• 대한전기학회논문지:시스템및제어부문D
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• 제51권6호
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• pp.267-271
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• 2002

The Hotelling transform is based on statistical properties of an image. The principal uses of this transform are in data compression. The basic concept of the Hotelling transform is that the choice of basis vectors pointing the direction of maximum variance of the data. This property can be used for rotation normalization. Many objects of interest in pattern recognition applications can be easily standardized by performing a rotation normalization that aligns the coordinate axes with the axes of maximum variance of the pixels in the object. However, this transform can not be used to rotation normalization of color images directly. In this paper, we propose a new method for rotation normalization of color images based on the Hotelling transform. The Hotelling transform is performed to calculate basis vectors of each channel. Then the summation of vectors of all channels are processed. Rotation normalization is performed using the result of summation of vectors. Experimental results showed the proposed method can be used for rotation normalization of color images effectively.

• Journal of Plant Biology
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• 제38권4호
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• pp.365-371
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• 1995

Binary vectors, pBI-ActR1, pBI-ActF1 and pBSH-ActR1, were constructed using pGA642, the replication origin of pTi12 and the rice actin promoter. The sizes of pBI-ActR1, pBI-ActF1 and pBSH-ActR1 were 12.9 kb, 13.2 kb and 11.95 kb, respectively. These vectors containing a rice actin promoter followed by a GUS structural gene could induce stronly the expression of GUS gene in transformed rice cells. Rice explants from 3-4 day old seedlings after germinatin were cocultured with A. tumefaceins harboring pBI-ActR1, pBI-ActF1 or pBSH-ActR1, and then GUS expression in the explants was assayed. Transformation of rice explants by these binary vectors was tissue-specific, such that the meristematic regions of shoot apex, root and hypocotyl were transformed by these binary vectors.

• 전자공학회논문지B
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• 제33B권9호
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• pp.117-123
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• 1996

In this paper, we present a new learning method for the fuzzy ARTMAP which is effective for the noisy input patterns. Conventional fuzzy ARTMAP employs only fuzzy AND operation between input vector and weight vector in learning both top-down and bottom-up weight vectors. This fuzzy AND operation causes excessive update of the weight vector in the noisy input environment. As a result, the number of spurious categories are increased and the recognition ratio is reduced. To solve these problems, we propose a new method in updating the weight vectors: the top-down weight vectors of the fuzzy ART system are updated using weighted average of the input vector and the weight vector itself, and the bottom-up weight vectors are updated using fuzzy AND operation between the updated top-down weitht vector and bottom-up weight vector itself. The weighted average prevents the excessive update of the weight vectors and the fuzzy AND operation renders the learning fast and stble. Simulation results show that the proposed method reduces the generation of spurious categories and increases the recognition ratio in the noisy input environment.

• 한국통신학회논문지
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• 제26권11A호
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• pp.1889-1896
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• 2001

본 논문에서는 보다 효율적이고 정확한 움직임 벡터를 추정하기 위하여 계층적 평활화 방법(hierachical regularization technique)을 이용한 움직임 추정 알고리듬을 제안한다. 계층적 평활화 기법을 이용하여 움직임 벡터들의 신뢰도를 증가시켰고, 주위 벡터와의 평활화를 통해 움직임 벡터들의 비트량을 감소시켰다. 또한 적은 후보 벡터를 이용하여 움직임 벡터를 예측하는 고속 움직임 추정 알고리듬을 적용하여 평활화 과정의 추가로 인해 생기는 많은 연산량을 감소시켰다. 실험 결과 제안한 계층적 평활화 방법을 이용한 고속 움직인 추정 알고리듬은 전방향 탐색(full search) 알고리듬과 비교하여 비슷한 영상 화질에서 많은 연산량 감소를 얻을 수 있었으며 잘못된 벡터의 추정 및 확산을 줄일 수 있음을 확인하였다.

• 전기학회논문지
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• 제63권5호
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• pp.696-702
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• 2014

Recently, researches on analyzing relationship between the state of emotion and musical stimuli using EEG are increasing. A selection of feature vectors is very important for the performance of EEG pattern classifiers. This paper proposes a comparison of EEG feature vectors for emotion classification according to music listening. For this, we extract some feature vectors like DAMV, IAV, LPC, LPCC from EEG signals in each class related to music listening and compare a separability of the extracted feature vectors using Bhattacharyya distance. So more effective feature vectors are recommended for emotion classification according to music listening.

