• Proceedings of the Korean Information Science Society Conference
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• 2011.06d
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• pp.167-170
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• 2011

많은 측위 알고리즘이 참조노드가 정사각형의 모서리에 위치한다고 가정 하고 있지만, 실제로는 다각형이 되거나 매쉬형으로 배치될 수 있다. 신호세기를 달리함으로써 동심원을 구성하여 측위하는 WMRL(Weighted Multiple Rings Localization)도 기본적으로 참조노드의 배치가 정사각형으로 가정하고 있다. 본 논문에서는 참조노드는 임의로 배치되어 있는 경우에서의 측위로 확장한다. 즉, 측위하는 센서 노드가 수신 가능한 전파를 송신하는 모든 참조노드로부터 링 번호를 기반으로 자신의 위치를 추청한다. WMRL의 다중 신호 세기 링 방식을 채용, 각 링의 도달거리를 기반으로 센서노드가 자신과 참조노드 간의 거리를 유추하고, 최소자승법을 이용해 자신의 좌표를 계산하는 알고리즘을 제안한다. 실험 결과 제안한 알고리즘은 에러가 없는 환경과 다수 참조노드 환경에서 WMRL 및 WCL(Weighted Centroid Localization)보다 2배 이상의 성능향상을 보였으며, 에러가 있는 전파환경에서는 DV-hop 보다 평균 6%, WCL 및 WMRL에는 평균 16% 정도의 성능 향상 결과를 보였다.

• The Journal of the Acoustical Society of Korea
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• v.19 no.5
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• pp.84-90
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• 2000

The bearing detection of radiated target noise is very important at underwater acoustic measurement and passive detection. It differs the arrival tines of received signal at each sensor. Therefore, the bearing can be obtained from the time delay. This paper proposes a new algorithm using the RLSL adaptive filter for TDE. The proposed method is particularly attractive when there is a limitation of priori information about the received signal spectra and when the delay is subject to variation. As the simulation results, it is shown that the proposed algorithm has better convergence characteristics and TDE speed, and so that the usefulness of proposed algorithm is confirmed.

• Journal of Korean Society of Transportation
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• v.18 no.6
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• pp.89-99
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• 2000

To cope with the limits of conventional O-D trip matrix collecting methods, several approaches have been developed. One of them is bilevel Programming method Proposed by Yang(1995), which uses Sensitivity Analysis Based(SAB) algorithm to solve Generalized Least Square(GLS) problem. However, the SAB a1gorithm has revealed two critical short-comings. The first is that when there exists a significant difference between target O-D matrix and true O-D matrix, SAB algorithm may not produce correct solution. This stems from the heavy dependance on the historical O-D information, in special when gravel Patterns are dramatically changed. The second is the assumption of iterative linear approximation to original Problem. Because of the approximation, SAB algorithm has difficulty in converging to Perfect Stackelberg game condition. So as to avoid the Problems. we need a more robust and stable solution method. The main purpose of this Paper is to show the problem of the dependency of Previous models and to Propose an alternative solution method to handle it. The Problem of O-D matrix estimation is intrinsically nonlinear and nonconvex. thus it has multiple solutions. Therefore it is necessary to require a method for searching globa1 solution. In this paper, we develop a solution algorithm combined with genetic algorithm(GA) , which is widely used as probabilistic global searching method To compare the efficiency of the algorithm, SAB algorithm suggested by Yang et al. (1992,1995) is used. From the results of numerical example, the Proposed algorithm is superior to SAB algorithm irrespective of travel patterns.

• Journal of Drive and Control
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• v.13 no.4
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• pp.59-67
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• 2016

This paper proposes a model-prediction-based collision-avoidance algorithm for excavators for which the recursive-least-squares (RLS) estimation of the excavator's rotational inertia is used. To estimate the rotational inertia of the excavator, the RLS estimation with multiple forgetting and two updating rules for the nominal parameter and the forgetting factors was conducted based on the excavator-swing dynamics. The average value of the estimated rotational inertia that is for the minimizing effects of the estimation error was computed using the recursive-average method with forgetting. Based on the swing dynamics, the computed average of the rotational inertia, the damping coefficient for braking, and the excavator's braking angle were predicted, and the predicted braking angle was compared with the detected-object angle for a safety evaluation. The safety level defined in this study consists of the three levels safe, warning, and emergency braking. The analytical rotational-inertia-based performance evaluation of the designed estimation algorithm was conducted using a typical working scenario. The results of the safety evaluation show that the predictive safety-evaluation algorithm of the proposed model can evaluate the safety level of the excavator during its operation.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.355-360
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• 2015

