• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.359-363
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• 1994

In this paper the system called the inverse model compensation system is proposed as a system whose input-output transfer function can be regarded as that of a model with uncertainty in spite of including an unknown plant. And their to construct the robust model following system, which is of low sensitivity and robust stability, in order to control the inverse model compensation system is proposed. The simulation experiments show that the robust model following system including the inverse model compensation system is practical and useful as a system which controls unknown plants.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.349-352
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• 1997

A Piezoelectric actuator yields hysteresis effect due to its composed ferroelectric. Hysteresis nonlinearity is neglected when a piezoelectric actuator moves with short stroke. However when it moves with long stroke and high frequency, the hysteresis nonlinearity can not be neglected. The hysteresis nonlinearity of piezoelectric actuator degrades the control performance in precision position control. In this paper, in order to improve the control performance of piezoelectric actuator, an inverse modeling scheme is proposed to compensate the hysteresis nonlinearity problem. And feedforward-feedforward-feedback controller is proposed to give a good tracking performance. The Feedforward controller is inverse hysteresis model, and PID control is sued as a feedback controller. To show the feasibility of the proposed controller and hysteresis modeling, some experiments have been carried out. It is concluded hat the proposed control scheme gives good tracking performance.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.675-688
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• 2020

In survival analysis of observational data, the inverse probability weighting method and the Cox proportional hazards model are widely used when estimating the causal effects of multiple-valued treatment. In this paper, the two kinds of weights have been examined in the inverse probability weighting method. We explain the reason why the stabilized weight is more appropriate when an inverse probability weighting method using the generalized propensity score is applied. We also emphasize that a marginal hazard ratio and the conditional hazard ratio should be distinguished when defining the hazard ratio as a treatment effect under the Cox proportional hazards model. A simulation study based on real data is conducted to provide concrete numerical evidence.

• 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.

• Journal of the Ergonomics Society of Korea
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• v.6 no.1
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• pp.3-8
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• 1987

This paper develops a statistical mode which estimates mental and physical loads of light work from sinus arrhythmia (SA) scores. During experiments, various levels of mental and physical loads (respectively scored by information processing and finger tapping rates) were imposed on subjects and SA scores were measured from the subjects. Two methods were used in developing workload estimation model. One is an algebraic inverse function of a multivariate regression equation, where mental and physical loads are independent variables and SA scores are dependent variables. The other is a statistical multivariate inverse regression. Of the two methods, inverse function resulted in larger mean squqre error in predicting mental and physical loads. Hence, inverse regression model is recommended for precise workload estimation.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.243-246
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• 1994

In this paper, a self-learning fuzzy controller is designed with a fuzzy approximation of an inverse model. The aim of an identification is to find an input command which is control of a system output. It is intuitional and easy to use a classical adaptive inverse modeling method for the identification, but it is difficult and complex to implement it. This problem can be solved with a fuzzy approximation of an inverse modeling. The fuzzy logic effectively represents the complex phenomena of the real world. Also fuzzy system could be represented by the neural network that is useful for a learning structure. The rule of a fuzzy inverse model is modified by the gradient descent method. The goal is to be obtained that makes the design of fuzzy controller less complex, and then this self-learning fuzz controller can be used for nonlinear dynamic system. We have applied this scheme to a nonlinear Ball and Beam system.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.3
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• pp.263-271
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• 2017

A captive trajectory simulation (CTS) system is used to investigate the separation behavior of the store model by moving the model to an arbitrary pose and position based on aerodynamic data. A CTS system operated inside a wind tunnel is designed to match the structure of the wind tunnel facility. As a result, each CTS system has different kinematic structure, and inverse kinematic analysis of the system is necessary. In this study, kinematic/inverse kinematic analysis for the CTS system with functional redundancy is performed. Inverse kinematic analysis with combined numerical and analytical approach is especially proposed. The suggested approach utilizes the redundancy to improve the safety of the system, and has advantages in real time analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.12 no.2
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• pp.176-188
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• 1987

This paper presents a method for Digital Image Motion Restoration by inverse filtering. In order to onstruct optimal Restoration filter, We exactly have to model the degradation process, and therefrom, derive the inverse filter which has inverse charateristics of the degradation model. An Image taken from object which moves fast, is o suffer blurring. it can be modeled by integration process mathematically and analyzed to convolve a rectangular window over an image. in this paper, We analyzed it in the frequency domain, and studied a method for motion restoration using inverse filter has a directional Sinc property.

• Coupled systems mechanics
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• v.7 no.2
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• pp.197-209
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• 2018

Fiber-reinforced concrete (FRC) is a material with increasing application in civil engineering. Here it is assumed that the material consists of a great number of rather small fibers embedded into the concrete matrix. It would be advantageous to predict the mechanical properties of FRC using nondestructive testing; unfortunately, many testing methods for concrete are not applicable to FRC. In addition, design methods for FRC are either inaccurate or complicated. In three-point bending tests of FRC prisms, it has been observed that fiber reinforcement does not break but simply pulls out during specimen failure. Following that observation, this work is based on an assumption that the main components of a simple and rather accurate FRC model are mechanical properties of the concrete matrix and fiber pullout force. Properties of the concrete matrix could be determined from measurements on samples taken during concrete production, and fiber pullout force could be measured on samples with individual fibers embedded into concrete. However, there is no clear relationship between measurements on individual samples of concrete matrix with a single fiber and properties of the produced FRC. This work presents an inverse model for FRC that establishes a relation between parameters measured on individual material samples and properties of a structure made of the composite material. However, a deterministic relationship is clearly not possible since only a single beam specimen of 60 cm could easily contain over 100000 fibers. Our inverse model assumes that the probability density function of individual fiber properties is known, and that the global sample load-displacement curve is obtained from the experiment. Thus, each fiber is stochastically characterized and accordingly parameterized. A relationship between fiber parameters and global load-displacement response, the so-called forward model, is established. From the forward model, based on Levenberg-Marquardt procedure, the inverse model is formulated and successfully applied.