• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.268-275
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• 2008

In this paper, we introduce a new method for detecting and estimating faults on a power line using the time-frequency domain reflectometry system. The system rests upon time-frequency signal analysis and uses a chirp signal which is multiplied by Gaussian envelope. The chirp signal is used as a reference signal, and we can get the reflected signal from a fault on a wire. To detect and estimate faults, we analyze the reflected signal by Wigner time-frequency distribution function and normalized time-frequency cross correlation function. In this paper we design an optimal reference signal for power line and implement a system for estimating fault distance on a power line with the TFDR implemented by PXI equipments. This approach is verified by some experiments with HIV 2.25mm power lines.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.538-544
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• 1999

In this paper, we propose a new fault detection algorithm for transmission line protection using ANFIS(Adaptive Network Fuzzy Inference System). The developed system consists of two subsystems: fault type classification, and fault location estimation. We use rms value, zero sequence component and positive sequence of current, and then using learning method of neural network, premise and consequent parameters are tuned properly. To prove the performance of the proposcd system, generated data by EMTP(Electr0- Magnetic Transient Program) sin~ulationi s used. It is shown that the proposed relaying classifies fault types accurately and advances fault location estimation.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.408-414
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• 2009

A GPS/INS system is widely used in the UGV to estimate position during the mission. However, there are few restrictions when a GPS/INS system used alone. For example, GPS provides precise location information but easily interrupted by external factors like weather, environment, etc. INS provides continuous location data but positioning errors grew very rapidly with time. Therefore, it is necessary to integrating multi-sensors for more continuous and correct position estimation. In this paper, we propose location estimation algorithm of the UGV for GPS/INS integrated system that combines Fuzzy Inference and Unscented Particle Filter(UPF) to improve navigation. Fuzzy inference provides the simplest method integrating GPS/INS and UPF is non-linear estimation filter well suited to the correction of errors. The performance of the proposed algorithm was tested to compare with other algorithms. the results show that the proposed algorithm is more accuracy in position estimation and ensures continuous position tracking.

• Journal of KIISE:Databases
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• v.29 no.6
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• pp.441-452
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• 2002

If continuous moving objects are managed by conventional database, it is not possible for them to store all position information changed over time in the database. Therefore, a time period of regular rate is determined and position information of moving objects are discretely stored in the system for every time period. However, if continuous moving objects are managed as discrete model, we will have problems which cannot properly answer to the query about uncertain past or future position information. To solve this problem, in this paper, we propose the method and algorithm which use the history information stored in the same database, to estimate the past or future location of moving objects. The cubic spline interpolation is used to estimate the past location and the mean movement value of the history information is used to predict the future location of moving objects. Finally, from the location estimation experimentation of using virtual trajectory and location sample, we proved that the proposed cubic spline function has less error than the linear function.

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.107-110
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• 1988

The quadratic cost function J(x) and the normalized residuals $r_N$ are conventionally used for identifying the presence and location of bed measurements in power system state estimation. These are "post estimation" tests and therefore require the complete re-estimation of system states whenever bed data is identified. This paper presents a pre-filter for reducing errors of the raw measurements using an adaptive filter. Each measurement is processed by the adaptive filter before being used in the state estimation. The performance of the adaptive filter is tested and the results are shown in this paper.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.877-884
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• 1994

In the power system state estimation, the J(x)-index test and normalized residuals ${\gamma}$S1NT have been the presence of bad measurements and identify their location. But, these methods require the complete re-estimation of system states whenever bad data is identified. This paper presents back-propagation neural network medel using autoregressive filter for identification of bad measurements. The performances of neural network method are compared with those of conventional mehtods and simulation results show the geed performance in the bad data identification based on the neural network under sample power system.

• Journal of KIISE:Databases
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• v.37 no.1
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• pp.33-43
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• 2010

In this paper, we propose a new location acquisition scheme based on RFID that reduces the computation cost of location acquisition and keeps the accuracy of the location. In addition, we propose an incremental location update policy to reduce the location update cost for moving objects. To show the superiority of our proposed scheme, we compare it with the existing researches. It is shown through various experiments that the proposed system reduces the computation cost of location estimation 500 times more than existing researches. Also, the proposed system significantly reduces the cost of location update using the RFID-based update policy.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.226-231
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• 2020

In order to maximize the production of landscape plants in optimal condition while coexisting with the environment in terms of precision agriculture, quick and accurate information gathering of the internal environmental elements of the growing container is necessary. This may depend on the accuracy of the positioning of numerous sensors connected to landscape plants cultivating system (LPCS) in containers. Thus, this paper presents a method for estimating the location of the sensors related to cultivation environment connected to LPCS by measuring the received signal strength (RSS) or time of arrival TOA received between oneself and adjacent sensors. The Small sensors connected to the LPCS of container are known for their locations, but the remaining locations must be estimated. For this in the paper, Rao-Cramer limits and maximum likelihood estimators are derived from Gaussian models and lognormal models for TOA and RSS measurements, respectively. As a result, this study suggests that both RSS and TOA range measurements can produce estimates of the exact locations of the cultivation environment sensors within the wireless sensor network related to the LPCS.

• Journal of the Korea Computer Industry Society
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• v.4 no.12
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• pp.986-998
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• 2003

A moving object management system manages spatiotemporal data o( moving objects which change their location continuously over time such as people, animals, cars, cellular phones, and so on. This system can be applied to location based services such as vehicle tracking systems, digital battlefields, and animal habitat management. The existing systems neither suggest location estimation of the moving objects nor handle the loss data of the moving objects in real-time environment. Thus the existing systems have problems that they give the uncertain results of the query processing to the user query. In this paper, we design a new moving object management system. The proposed system processes the past and future location information of the moving objects by the location change function. Also we propose a location triggering method, which supplements loss of the location data of the mobile objects in real-time environment. Finally, we implement and apply the proposed system to a vehicle tracking system based on PDA. Thus we ascertain that the proposed system can be applied to the location based system.

• The Journal of Korea Robotics Society
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• v.1 no.2
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• pp.125-134
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• 2006

The problem of establishing the servo system to reach the desired location keeping all features in the field of view and following a straight line is considered. In addition, robustness of camera calibration parameters is considered in this paper. The proposed approach is based on switching from position-based visual servoing (PBVS) to image-based visual servoing (IBVS) and allows the camera path to follow a straight line. To achieve the objective, a pose estimation method is required; the camera's target pose is estimated from the obtained images without the knowledge of the object. A switched control law moves the camera equipped to a robot end-effector near the desired location following a straight line in Cartesian space and then positions it to the desired pose with robustness to camera calibration error. Finally simulation results show the feasibility of the proposed visual servoing technique.