• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.163-168
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• 2008

Navigation system based on indoor location estimation is one of the core technologies in mobile robot systems. Wireless sensor network has great potential in the indoor location estimation due to its characteristics such as low power consumption, low cost, and simplicity. In this paper we present an algorithm to estimate the indoor location of mobile robot based on wireless sensor network and fuzzy modeling. ZigBee-based sensor network usually uses RSSI(Received Signal Strength Indication) values to measure the distance between two sensor nodes, which are affected by signal distortion, reflection, channel fading, and path loss. Therefore we need a proper correction method to obtain accurate distance information with RSSI. We develop the fuzzy distance models based on RSSI values and an efficient algorithm to estimate the robot location which applies to the navigation algorithm incorporating the time-varying data of environmental conditions which are received from the wireless sensor network.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.805-810
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• 2011

This paper deals with the method and algorithm used to find fault locations in gas insulated transmission line. The method uses UHF sensors and digital oscilloscope to detect discharge signals emitted to the outside through insulating spacer in the event of breakdown inside GIL. UHF sensors are the external type and installed at outside of insulating spacers of GIL. And we used wavelet signal processing to analyze the discharge signals and confirm the exact fault location findings in the GIL test line. This method can overcome demerit of TDR(Time Domain Reflectometer) method having been applied to detect fault location for conventional underground transmission lines, and Ground Fault Sensors used in conventional GIS systems. TDR method requires high level of specialty and experience in analyzing the measured signals. Ground fault sensors are installed inside GIL and can be destroyed by high transient voltage. This paper's method can simplify the fault location process and minimize the damage of sensors. In addition, this method can estimate the fault location only by the time difference when discharge signals are arrived to detecting sensors at the ends of GIL sections without reasons of breakdown. To test the performance of our method, we installed sensors at the ends of test line of GIL(84m) and sensed discharge signals occurred in GIL, energized with AC voltage generator up to 700kV.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.2
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• pp.133-139
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• 2011

Estimation of biomass carbon stock is an important research for estimation of public benefit of forest. Previous studies about biomass carbon stock estimation have limitations, which come from the used deterministic models. The most serious problem of deterministic models is that deterministic models do not provide any explanation about the relevant effects of errors. In this study, the effects of location errors were analyzed in order to estimation of biomass carbon stock of Danyang area using Monte Carlo simulation method. More specifically, the k-Nearest Neighbor(kNN) algorithm was used for basic estimation. In this procedure, random and systematic errors were added on the location of Sample plot, and effects on estimation error were analyzed by checking the changes of RMSE. As a result of random error simulation, mean RMSE of estimation was increased from 24.8 tonC/ha to 26 tonC/ha when 0.5~1 pixel location errors were added. However, mean RMSE was converged after the location errors were added 0.8 pixel, because of characteristic of study site. In case of the systematic error simulation, any significant trends of RMSE were not detected in the test data.

• Journal of information and communication convergence engineering
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• v.5 no.4
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• pp.295-299
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• 2007

This paper presents the ASF(Additional Secondary Factor) modeling of DGPS beacon signal. In addition to DGPS's original purpose, the feasibility to utilize DGPS system for timing and navigation has been studied. For timing and navigation, the positioning system must know the accurate time delay of signal traveling from the transmitter to receiver. Then the delay can be used to compute the user position. The DGPS beacon signal transmits the data using medium frequency, which travels through the surface and cause the additional delay rather than the speed of light according to conductivities and elevations of the irregular terrain. We introduce the modeling of additional delay(ASF) and present the results of implementation. The similar approach is Locan-C. Loran-C has been widely used as the maritime location system and was enhanced to E-Loran(Enhanced Loran). E-Loran system uses the ASF estimation method and is able to provide the more precise location service. However there was rarely research on this area in Korea. Hence, we introduce the ASF and its estimation model. With the comparison of the same condition and data from the original Monteath model and ASF estimation data of Loran system respectively, we guarantee that the implementation is absolutely perfect. For further works, we're going to apply the ASF estimation model to Korean DGPS beacon system with the Korean terrain data.

