• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.9
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• pp.1684-1692
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• 2011

Recently, several vector hysteresis models such as vector Preisach, vector Jiles-Atherton and dynamic E&S model have been proposed to describe vector magnetic properties of electrical steel sheets. However, it is still difficult to find an adequate vector hysteresis model in finite element application for both the Non-oriented and Grain-oriented electrical steel sheets under alternating and rotating field conditions. In this paper, an improved E&S vector hysteresis model is suggested to describe the vector magnetic properties of both Non-oriented and Grain-oriented electrical steel sheets under various magnetic field conditions including alternating and rotating magnetic field conditions. The validity of the proposed model is tested through comparisons with the experimental results under various magnetic field conditions.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.655-660
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• 2003

In general, Support Vector Machine has a good performance in binary classification, but it has the limitation on multi-pattern classification. So, we proposed an Import Vector Voting model for two or more labels classification. This model applied kernel bagging strategy to Import Vector Machine by Zhu. The proposed model used a voting strategy which averaged optimal kernel function from many kernel functions. In experiments, not only binary but multi-pattern classification problems, our proposed Import Vector Voting model showed good performance for given machine learning data.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.11a
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• pp.88-89
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• 2014

A vector-photo is a photography which contain image and 5W1H information. Objects pictured and an image in a vector-photo can be linked by 5W1H information in the vector-photo. This study discuss a positioning module to link between vector-photos and 3D BIM model. The module developed in this study can be utilized in a system that can link the vector-photos with objects in BIM model.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.263-272
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• 2024

A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of their status. This model improves the predictive effect of support vector machines. The genetic optimization algorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, and gamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain the optimal solution. The experimental model was trained on open-source datasets and validated through comparisons with random forest, backpropagation neural network, and GA-SVM models. The test results show that the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtain higher accuracy rates when used for early warning in university learning.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.12
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• pp.1813-1819
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• 2012

This paper presents vector magnetic properties of an electrical steel sheet (ESS) employed for electric machine and iron loss analysis considering the vector magnetic properties of the ESS. The vector magnetic properties of the ESS are measured by using a two-dimensional single sheet tester and modeled by an E&S vector hysteresis model to be applied to finite element method. The finite element analysis considering the vector magnetic properties is applied to iron loss analysis of a three-phase induction motor model, and the influences of the vector magnetic properties on the iron loss distribution are verified by comparing with numerical results from a typical B-H curve model.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.1
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• pp.36-42
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• 2004

In this paper, we compare the vector model performance with the probabilistic model of information system. We use LSI(Latent Semantic Indexing) model for vector model, while Condor information search system that is ready to sell on business is used as a probabilistic model. Each model produces the search results from the original queries and the queries extended by a dictionary definition. We compare those results between two models and find out the vector model is much better than the probabilistic model for the most queries.

• The Journal of Information Technology and Database
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• v.3 no.2
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• pp.75-96
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• 1996

This study is intended to compare the effectiveness of the neural network inductive learning model with a vector space model in information retrieval. As a result, searches responding to incomplete queries in the neural network inductive learning model produced a higher precision and recall as compared with searches responding to complete queries in the vector space model. The results show that the hybrid methodology of integrating an inductive learning technique with the neural network model can help solve information retrieval problems that are the results of inconsistent indexing and incomplete queries--problems that have plagued information retrieval effectiveness.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.52 no.2
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• pp.87-92
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• 2003

This paper presents a flux estimator for the sensorless vector control of induction motors. The proposed method utilize the combination of the voltage model based on stator equivalent model and the current model based on rotor equivalent model, which enables stable estimation of rotor flux in high speed region and in low speed region. The dynamic performance of proposed method is verified through the experiment. The experimental results show that motors ran easily start even under 150[%] load condition and operate continuously below 0.5[Hz].

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.679-682
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• 1997

This paper presents finite element analysis algorithm combined with vector hysteresis model for accurate analysis of the hysteresis motor. Magnetization-dependent vector model is adapted to calculate the vector magnetization. That is to say, from the magnitude and direction of the magnetic field intensity, the magnetization of each ring element is computed by the vector model. By comparing the simulation results with the experimental ones, it is found that good results are obtained.

• Journal of the Korean Statistical Society
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• v.34 no.4
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• pp.345-366
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• 2005

This paper considers the cointegrating vector estimator in the error correction model with stationary covariates, which combines the stationary vector autoregressive model and the nonstationary error correction model. The cointegrating vector estimator is shown to follow the locally asymptotically mixed normal distribution. The variance of the estimator depends on the co­variate effect of stationary regressors, and the asymptotic efficiency improves as the magnitude of the covariate effect increases. An economic application of the money demand equation is provided.