• Journal of the Korean Society for Precision Engineering
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• v.12 no.11
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• pp.52-62
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• 1995

In the laser surface hardening process the geometrical parameters, especially the depth, of the hardened layer are utilized to assess the integrity of the hardening layer quality. Monitoring of this geometrical parameter ofr on-line process control as well as for on-line quality evaluation, however, is an extremely difficult problem because the hardening layer is formed beneath a material surface. Moreover, the uncertainties in monitoring the depth can be raised by the inevitable use of a surface coating to enhance the processing efficiency and the insufficient knowledge on the effects of coating materials and its thicknesses. The paper describes the extimation results using neural network to estimate the hardening layer depth from measured surface temperanture and process variables (laser beam power and feeding velocity) under various situations. To evaluate the effec- tiveness of the measured temperature in estimating the harding layer depth, estimation was performed with or without temperature informations. Also to investigate the effects of coating thickness variations in the real industry situations, in which the coating thickness cannot be controlled uniform with good precision, estimation was done over only uniformly coated specimen or various thickness-coated specimens. A series of hardening experiments were performed to find the relationships between the hardening layer depth, temperature and process variables. The estimation results show the temperature informations greatly improve the estimation accuracy over various thickness-coated specimens.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.1
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• pp.13-20
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• 2020

Accurate parameters based on equivalent circuit are required for high-performance field-oriented control in a three-phase induction motor. In a normal case, stator resistance can be accurately measured using a measuring equipment. Except for stator resistance, all machine parameters on the equivalent circuit should be estimated with particular algorithms. In the viewpoint of traditional regions, the parameters of an induction motor can be identified through the no-load and standstill test. This study proposes an identification method that uses the d-axis model of the induction motor in a stationary frame with the predefined information on stator resistance. Mutual inductance is estimated on the rotational dq coordination similar to that in the traditional no-load experiment test. The leakage inductance and rotor resistance can be estimated simply by applying different voltages and frequencies in the d-axis model of the induction motor. The proposed method is verified through simulation and experimental results.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.25 no.5
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• pp.673-679
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• 2001

The objective of this work is to estimate the temperature dependent thermal properties of the APC-2 composite using a inverse parameter estimation technique. The present inverse method features the estimation of the thermal conductivity and the volumetric heat capacity, which are dependent on the temperature inside the composite. Furthermore, the thermal conductivity is directionally dependent because of the aniosotropy of the composite. An on-line temperature measurement system with a suitable method of heating is built. A composite slab is fabricated using thermoplastic prepreg for the investigation. The corresponding computer code for evaluating the thermal properties inversely using the temperature reading transmitted from the measurement system is developed. The parameterized form is used for the rapid and stable estimation. The modified Newtons method is adopted for the solution technique of the inverse analysis. The estimated results are compared with the measured data from a previous study for the verification.

• Journal of Ocean Engineering and Technology
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• v.20 no.4 s.71
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• pp.24-30
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• 2006

Different methods exist for the estimation of thermaldeformation of plates in the line heating process. These are based on the assumption of residual strains in the heat-affected zone, known as the method of inherent strains, or simulated relations between heating conditions and residual deformations. The purpose of this paper is to develop a simulator of thermal deformation in the line heating, using the artificial neural network. Curvature deformations for the plate-forming are investigated, which can be used as a prime deformation parameter in the process. The curvature of plates are calculated using the approximation of plate surface by NURBS. Line heating experiments for 11 specimens of different thickness and heating conditions were performed. Two neural networks predicting the maximum temperature and curvature deformations at the heating line are studied. It was concluded that the thermal deformations predicted by the neural network can be used in a line heating simulator, which is considered an attractive and practical alternative to the existing methods.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.164-167
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• 1996

This paper presents an off-line P.I.D parameter estimation method during normal operation in power plant. The process parameters are estimated using the recursive least square method. The controller parameters are estimated on the basis of desired characteristics of the dynamic model of the closed-loop control.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.188-191
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• 2002

This paper presents a method for on-line estimation of rotor time constant of induction motor. The proposed method applies a model reference adaptive system(MRAS) using rotor flux vector. The MRAS consists of two independent observers to estimate the rotor flux vector; one based on voltage equations of rotor flux vector, the other based on current equations of them. The MRAS utilizes concept of auxiliary variables to normalize observer output and decrease high-frequency components of its input. Experimental results verify the validity and usefulness of proposed method

• The KIPS Transactions:PartB
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• v.12B no.5 s.101
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• pp.567-576
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• 2005

This paper proposes an image-based modeling method which recovers 3D models using projected line segments in multiple images. Using the method, a user obtains accurate 3D model data via several steps of simple manual works. The embedded nonlinear minimization technique in the model parameter estimation stage is based on the distances between the user provided image line segments and the projected line segments of primitives. We define an error using a finite line segment and thus increase accuracy in the model parameter estimation. The error is defined as the sum of differences between the observed image line segments provided by the user and the predicted image line segments which are computed using the current model parameters and camera parameters. The method is robust in a sense that it recovers 3D structures even from partially occluded objects and it does not be seriously affected by small measurement errors in the reconstruction process. This paper also describesexperimental results from real images and difficulties and tricks that are found while implementing the image-based modeler.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.40.5-40
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• 2001

In this paper, a new on-line parameter estimation methodology for the general continuous time Takagi-Sugeno(T-S) fuzzy model whose parameters are poorly known or uncertain is presented. An estimator with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory. The adaptive law is designed so that the estimation model follows the plant parameterized model. By the proposed estimator, the parameters of the T-S fuzzy model can be estimated by observing the behavior of the system and it can be a basis for the indirect adaptive fuzzy control.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.386-398
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• 2019

The transmission capacity of transmission lines is affected by environmental parameters such as ambient temperature, wind speed, wind direction and so on. The environmental parameters can be measured by the installed measuring devices. However, it is impossible to install the environmental measuring devices throughout the line, especially considering economic cost of power grid. Taking into account the limited number of measuring devices and the distribution characteristics of environment parameters and transmission lines, this paper first studies the environmental parameter estimating method of inverse distance weighted interpolation and ordinary Kriging interpolation. Dynamic thermal rating of transmission lines based on IEEE standard and CIGRE standard thermal equivalent equation is researched and the key parameters that affect the load capacity of overhead lines is identified. Finally, the distributed thermal rating of transmission line is realized by using the data obtained from China meteorological data network. The cost of the environmental measurement device is reduced, and the accuracy of dynamic rating is improved.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.225-229
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• 1998

If there is a mismatch between the controller programmed rotor time constant and the actual time constant of motor, the decoupling between the flux and torque is lost in an indirect rotor field oriented control. This paper presents a new estimation scheme for rotor time constant using artificial neural networks. The parameters of induction motor model organize 2 layer neural to be weight between neuron, which is proposed new in this paper. This method makes networks simple, so its brings not only the improvement in speed but simplification in calculation. Furthermore, it is possible to estimated rotor time constant real time through on-line learning without using off-line learning. The digital simulation and the experimental results to verify the effectiveness of the new method are described in this paper.