• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.12
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• pp.1041-1047
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• 1989

In recent years, a.c servomotors have been gradually replacing d.c servomotors in various high-performance applications such as machine tools and industrial robots. Inparticular, the high performance slip-frequency control of an induction motor, which is often called the vector control, is considered ane of th ebest a.c drives. In this paper, the transient state equations and vector control algorithms of an induction type servomotor are described mathematically by using the two- axis theory (d-q coordinates). According to the result of these algorithms, we scheme the speed control system for the motor in which the vector control is adopted to give high performance. Motor drive through a PWM inverter with power MOSFET is controlled so that the actual input current to the motor may track the current reference obtained from a micro-computer (8086 CPU). Driving experiments are performed in the range of 0 to 3000 rpm, and it is verified that high speed response is obtained for this system.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.103-105
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• 1993

This paper proposed a new direct vector control scheme using high frequency current injection. This scheme has some attractive features such as using no flux sensor or speed sensor, robustness of parameter variation, capability of accurate vector control at very low speed. Compared to other direct vector control scheme, this scheme doesn't depends on phase delay and difference of magnitude of estimated flux, rotor resistance and all inductances except stator resistance. These features are proved by computer simulation and experimental results.

• International journal of advanced smart convergence
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• v.5 no.3
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• pp.8-15
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• 2016

In recent years, one of deep learning models called Deep Belief Network (DBN) which formed by stacking restricted Boltzman machine in a greedy fashion has beed widely used for classification and recognition. With an ability to extracting features of high-level abstraction and deal with higher dimensional data structure, this model has ouperformed outstanding result on image and speech recognition. In this research, we assess the applicability of deep learning in dna classification level. Since the training phase of DBN is costly expensive, specially if deals with DNA sequence with thousand of variables, we introduce a new encoding method, using decimal-binary vector to represent the sequence as input to the model, thereafter compare with one-hot-vector encoding in two datasets. We evaluated our proposed model with different contrastive algorithms which achieved significant improvement for the training speed with comparable classification result. This result has shown a potential of using decimal-binary vector on DBN for DNA sequence to solve other sequence problem in bioinformatics.

• Journal of Korea Multimedia Society
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• v.18 no.7
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• pp.834-842
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• 2015

We propose a method to construct composite feature vector based on discriminant analysis for face recognition. For this, we first extract the holistic- and local-features from whole face images and local images, which consist of the discriminant pixels, by using a discriminant feature extraction method. In order to utilize both advantages of holistic- and local-features, we evaluate the amount of the discriminative information in each feature and then construct a composite feature vector with only the features that contain a large amount of discriminative information. The experimental results for the FERET, CMU-PIE and Yale B databases show that the proposed composite feature vector has improvement of face recognition performance.

• Kyungpook Mathematical Journal
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• v.52 no.1
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• pp.49-59
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• 2012

In this article, using the example of C. Camci([7]) we reconfirm necessary sufficient condition for a slant curve. Next, we find some necessary and sufficient conditions for a slant curve in a Sasakian 3-manifold to have: (i) a $C$-parallel mean curvature vector field; (ii) a $C$-proper mean curvature vector field (in the normal bundle).

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.493-496
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• 1998

Several sensorless vector control methods of induction motor have been proposed, but these methods don't have the satisfying performance to the change of the rotor time constant. Therefore, this paper proposes the sensorless vector control method which estimates the rotor speed using MRAS and compensates the rotor time constant using current error feedback at the same time. This method can guarantees the accurate performance of sensorless vector control while the rotor speed and the rotor time constant are changing. This method is verified by computer simulation using SIMULINK in MATLAB.

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.63-65
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• 2018

This paper presents a DTC-PWM(Direct Torque Control-Pluse Width Modulation) method of PMSM(Permanent Magnet Synchronous Motor) using the flux-torque hysteresis band. In order to keep the flux and torque error of the PMSM within the hysteresis band, the optimal PWM duty ratio is calculated by the error of the flux and torque with the flux and torque vector of the selected voltage vector. According to the flux duty ratios and the torque duty ratios, the optimized duty ratio to reduce the errors is selected by the calculated duty ratios. In the proposed method, the selected voltage vector is divided into d-q axis components with a simple method. And the flux duty ratios and torque duty ratios are estimated by the applied voltage vector. The proposed DTC-PWM for PMSM was verified by computer simulation.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.435-442
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• 2017

Consider the problem of estimating a $p{\times}1$ mean vector ${\theta}$ (p ${\geq}$ 3) under the quadratic loss from multi-variate normal population. We find a James-Stein type estimator which shrinks towards the projection vectors when the underlying distribution is that of a variance mixture of normals. In this case, the norm ${\parallel}{\theta}-K{\theta}{\parallel}$ is known where K is a projection vector with rank(K) = q. The class of this type estimator is quite general to include the class of the estimators proposed by Merchand and Giri (1993). We can derive the class and obtain the optimal type estimator. Also, this research can be applied to the simple and multiple regression model in the case of rank(K) ${\geq}2$.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.5
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• pp.59-67
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• 1998

This paper propose a new vector-quantizer design method for coefficients of wavelet transformed images. In conventional wavelet transform, it is quite often to employ wavelet transformed coefficients, not containing images to be encoded, as training sequences for designing a vector-quantizer. This method has a serious drawback ; it is not known how to find a proper set of training images. This paper investigates characteristics of images that should be considered in the design of vector-quantizers for wavelet transformed images. Besides the statistical parameters such as correlation and standard deviation, edge components are shown to characterise wavelet transform images. Training sequences established in accordance with the above knowledge are used in the design of quantizers having guaranteed range of applicable images. Results of computer simulations are shown to demonstrate the effectiveness of the proposed method.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.5
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• pp.75-82
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• 1998

Conventional vector quantization (VQ) codebook design methods have several drawbacks such as edge degradation and high computational complexity. In this paper, we first made activity coordinates from the horizonatal and the vertical activity of the input block. Then it is mapped on the 2-dimensional interconnected codebook, and the codebook is designed using kohonen self-organizing map (KSFM) learning algorithm after the search of a codevector that has the minumum distance from the input vector in a small window, centered by the mapped point. As the serch area is restricted within the window, the computational amount is reduced compared with usual VQ. From the resutls of computer simulation, proposed method shows a better perfomance, in the view point of edge reconstruction and PSNR, than previous codebook training methods. And we also obtained a higher PSNR than that of classified vector quantization (CVQ).