• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.526-534
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• 2019

Localization methods using radio signals should obtain range measurements from three or more anchors. However, a typical building consists of narrow, long hallways and corners, making it difficult to secure more than three light of sight anchors. The result is a multi-modal solution that makes it difficult to estimate the user's location. In order to overcome this problem, this paper proposes a method for estimating the location using artificial neural networks. Using the artificial neural network, even if a multi-modal solution occurs, the position can be estimated by acquiring user movement pattern information based on accumulated range measurements. The method does not require any additional equipment or sensors, and only anchor-based range measurements can estimate the user's location. In order to verify the proposed method, location estimation tests were performed in situations where the multi-modal solution occurred by installing an insufficient number of anchors in a building. As a result, it was confirmed that the location can be estimated even when the number of anchors is insufficient.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.9
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• pp.95-103
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• 1998

This paper proposes an image segmentation algorithm based on fusion of range and intensity images. Based on Bayesian theory, a priori knowledge is encoded by the Markov random field (MRF). A maximum a posteriori (MAP) estimator is constructed using the features extracted from range and intensity images. Objects are approximated by local planar surfaces in range images, and the parametric space is constructed with the surface parameters estimated pixelwise. In intensity images the ${\alpha}$-trimmed variance constructs the intensity feature. An image is segmented by optimizing the MAP estimator that is constructed using a likelihood function based on edge information. Computer simulation results shw that the proposed fusion algorithm effectively segments the images independentl of shadow, noise, and light-blurring.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.85-91
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• 2013

For location based service and context awareness services, accurate indoor positioning technology is essential. The TDOA method that uses the range difference between signal source and receivers for estimating the location of the signal source, has estimation error due to measurement error. In this paper, a new algorithm is proposed to select the real root among calculated roots using the range difference information, and the estimated position of the signal source shows good accuracy compared to the existing method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.408-413
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• 2005

This paper presents a technique which extracts a moving object from omni-directional images and estimates a real coordinates of the moving object using 3D parabolic coordinate transformation. To process real-time, a moving object was extracted by proposed Hue histogram Matching Algorithms. We demonstrate our proposed technique could extract a moving object strongly without effects of light changing and estimate approximation values of real coordinates with theoretical and experimental arguments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.837-839
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• 2014

In radar systems, information of three-dimensional (3D) trajectory is necessary for tracking targets. The information of 3D trajectory for a 3D radar can be obtained by estimating the azimuth angle, the elevation angle, and the distance. The estimated information of the angles and the distance has errors according to received signals. Since these errors affect performances of 3D radar systems, performance analysis of 3D radar for the angles and the distance errors is required. In this paper, the performance of 3D radar systems is analyzed by root mean square error (RMSE) between true trajectory information and the estimated trajectory information according to the angles and the distance errors.

• The Journal of the Acoustical Society of Korea
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• v.20 no.6
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• pp.75-81
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• 2001

In this paper we analyzed the localization error of near-field detection algorithm in the sea. The near-field detection algorithms using triangulation and wavefront curvature basically assume a signal in two dimension of bearing and range. But the assumption causes localization error because there is three dimension of bearing, range, and depth in the sea. Even through three dimensional effect is considered, the localization error is occurred if multipath propagation in the sea is ignored. To analyze the localization error in the sea, we simulate the near-field localization using acoustic propagation model and focused beamforming considering wavefront curvature. The simulation results indicate that localization error always occurs in the sea and the error varied with sound velocity profile, water depth, bottom slope, source range, etc.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.235-237
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• 2017

We suggest a way to improve the performance of a target distance estimation algorithm using Kalman Filter to compensate for the error that occurs when the target track information over the Combat Radio Network is lost.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.4
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• pp.175-182
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• 2000

Spatial interpolation of daily temperature forecasts and observations issued by public weather services is frequently required to make them applicable to agricultural activities and modeling tasks. In contrast to the long term averages like monthly normals, terrain effects are not considered in most spatial interpolations for short term temperatures. This may cause erroneous results in mountainous regions where the observation network hardly covers full features of the complicated terrain. We developed a spatial interpolation model for daily minimum temperature which combines inverse distance squared weighting and elevation difference correction. This model uses a time dependent function for 'mountain slope lapse rate', which can be derived from regression analyses of the station observations with respect to the geographical and topographical features of the surroundings including the station elevation. We applied this model to interpolation of daily minimum temperature over the mountainous Korean Peninsula using 63 standard weather station data. For the first step, a primitive temperature surface was interpolated by inverse distance squared weighting of the 63 point data. Next, a virtual elevation surface was reconstructed by spatially interpolating the 63 station elevation data and subtracted from the elevation surface of a digital elevation model with 1 km grid spacing to obtain the elevation difference at each grid cell. Final estimates of daily minimum temperature at all the grid cells were obtained by applying the calculated daily lapse rate to the elevation difference and adjusting the inverse distance weighted estimates. Independent, measured data sets from 267 automated weather station locations were used to calculate the estimation errors on 12 dates, randomly selected one for each month in 1999. Analysis of 3 terms of estimation errors (mean error, mean absolute error, and root mean squared error) indicates a substantial improvement over the inverse distance squared weighting.

• Journal of Korea Water Resources Association
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• v.44 no.5
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• pp.363-376
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• 2011

1-D hydrodynamic numerical models have been most widely used in the field of flood analysis. The model's input data are upstream/downstream boundaries, roughness coefficients, cross-sections, and so on, and computational distance step and time step are the most important factors in order to guarantee the computational accuracy, stability, and efficiency. In this study, a theoretical explanation is presented for the basis of the previous empirical selection criteria of cross-section's location; also, the estimation technique of computationally variable distance step is proposed to reflect the properties of flow at every computational time step. Combining this technique with 1-D unsteady numerical model, it was applied to two events of Teton dam failure flood and the Han River flood. The numerical experimental results demonstrate that the accuracy and stability is increased when used more interpolated cross-sections and show that the proposed technique of computationally variable distance step has the same order of accuracy with smaller numbers of cross-section than previous empirical selection criteria. The practical use of this technique will be possible to analyze the river floods with high efficiency as well as accuracy and stability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11C
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• pp.957-964
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• 2010

In this paper, we propose an indoor positioning algorithm based on 3 points near field angle-of-arrival estimation without side information. The conventional angle-of-arrival based positioning scheme requires the distance between the listener and the center of two points which is obtained by a received signal strength based range estimation. However, a received signal strength is affected by structure of room, placement of furniture, and characteristic of signal, these effects cause a large error to estimation of angle. In this paper, the proposed positioning scheme based on near field angle-of-arrival estimation can be used to estimate the position of listener without a prior distance information, just using time-difference-of-arrival information given from 3 points microphones. The performance of the proposed scheme is shown by cumulative distribution function of root mean squared error.