• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.408-421
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• 2015

Because the wheel of V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor (PMSM) has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming job. In order to overcome difficulties for design of the linear controllers, a hybrid recurrent Chebyshev neural network (NN) control system is proposed to control for a PMSM servo-driven V-belt CVT system under the occurrence of the lumped nonlinear load disturbances. The hybrid recurrent Chebyshev NN control system consists of an inspector control, a recurrent Chebyshev NN control with adaptive law and a recouped control. Moreover, the online parameters tuning methodology of adaptive law in the recurrent Chebyshev NN can be derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, the optimal learning rate of the parameters based on discrete-type Lyapunov function is derived to achieve fast convergence. The recurrent Chebyshev NN with fast convergence has the online learning ability to respond to the system's nonlinear and time-varying behaviors. Finally, to show the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.125-126
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• 2019

In this paper, we propose an improved kNN classification method. Through improved the mothed and normalizing the data, the purpose of improving the accuracy is achieved. Then we compared the three classification algorithms and the improved algorithm by experimental data.

• Journal of Power Electronics
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• v.6 no.4
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• pp.340-346
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• 2006

A high efficiency drive technique for synchronous reluctance motors (SynRM) using a neural network (NN) is presented in this paper. High efficiency drive condition depends on the mathematical model of SynRM. A NN is employed as an adaptive model of SynRM. The proposed high efficiency drive technique does not require an accurate mathematical model of SynRM. Moreover, the proposed method shows robustness against machine parameter variations because the training algorithm of the NN is executed on-line. The usefulness of the proposed method is confirmed through experimentation.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.6
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• pp.395-402
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• 2013

We propose an accelerometer-based gesture recognition method for smartphone users. In our method, similarities between a new time series accelerometer data and each gesture exemplar are computed with DTW algorithm, and then the best matching gesture is determined based on k-NN algorithm. In order to investigate the performance of our method, we implemented a gesture recognition program working on an Android smartphone and a gesture-based teleoperating robot system. Through a set of user-mixed and user-independent experiments, we showed that the proposed method and implementation have high performance and scalability.

• Journal of the Korean Society for Library and Information Science
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• v.39 no.2
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• pp.123-146
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• 2005

This study aims to find consistent strategies for feature selection and feature weighting methods, which can improve the effectiveness and efficiency of kNN text classifier. Feature selection criteria and feature weighting methods are as important factor as classification algorithms to achieve good performance of text categorization systems. Most of the former studies chose conflicting strategies for feature selection criteria and weighting methods. In this study, the performance of several feature selection criteria are measured considering the storage space for inverted index records and the classification time. The classification experiments in this study are conducted to examine the performance of IDF as feature selection criteria and the performance of conventional feature selection criteria, e.g. mutual information, as feature weighting methods. The results of these experiments suggest that using those measures which prefer low-frequency features as feature selection criterion and also as feature weighting method. we can increase the classification speed up to three or five times without loosing classification accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.828-833
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• 2005

Kernel methods have shown to improve the performance of conventional linear classification algorithms for complex distributed data sets, as mapping the data in input space into a higher dimensional feature space(7). In this paper, we propose a fuzzy kernel K-nearest neighbor(fuzzy kernel K-NN) algorithm, which applies the distance measure in feature space based on kernel functions to the fuzzy K-nearest neighbor(fuzzy K-NN) algorithm. In doing so, the proposed algorithm can enhance the Performance of the conventional algorithm, by choosing an appropriate kernel function. Results on several data sets and segmentation results for real images are given to show the validity of our proposed algorithm.

• Journal of Forest and Environmental Science
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• v.24 no.3
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• pp.151-154
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• 2008

Dramatic price increases of fossil fuels and the economic development of emerging nations accelerates the transformation of forest lands into monocultures, e.g. for biofuel production. On this account, cost efficient methods to enable the monitoring of land resources has become a vital ambition. The application of remote sensing techniques has become an integral part of forest attribute estimation and mapping. The aim of this study was to evaluate the potentials of the kNN method by combining terrestrial with remotely sensed data for the development of a pixel-based monitoring system for the small scaled mosaic of different land use types of the off-reserve forests of the Goaso forest district in Ghana, West Africa. For this reason, occurrence and distribution of land use types like cocoa and non-timber forest resources, such as bamboo and raphia palms, were estimated, applying the kNN method to ASTER satellite data. Averaged overall accuracies, ranging from 79% for plantain, to 83% for oil palms, were found for single-attribute classifications, whereas a multi-attribute approach showed overall accuracies of up to 70%. Values of k between 3 and 6 seem appropriate for mapping bamboo. Optimisation of spectral bands improves results considerably.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.95-107
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• 2008

Survey on the rural living indicators was the statistic approved from National Statistical Office and the survey executed by rural resources development institute. This study was used the raw data of survey on the rural living indicators in 2005. After editing procedure for raw data, we were studied 1,582 households which is acquired through elimination of case included nonresponses, and imputed a nonresponses of 15 item selected from 146 item. The imputation methods and efficiency of imputation for simulation was adapted differently from type of data. For continuous data, we imputed the nonresponses with mean imputation, regression imputation, adjusted grey-based k-NN imputation(DU, DW, WU, WW) and compared the results with RMSE. For categorical data, we imputed the nonresponses with mode method, probability imputation, conditional mode method, conditional probability method, hot-deck imputation, and compared the results with Accuracy. By the results, regression imputation and adjusted grey-based k-NN imputation appropriated for continuous data and hot-deck imputation appropriated for categorical data.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.624-627
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• 2005

This paper presents a new data classification method using the Robbins Monro stochastic approximation algorithm k-nearest neighbor and distribution analysis. To cluster the data set, we decide the centroid of the test data set using k-nearest neighbor algorithm and the local area of data set. To decide each class of the data, the Robbins Monro stochastic approximation algorithm is applied to the decided local area of the data set. To evaluate the performance, the proposed classification method is compared to the conventional fuzzy c-mean method and k-nn algorithm. The simulation results show that the proposed method is more accurate than fuzzy c-mean method, k-nn algorithm and discriminant analysis algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.423-426
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• 2005

기억기반학습의 일종인 최근접 이웃(k nearest neighbor) 알고리즘은 과거의 데이터들 중에서 새로운 개체와 유사한 데이터들을 이용해서 새로운 개체의 목적 값을 예측하는 것이다. 이 경우 속성의 가중치를 계산하는 방식은 kNN의 성능을 결정하는 중요한 요소가 된다. 본 논문에서는 기존의 다른 이론들과 달리 정보이론에서 사용되는 엔트로피 개념을 이용해서 속성의 가중치를 이론적이고, 효과적으로 계산하는 새로운 방법을 제시하고자한다. 제안된 방법은 각 속성이 목적속성에 제공하는 정보의 양에 따라 가중치를 자동으로 계산하여 kNN의 성능을 향상시킨다. 마지막으로 이러한 방식의 성능을 다수의 실험을 통해 비교하였다.