• 한국데이터정보과학회:학술대회논문집
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• 2002.06a
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• pp.89-95
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• 2002

This paper considers the problem of estimating paramaters of the bivariate exponential distribution with a loaction parameter for a two-component shared parallel system using component data from system-level life test terminated at the time of the prespecified number of system failure. In the system-level life testing, there are three patterns of failure types; 1) both component failed 2) both component censored 3) one is failed and the other is censored. In the third case, we assume that the failure time might be known or unknown. The maximum likelihood estimators are obtained for the case of known/unknown failure time when the other component is censored.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.129-132
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• 2001

she purpose of this paper is to propose an approach to suppress the vibration of three-mass inertia system based on the LMI theory. and confirm its validity through simulations under the condition of parameter variation. First, the existing $H_{\infty}$ servo problem is modified to a structure to which the LMI theory can be applied by virtue of the interval model principle. By adopting this structure, we can divide given specifications fur the vibration suppression problem into $H_2$and $H_{\infty}$ performance criteria. The results of simulation for the three-mass inertia system show that the proposed design approach is quite effective.

• International Journal of Precision Engineering and Manufacturing
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• v.4 no.3
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• pp.62-67
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• 2003

The grinding force generated during the grinding process causes an elastic deformation of the workpiece, grinding wheel, and machine system. Thus, the true depth of cut is always smaller than the apparent depth of cut. This is known as machining elasticity phenomenon. The machining elasticity parameter is defined as a ratio between the true depth of cut and the apparent depth of cut. It is an important factor to understand the material removal mechanism of the grinding process. To increase productivity, the value of this machining elasticity parameter must be large. Therefore, it is essential to know the characteristics of this parameter. The objective of this research is to study the effect of the major grinding conditions, such as table speed, depth of cut, on this parameter experimentally, Through this research, it is found that this parameter value is increasing when the table speed is decreasing or the depth of cut is increasing. Also, this parameter value depends on the grinding mode (up grinding, down grinding).

• Journal of Korean Neurosurgical Society
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• v.57 no.3
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• pp.152-158
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• 2015

Objective : The purpose of this study to develop new deep-brain stimulation system for long-term use in animals, in order to develop a variety of neural prostheses. Methods : Our system has two distinguished features, which are the fully implanted system having wearable wireless power transfer and ability to change the parameter of stimulus parameter. It is useful for obtaining a variety of data from a long-term experiment. Results : To validate our system, we performed pre-clinical test in Parkinson's disease-rat models for 4 weeks. Through the in vivo test, we observed the possibility of not only long-term implantation and stability, but also free movement of animals. We confirmed that the electrical stimulation neither caused any side effect nor damaged the electrodes. Conclusion : We proved possibility of our system to conduct the long-term pre-clinical test in variety of parameter, which is available for development of neural prostheses.

• Journal of Electrical Engineering and Technology
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• v.7 no.3
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• pp.304-311
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• 2012

So far, the importance for an accurate load model has been constantly raised and its necessity would be further more emphasized. Currently used load model for analysis of power system in Korea was developed 10 years ago, which is aggregated by applying the statistically estimated load compositions to load models based on individual appliances. As modern appliances have diversified and rapidly changed, the existing load model is no longer compatible with current loads in the Korean power system. Therefore, a measurement based load model is more suitable for modern power system analysis because it can accurately include the load characteristics by directly measuring target load. This paper proposes a ZIP model employing a Kalman-filter as the estimation algorithm for the model parameters. The Kamlan-filter based parameter identification offers an advantage of fast parameter determination by removing iterative calculation. To verify the proposed load model, the four-second-interval real data from the Korea Energy Management System (K-EMS) is used.

• Transactions of the Korean Society of Automotive Engineers
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• v.3 no.1
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• pp.89-96
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• 1995

The application of the 4-pole parameter method with 1 - D theory is acceptable for intake system analysis. However, the limitaion appears during the analysis of complicated intake system since this method is developed based on the plane wave thoery. For the intake system analysis, the usage of BEM(Boundary Element Method) is introduced describing its disadvantage. To combine benefits of both method. a hybrid method is introduced. This hybrid method consists of the 4-pole parameter with I-D theory and BEM. The developed method is applied to an automobile intake system analysis to obtain the transmission loss.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.3
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• pp.188-195
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• 2004

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• Journal of Power Electronics
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• v.6 no.3
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• pp.235-244
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• 2006

Recently, speed sensorless vector control for synchronous reluctance motors (SYRMs) has deserved attention because of its advantages. Although rotor angle calculation using flux estimation is a straightforward approach, the DC offset can cause an increasing pure integrator error in this estimator. In addition, this method is affected by parameter fluctuation. In this paper, to control the motor at the low speed region, a modified programmable cascaded low pass filter (MPCPLF) with sensorless online parameter identification based on a block pulse function is proposed. The use of the MPCLPF is suggested because in programmable, cascade low pass filters (PCLPF), which previously have been applied to induction motors, the drift increases vastly wl)en motor speed decreases. Parameter identification is also used because it does not depend on estimation accuracy and can solve parameter fluctuation effects. Thus, sensorless speed control in the low speed region is possible. The experimental system includes a PC-based control with real time Linux and an ALTERA Complex Programmable Logic Device (CPLD), to acquire data from sensors and to send commands to the system. The experimental results show the proposed method performs well, speed and angle estimation are correct. Also, parameter identification and sensorless vector control are achieved at low speed, as well as, as at high speed.

• Journal of the Korean Mathematical Society
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• v.60 no.1
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• pp.71-90
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• 2023

We construct generalized Cauchy-Riemann equations of the first order for a pair of two ℝ-valued functions to deform a minimal graph in ℝ³ to the one parameter family of the two dimensional minimal graphs in ℝ⁴. We construct the two parameter family of minimal graphs in ℝ⁴, which include catenoids, helicoids, planes in ℝ³, and complex logarithmic graphs in ℂ². We present higher codimensional generalizations of Scherk's periodic minimal surfaces.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.3
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• pp.67-73
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• 2006

In this paper, a reconfigurable model following flight control method is proposed based on direct adaptive scheme using parameter estimation. Adaptive control scheme updates the control gains to make the system output follow the reference output even when fault occurs. By adopting the frequency domain parameter estimation method, system changes by the fault can be estimated. Recursive Fourier transformation is used for system identification. Using recursive Fourier transform, the proposed adaptive control algorithm guarantees the system stability and improves the system characteristics. To evaluate the performance of proposed control method, numerical simulations are performed.