• Advances in robotics research
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• v.2 no.2
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• pp.129-140
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• 2018

With the increasing demand for the development of human pose estimation, such as human-computer interaction and human activity recognition, there have been numerous approaches to detect the 2D poses of people in images more efficiently. Despite many years of human pose estimation research, the estimation of human poses with images remains difficult to produce satisfactory results. In this study, we propose a robust 2D human body pose estimation method using an RGB camera sensor. Our pose estimation method is efficient and cost-effective since the use of RGB camera sensor is economically beneficial compared to more commonly used high-priced sensors. For the estimation of upper-body joint positions, semantic segmentation with a fully convolutional network was exploited. From acquired RGB images, joint heatmaps accurately estimate the coordinates of the location of each joint. The network architecture was designed to learn and detect the locations of joints via the sequential prediction processing method. Our proposed method was tested and validated for efficient estimation of the human upper-body pose. The obtained results reveal the potential of a simple RGB camera sensor for human pose estimation applications.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.2
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• pp.51-60
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• 2012

An improved method for detecting fault distance in overhead transmission lines is described in this paper. Based on single-ended measurement, propagation theory of traveling waves together with the wavelet transform technique is used. In estimating fault location, a simple, but fundamental method using the time difference between the two consecutive peaks of transient signals is considered; however, a new method to enhance measurement sensitivity and its accuracy is sought. The algorithm is developed based on the lattice diagram for traveling waves. Representing both the ground mode and alpha mode of traveling waves, in a lattice diagram, several relationships to enhance recognition rate or estimation accuracy for fault location can be found. For various cases with fault types, fault locations, and fault inception angles, fault resistances are examined using the proposed algorithm on a typical transmission line configuration. As a result, it is shown that the proposed system can be used effectively to detect fault distance.

• Journal of IKEEE
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• v.20 no.1
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• pp.1-8
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• 2016

In this paper, we propose an effective time-of-arrival (TOA)-based localization method with adaptive bias computation in indoor environments. The goal of the localization is to estimate an accurate target's location in wireless localization system. However, in indoor environments, non-line-of-sight (NLOS) errors block the signal propagation between target device and base station. The NLOS errors have significant effects on ranging between two devices for wireless localization. In TOA-based localization, finding the target's location inside the overlapped area in the TOA-circles is difficult. We present an effective localization method using compensated distance with adaptive bias computation. The proposed method is possible for the target's location to estimate an accurate location in the overlapped area using the measured distances with subtracted adaptive bias. Through localization experiments in indoor environments, estimation error is reduced comparing to the conventional localization methods.

• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.19-28
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• 2011

Recently, ubiquitous computing and related technologies is more and more growing concern about. Depending on the trend, the moving object recognition and tracking research have been required in order to meet the diverse needs of the user. In the location-based services, one of the most important issues in the indoor environment is to provide location-aware services. In this paper, the effective algorithm to help estimate the position of moving objects in an indoor environment is proposed. We propose an algorithm that combined the existing trilateration measurement and the improved measurement of environmental adaptation scene analysis. The proposed indoor location estimation algorithm use the trilateration measurement when we have enough anchor in the line-of-sight environment. Otherwise that use measurement of environmental adaptation scene analysis. Consequently, the proposed algorithm has been improved the localization accuracy of a moving object as well as was able to reduce complexity of the algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.785-791
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• 2021

In this paper, we propose a methodology for estimating the optimal traversable destination from the location-based information of the object recognized by the mobile robot to perform the object delivery service. The location estimation process is to apply the generalized Voronoi graph to the grid map to create an initial topology map composed of nodes and links, recognize objects and extract location data using RGB-D sensors, and collect the shape and distance information of obstacles. Then, by applying the hybrid approach that combines the center of gravity and thinning method, the optimal moving position for the service robot to perform the task of grabbing is estimated. And then, the optimal node information for the robot's work destination is updated by comparing the geometric distance between the estimated position and the existing node according to the node update rule.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.12
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• pp.2184-2188
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• 2007

We developed a novel method of partial discharge(PD) location based on the fact that the waveform of PD signal propagate along the GIB (Gas Insulated Bus) is composed of several modes of electromagnetic wave with different group velocities and cut-off frequencies. From the PD waveform, measured at a broadband PD sensor attached on the GIB, we could derive arrival time and frequency components of different modes using the short term Fourier transform or etc. After the group velocities of different modes are calculated, the location of the PD source could be estimated. To show the effectiveness of this new locating method in a real on site application, we used this method to locate the position of a PD source at a 76 m long 345 kV GIB substation. The estimated location of the PD source using the method proposed above was in good agreement with the actual location found from the inspection result of internal component in the GIB with 2.4% of the estimation error.