• Spatial Information Research
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• v.21 no.1
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• pp.87-100
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• 2013

Recently, the use of locational information is growing rapidly. GPS technology has been adopted generally for obtaining locational information in outdoor spaces. In the other hand, the researches on indoor positioning have been carried out applying WLAN, RFID or Bluetooth technology because of the multi-path interference of GPS signal caused by the physical obstacles such as walls or columns in buildings. However, such technologies for indoor positioning cost too much to build sensing infrastructure and compute-intensive processes are involved. Furthermore, the accuracy of location estimation is variable caused by interior structures in buildings. In this study, to make up for the limitations, descriptive data such as phone number, unique room numbers, or business names readily available in mixed-use buildings is used for extracting location information. Furthermore, during the process, a geocoding method using character matching is applied to this study enabling prompt location estimation and sublating the fluctuation of accuracy caused by interior structures. Based on the proposed method in this study, an architecture is designed, and three-dimensional viewer program is developed for the implementation of this study. Also, this research is quantitatively analyzed through match rate and processing time of proposed method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.147-153
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• 2018

Beacon enables one to measure distance indoors based on low-power Bluetooth low energy (BLE) technology, while GPS (Global Positioning System) only can be used outdoors. In measuring indoor distance using Beacon, RSSI (Received Signal Strength Indication) is considered as the one of the key factors, however, it is influenced by various environmental factors so that it causes the huge gap between the estimated distance and the real. In order to handle this issue, we propose the adaptive ANN (Artificial Neural Network) based approach to measuring the exact distance using Beacon. First, we has carried out the preprocessing of the RSSI signals by applying the extended Kalman filter and the signal stabilization filter into decreasing the noise. Then, we suggest the multi-layered ANNs, each of which layer is learned by specific training data sets. The results showed an average error of 0.67m, a precision of 0.78.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.2
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• pp.213-219
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• 2013

Recently, with the rapid advancement in information and communication technology, indoor location-based services(LBSs) that require precise position tracking have been actively studied with outdoor-LBS using GPS. However, in case of a ship which consists of steel structure, it is difficult to measure a precise localization due to significant ranging error by the diffraction and refraction of radio waves. In order to reduce location measurement errors that occur in these indoor environments, this paper presents distance compensation algorithms that are suitable for a narrow passage such as ship corridors without any additional sensors by using UWB(Ultra-wide-band), which is robust to multi-path and has an error in the range of a few centimeters in free space. These improvement methods are that Pythagorean theory and adjacent node technique are used to solve the distance error due to the node deployment and distance variation analysis technique is applied to reduce the ranging errors which are significantly fluctuated in the corner section. The experimental results show that the number of nodes and the distance error are reduced to 66% and 57.41%, respectively, compared with conventional CSS(Chirp spread spectrum) method.