In this paper, we propose the design of Radial Basis Function Neural Network based on PCA in order to recognize handwritten digits. The proposed pattern classifier consists of the preprocessing step of PCA and the pattern classification step of pRBFNNs. In the preprocessing step, Feature data is obtained through preprocessing step of PCA for minimizing the information loss of given data and then this data is used as input data to pRBFNNs. The hidden layer of the proposed classifier is built up by Fuzzy C-Means(FCM) clustering algorithm and the connection weights are defined as linear polynomial function. In the output layer, polynomial parameters are obtained by using Least Square Estimation (LSE). MNIST database known as one of the benchmark handwritten dataset is applied for the performance evaluation of the proposed classifier. The experimental results of the proposed system are compared with other existing classifiers.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.159-165
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• 2017

In order to solve problems in automatic quality inspection of tablet capsules, computation-efficient image processing technique, appropriate threshold setting, edge detection and segmentation methods are required. And since existing automatic system for quality inspection of tablet capsules is of very high cost, it needs to be reduced through the realization of low-price hardware system. This study suggests a technique that uses low-cost camera module to obtain image and inspects dents on tablet capsules and sorting them by applying TLS curve fitting technique and edge-based image segmentation. In order to assess the performance, the major classifications algorithm of PCA, ICA and SVM are used to evaluate training time, test time and accuracy for capsule image area and curve fitting edge data sets.

• Journal of the Korean Geophysical Society
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• v.5 no.3
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• pp.233-245
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• 2002

The spectral-analysis-of-surface-waves (SASW) method is a nondestructive testing method based upon generation and detection of elastic stress waves. SASW is widely used as one of the techniques to determine stiffness profile in engineering geophysics. The essential steps involved are construction of an experimental dispersion curve from data collected in situ, and inversion of the dispersion curve to determine the stiffness profile. The main object of this study is to derive an analytical Jacobian for the inversion. If we set the subsurface to N homogeneous layer, it could save 2N times Jacobian calculation compared to numerical jacobian calculation during inversion. To reconstruct a stiffness profile, constrained damped least square method was applied for the inversion. The algorithm was tested for the numerical data and for the real asphalt and tunnel data, which were able to verify the stiffness profile. The stiffness profile reconstructed by the algorithm showed the possibility to appraise the soundness of tunnel with applications SASW.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.1
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• pp.56-67
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• 2012

In this thesis, to compensate the sweep nonlinearity occurring in the high resolution radar system using FMICW or FMCW, the method of the estimation of the nonlinearity is proposed. The nonlinear phase component caused by the nonlinear characteristic of the radar system is modelled as a linear combination of the sinusoidal functions consisting of various magnitudes and phases(systematic nonlinear phase error) and a random component(stochastic nonlinear phase error). From two IF signals that are measured respectively independently for two reference point targets lying in different distances which are known, a sparse linear equation is made and solved by least squares method to estimate the nonlinear phase component. The estimated component can be used for predistortion method to compensate the sweep nonlinearity.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.8
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• pp.921-931
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• 2011

This paper presents a data-fitting technique in which a B-spline hypervolume is used to approximate a given data set of scattered data samples. We describe the implementation of the data structure of a B-spline hypervolume, and we measure its memory size to show that the representation is compact. The proposed technique includes two algorithms. One is for the determination of the knot vectors of a B-spline hypervolume. The other is for the control points, which are determined by solving a linear least-squares minimization problem where the solution is independent of the data-set complexity. The proposed approach is demonstrated with various data-set configurations to reveal its performance in terms of approximation accuracy, memory use, and running time. In addition, we compare our approach with existing methods and present unconstrained optimization examples to show the potential for various applications.

• Journal of Drive and Control
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• v.14 no.2
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• pp.16-22
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• 2017

This paper presents the algorithm of an adaptive model-free-control-based steering control for multi-axle all-terrain cranes for which the recursive least squares with forgetting are applied. To optimally control the actual system in the real world, the linear or nonlinear mathematical model of the system should be given for the determination of the optimal control inputs; however, it is difficult to derive the mathematical model due to the actual system's complexity and nonlinearity. To address this problem, the proposed adaptive model-free controller is used to control the steering angle of a multi-axle crane. The proposed model-free control algorithm uses only the input and output signals of the system to determine the optimal inputs. The recursive least-squares algorithm identifies first-order systems. The uncertainty between the identified system and the actual system was estimated based on the disturbance observer. The proposed control algorithm was used for the steering control of a multi-axle crane, where only the steering input and the desired yaw rate were employed, to track the reference path. The controller and performance evaluations were constructed and conducted in the Matlab/Simulink environment. The evaluation results show that the proposed adaptive model-free-control-based steering-control algorithm produces a sound path-tracking performance.