• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1151-1160
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• 2006

The similarity weight, the pearson's correlation coefficient, which is used in the recommender system has a weak point that it cannot predict all of the prediction value. The similarity weight, the vector similarity, has a weak point of the high MAE although the prediction coverage using the vector similarity is higher than that using the pearson's correlation coefficient. The purpose of this study is to suggest how to raise the prediction coverage. Also, the MAE using the suggested method in this study was compared both with the MAE using the pearson's correlation coefficient and with the MAE using the vector similarity, so was the prediction coverage. As a result, it was found that the low of the MAE in the case of using the suggested method was higher than that using the pearson's correlation coefficient. However, it was also shown that it was lower than that using the vector similarity. In terms of the prediction coverage, when the suggested method was compared with two similarity weights as I mentioned above, it was found that its prediction coverage was higher than that pearson's correlation coefficient as well as vector similarity.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.4
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• pp.643-649
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• 2012

The diagnosis technique of stator winding faults based on Motor Current Signature Analysis(MCSA) was suggested. Park's vector pattern, the circle that is drawn by d-q transformed currents($i_d$, $i_q$), is widely used for stator winding faults detection. The current Distortion Ratio(DR), defined by the ratio of max axis and min axis of ellipse of Park's vector's pattern, was more simple and powerful method than the Park's vector pattern. In this study, a calculation method of distortion ratio of Park's vector pattern was suggested for auto diagnosis of stator winding short fault and usefulness of suggested calculation method of distortion ratio was verified through simulation using LabVIEW program.

• Communications for Statistical Applications and Methods
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• v.23 no.1
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• pp.71-83
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• 2016

In this paper we discuss a deconvolution density estimator obtained using the support vector machines (SVM) and Tikhonov's regularization method solving ill-posed problems in reproducing kernel Hilbert space (RKHS). A remarkable property of SVM is that the SVM leads to sparse solutions, but the support vector deconvolution density estimator does not preserve sparsity as well as we expected. Thus, in section 3, we propose another support vector deconvolution estimator (method II) which leads to a very sparse solution. The performance of the deconvolution density estimators based on the support vector method is compared with the classical kernel deconvolution density estimator for important cases of Gaussian and Laplacian measurement error by means of a simulation study. In the case of Gaussian error, the proposed support vector deconvolution estimator shows the same performance as the classical kernel deconvolution density estimator.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.9
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• pp.133-141
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• 1994

Conventional competitive learning algorithms compute the Euclidien distance to determine the winner neuron out of all predetermined output neurons. In such cases, there is a drawback that the performence of the learning algorithm depends on the initial reference(=weight) vectors. In this paper, we propose a new competitive learning algorithm that dynamically generates output neurons. The proposed method generates output neurons by dynamically changing the class thresholds for all output neurons. We compute the similarity between the input vector and the reference vector of each output neuron generated. If the two are similar, the reference vector is adjusted to make it still more like the input vector. Otherwise, the input vector is designated as the reference vector of a new outputneuron. Since the reference vectors of output neurons are dynamically assigned according to input pattern distribution, the proposed method gets around the phenomenon that learning is early determined due to redundant output neurons. Experiments using speech data have shown the proposed method to be superior to existint methods.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.48 no.2
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• pp.89-96
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• 1999

This paper proposes a common-mode voltage reduction method base on SVPWM(Space-Vector Pulsewidth Modulation) in three phase PWM converter/inverter system. By shifting the active voltage vector of inverter and aligning this to the active vector of converter, it is possible to eliminate a common-mode voltage pulse in one control period. Since the proposed PWM method maintains the active voltage vector, it does not affect the control performance of PWM converter/inverter system. Without any extra hardware, overall common mode voltage dv/dt and conrresponding leakage current can be reduced to two-third of the conventional three phase symmetric SVPWM scheme.

• Journal of the Korean Society of Safety
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• v.16 no.4
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• pp.88-95
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• 2001

Stator core loss has significant adverse effects when an induction motor is controlled by the conventional vector control method. Therefore, taking core toss into account should make it possible to control the torque very precisely. This paper proposes a speed sensorless vector control method for an induction motor at optimum efficiency and high response taking core loss account. The proposed vector control system consists of a speed adaptive rotor flux observer which takes core loss into account and employs a direct vector control which compensates for the influence of core loss. Also, in this paper, a vector controlled induction motor with a deadbeat rotor flux controller is developed. The method ensures optimum efficiency in the steady state without degradation of the dynamic response. The validity of the proposed technique is confirmed by simulation results for induction motor drive system.

• Journal of Industrial Convergence
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• v.16 no.4
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• pp.33-46
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• 2018

Personal credit scoring is an effective tool for banks to properly guide decision profitably on granting loans. Recently, many classification algorithms and models are used in personal credit scoring. Personal credit scoring technology is usually divided into statistical method and non-statistical method. Statistical method includes linear regression, discriminate analysis, logistic regression, and decision tree, etc. Non-statistical method includes linear programming, neural network, genetic algorithm and support vector machine, etc. But for the development of the credit scoring model, there is no consistent conclusion to be drawn regarding which method is the best. In this paper, we will compare the performance of the most common scoring techniques such as logistic regression, neural network, and support vector machines using personal credit data of the financial institution in China. Specifically, we build three models respectively, classify the customers and compare analysis results. According to the results, support vector machine has better performance than logistic regression and neural networks.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1994.10a
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• pp.145-151
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• 1994

Welded connections have been designed on basis of allowable stresses, wherein the response to loading is assumed to be totally elastic. This is the vector analysis method, which resolves the stresses determined from the direct stress formula and the torsion formula into a vector combination to obtain a solution. It has been known that this method gives conservative answers and typically a very high factor of safety. An analytical method based on the Instantaneous Center of Rotation has been developed which predicts the ultimate strength of an eccentically loaded fillet welded connection. The method of Instantaneous Center of Rotation results in weld resistance capacities greater than the vector analysis method, by recognizing the variation in fillet weld strength with respect to the direction of the applied loading and actual load-deformation response of elemental fillet welds. The procedure of numerical analysis is iterative and complex. The relations between vector analysis method and the method of Instantaneous Center of Rotation on eccentrical distance subjected to variation of load direction are presented in this paper. Considering of the effects on configuration of weld groups, the method of Instantaneous Center of Rotation are provided a more exact results of the numerical analysis.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.243-246
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• 2003

In this paper, we propose a novel navigation method combined limit-cycle method and the vector field method for avoidance of unexpected obstacles in the dynamic environment. The limit-cycle method is used to obstacle avoidance in front of the robot and the vector field method is used to obstacle avoidance in the side of robot. The proposed method is tested on pioneer 2-DX mobile robot. The simulations and experiments demonstrate in the effectiveness of the proposed method for navigation of a mobile robot in the complicated and dynamic environments.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2281-2283
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• 2004

In this paper, we show the vector control performances for the parallel-connected motor drive system using the indirect vector control and the proposed vector control. The suggested estimation scheme of the rotor flux position is presented to reduce the sensitivity due to the load difference between the motors. To confirm the validity of the proposed control method, we compare the simulation results of the proposed control method with those of the conventional indirect vector control method. The simulation results show that the proposed control method is more effective for a change in the load torque.