• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.10
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• pp.889-900
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• 2012

In wireless sensor networks, the positioning scheme using received signal strength (RSS) has been widely considered. Appropriate estimation of path-loss exponent (PLE) between a sensor node and an anchor node plays a key role in reducing position error in this RSS-based positioning scheme. In the conventional researches, a sensor node directly uses the PLEs measured by its nearest anchor node to calculate its position. However, the actual PLE between a sensor node and the anchor node can be different from the PLE measured by its nearest anchor node. Thus, if a sensor node directly uses the PLEs measured by its nearest anchor node, the estimated position is different from the actual position of the sensor node with a high probability. In this paper, we describe the method how a sensor node estimates PLEs from the anchor nodes of interest by itself and calculates its position based on these self-estimated PLEs. Especially, our proposal suggests the mechanism to iteratively calculate the PLEs depending on the estimated distances between a sensor node and anchor nodes. Based on the recalculated PLEs, the sensor node reproduces its position. Through simulations, we show that our proposed positioning scheme outperforms the traditional scheme in terms of position error.

• Journal of Korea Multimedia Society
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• v.12 no.10
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• pp.1436-1449
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• 2009

The value of sensing information is decided according to positions of sensor nodes, which are very important in sensor networks. In this paper, we propose a method that estimates positions of nodes by using adjacent node information and received signal strength in a sensor network. With the proposed method, we can find positions of nodes easily because we use information that nodes have. Moreover, we can find distribution easily for all the nodes because we can measure a relative position for a node whose position is not known based on anchor nodes whose positions are already known. We utilized Use case diagram, activity diagram and State machine diagram among several diagrams of UML to implement proposed method in sensor networks that is dynamic system. We can understand exact flow for each function of the proposed method in node position estimation system can be implemented easily. And we can be confirmed that the position of estimated nodes has a little error.

• Proceedings of the KIEE Conference
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• 2006.10d
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• pp.238-240
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• 2006

For the method of train position detection, ground-based train position estimation mainly has been applied so far. Ground-based position detection is the way to detect train current positions by installing train position equipments on railroad lines. However, the ground-based methods should install detection equipments on each section, and can only be able to detect train positions from main command center. So this method has several disadvantages such as an discontinuous position detection, an increment in cost of installation and maintenance. To make possible continuous train position detection, and to minimize amount of the cost, the vehicle-based position detection method should be chosen to determine train positions by loading position equipments on vehicles. In this paper, to realize the vehicle-based train position detection method, configuration scheme of train position detection equipment is suggested by using GPS, inertial sensor, speed sensor and its performance is verified by simulations.

• Journal of Ubiquitous Convergence Technology
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• v.2 no.2
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• pp.78-87
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• 2008

Received signal strength indicator (RSSI) is used as one of the ranging techniques to locate dynamic sensor nodes in wireless sensor network. Before it can be used for position estimation, RSSI values must be converted to distances using path loss model. These distances among sensor nodes are combined using trilateration method to find position. This paper presents an idea which attempts to integrate both path loss model and trilateration as one algorithm without going through RSSI-distance conversion. This means it is not simply formulas combination but a whole new model was developed. Several advantages were found after integration: it is able to reduce processing load, and ensure that all values do not exceed the maximum range of 16-bit signed or unsigned numbers due to antilog operation in path loss model. The results also show that this method is able to reduce estimation error while inaccurate environmental parameters are used for RSSI-distance conversion.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1478-1482
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• 2005

Since it is a very issue that figures out a current position of mobile robots, various methods have been proposed until nowadays. This paper proposes the sensor model of RFID(Radio Frequency Identification) and position estimation algorithm for mobile robots. We designed the sensor model of RFID in experimenting repeatedly. The sensor model of RFID in this case is that of stochastics according to sensing rate. Based on this stochastic sensor model, we designed the algorithm which estimates distance and direction of RFID tag. Therefore we made sure that RFID tag is used as landmark.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.9
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• pp.1692-1698
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• 2010

In this paper we use wireless sensor network and ultrasonic sensors to estimate local position of mobile robots, and to navigate it. Ultra sonic sensor is simple and accurate so it is good to use in local estimation and navigation of mobile robots. But to obtain accurate distance of two sensors they need to face each others as possible as they can. To solve this problem we rotate ultra sonic sensor which is attached to robot in 360 degrees and obtain accurate distance. We can estimate precise position of mobile robot by triangulation using obtained distance information. A mobile robot navigates using embedded encoder and compensates its coordinates by ultrasonic sensors. Results of Experiments show proposed method obtains accurate distance between sensors and coordinates of position of robot. And mobile robots can navigate designated path well.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.819-821
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• 2001

This paper describes a characteristics of MFL according to the position of Hall sensor Magnetic Flux Leakage(MFL) Method is used to detect surface defect in ferromagnetic plate. A plate has a surface defect and magnetizing equipment are producted to perform Non-Destructive Testing(NDT) using MFL. The SM 45C carbon steel plate is adopted to this experiment. there is a artifical defect with a twice of thickness and a half of depth of plate. Magnetizing equipment is composed of yoke made by layer-built of silicon sheet steel, NdFeB magnetic and iron brushes. Detecting defect is performed by MFL NDT using Hall sensor. It is shown that magnetic flux detected by Hall sensor is affected according to the position of Hall sensor through MFL experiment and numerical analysis.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1799-1800
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• 2006

ATPS(Automatic Transmission Position Sensor) is the position sensor of an automatic transmission which transforms the gear changes into the electrical signals and transmits them to TCU. This paper deals with an development of the sensor output signal analysis system for ATPS inspection using LabVIEW. The developed system is designed to carry easily, and it can be installed for inspection directly in a car fitted with an automatic transmission. The system can not only detect the output signals of the sensor but also inspect the sensor behavior under the running state of a car. For this, the system is designed to control the position and the speed of motor. Also, it has the database system for the systematic output data management and the convenient GUI for user.

• International Journal of Ocean System Engineering
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• v.1 no.3
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• pp.165-170
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• 2011

In this paper, for the accurate estimation of the position and orientation of the UUV (unmanned underwater vehicle), an AHRS (Attitude Heading Reference System) was developed using the IMU (inertial measurement unit) sensor which provides information on acceleration and orientation in the object coordinate and the initial alignment algorithm and the E-KF (extended Kalman Filter). The initial position and orientation of the UUV are estimated using the initial alignment algorithm with 3-axis acceleration and geomagnetic information of the IMU sensor. The position and orientation of the UUV are estimated using the AHRS composed of 3-axis acceleration, velocity, and geomagnetic information and the E-KF. For the performance test of the orientation estimation of the AHRS, a testbed using IMU sensor(ADIS16405) and DSP28335 coded with an E-KF algorithm was developed and its performance was verified through tests.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.363-365
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• 1994

In switched reluctance motors(SRM), shaft position sensing is essential in order to synchronize the excitation pulse of a pertinent phase to the rotor position. This paper describes the operation of SRM drive using a simple position sensor of low cost. The position sensor is composed of a slotted disk similar to the rotor core shape of a prototype 6/4 SRM and three opto-interrupters disposed at an angle of 30 degrees. The phase current waveforms measured at several rotor speeds in experiment arc compared with those obtained through the computer simulation.