrated in both simulation and experiment.

• Communications for Statistical Applications and Methods
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• v.19 no.6
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• pp.837-847
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• 2012

Inverse Gaussian distribution is widely used in applications to analyze and model right-skewed data. To assess the appropriateness of the distribution prior to data analysis, Mudholkar and Tian (2002) proposed an entropy-based test of fit. The test is based on the entropy power fraction(EPF) index suggested by Gokhale (1983). The simulation results report that the power of the entropy-based test is superior compared to other goodness-of-fit tests; however, this observation is based on the small-scale simulation results on the standard exponential, Weibull W(1; 2) and lognormal LN(0:5; 1) distributions. A large-scale simulation should be performed against various alternative distributions to evaluate the power of the entropy-based test; however, the use of a theoretical method is more effective to investigate the powers. In this paper, utilizing the information discrimination(ID) index defined by Ehsan et al. (1995) as a mathematical tool, we scrutinize the power of the entropy-based test. The selected alternative distributions are the gamma, Weibull and lognormal distributions, which are widely used in data analysis as an alternative to inverse Gaussian distribution. The study results are provided and an illustrative example is analyzed.

• Geomechanics and Engineering
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• v.33 no.5
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• pp.427-437
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• 2023

The objective of this work is to study the wave propagation of an FGM plate via a new integral inverse shear model with temperature-dependent material properties. In this contribution, a new model based on a high-order theory field of displacement is included by introducing indeterminate integral variables and inverse co-tangential functions for the presentation of shear stress. The temperature-dependent properties of the FGM plate are assumed mixture of metal and ceramic, and its properties change by the power functions of the thickness of the plate. By applying Hamilton's principle, general formulas of wave propagation were obtained to plot the phase velocity curves and wave modes of the FGM plate with simply supported edges. The effects of the temperature and volume fraction by distributions on wave propagation of the FGM plate are investigated in detail. The results of the dispersion and the phase velocity curves of the propagation wave in the functionally graded plate are compared with previous research.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.12
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• pp.1668-1677
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• 2013

This paper deals with the power loss and thermal characteristics of induction motor for machine tools according to the rated power and speed. To reduce the fabrication error by thermal strain in rotational machine tools, we calculated the power loss and thermal behavior of induction motors. Firstly, the inverse design of general induction motors for machine tool spindle has been performed. The inverse design results are compared with the torque-speed characteristic curve in motor's catalog. The power loss are calculated by finite element method(FEM) at rated condition. Secondary, the transient thermal characteristics of induction motors are calculated by equivalent thermal resistance model from Motor-CAD S/W. The inverse design, power loss and thermal behavior calculation for induction motors with various rated power and speed has been performed. Finally, to verify the design and calculation process of induction motor, we implemented the experimental set with 0.4kW 1710rpm class industrial induction motor model. The obtained thermal characteristics of experimental model confirmed that the design and power loss calculation processes are appropriate to the prediction of thermal strain in rotational machine tools.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2642-2649
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• 2023

A real-time unmeasured dynamic response prediction process for the nuclear power plant pressure pipeline is proposed and its performance is tested in the test-loop system (KAERI). The aim of the process is to predict unmeasurable or unreachable dynamic responses such as acceleration, velocity, and displacement by using a limited amount of directly measured physical responses. It is achieved by combining a well-constructed finite element model and robust inverse force identification algorithm. The pressure pipeline system is described by using the displacement-pressure vibro-acoustic formulation to consider fully filled liquid effect inside the pipeline structure. A robust multiphysics modal projection technique is employed for the real-time sensor synchronized prediction. The inverse force identification method is also derived and employed by using Bathe's time integration method to identify the full-field responses of the target system from the modal domain computation. To validate the performance of the proposed process, an experimental test is extensively performed on the nuclear power plant pressure pipeline test-loop under operation conditions. The results show that the proposed identification process could well estimate the unmeasured acceleration in both frequency and time domain faster than 32,768 samples per sec.