• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.8
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• pp.1666-1671
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• 2002

A lane sensing algorithm using vision sensors is developed based on lane geometry models. The parameters of the lane geometry models are estimated by a Kalman filter and utilized to reconstruct the lane geometry in the global coordinate. The inverse perspective mapping from image plane to global coordinate assumes earth to be flat, but roll and pitch motions of a vehicle are considered from the perspective of the lane sensing. The proposed algorithm shows robust lane sensing performance compared to the conventional algorithms.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.22-25
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• 2003

EOC(Electro Optical Camera)는 한반도 및 전 세계 육지 영역 관측용으로 설계되었다. EOC는 1999년 12월 21일 발사된 다목적 실용위성 1호에 탑재되어 가시광 대역(510 ～730nm)으로 입사하는 복사 정보를 수집해 왔다. 획득된 EOC 영상 자료는 다목적 실용위성 1호의 탑재체 자료전송 시스템(Payload Data Transmission System, PDTS)을 통해 지상으로 전송되며, 수신된 자료에 대한 방사 보정 및 기하 보정 등의 일련의 전처리(Pre-processing) 과정을 거쳐 EOC 표준 영상이 생성된다. EOC 영상에 대한 MTF 보상은 방사 보정 후 수행될 수 있으며, 다목적 실용위성 지상국에서는 사용자의 요구에 따라 EOC 영상에 대한 MTF 보상을 수행하고 그 결과를 제공한다. MTF 보상은 EOC의 점 확산 함수(Point Spread Function)를 이용하여 수행되며, 현재 Wiener 필터를 이용하여 수행되고 있다. 본문에서는 현재 다목적 실용위성 1호 영상처리시스템의 EOC 영상에 대한 MTF 보상을 소개하고, EOC의 점 확산 함수에 기초하여 역 필터(Inverse Filter) 및 의사 역 필터(Pseudo Inverse Filter)를 제작, EOC 영상에 대한 MTF 보상 수행 후 그 결과를 Wiener 필터를 이용한 결과와 비교, 분석한다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1044-1049
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• 2001

Classical deconvolution methods for source identification following linear filtering can only be used if the transfer function of the system is known. For many practical situations, however, this information is not accessible and/or is time varying. The problem addressed here is that of reconstruction of the original input from only the measured signal. This is known as 'blind deconvolution'. By using Higher Order Statistics (HOS), the restoration of the input signal is established through the maximisation of higher order moments (cumulants) with respect to the characteristics of the signals concerned. This restoration is achieved by constructing an inverse filter considering the choice of the initial inverse filter type. As a practical application, an experimental verification is carried out for the restoration of our impacting signal arising in the response of a cantilever beam with an end stop when randomly excited.

• Wind and Structures
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• v.17 no.4
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• pp.451-464
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• 2013

This study develops an extended inverse input estimation algorithm with intelligent adaptive fuzzy weighting to effectively estimate the unknown input wind load of nonlinear structural systems. This algorithm combines the extended Kalman filter and recursive least squares estimator with intelligent adaptive fuzzy weighting. This study investigated the unknown input wind load applied on a tower structural system. Nonlinear characteristics will exist in various structural systems. The nonlinear characteristics are particularly more obvious when applying larger input wind load. Numerical simulation cases involving different input wind load types are studied in this paper. The simulation results verify the nonlinear characteristics of the structural system. This algorithm is effective in estimating unknown input wind loads.

• ETRI Journal
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• v.34 no.3
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• pp.379-387
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• 2012

In this paper, an error compensation technique for a dead reckoning (DR) system using a magnetic compass module is proposed. The magnetic compass-based azimuth may include a bias that varies with location due to the surrounding magnetic sources. In this paper, the DR system is integrated with a Global Positioning System (GPS) receiver using a finite impulse response (FIR) filter to reduce errors. This filter can estimate the varying bias more effectively than the conventional Kalman filter, which has an infinite impulse response structure. Moreover, the conventional receding horizon Kalman FIR (RHKF) filter is modified for application in nonlinear systems and to compensate the drawbacks of the RHKF filter. The modified RHKF filter is a novel RHKF filter scheme for nonlinear dynamics. The inverse covariance form of the linearized Kalman filter is combined with a receding horizon FIR strategy. This filter is then combined with an extended Kalman filter to enhance the convergence characteristics of the FIR filter. Also, the receding interval is extended to reduce the computational burden. The performance of the proposed DR/GPS integrated system using the modified RHKF filter is evaluated through simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.12 no.2
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• pp.83-91
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• 1987

