• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.4
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• pp.45-56
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• 2011

This paper is proposed artificial neural network(ANN) rotor resistance estimation of induction motor drive controlled by multi-adaptive fuzzy learning controller(AFLC). A simple double layer feedforward ANN trained by the back-propagation technique is employed in the rotor resistance identification. In this estimator, double models of the state variable estimations are used; one provides the actual induction motor output states and the other gives the ANN model output states. The total error between the desired and actual state variables is then back propagated to adjust the weights of the ANN model, so that the output of this model tracks the actual output. When the training is completed, the weights of the ANN correspond to the parameters in the actual motor. The estimation and control performance of ANN and multi-AFLC is evaluated by analysis for various operating conditions. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• Journal of the Korea Computer Industry Society
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• v.3 no.9
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• pp.1315-1328
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• 2002

Recently there has been increasing demand on developing methodologies and tools for measuring the information security level of organizations for the efficient security management, as the growth of security incidents. However, most methodologies from foreign countries are not realistic in constructing the checklists, moreover their tools provide neither the ease of use nor the inexpensiveness, and most domestic works are not properly considering the characteristics of the organizations. In this study, based on the recently developed standard for information security management, an information security levelling tool is designed and implemented which can be used before building an information security management system while considering the characteristics of organizations more efficiently. The efficiency comes from applying multiple variable weights for security levelling according to the characteristics of organizations.

• Journal of IKEEE
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• v.24 no.2
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• pp.570-578
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• 2020

In this paper, we propose an efficient method for implementing the adaptive second-order Volterra filter. To reduce computational load, the UCFD-SVF has been proposed. The UCFD-SVF, however, shows deteriorated convergence performance. We propose a new method that initializes the adaptive filter weights periodically on the fact that the energy of the filter weights is slowly increased. Furthermore, we propose another method that the interval for the weight initialization is variable to guarantee the performance and we shows the method gives the better performance under the non-stationary environment through the computer simulation for the adaptive system identification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.786-792
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• 2022

In the cloud computing environment, there has been a lot of research into the Fog/Edge Computing (FEC) paradigm for securing user proximity of application services and computation offloading to alleviate service delay difficulties. The method of predicting dynamic location change patterns of edge devices (moving objects) requesting application services is critical in this FEC environment for efficient computing resource distribution and deployment. This paper proposes an optimal moving pattern extraction algorithm in which variable weights (distance, time, congestion) are applied to selected paths in addition to a support factor threshold for frequency patterns (moving objects) of edge devices. The proposed algorithm is compared to the OPE_freq [8] algorithm, which just applies frequency, as well as the A^* and Dijkstra algorithms, and it can be shown that the execution time and number of nodes accessed are reduced, and a more accurate path is extracted through experiments.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.961-975
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• 2016

The hierarchically penalized support vector machine (H-SVM) has been developed to perform simultaneous classification and input variable selection when input variables are naturally grouped or generated by factors. However, the H-SVM may suffer from estimation inefficiency because it applies the same amount of shrinkage to each variable without assessing its relative importance. In addition, when analyzing imbalanced data with uneven class sizes, the classification accuracy of the H-SVM may drop significantly in predicting minority class because its classifiers are undesirably biased toward the majority class. To remedy such problems, we propose the weighted adaptive H-SVM (WAH-SVM) method, which uses a adaptive tuning parameters to improve the performance of variable selection and the weights to differentiate the misclassification of data points between classes. Numerical results are presented to demonstrate the competitive performance of the proposed WAH-SVM over existing SVM methods.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.111-122
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• 2012

There has been a strong demand for providing diverse services to drivers utilizing existing ITS infrastructure. To this end, this study is aiming at improving the accuracy of a variable speed limit system by determining recommended speeds for the system utilizing the information from Urban Traffic Information System(UTIS) and Weather Information System(WIS). In order to determine appropriate speed limits under inclement weather conditions for the variable speed limit system, this study examined three methods: i) the method utilizing the information from WIS, ii) the method utilizing the information from UTIS, and iii) the method which combines the information from WIS and UTIS using different weights for diverse weather conditions. Finally, this study selected the third method which determines an appropriate speed limit using the relationship between the vehicle operating speed and the minimum stopping distance which is estimated using the existing speed limit, surface coefficient of friction and superelevation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.47-53
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• 2018

Various types of robotic arms are being used for industrial purposes, particularly with the small production of multi-products, and the importance of the gripper, which can be used in industrial fields, is increasing. This study evaluated a variable stiffness mechanism gripper that can change the stiffness using the nonlinearity of a flexible material. A prototype of the gripper was fabricated and examined to confirm the change in stiffness. The previous gripper was unable to grip objects in some situations with three variable stiffness mechanism. In addition, these mechanisms were not balanced and rarely rotated when the object was gripped. Therefore, a new type of gripper was needed to solve this problem. Inspired by the movements of the human palm and Venus Flytrap, a new type of a variable stiffness soft robot hand was designed. The possibility of grasping could be increased by interlocking the palm folding mechanism by pulling the tendon attached to the variable stiffness mechanism. The soft robotic hand was used to grasp objects of various shapes and weights more stably than the previous variable stiffness mechanism gripper. This new variable stiffness soft robot hand can be used selectively depending on the application and environment to be used.

• Journal of KIISE:Software and Applications
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• v.28 no.11
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• pp.855-863
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• 2001

We propose an image alignment algorithm based on variable-sized blocks of cylindrical prototype model to generate a face texture for a realistic 3D face model. This is a block matching algorithm which aligns 2D images of a 3D cylindrical model using th correlation between them. While matching blocks, it does not use same sized blocks with considering a curvature of 3D model. And we make a texture of aligned images using a technique of image mosaic. For this purpose, we stitch them with assigning linear weights according to the overlapped region and using the cross-dissolve technique.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.3
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• pp.316-324
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• 2001

A table compression method for reversible variable length code is proposed in this paper. TNWT(Transition Number and Weight of Tree) method, which uses the transition number of bits within a symbol and the level of a code tree, is proposed. Compression of table values is performed after arrangment of values that is not distinghishable by transition number and weights. In decoding, the transition number and weight of code are used. In this method, the table for RVLC decoding can be implemented with a smaller memory.

• Journal of Power Electronics
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• v.19 no.3
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• pp.771-783
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• 2019

Due to nonlinear-synthetic uncertainty including the total unknown nonlinear load torque, the total parameter variation and the fixed load torque, a synchronous reluctance motor (SynRM) driving a continuously variable transmission (CVT) system causes a lot of nonlinear effects. Linear control methods make it hard to achieve good control performance. To increase the control performance and reduce the influence of nonlinear time-synthetic uncertainty, an admixed recurrent Gegenbauer orthogonal polynomials neural network (ARGOPNN) with a modified particle swarm optimization (MPSO) control system is proposed to achieve better control performance. The ARGOPNN with a MPSO control system is composed of an observer controller, a recurrent Gegenbauer orthogonal polynomial neural network (RGOPNN) controller and a remunerated controller. To insure the stability of the control system, the RGOPNN controller with an adaptive law and the remunerated controller with a reckoned law are derived according to the Lyapunov stability theorem. In addition, the two learning rates of the weights in the RGOPNN are regulating by using the MPSO algorithm to enhance convergence. Finally, three types of experimental results with comparative studies are presented to confirm the usefulness of the proposed ARGOPNN with a MPSO control system.