• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.283-285
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• 2005

This paper presents a new two-terminal numerical algorithm for fault location estimation and for faults recognition using the synchronized phasor in time-domain. The proposed algorithm is also based on the synchronized voltage and current phasor measured from the PMUs(Phasor Measurement Units) installed at both ends of the transmission lines. Also the arc voltage wave shape is modeled numerically on the basis of a great number of arc voltage records obtained by transient recorder. From the calculated arc voltage amplitude it can make a decision whether the fault is permanent or transient. In this paper the algorithm is given and estimated using DFT(Discrete Fourier Transform) and the LES(Least Error Squares Method). The algorithm uses a very short data window and enables fast fault detection and classification for real-time transmission line protection. To test the validity of the proposed algorithm, the Electro-Magnetic Transient Program(EMTP/ATP) and MATLAB is used.

• Journal of Sensor Science and Technology
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• v.28 no.6
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• pp.395-401
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• 2019

This paper proposes a method for estimating the location of a magnetized tip that is inside a non-transparent space or body by using arrayed giant magnetoresistance (GMR) sensors. In general, an object located in such an opaque space can be detected using X-rays, magnetic fields, ultra-sonic sensors, etc., depending on its characteristics. X-ray is mostly used for medical purposes but frequent exposure to it could cause harm to patients as well as doctors. In this study, how well a GMR sensor is applicable instead of an X-ray is investigated. The sensor's voltage output is experimentally fitted to distance with a relationship of 3rd degree polynomial. To detect a small magnetized tip with 900 Oe inside a human body, a 2×2 arrayed GMR sensor and a location estimation algorithm based on information acquired from four sensors is developed. Evaluation tests show that the suggested method is applicable to limited cases with a distance less than 33-55 mm, and the location of a magnet tip is estimated relatively well with an error less than 1.5 mm.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.752-759
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• 2008

In this paper, we propose a high accurate geo-location system based on a single base station, where its location is obtained by Time-of-Arrival(ToA) and Direction-of-Arrival(DoA) of the radio signal. For estimating accurate ToA and DoA information, a MUltiple SIgnal Classification(MUSIC) is adopted. However, the estimation of ToA and DoA using MUSIC algorithm is a time-consuming process. The position tracking bias is occurred by the time delay caused by the estimation process. In order to mitigate the bias error, we propose the estimation method of the position tracking bias and compensate the location error produced by the time delay using the position tracking bias mitigation. For accurate self-localization of mobile robot, the Unscented Kalman Filter(UKF) with position tracking bias is applied. The simulation results show the efficiency and accuracy of the proposed geo-location system and the enhanced performance when the Unscented Kalman Filter is adopted for mobile robot application.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.125-133
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• 2011

For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. As fingerprinting method, k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighbors k and positions of reference points(RPs). So intuitive fuzzy c-means(IFCM) clustering algorithm is applied to improve KNN, which is the KNN/IFCM hybrid algorithm presented in this paper. In the proposed algorithm, through KNN, k RPs are firstly chosen as the data samples of IFCM based on signal to noise ratio(SNR). Then, the k RPs are classified into different clusters through IFCM based on SNR. Experimental results indicate that the proposed KNN/IFCM hybrid algorithm generally outperforms KNN, KNN/FCM, KNN/PFCM algorithm when the locations error is less than 2m.

• Journal of Internet Computing and Services
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• v.11 no.2
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• pp.61-72
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• 2010

This paper addresses the problem of moving object localization using Ultra-Wide-Band(UWB) range measurement and the method of location accuracy improvement of the indoor moving object. Unlike outdoor environment, it is difficult to track moving object position due to various noises in indoor. UWB is a radio technology that has attention for localization applications recently. UWB's ranging technique offer the cm accuracy. Its capabilities for data transmission, range accurate estimation and material penetration are suitable technology for indoor positioning application. This paper propose a positioning algorithm of an moving object using UWB ranging technique and particle filter. Existing positioning algorithms eliminate estimation errors and bias after location estimation of mobile object. But in this paper, the proposed algorithm is that eliminate predictable UWB range distance error first and then estimate the moving object's position. This paper shows that the proposed positioning algorithm is more accurate than existing location algorithms through experiments. In this study, the position of moving object is estimated after the triangulation and eliminating the bias and the ranging error from estimation range between three fixed known anchors and a mobile object using UWB. Finally, a particle filter is used to improve on accuracy of mobile object positioning. The results of experiment show that the proposed localization scheme is more precise under the indoor.

• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.310-312
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• 2005

This paper presents an improved two-terminal technique for fault location estimation. The proposed algorithm is also based on the synchronized phasors measured from the synchronized PMUs installed at two-terminals of the transmission lines. Also the arc voltage wave shape is modeled numerically on the basis of a great number of arc voltage records. Also, the two-terminal fault algorithm for the long line model is derived in the spectral domain.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.227-237
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• 2004

In this paper we presented a Bayesian inference approach for estimating the location and scale parameters of the lognormal distribution using iterative Gibbs sampling algorithm. We also presented estimation of location parameter by two non iterative methods, importance sampling and weighted bootstrap assuming scale parameter as known. The estimates by non iterative techniques do not depend on the specification of hyper parameters which is optimal from the Bayesian point of view. The estimates obtained by more sophisticated Gibbs sampler vary slightly with the choices of hyper parameters. The objective of this paper is to illustrate these tools in a simpler setup which may be essential in more complicated situations.

• The KIPS Transactions:PartC
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• v.19C no.4
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• pp.239-246
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• 2012

The function of location estimation in WPAN has been studied and specified on the ultra wide band optionally. But the devices based on CSS(Chirp Spread Spectrum) specification has been used widely in the market because of its functionality, cheapness and support of development. As the CSS device uses 2.4GHz for a carrier frequency and the sampling frequency is lower than that of the UWB, the resolution of a timestamp is very coarse. Then actually the error of a measured distance is very large about 30cm~1m at 10 m depart. And the location error in ($10m{\times}10m$) environment is known as about 1m~2m. So for some applications which require more accurate location information, it is very natural and important to develop a sophisticated post processing algorithm after distance measurements. In this paper, we have studied extended Kalman filter with the frequency offsets of anchor nodes, and proposed a novel algorithm frequency offset compensated extended Kalman filter. The frequency offsets are composed with a variable as a common frequency offset and constants as individual frequency offsets. The proposed algorithm shows that the accurate location estimation, less than 10cm distance error, with CSS WPAN nodes is possible practically.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4B
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• pp.300-308
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• 2012

Many indoor location tracking technologies have been proposed. Generally indoor location tracking using TOA signal is used, there is a weak point that it's difficult to track the location due to obstacles like a refraction, reflection and dispersion of radio wave. In this paper, we apply ETOA(Estimated-TOA) algorithm in NLOS(Non-Line-Of-Sight) environment to solve above problem. In NLOS environment, TOA value between Beacon and Mobile node is predicted by ETOA algorithm and the tracking of indoor location is also possible to identify using two NLOS beacons of three beacons by this algorithm. We show that the proposed algorithm is accurate location tracking is accomplished using the applying the proposed algorithm to indoor moving robot and the inertia sensor of robot and Kalman filter algorithm.

• Structural Engineering and Mechanics
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• v.17 no.3_4
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• pp.483-492
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• 2004

This paper presents an efficient algorithm for the estimation of damage location and severity in bridge structures using Probabilistic Neural Network (PNN). Generally, the Back Propagation Neural Network (BPNN)-based damage detection methods need a lot of training patterns for neural network learning process and the optimum architecture of a BPNN is selected by trial and error. In this paper, the PNN instead of the conventional BPNN is used as a pattern classifier. The modal properties of damaged structure are somewhat different from those of undamaged one. The basic idea of proposed algorithm is that the PNN classifies a test pattern which consists of the modal characteristics from damaged structure, how close it is to each training pattern which is composed of the modal characteristics from various structural damage cases. In this algorithm, two PNNs are sequentially used. The first PNN estimates the damage location using mode shape and the results of the first PNN are put into the second PNN for the damage severity estimation using natural frequency. The proposed damage assessment algorithm using the PNN is applied to a cable-stayed bridge to verify its applicability.