• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.562-567
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• 2000

This paper proposed a speed estimator using MRAC(Model Reference Adaptive Control) for sensorless vector control. It is robust for parameter variation and the estimated speed is used as feedback in a vector control system. Computer simulation is presented to confirm the theoretical analysis.

• Journal of the Korean Mathematical Society
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• v.52 no.1
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• pp.177-190
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• 2015

Let X be a completely regular Hausdorff space, and E and F be Banach spaces. Let $C_{rc}(X,E)$ be the Banach space of all continuous functions $f:X{\rightarrow}E$ such that f(X) is a relatively compact set in E. We establish an integral representation theorem for bounded linear operators $T:C_{rc}(X,E){\rightarrow}F$. We characterize continuous operators from $C_{rc}(X,E)$, provided with the strict topologies ${\beta}_z(X,E)$ ($z={\sigma},{\tau}$) to F, in terms of their representing operator-valued measures.

• Journal of Integrative Natural Science
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• v.12 no.4
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• pp.105-115
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• 2019

In this paper, a prediction system is proposed to control the brightness of smart street lamps by predicting the moving path through the reduction of consumption power and information of pedestrian's past moving direction while meeting the function of existing smart street lamps. The brightness of smart street lamps is adjusted by utilizing the walk tracking vector and soft hand-off characteristics obtained through the motion sensing sensor of smart street lamps. In addition, the motion vector is used to analyze and predict the pedestrian path, and the GPU is used for high-speed computation. Pedestrians were detected using adaptive Gaussian mixing, weighted difference imaging, and motion vectors, and motions of pedestrians were analyzed using the extracted motion vectors. The preprocessing process using linear interpolation is performed to improve the performance of the proposed prediction system. Fuzzy prediction system and neural network prediction system are designed in parallel to improve efficiency and rough set is used for error correction.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1056-1059
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• 1996

In this paper, we analyzed the effect of the change of the rotor time constant on the performance of the indirect vector control system. By employing indirect field orientation technique, we have also suggested an optimal control algorithm that allows an induction motor to maintain the maximum torque under the changing environment of rotor time constant. A computer simulation on the transient response of the output torque was demonstrated. To verify the validity of the method that has been proposed in this paper, an experiment has been performed utilizing TMS32OC31(40MHz) DSP chip which is capable of performing floating-point arithmetic in real time.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.645-646
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• 2008

Silhouette extraction is widely used in many computer graphics applications. In this paper, we proposed a method for extracting 3D silhouette and internal pattern from 3D vector data. To do this, we first make an edge-list, secondly define the silhouette, and finally remove hidden lines. After getting the silhouette, we extract internal pattern using adjacent edge's dihedral. The proposed method not only effectively improves the performance of extracting 3D silhouette and internal pattern from 3D vector data but also reduces the computational complexity.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.585-591
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• 1998

It is proposed a new compensation method in the rotor time constant of indirect vector controlled induction motor. The proposed scheme is an on-line method using the stator current error that is the difference between current command and estimated current calculated from terminal voltages and currents. As the current error becomes to zero, the rotor time constant in the vector controller approaches the real value. The proposed method shows good performances in the transient region as well as in the steady state region regardless of load torque variation, and it is verified by the computer simulation using SIMULINK in Matlab.

• International Journal of Advanced Culture Technology
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• v.6 no.3
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• pp.142-150
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• 2018

This paper describes the algorithm that lowers the dimension, maintains the object recognition and significantly reduces the eigenspace configuration time by combining the edge orientation histogram and principle component analysis. By using the detected object region as a recognition input image, in this paper the object recognition method combined with principle component analysis and the multi-layer network which is one of the intelligent classification was suggested and its performance was evaluated. As a pre-processing algorithm of input object image, this method computes the eigenspace through principle component analysis and expresses the training images with it as a fundamental vector. Each image takes the set of weights for the fundamental vector as a feature vector and it reduces the dimension of image at the same time, and then the object recognition is performed by inputting the multi-layer neural network.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.5
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• pp.390-395
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• 2000

We present a fingerprint identification algorithm using the wavelet transform and correlation. The wavelet transform is used because of its simple operation to extract fingerprint minutiaes features for fingerprint classification. We perform the rowwise 1-D wavelet transform for a $256\times256$ fingerprint image to get a $1\times256$ column vector using the Haar wavelet and repeat 1-D wavelet transform for a 1$\times$256 column vector to get a $1\times4$ feature vector. Using PNN(Probabilistic Neural Network), we select the possible candidates from the stored feature vectors for fingerprint images. For those candidates, we compute the correlation between the input binary image and the target binary image to find the most similar fingerprint image. The proposed algorithm may be the key to a low cost fingerprint identification system that can be operated on a small computer because it does not need a large memory size and much computation.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.363-366
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• 1990

In the indirect vector control method, the rotor resistance variation caused by operating temperature change is an undesirable nature. A new adaptation algorithm to compensate for the rotor resistance change based on the on-line estimation of field vector which requires the measurements of stator voltage and rotor speed is presented in this paper. Also minimum variance controller is presented for the adaptive control performance. This algorithm has been tested by simulating the induction machine using a digital computer and the results are discussed.

• Communications for Statistical Applications and Methods
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• v.20 no.6
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• pp.481-490
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• 2013

Classification is an important research area as data can be easily obtained even if the number of predictors becomes huge. The support vector machine(SVM) is widely used to classify a subject into a predetermined group because it gives sound theoretical background and better performance than other methods in many applications. The SVM can be viewed as a penalized method with the hinge loss function and penalty functions. Instead of $L_2$ penalty function Fan and Li (2001) proposed the smoothly clipped absolute deviation(SCAD) satisfying good statistical properties. Despite the ability of SVMs, they have drawbacks of non-robustness when there are outliers in the data. We develop a robust SVM method using a weight function with the SCAD penalty function based on the local quadratic approximation. We compare the performance of the proposed SVM with the SVM using the $L_1$ and $L_2$ penalty functions.