In this paper, the extended inverse Chebyshev function have been derived from Chebyshev function. We presented normalized biquads coefficients of n=5, 6 for passband attenuation Ap(dB)=0.1, 0.2, 0.5, 1.0, 2.0, 3.0 and stopband frequency Ws(rad/s)=1.2, 1.3, 1.4, 1.5, 1.6. A designed low-pass filter from extended inverse Chebyshev transfer function produces the magnitude haracteristic which is maximally flat in the passband and equalripple in the stopband as shown in fig. 3(c), (d). Finally, it showed the magnitude and loss characteristics through realistic circuit simulation, and presented element values.

• Journal of The Korean Association of Information Education
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• v.15 no.3
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• pp.517-524
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• 2011

This paper presents a fast blind deconvolution method that produces a deblurring result from a single image in only a few seconds. The high speed of our method is enabled by considering the Discrete Fourier Transform (DFT), and its relation to filtering and convolution, and fast computation of Moore-Penrose inverse matrix. How can we predict the behavior of an arbitrary filter, or even more to the point design a filter to achieve certain specifications. The idea is to study the frequency response of the filter. This concept leads to an useful convolution formula. A Matlab implementation of our method usually takes less than one minute to deblur an image of moderate size, while the deblurring quality is comparable.

• Structural Monitoring and Maintenance
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• v.3 no.2
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• pp.115-127
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• 2016

It is a challenging problem of assessing the location and extent of structural damages with vibration measurements. In this paper, an improved Extended Kalman filter (EKF) with Tikhonov regularization is proposed to identify structural damages. The state vector of EKF consists of the initial values of modal coordinates and damage parameters of structural elements, therefore the recursive formulas of EKF are simplified and modal truncation technique can be used to reduce the dimension of the state vector. Then Tikhonov regularization is introduced into EKF to restrain the effect of the measurement noise for improving the solution of ill-posed inverse problems. Numerical simulations of a seven-story shear-beam structure and a simply-supported beam show that the proposed method has good robustness and can identify the single or multiple damages accurately with the unknown initial structural state.

• Wind and Structures
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• v.11 no.1
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• pp.19-33
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• 2008

This research investigates the adaptive input estimation method applied to the multilayer shearing stress structure. This method is to estimate the values of wind load inputs by analyzing the active reaction of the system. The Kalman filter without the input term and the adaptive weighted recursive least square estimator are two main portions of this method. The innovation vector can be produced by the Kalman filter, and be applied to the adaptive weighted recursive least square estimator to estimate the wind load input over time. This combined method can effectively estimate the wind loads to the structure system to enhance the reliability of the system active performance analysis. The forms of the simulated inputs (loads) in this paper include the periodic sinusoidal wave, the decaying exponent, the random combination of the sinusoidal wave and the decaying exponent, etc. The active reaction computed plus the simulation error is regard as the simulated measurement and is applied to the input estimation algorithm to implement the numerical simulation of the inverse input estimation process. The availability and the precision of the input estimation method proposed in this research can be verified by comparing the actual value and the one obtained by numerical simulation.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.23 no.10
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• pp.820-825
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• 2010

In this study, we intended to design the optimal Fermi filter to apply the k-space date that is knee image of the rheumatoid arthritis patient acquired from the MRI (magnetic resonance imaging) instrument. After deciding the suitable coefficient for the Fermi filter, the results were compared with modified Fermi filter and inverse Chebyshev filter, Chebyshev filter, Elliptic filter and Butterworth filter. Firstly, in comparison to the results, the radiologist confirmed that modified Fermi filter was best decision for boundary of the rheumatoid arthritis images. The number of the black voxels of the histogram showed the quantity of the results. At the proposed filter images, numbers of the blacks voxels were statistically decreased. That meant voxels only appeared the black color were changed to others voxels color. Because the number of the total voxels was fixed, the area appeared block color could be effected to the other areas. If the modified Fermi filter were used for rheumatoid arthritis patient, the result will be better than other filters.