• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.95-100
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• 2001

In this paper, we proposed the robust control scheme for a class of nonlinear dynamical systems using output feedback linearization method. The presented control scheme is based on the VSS. We assume that the nonlinear dynamical system is minimum phase, the relative degree of the system is r＜n and zero dynamics is stable. It is also shown that the global asymtotically stability is guaranted. And we verified that the proposed control scheme Is the feasible through a computer simulation.

• Journal of Korean Society for Quality Management
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• v.30 no.4
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• pp.68-77
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• 2002

EM algorithm has good convergence rate for numerical procedures which converges on very small step. In the case of proportion estimation in a mixed distribution which has very big incomplete data or of update of new data continuously, however, EM algorithm highly depends on a initial value with slow convergence ratio. There have been many studies to improve the convergence rate of EM algorithm in estimating the proportion parameter of a mixed data. Among them, dynamic EM algorithm by Hurray Jorgensen and Titterington algorithm by D. M. Titterington are proven to have better convergence rate than the standard EM algorithm, when a new data is continuously updated. In this paper we suggest dynamic EM algorithm and Titterington algorithm for the estimation of a mixed Poisson distribution and compare them in terms of convergence rate by using a simulation method.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.5 s.170
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• pp.45-54
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• 2005

The purpose of this research is to find a simple and accurate NURBS interpolator for CNC systems such as robot, CMM and CNC machine tools. This paper presents a new design of NURBS interpolator for CNC system. The proposed algorithm used the recursive characteristics of NURBS equation, the previous incremental value and chord length for the sake of a constant chord length. Simulation study was conducted to see the performance of the proposed interpolator with reference-word and reference-pulse method. Consequently, an accurate and simple NURBS interpolator was possible for modem CNC systems.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.763-770
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• 2017

A radome mounted on the front of an aircraft can cause refraction errors for various reasons that occur during maneuver in seeking and tracking a target. This refraction error means that the microwave seeker is detecting apparent target. An Interactive Multiple Model (IMM) algorithm is applied to estimate radome slope mounted on an aircraft in 3D space. However, even though the parameter of uncertain system model such as radome slope can be estimated, the estimated performance can not be guaranteed when it exceeds the range of the predicted value. In this paper, we propose a method to update the predicted value by using the radome slope as the mode parameter of the IMM algorithm, and confirm the radome slope estimation performance of the proposed method.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.4
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• pp.411-419
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• 2005

In this paper, a sliding mode observer for solar array current estimation in the photovoltaic power generation system is presented. The solar array current estimation Information is obtained from the sliding mode observer and fed into the maximum power point tracker to update the reference voltage. The parameter values such as inverter dc link capacitances cm be changed up to 50$\%$ from their nominal values and the linear observer can't estimate the correct state values under the parameter variations and noisy environments. The configuration of sliding mode observer is simple, but it shows the robust tracking performance against parameter variations and modeling uncertainties. In this paper, the method for constructing the sliding mode observer using equivalent control input is presented and the convergence of the proposed observer is verified by the Lyapunov method. The mathematical modeling and the experimental results verify the validity of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.22-28
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• 2017

The Convolutional Neural Network(CNN) can be divided into two stages: feature extraction and classification. The hyperparameters such as kernel size, number of channels, and stride in the feature extraction step affect the overall performance of CNN as well as determining the structure of CNN. In this paper, we propose a method to optimize the hyperparameter in CNN feature extraction stage using Parameter-Setting-Free Harmony Search (PSF-HS) algorithm. After setting the overall structure of CNN, hyperparameter was set as a variable and the hyperparameter was optimized by applying PSF-HS algorithm. The simulation was conducted using MATLAB, and CNN learned and tested using mnist data. We update the parameters for a total of 500 times, and it is confirmed that the structure with the highest accuracy among the CNN structures obtained by the proposed method classifies the mnist data with an accuracy of 99.28%.

