• 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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.262-265
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• 2012

In this paper we propose a new deinterlacing method with block classification and motion vector smoothing. The proposed method classifies a block, then depending on the region it belongs to, the motion estimation or line averaging is applied. To classify a block its variance is calculated. Then, for those blocks that belong to simple non-texture region the line averaging is done while motion estimation is applied to complex region. The motion vector field is smoothed using median filter what yields more accurate interpolation. In the experiments for the subjective evaluation, the proposed method bas shown satisfying results in terms of computation time consumption and peak signal-to-noise ratio. Due to the simplicity of the algorithm computation time was drastically decreased.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.52 no.11
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• pp.577-585
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• 2003

This paper proposes a new speed sensorless control method of a permanent magnet synchronous motor using an instantaneous reactive power. In the proposed algorithm, the line currents are estimated by a observer and the estimated speed can be yielded from the voltage equation because the information of speed is included in back emf. But the speed estimation error between the estimated and the real speeds is occured by errors due to measuring the motor parameters and sensing the line current and the input voltage. To minimize the speed estimation error, the estimated speed is compensated by using an instantaneous reactive power. In this paper, the proposed algorithm is not affected by mechanical motor parameters because the mechanical equation is not used. The effectiveness of algorithm is confirmed by the experiments.

• Proceedings of the KIEE Conference
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• 2004.10a
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• pp.224-226
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• 2004

Digital Kalman filter is presented as a powerful approach to obtain the reference estimation of the control current for shunt active power filter. This algorithm provides the best estimate of the fundamental and harmonic frequency components from the sampled values of the line current or voltage. By adopting of the digital Kalman filtering algorithm, the structure of the control algorithm eliminates the need of a Phase locked loop(PLL) for the synchronization of the reference signal used in the compensation and it not sensitive to the distortion of the line voltage. The effectiveness of the algorithm is confirmed by the computer simulations.

• Proceedings of the KSR Conference
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• 2004.10a
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• pp.1480-1485
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• 2004

In this paper, a real-time digital kalman filtering algorithm is used to obtain the reference estimation of the control current for shunt active power filter. This algorithm provides the best estimate of the fundamental and harmonic frequency components from the sampled values of the line current or voltage waveform. By adopting of the digital Kalman filtering algorithm, the structure of the control algorithm eliminates the need of a Phase locked loop(PLL) for the synchronization of the reference signal used in the compensation and it not sensitive to the distortion of the line voltage. The effectiveness of the algorithm is confirmed by the computer simulations.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.742-747
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• 2006

This paper describes online stator-resistance estimation of a linear induction motor(LIM) with cage-type secondary using direct thrust control(DTC), where the resistance value is derived from stator-winding temperature estimation using thermocouple. In this paper, corrected stator resistance has an error in actuality measurement resistance. So compensation coefficient $\kappa$ which is decided through comparison and verifying several times relation of calculated resistance and measured motor line-line resistance. The stator-winding temperature information can also be used for monitoring, protection, and fault-tolerant control of the machine. Also, this paper reports the LIM's responses of the flux measured by the proposed stator resistance compensation algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.538-544
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• 1999

In this paper, we propose a new fault detection algorithm for transmission line protection using ANFIS(Adaptive Network Fuzzy Inference System). The developed system consists of two subsystems: fault type classification, and fault location estimation. We use rms value, zero sequence component and positive sequence of current, and then using learning method of neural network, premise and consequent parameters are tuned properly. To prove the performance of the proposcd system, generated data by EMTP(Electr0- Magnetic Transient Program) sin~ulationi s used. It is shown that the proposed relaying classifies fault types accurately and advances fault location estimation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.5
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• pp.885-890
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• 2009

A fault location algorithm using estimated local source impedance after a fault is proposed in this paper. The method uses after fault data only at the local end. It uses the negative sequence current distribution factor for more accurate estimation. The proposed algorithm can keep up with the variation of the local source impedance. Therefore, the proposed algorithm especially is valid for a transmission line interconnected to a wind farm that the equivalent source impedance changes continuously. The performance of the proposed algorithm was verified under various fault conditions using the Simpowersystem of MATLAB Simulink. The proposed algorithm is largely insensitive to the variation in fault distance and fault resistance. The test results show a very high accurate performance.

• Asia pacific journal of information systems
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• v.15 no.3
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• pp.209-221
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• 2005

One of the most fundamental problems in business is to evaluate the value of each product. The difficulty is that the profit of one product not only comes from its own sales, but also its influence on the sales of other products, i.e., the "cross-selling effect". This study integrates a measure for cross selling and an algorithm for profit estimation. Sales transaction data and post sales survey data from on-line and off-line shopping mall is used to show the effectiveness of the method against other heuristic for profit estimation based on product-specific profitability. We show that with the use of the new method we are able to identify the cross-selling potential of each product and use the information for better product selection.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.1-7
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• 2019

Recently, smart factories have attracted much attention as a result of the 4th Industrial Revolution. Existing factory automation technologies are generally designed for simple repetition without using vision sensors. Even small object assemblies are still dependent on manual work. To satisfy the needs for replacing the existing system with new technology such as bin picking and visual servoing, precision and real-time application should be core. Therefore in our work we focused on the core elements by using deep learning algorithm to detect and classify the target object for real-time and analyzing the object features. We chose YOLO CNN which is capable of real-time working and combining the two tasks as mentioned above though there are lots of good deep learning algorithms such as Mask R-CNN and Fast R-CNN. Then through the line and inside features extracted from target object, we can obtain final outline and estimate object posture.