• Journal of Power Electronics
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• 제19권6호
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• pp.1477-1485
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• 2019

This paper proposes an effective model predictive current control (MPCC) that involves using 10 virtual voltage vectors to reduce the current harmonics and common-mode voltage (CMV) for a two-level five-phase voltage source inverter (VSI). In the proposed scheme, 10 virtual voltage vectors are included to reduce the CMV and low-order current harmonics. These virtual voltage vectors are employed as the input control set for the MPCC. Among the 10 virtual voltage vectors, two are applied throughout the whole sampling period to reduce current ripples. The two selected virtual voltage vectors are based on location information of the reference voltage vector, and their duration times are calculated using a simple algorithm. This significantly reduces the computational burden. Simulation and experimental results are provided to verify the effectiveness of the proposed scheme.

• 전자공학회논문지B
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• 제31B권7호
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• pp.170-178
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• 1994

The nearest neighbor classification rule is widely used because it is not only simple but the error rate is asymptotically less than twice Bayes theoretical minimum error. But the method basically use the whole training patterns as the reference vectors. so that both storage and classification time increase as the number of training patterns increases. LVQ(Learning Vector Quantization) resolved this problem by training the reference vectors instead of just storing the whole training patterns. But it is a heuristic algorithm which has no theoretic background there is no terminating condition and it requires a lot of iterations to get to meaningful result. This paper is to propose a new training method of the reference vectors. which minimize the given error function. The nearest neighbor network,the network version of the nearest neighbor classification rule is proposed. The network is funtionally identical to the nearest neighbor classification rule is proposed. The network is funtionally identical to the nearest neighbor classification rule and the reference vectors are represented by the weights between the nodes. The network is trained to minimize the error function with respect to the weights by the steepest descent method. The learning algorithm is derived and it is shown that the proposed method can adjust more reference vectors than LVQ in each iteration. Experiment showed that the proposed method requires less iterations and the error rate is smaller than that of LVQ2.

• Journal of Power Electronics
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• 제17권2호
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• pp.368-379
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• 2017

In this paper, a direct torque control algorithm with novel duty cycle-based modulation is proposed for permanent magnet synchronous motor drives fed by neutral-point clamped three-level inverters. Compared with the standard DTC, the proposed algorithm can suppress steady-state torque ripples as well as ensure neutral-point potential balance and smooth vector switching. A unified torque/flux evaluation table with multiple voltage vectors and precise control levels is established and used in this method. This table can be used to evaluate the effects of duty-cycle vectors on torque and flux directly, and the elements of the table are independent of the motor parameters. Consequently, a high number of appropriate voltage vectors and their corresponding duty cycles can be selected as candidate vectors to reduce torque ripples by looking up the table. Furthermore, small vectors are incorporated into the table to ensure the neutral-point potential balance with the numerous candidate vectors. The feasibility and effectiveness of the proposed algorithm are verified by both simulations and experiments.

• 전력전자학회논문지
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• 제22권1호
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• pp.18-26
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• 2017

This paper proposes the model predictive control with space vector modulation (SVM) method for current control of voltage-source inverter. Unlike the conventional method using a limited number of voltage vectors by switching states, the proposed method can consider various voltage vectors to identify the optimized voltage vector. The various voltage vectors are obtained by subdividing existing voltage vectors. The optimized voltage vector that minimizes the cost function is selected and applied to the inverter by using the SVM. The various voltage vectors and SVM reduce current ripples in the output AC side of the inverter compared with the conventional method. The effectiveness and performance of the proposed method are verified through simulation and experiment with a three-phase two-level voltage-source grid-connected inverter.

• Journal of Power Electronics
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• 제18권5호
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• pp.1398-1408
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• 2018

In order to further improve the steady-state control performance of model predictive torque control (MPTC), a double-vector-based model predictive torque control without a weighting factor is proposed in this paper. The extended voltage vectors synthesized by two basic voltage vectors are used to increase the number of feasible voltage vectors. Therefore, the control precision of the torque and the stator flux along with the steady-state performance can be improved. To avoid testing all of the feasible voltage vectors, the solution of deadbeat torque control is calculated to predict the reference voltage vector. Thus, the candidate voltage vectors, which need to be evaluated by a cost function, can be reduced based on the sector position of the predicted reference voltage vector. Furthermore, a cost function, which only includes a reference voltage tracking error, is designed to eliminate the weighting factor. Moreover, two voltage vectors are applied during one control period, and their durations are calculated based on the principle of reference voltage tracking error minimization. Finally, the proposed method is tested by simulations and experiments.