• Journal of Internet Computing and Services
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• v.5 no.4
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• pp.77-94
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• 2004

Recently, the needs of index structure which manages moving objects efficiently have been increased because of the rapid development of location-based techniques. Existing index structures frequently need updates because moving objects change continuatively their positions. That caused entire performance loss of the index structures. In this paper, we propose a new index structure called the TPKDB-tree that is a spatio-temporal index structure based on KDB-tree. Our technique optimizes update costs and reduces a search time for moving objects and reduces unnecessary updates by expressing moving objects as linear functions. Thus, the TPKDB-tree efficiently supports the searches of future positions of moving objects by considering the changes of moving objects included in the node as time-parameter. To maximize space utilization, we propose the new update and split methods. Finally, we perform various experiments to show that our approach outperforms others.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.46-53
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• 2016

In mobile communication systems, location management deals with the location determination of users in a network. One of the strategies used in location management is to partition the network into location areas. Each location area consists of a group of cells. The goal of location management is to partition the network into a number of location areas such that the total paging cost and handoff (or update) cost is a minimum. Finding the optimal number of location areas and the corresponding configuration of the partitioned network is a difficult combinatorial optimization problem. This cell grouping problem is to find a compromise between the location update and paging operations such that the cost of mobile terminal location tracking is a minimum in location area wireless network. In fact, this is shown to be an NP-complete problem in an earlier study. In this paper, artificial bee colony (ABC) is developed and proposed to obtain the best/optimal group of cells for location area planning for location management system. The performance of the artificial bee colony (ABC) is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters. The important control parameter of ABC is only 'Limit' which is the number of trials after which a food source is assumed to be abandoned. Simulation results for 16, 36, and 64 cell grouping problems in wireless network show that the performance of our ABC is better than those alternatives such as ant colony optimization (ACO) and particle swarm optimization (PSO).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.9
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• pp.829-840
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• 2012

Recently, in network communication environments, it is changing very fast from wired to wireless. The open shortest path firtst (OSPF), one of link state routing protocols, mainly used in wired networks, is the routing method to select optimal traffic path as identifying the link state of neighbor routers. The traditional OSPF cost functions performs with first fixed cost permanently, unless the router link is changed. However, in wireless networks, the performance of links show big difference by other environment factors. The bit error rate (BER), a parameter which can quite affect link state in wireless networks, is not considered in the traditional OSPF cost functions. Only a link bandwidth is considered in the traditional OSPF cost functions. In this paper, we verify the various parameters which can affect link performance, whether it is permissible to use as the parameter of proposed cost functions. To propose new cost functions, we use the effective bandwidth. This bandwidth is calculated by proposed formula using the BER of the network link and link bandwidth. As applied by the proposed triggering condition, the calculated effective bandwidth decrease the unstable of network by generating less link state update messages in wireless networks that frequently changes the link state. Simulation results show that the proposed cost functions significantly outperforms the traditional cost functions in wireless networks in terms of the services of VoIP and data transmission.

• Journal of the Korean earth science society
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• v.37 no.5
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• pp.277-285
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• 2016

We introduce a depth scaling strategy to improve the accuracy of frequency-domain elastic full waveform inversion (FWI) using the new pseudo-Hessian matrix for seismic data without low-frequency components. The depth scaling strategy is based on the fact that the damping factor in the Levenberg-Marquardt method controls the energy concentration in the gradient. In other words, a large damping factor makes the Levenberg-Marquardt method similar to the steepest-descent method, by which shallow structures are mainly recovered. With a small damping factor, the Levenberg-Marquardt method becomes similar to the Gauss-Newton methods by which we can resolve deep structures as well as shallow structures. In our depth scaling strategy, a large damping factor is used in the early stage and then decreases automatically with the trend of error as the iteration goes on. With the depth scaling strategy, we can gradually move the parameter-searching region from shallow to deep parts. This flexible damping factor plays a role in retarding the model parameter update for shallow parts and mainly inverting deeper parts in the later stage of inversion. By doing so, we can improve deep parts in inversion results. The depth scaling strategy is applied to synthetic data without lowfrequency components for a modified version of the SEG/EAGE overthrust model. Numerical examples show that the flexible damping factor yields better results than the constant damping factor when reliable low-frequency components are missing.