• Journal of IKEEE
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• v.26 no.2
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• pp.169-175
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• 2022

This paper introduces an interference elimination method using k-nearest neighbor (KNN) algorithm for precise distance estimation by reducing interference between ultrasonic sensors. Conventional methods compare current distance measurement result with previous distance measurement results. If the difference exceeds some thresholds, conventional methods recognize them as interference and exclude them, but they often suffer from imprecise distance prediction. KNN algorithm classifies input values measured by multiple ultrasonic sensors and predicts high accuracy outputs. Experiments of distance measurements are conducted where interference frequently occurs by multiple ultrasound sensors of same type, and the results show that KNN algorithm significantly reduce distance prediction errors. Also the results show that the prediction performance of KNN algorithm is superior to conventional voting methods.

• Journal of Korea Multimedia Society
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• v.15 no.12
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• pp.1430-1441
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• 2012

In this paper, we present an algorithm that quickly and effectively estimates orthogonal vanishing points in equirectangular images of urban environment. Our algorithm is based on the RANSAC (RANdom SAmple Consensus) algorithm and on the characteristics of the line segment in the spherical panorama image of the $360^{\circ}$ longitude and $180^{\circ}$ latitude field of view. These characteristics can be used to reduce the geometric ambiguity in the line segment classification as well as to improve the robustness of vanishing point estimation. The proposed algorithm is validated experimentally on a wide set of images. The results show that our algorithm provides excellent levels of accuracy for the vanishing point estimation as well as line segment classification.

• ETRI Journal
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• v.35 no.5
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• pp.757-766
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• 2013

This paper deals with the estimation of the time of arrival (TOA) of ultra-wideband signals under IEEE 802.15.4a channel models. The proposed approach is based on a randomness test and consists of determining whether an autoregressive (AR) process modeling an energy frame is random or not by using a distance to measure the randomness. The proposed method uses a threshold that is derived analytically according to a preset false alarm probability. To highlight the effectiveness of the developed approach, simulation setups as well as real data experiments are conducted to assess the performance of the new TOA estimation algorithm. Thereby, the proposed method is compared with the cell averaging constant false alarm rate technique, the threshold comparison algorithm, and the technique based on maximum energy selection with search back. The obtained results are promising, considering both simulations and collected real-life data.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.5
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• pp.115-121
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• 2001

A driving load estimation method for intelligent cruise control(ICC) vehicles has been proposed in this paper. Vehicle driving load is one of the most important factors of perturbations in vehicle control and can affect the control performance critically. The effect of the control with driving load estimation on vehicle-to-vehicle distance control has been presented and investigated via computer simulations and vehicle tests. The results show that vehicle-driving-load-adaptive control can provide an ICC system with a good acceleration tracking performance. In addition, the results show that driving load estimation can compensate not only the variation of driving load but also the modeling errors.

• Journal of the Korean Statistical Society
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• v.29 no.3
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• pp.361-373
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• 2000

A simple and efficient algorithm is introduced for generalized least squares estimation under nonnegativity constraints in the components of the parameter vector. This algorithm gives the exact solution to the estimation problem within a finite number of pivot operations. Besides an illustrative example, an empirical study is conducted for investigating the performance of the proposed algorithm. This study indicates that most of problems are solved in a few iterations, and the number of iterations required for optimal solution increases linearly to the size of the problem. Finally, we will discuss the applicability of the proposed algorithm extensively to the estimation problem having a more general set of linear inequality constraints.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2359-2364
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• 2017

This paper proposes a scale factor based range estimation method using a sum of squares (SOS) method. Many previous studies measured distance by using a camera, which usually required two cameras and a long computation time for image processing. To overcome these disadvantages, we propose a range estimation method for an object using a single moving camera. A SOS-based Luenberger observer is proposed to estimate the range on the basis of the Euclidean geometry of the object. By using a scale factor, the proposed method can realize a faster operation speed compared with the previous methods. The validity of the proposed method is verified through simulation results.

• Journal of the Korean Society of Safety
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• v.22 no.5
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• pp.65-70
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• 2007

This paper is estimation of structure damage caused by VCE(Vapor Cloud Explosion) in enclosure. As we estimate the influence of damage which occur at gas facility in factory. We can utilize the elementary data of safety distance. In this study, the influence of over-pressure caused by VCE in enclosure was calculated by using the Hopkinson's scaling law and the accident damage was estimated by applying the influence on the adjacent structure into the probit model. As a result of the damage estimation conducted by using the probit model, both the damage possibility of explosion overpressure to structures of 20 meters away and to glass bursting of 80 meters away was nearly zero in open space explosion.

• Proceedings of the KIPE Conference
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• 2004.07a
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• pp.316-320
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• 2004

This paper presents an advanced method for an initial pole position estimation of a PMLSM (Permanent Magnet Linear Synchronous Motor) that has an incremental encoder for servo applications but does not have Hall sensors as a magnetic pole sensor. The proposed algorithm finds either of two zero force positions and then the correct d-axis by appropriately using the secant method as a numerical method. It only requires the tuned current controller and the relative position information and so it can be simply applicable to a rotary PMSM. The experimental results show the validity of the proposed method with respect to accurate pole position estimation under the minimal moving distance during estimation process.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.3
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• pp.143-150
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• 2008

In this paper, we investigate a real-time environment recognition system based on stereo vision for moving object. This system consists of stereo matching, obstacle detection and distance estimation. In stereo matching part, depth maps can be obtained real road images captured adjustable baseline stereo vision system using belief propagation(BP) algorithm. In detection part, various obstacles are detected using only depth map in case of both v-disparity and column detection method under the real road environment. Finally in estimation part, asymmetric parabola fitting with NCC method improves estimation of obstacle detection. This stereo vision system can be applied to many applications such as unmanned vehicle and robot.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.2
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• pp.100-107
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• 2009

Velocity estimation is one of important issues for efficient system management in mobile cellular systems. In this paper, a modified velocity estimation scheme which works in Adaptive Antenna System (AAS) is proposed. The proposed scheme estimates user velocity based on moving distance information and sojourn time information. From numerical results, it is shown that the proposed scheme can estimate user velocity accurately with low cost.