• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.1-10
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• 2009

One of major purposes of a RFID system is to track moving objects using tags attached to the objects. Because a tagged object has both location and time information expressed as the location of the reader, we can index the trajectory of the object like existing spatiotemporal objects. More efficient tracking may be possible if a spatiotemporal trajectory can be formed of a tag, but there has not been much research on tag trajectory indexes. A characteristic that distinguishes tags from existing spatiotemporal objects is that a tag creates a separate trajectory in each reader by entering and then leaving the reader. As a result, there is a trajectory interruption interval between readers, in which the tag cannot be located, and this makes it difficult to track the tag. In addition, the point tags that only enter and don't leave readers do not create trajectories, so cannot be tracked. To solve this problem, we propose a tag trajectory index called TR-tree (tag trajectory R-tree in RFID system) that can track a tag by combining separate trajectories among readers into one trajectory. The results show that TR-tree, which overcomes the trajectory interruption superior performance than TPIR-tree and R-tree.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.578-581
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• 2007

For tracing tag locations, a trajectories should be modeled and indexed in radio frequency identification (RFID) systems. The trajectory of a tag can be represented as a line that connects two spatiotemporal locations captured when the tag enters and leaves the vicinity of a reader. If a tag enters but does not leave a reader, its trajectory is represented only as a point captured at entry. Because the information that the tag stays in the reader is missing from the trajectory represented only as a point, we should extend the region of a query to find the tag that remains in a reader. In this paper, we propose an interval data model of tag's trajectory in order to solve the problem. Trajectories of tags are represented as two kinds of intervals; dynamic intervals which are time-dependent lines and static intervals which are fixed lines. We also show that the interval data model has better performance than others with a cost model

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.143-146
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• 2008

For tracing tag locations, a trajectories should be modeled and indexed in radio frequency identification (RFID) systems. The trajectory of a tag can be represented as a line that connects two spatiotemporal locations captured when the tag enters and leaves the vicinity of a reader. If a tag enters but does not leave a reader, its trajectory is represented only as a point captured at entry and we should extend the region of a query to find the tag that remains in a reader. In this paper, we propose an interval data model of tag's trajectory in order to solve the problem. For the interval data model. we propose a new index scheme called the IR-tree(Interval R-tree) and algorithms of insert and split for processing query efficiently. We also evaluate the performance of the proposed index scheme and compare it with the previous indexes.

• Journal of Korea Spatial Information System Society
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• v.7 no.2 s.14
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• pp.67-79
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• 2005

For tracing tag locations, the trajectories should be modeled and indexed in a radio frequency identification (RFID) system. The trajectory of a tag is represented as a line that connects two spatiotemporal locations captured when the tag enters and leaves the vicinity of a reader. If a tag enters but does not leave a reader, its trajectory is represented only as a point captured at entry. Because the information that a tag stays in a reader is missing from the trajectory represented only as a point, it is impossible to find the tag that remains in a reader. To solve this problem we propose the data model in which trajectories are defined as intervals and new index scheme called the Interval R-tree. We also propose new insert and split algorithms to enable efficient query processing. We evaluate the performance of the proposed index scheme and compare it with the R-tree and the R*-tree. Our experiments show that the new index scheme outperforms the other two in processing queries of tags on various datasets.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.56-68
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• 2006

For tracing tag locations, the trajectories should be modeled and indexed in radio frequency identification (RFID) systems. The trajectory of a tag can be represented as a line that connects two spatiotemporal locations captured when the tag enters and leaves the vicinity of a reader. If a tag enters but does not leave a reader, its trajectory is represented only as a point captured at entry. Because the information that a tag stays in a reader is missing from the trajectory represented only as a point, it is impossible to find the tag that remains in a reader. To solve this problem we propose the data model in which trajectories are defined as time-parameterized intervals and new index scheme called the Time Parameterized Interval R-tree. We also propose new insert and split algorithms that reduce the area of nodes to enable efficient query processing. We evaluate the performance of the proposed index scheme and compare it with previous indexes on various datasets.

• Journal of KIISE:Databases
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• v.34 no.1
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• pp.59-69
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• 2007

The trajectory of a tag in RFID system is represented as a interval that connects two spatiotemporal locations captured when the tag enters and leaves the vicinity of a reader. Whole trajectories of a tag are represented as a set of unconnected interval because the location of the tag which left the vicinity of a reader is unknown until it enters the vicinity of another reader. The problems are that trajectories of a tag are not connected. It takes a long time to find trajectories of a tag because it leads to searching the whole index. To solve this problem, we propose a technique that links two intervals of the tag and an index scheme called SLR-tree. We also propose a sharing technique of link information between two intervals which enhances space utilization of nodes, and propose a split policy that preserves shared-link information. And finally, we evaluate the performance of the proposed index and prove that the index processes history queries efficiently.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.1
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• pp.9-14
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• 2006

This paper presents methods of robot localization using recent radio frequency identification technology. If the absolute position and orientation of a tag are given in an indoor environment where RFID tags are installed, a robot can estimate its location using the relationship of the identified tag and the robot in a relative coordinate. To derive this relationship, we propose three estimation techniques using a model of a RFID reader, the direction of identification and the detection range. In this algorithm, a suitable estimation method is selected out of the three proposed techniques depending on the situations and trajectory of robot in the detection range. Simulation and experimental results show that the proposed methods can provide good performance for localization.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.8
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• pp.804-809
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• 2010

This paper proposes a Taylor-series design method which reduces the height error of the tag when readers are arranged at the same height in 3-dimensional space. The proposed method consists of two steps. Firstly, the planar position is estimated by the Taylor-series method using the TDOA measurement. Next, the height is estimated from the estimated planar position. In order to show the validity of the proposed method, computer simulations were performed for the static case and linear trajectory of the tag. Results show that the proposed method gives convergent estimated position and better height estimate than the Taylor series method.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.344-352
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• 2022

Accurate indoor localization of construction workers and mobile assets is essential in safety management. Existing positioning methods based on GPS, wireless, vision, or sensor based RTLS are erroneous or expensive in large-scale indoor environments. Tightly coupled sensor fusion mitigates these limitations. This research paper proposes a state-of-the-art positioning methodology, addressing the existing limitations, by integrating Stereo Visual Inertial Odometry (SVIO) with fiducial landmarks called AprilTags. SVIO determines the relative position of the moving assets or workers from the initial starting point. This relative position is transformed to an absolute position when AprilTag placed at various entry points is decoded. The proposed solution is tested on the NVIDIA ISAAC SIM virtual environment, where the trajectory of the indoor moving forklift is estimated. The results show accurate localization of the moving asset within any indoor or underground environment. The system can be utilized in various use cases to increase productivity and improve safety at construction sites, contributing towards 1) indoor monitoring of man machinery coactivity for collision avoidance and 2) precise real-time knowledge of who is doing what and where.