• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.404-414
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• 2005

In the packet data network systems beyond 3G, the service quality of current users is affected by traffic load due to the packet burstiness. There also exists a hot cell problem, a well-known problem of cellular systems, caused by traffic centraliBation. Hot cell problem is one of the major reasons of degrading system performance because hot cell increases the call drop rate without fully utilization of system resource. Therefore, it is very important to distribute the traffic on the several neighboring cells so that system uses its resource effectively and maintains the quality of service. In this paper, we propose the adaptive handoff algorithms for distributing traffic in the packet data network systems. In addition, we propose a new load estimation method with MAC state diagram suitable for packet data network systems. Through the simulation results, we could find that proposed algorithm is able to improve efficiency of system resource and to assure the service quality of users through traffic distribution.

• Journal of Korea Water Resources Association
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• v.51 no.8
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• pp.687-701
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• 2018

A multi-function weir is representative control structure in the stream flow. Estimation of accurate flood discharge according to gate operations and prediction of floodwave travel times at the downstream are very important in terms of water use and river management. This study analyzed the limitation and improvement through the current gate operation data on the Young-san river. in addition, flood discharge was calculated considering lower and upper water level condition and gate operating using the modified slope-area method in the Seoung-chon weir. As a result, the current state was required improvement because exceed the theoretical range and rapidly fluctuation of discharge coefficient, can not be considered difference between the upper and lower water level and the estimation by the regression equation. As a result of applying the proposed method in this study, the above mentioned limitations can be compensated, compared with the current discharge data. Also it was analyzed as more physically valid because using the evaluated hydraulic equation and estimate the slope and friction loss of natural stream by iteration and to reduce the error. In conclusion, the process carried out serves as a representative flow control point of the water system through reliable discharge estimation, it is expected that it will be possible to properly river management.

• Smart Structures and Systems
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• v.6 no.9
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• pp.1057-1077
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• 2010

An early detection of structural damages is critical for the decision making of repair and replacement maintenance in order to guarantee a specified structural reliability. Consequently, the structural damage detection, based on vibration data measured from the structural health monitoring (SHM) system, has received considerable attention recently. The traditional time-domain analysis techniques, such as the least square estimation (LSE) method and the extended Kalman filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for some SHM systems. Recently, these two approaches have been extended to cover the general case where some of the external excitations (inputs) are not measured, referred to as the adaptive LSE with unknown inputs (ALSE-UI) and the adaptive EKF with unknown inputs (AEKF-UI). Also, new analysis methods, referred to as the adaptive sequential non-linear least-square estimation with unknown inputs and unknown outputs (ASNLSE-UI-UO) and the adaptive quadratic sum-squares error with unknown inputs (AQSSE-UI), have been proposed for the damage tracking of structures when some of the acceleration responses are not measured and the external excitations are not available. In this paper, these newly proposed analysis methods will be compared in terms of accuracy, convergence and efficiency, for damage identification of structures based on experimental data obtained through a series of laboratory tests using a scaled 3-story building model with white noise excitations. The capability of the ALSE-UI, AEKF-UI, ASNLSE-UI-UO and AQSSE-UI approaches in tracking the structural damages will be demonstrated and compared.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.13 no.1
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• pp.75-85
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• 2000

When applying a system identification technique, which incorporates an experimental model to a corresponding finite element model of a structure, one of the major problems is the large difference in the numbers of degrees of freedom (dof) between the two models. While there are large number of dofs in a finite element model, the number of measurement points is practically limited. So it is very difficult to incorporate them. Especially rotational dofs are hard to measure. In this study a method is presented for estimating structural dynamic responses at unmeasurable locations in frequency domain. The proposed method is tested numerically and the feasibility for practical application has been demonstrated through an example structure under moving loads, where translational and rotational dofs of beam at a center point are estimated from the partial measurements of responses at accessible points.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.237-244
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• 2020

This paper describes an implement of Simultaneous Localization and Mapping(SLAM) in two dimensional space. The method uses Invariant Extended Kalman Filter(IEKF), which transforms the state variables and measurement variables so that the transformed variables constitute a linear space when variables called the invariant quantities are kept constant. Therefore, the IEKF guarantees convergence provided in the invariant quantities are kept constant. The proposed IEKF approach uses Lie group matrix for the transformation. The method is tested through simulation, and the results show that the Kalman gain is constant as it is the case for the linear Kalman filter. The coherence between the estimated locations of the vehicle and the detected objects verifies the estimation performance of the method.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.245-254
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• 2004

