• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.508-511
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• 1997

Recently the development of motor speed control systems with both good dynamic performance and simple implementation has been required. The vector control scheme considering flux harmonics for the permanent-magnet AC servo motor having low inertia, low weight, and high efficiency is proposed. To reduce the torque harmonics, current harmonics is employed. The vector control strategy is verified through digital simulation.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.689-697
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• 2016

The most useful method for financial market risk management may be Value at Risk (VaR) which estimates the maximum loss amount statistically. The VaR is used as a risk measure for one industry. Many real cases estimate VaRs for many industries or nationwide industries; consequently, it is necessary to estimate the VaR for multivariate distributions when a specific portfolio is established. In this paper, the multivariate quantile vector is proposed to estimate VaR for multivariate distribution, and the Vector at Risk for multivariate space is defined based on the quantile vector. When a weight vector for a specific portfolio is given, one point among Vector at Risk could be found as the best VaR which is called as an alternative VaR. The alternative VaR proposed in this work is compared with the VaR of Morgan with bivariate and trivariate examples; in addition, some properties of the alternative VaR are also explored.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.900-901
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• 2008

In this research uses a predetermined initial weight vectors of 1-dimensional string, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are moved toward or reverse the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• The Journal of the Acoustical Society of Korea
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• v.27 no.5
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• pp.252-255
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• 2008

In this paper, we apply a discriminative weight training to a support vector machine (SVM) based gender identification. In our approach, the gender decision rule is expressed as the SVM of optimally weighted mel-frequency cepstral coefficients (MFCC) based on a minimum classification error (MCE) method which is different from the previous works in that different weights are assigned to each MFCC filter bank which is considered more realistic. According to the experimental results, the proposed approach is found to be effective for gender identification using SVM.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.1
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• pp.50-61
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• 2005

To provide adaptively video streaming services on network environment, video transcoding is introduced. The one of transcoding methods is the frame-rate conversion. it needs a re-estimation about a motion vector of the frame to refer a skipping frame. This re-estimation makes higher the computational complexity in video transcoding. To reduce the computational complexity of a motion vector refinement, this paper proposes a region & activity based motion vector composition scheme that refine the moving vector of a skipping frame. This scheme composes each motion vector from the weight based on the activity information of a macroblock and the site of the overlapped area. The experiment result shows that RABVC has a higher PSNR than the value of existing weight-based motion vector selection schemes though the computational complexity of our scheme is similar to that of other schemes.

• Journal of Korean Association for Spatial Structures
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• v.8 no.3
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• pp.75-82
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• 2008

In this study, the modified genetic algorithm, D-GA, is proposed. D-GA is a hybrid genetic algorithm combined a simple genetic algorithm and the local search algorithm using direction vectors. Also, two types of direction vectors, learning direction vector and random direction vector, are defined without the sensitivity analysis. The accuracy of D-GA is compared with that of simple genetic algorithm. It is demonstrated that the proposed approach can be an effective optimization technique through a minimum weight structural optimization of ten bar truss.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.12
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• pp.954-961
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• 2013

In this paper, we propose a new learning method using a variable learning to improve pattern recognition in the FCSR(Fast Commit Slow Recode) learning method of the Fuzzy ART. Traditional learning methods have used a fixed learning rate in updating weight vector(representative pattern). In the traditional method, the weight vector will be updated with a fixed learning rate regardless of the degree of similarity of the input pattern and the representative pattern in the category. In this case, the updated weight vector is greatly influenced from the input pattern where it is on the boundary of the category. Thus, in noisy environments, this method has a problem in increasing unnecessary categories and reducing pattern recognition capacity. In the proposed method, the lower similarity between the representative pattern and input pattern is, the lower input pattern contributes for updating weight vector. As a result, this results in suppressing the unnecessary category proliferation and improving pattern recognition capacity of the Fuzzy ART in noisy environments.

• The Journal of Korean Society for Radiation Therapy
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• v.28 no.1
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• pp.27-34
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• 2016

Purpose : Retrospective evaluation of setup changes using the corrected position during helical tomotherapy Materials and Methods : Head and neck cancer patients were randomly sampled and summarized into 3 groups: Group 1(32) Brain, Group 2 2(28)Maxillar, Nasal cavity, Group 3 (35) Nasopharynx(NPX), Tongue, Tonsil, and Oropharynx(OPX). In 3 groups, the statistical tests based on repeated measurements among 30 times of the duration of treatment by applying X, Y, Z axis errors, roll, weight changes, and vectors as variables. Results : The statistical test results showed that there was no difference between x-axis (p = 0.458) and y-axis (p=0.986) and in roll (p = 0.037), weight change (p <0.001), and the vector (p <0.001). In addition, the pattern between the three groups based on the fraction revealed no difference in x-axis (p = 0.430) and roll (p = 0.299) but a difference in y-axis (.023), weight change (p = 0.001), and vector (p = 0.028). Conclusion : The results of the retrospective evaluation found the change in the group 3 with respect Y, Z, weight, and vector and a larger random error during the treatment including low neck.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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• pp.159-167
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• 1996

In this paper, a new competitive learning algorithm called dynamic competitive learning (DCL) is presented. DCL is a supervised learning mehtod that dynamically generates output neuraons and nitializes weight vectors from training patterns. It introduces a new parameter called LOG (limit of garde) to decide whether or not an output neuron is created. In other words, if there exist some neurons in the province of LOG that classify the input vector correctly, then DCL adjusts the weight vector for the neuraon which has the minimum grade. Otherwise, it produces a new output neuron using the given input vector. It is largely learning is not limited only to the winner and the output neurons are dynamically generated int he trining process. In addition, the proposed algorithm has a small number of parameters. Which are easy to be determined and applied to the real problems. Experimental results for patterns recognition of remote sensing data and handwritten numeral data indicate the superiority of dCL in comparison to the conventional competitive learning methods.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.55-65
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• 2020

Image classification needs the spectral similarity comparison between spectral features of each pixel and the representative spectral features of each class. The spectral similarity is obtained by computing the spectral feature vector distance between the pixel and the class. Each spectral feature contributes differently in the image classification depending on the class separability of the spectral feature, which is computed using a suitable vector distance measure such as the Bhattacharyya distance. We propose a method to determine the weight value of each spectral feature in the computation of feature vector distance for the similarity measurement. The weight value is determined by the ratio between each feature separability value to the total separability values of all the spectral features. We created ten spectral features consisting of seven bands of Landsat-8 OLI image and three indices, NDVI, NDWI and NDBI. For three experimental test sites, we obtained the overall accuracies between 95.0% and 97.5% and the kappa coefficients between 90.43% and 94.47%.