• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1824-1833
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• 2016

In this paper, we propose networked airborne relay-based positioning scheme (N-ARPS) based on time division multiple access (TDMA) networks to improve the performance of relative navigation (RelNav). To integrate the ARPS into TDMA, there are three problems such as slot allocation, selection of airborne relays, and method for signal loss to be solved. A subframe of N-ARPS is designed to assign the slots for broadcast and relay of navigation signals consecutively to minimize the effect of mobility. The selection algorithm determines the optimum set of airborne relays by selecting evenly distributed nodes depending on their distances to the master station. Finally, we uses precise position location information (PPLI) messages, which are received in data transmission period, to estimate a user position when the navigation signals are missing. The simulation results indicate that N-ARPS significantly improves user accuracy over RelNav.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.469-474
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• 2011

This paper presents the intelligent tracking method for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. K-means clustering and TS fuzzy system are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by K-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. While calculating expected value, the non-linearity of the maneuvering target is recognized as linear one by dividing acceleration and the capability of Kalman filter is kept in the filtering process. The error for the non-linearity is compensated by approximated acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.10
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• pp.51-59
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• 2003

A method of autonomous update is presented for onboard orbit propagator. On board propagator is an alternative means that could be used for navigation purpose in case of CPS receiver's failure. Although the ground station is not a able to upload a new propagator, the onboard propagator must be maintained most up-to-date. For this, a filtering technique is proposed wherein GPS data are effectively used to continuously update the on board propagator which was uploaded previously. Even if the ground station has generated the on board propagator based on the wrong information, the onboard propagator with updating scheme can automatically correct the errors in the coefficients of residual reconstruction function. Several scenarios were used to show the validity of the scheme for updating the onboard propagator using KOMPSAT-1 orbit data.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.2
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• pp.133-140
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• 2015

In this paper, a roll angle estimation method of a rolling airframe using a low grade GPS and a roll rate gyro is proposed. The strength of the received signal of the GPS antenna attached on the rolling airframe is maximized when the GPS satellite is placed on the plane determined by the x-axis of the rolling airframe and the GPS antenna axis. Under the assumption that the x-axis of the rolling airframe is coincident with its velocity vector, the roll angle of the rolling airframe is calculated from the relative position vector of the satellite to the GPS when the GPS signal strength becomes maximum. The Kalman filter combined with a roll rate gyro is introduced to increase the determination accuracy of the roll angle. The performance of the proposed method is verified via 6-DOF simulations.

• Journal of Korea Water Resources Association
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• v.33 no.3
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• pp.341-350
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• 2000

The objectives of this study are to develop a state-space form of stochastic dynamic storage function routing model and to test the model performance for real-time flow forecast. The selected study area is the main Han River starting from Paldang Dam site to Indogyo station and the 13 flood events occurred from 1987 to 1998 are selected for computing model parameters and testing the model performance. It was shown that the optimal model parameters are quite different depending on Hood events, but the values used on field work also give reasonable results in this study area. It is also obvious that the model performance from the stochastic-dynamic model developed in this study gives more accurate and reliable results than that from the existing deterministic model. Analysis for allowable forecast lead time leads that under the current time step the reasonable predicted downstream flows in 5 hours time advance are obtained from the stochastic dynamic model on relatively less lateral inflow event in the study area.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.6
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• pp.53-61
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• 2014

For monitoring the flight trajectory and the status of a launch vehicle, the mission control system in NARO space center process data acquired from the ground tracking system, which consists of two tracking radars, four telemetry stations, and one electro-optical tracking system. Each tracking unit exhibits its own tracking error mainly due to multi-path, clutter and radio refraction, and by utilizing only one among transmitted informations, it is not possible to determine the actual vehicle trajectory. This paper presents a way of generating flight trajectory via post-processing the data received from the ground tracking system. The post-processing algorithm is divided into two parts: compensation for atmosphere radio refraction and multi-sensor fusion, for which a decentralized Kalman filter was adopted and implemented based on constant acceleration model. Applications of the present scheme to real data resulted in the flight trajectory where the tracking errors were minimized than done by any one sensor.

• 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 Korean Society for Geospatial Information Science
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• v.14 no.3 s.37
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• pp.47-53
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• 2006

In this contribution, an integration of seamless navigation system for the pedestrian is introduced. To overcome the GPS outages in various situations, multi-sensor of GPS, INS, electronic barometer and compass are considered in one Extented Kalman filter. Especially, the integrated system is designed for low-cost for the practical applications. Therefore, a MEMS IMU is considered, and the low quality of the heading is compensated by the electronic compass. In addition, only the pseudoranges from GPS measurements are considered for possible real-time application so that the degraded height is also controlled by a barometer. The mathematical models for each sensor with systematic errors such as biases, scale factors are described in detail and the results are presented in terms of a covariance analysis as well as the position and attitude errors compared to the high-grade GPS/INS combined solutions. The real application scenario of GPS outage is also investigated to assess the feasible accuracy with respect to the outage period. The description on the current status of the development and future research directions are also stated.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.2 s.302
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• pp.1-10
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• 2005

This paper presents a technique for localization of a mobile robot using a single ultrasonic sensor. The mobile robot is designed for operating in a well-structured environment that can be represented by planes, edges, corners and cylinders in the view of structural features. In the case of ultrasonic sensors, these features have the range information in the form of the arc of a circle that is generally named as RCD (Region of Constant Depth). Localization is the continual provision of a knowledge of position which is deduced from it's a priori position estimation. The environment of a robot is modeled into a two dimensional grid map. we defines a physically-based sonar sensor model and employs an extended Kalman filter to estimate position of the robot. The performance and simplicity of the approach is demonstrated with the results produced by sets of experiments using a mobile robot.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.609-614
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• 2014

Cerebellar ataxia is a steadily progressive neurodegenerative disease associated with loss of motor control, leaving patients unable to walk, talk, or perform activities of daily living. Direct motor instruction in cerebella ataxia patients has limited effectiveness, presumably because an inappropriate closed-loop cerebellar response to the inevitable observed error confounds motor learning mechanisms. Recent studies have validated the age-old technique of employing motor imagery training (mental rehearsal of a movement) to boost motor performance in athletes, much as a champion downhill skier visualizes the course prior to embarking on a run. Could the use of EEG based BCI provide advanced biofeedback to improve motor imagery and provide a "backdoor" to improving motor performance in ataxia patients? In order to determine the feasibility of using EEG-based BCI control in this population, we compare the ability to modulate mu-band power (8-12 Hz) by performing a cued motor imagery task in an ataxia patient and healthy control.