This paper deals with the problem of estimating structure and motion from long continuous image sequences, applying the Expectation Maximization algorithm based on extended Kalman smoother to impose the time-continuity of the motion parameters. By repeatedly estimating the state transition matrix of the dynamic equation and the parameters of noise processes in the dynamic and measurement equations, this optimization gives the maximum likelihood estimates of the motion and structure parameters. Practically, this research is essential for dealing with a long video-rate image sequence with partially unknown system equation and noise. The algorithm is implemented and tested for a real image sequence.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.547-557
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• 2009

LIDAR (LIght Detection And Ranging) is an active remote sensing technology which provides 3D coordinates of the Earth's surface by performing range measurements from the sensor. Early small footprint LIDAR systems recorded multiple discrete returns from the back-scattered energy. Recent advances in LIDAR hardware now make it possible to record full digital waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of components which are then used to characterize the original data. The most common statistical mixture model used for this process is the Gaussian mixture. Waveform decomposition plays an important role in LIDAR waveform processing, since the resulting components are expected to represent reflection surfaces within waveform footprints. Hence the decomposition results ultimately affect the interpretation of LIDAR waveform data. Computational requirements in the waveform decomposition process result from two factors; (1) estimation of the number of components in a mixture and the resulting parameter estimates, which are inter-related and cannot be solved separately, and (2) parameter optimization does not have a closed form solution, and thus needs to be solved iteratively. The current state-of-the-art airborne LIDAR system acquires more than 50,000 waveforms per second, so decomposing the enormous number of waveforms is challenging using traditional single processor architecture. To tackle this issue, four parallel LIDAR waveform decomposition algorithms with different work load balancing schemes - (1) no weighting, (2) a decomposition results-based linear weighting, (3) a decomposition results-based squared weighting, and (4) a decomposition time-based linear weighting - were developed and tested with varying number of processors (8-256). The results were compared in terms of efficiency. Overall, the decomposition time-based linear weighting work load balancing approach yielded the best performance among four approaches.

• Earthquakes and Structures
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• v.25 no.3
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• pp.187-198
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• 2023

Model Predictive Control (MPC) is an advanced control approach that uses the current states of the system model to predict its future behavior. In this article, according to the seismic dynamics of structural systems, the Predictive Functional Control (PFC) method is used to solve the control problem. Although conventional PFC is an efficient control method, its performance may be impaired due to problems such as uncertainty in the structure of state sensors and process equations, as well as actuator saturation. Therefore, it requires the utilization of appropriate estimation algorithms in order to accurately evaluate responses and implement actuator saturation. Accordingly, an extended PFC is presented based on the H-ifinity (H∞) filter (HPFC) while considering simultaneously the saturation actuator. Accordingly, an extended PFC is presented based on the H-ifinity (H∞) filter (HPFC) while considering the saturation actuator. Thus, the structural responses are formulated by two estimation models using the H∞ filter. First, the H∞ filter estimates responses using a performance bound (𝜃). Second, the H∞ filter is converted into a Kalman filter in a special case by considering the 𝜃 equal to zero. Therefore, the scheme based on the Kalman filter (KPFC) is considered a comparative model. The proposed method is evaluated through numerical studies on a building equipped with an Active Tuned Mass Damper (ATMD) under near and far-field earthquakes. Finally, HPFC is compared with classical (CPFC) and comparative (KPFC) schemes. The results show that HPFC has an acceptable efficiency in boosting the accuracy of CPFC and KPFC approaches under earthquakes, as well as maintaining a descending trend in structural responses.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.7
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• pp.837-842
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• 2010

The wiper system of a vehicle is important because it wipes the windshield, thereby enabling drivers to see through the windshield even under conditions of rain and snow. The blade is the key component of the wiper system because it wipes the windshield. When wiper-arm spring causes the blade to be pressed on the windshield optimum performance of wiping can be achieved when appropriate contact pressure is maintained. In this study, a dynamic analysis of the wiper system is carried out. A three-dimensional finite-element model of the wiper system is generated using SAMCEF, a commercial structural dynamic analysis program. The distribution of the contact pressure of the blade in its dynamic state is calculated. The simulation result is compared to the experiment result. Using the results of this study, the contact pressure of the blade can be estimated.

• The Journal of the Acoustical Society of Korea
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• v.13 no.2
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• pp.30-37
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• 1994

Since the morden targets are of high speed and getting quiet in both active and passive mode, the necessities of developing advanced SONAR system capable of performing target motion analysis (TMA) and target classification are evident. In order to develop such a system, the scattering mechanism of complex bodies needs to be, some extent, fully understood and modeled. In this paper, MOving Spread Target(MOST) signal simulation model is presented and discussed. The model is based on the highlight distribution method, and simulates pulse elongation of spread target, doppler effect due to kinematics of the target as well as SONAR platform, and distribution target strength of each highlight point (HL) with directivity. The model can be used in developing and evaluating advanced SONAR system through system simulation, and can also be used in the development of target state estimation algorithm.