• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.355-357
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• 2022

In this paper, as a positioning technology for predicting the movement path of a moving object using a recurrent neural network (RNN) model, which is a deep learning network, in an indoor environment, continuous location information is used to predict the path of a moving vehicle within a local path. We propose a movement path generation technique that can reduce decision errors. In the case of an indoor environment where GPS information is not available, the data set must be continuous and sequential in order to apply the RNN model. However, Wi-Fi radio fingerprint data cannot be used as RNN data because continuity is not guaranteed as characteristic information about a specific location at the time of collection. Therefore, we propose a movement path generation technique for a vehicle moving a local path in an indoor environment by giving the necessary sequential location continuity to the RNN model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.458-459
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• 2021

Recently, researches using deep learning technology based on Wi-Fi fingerprints have been conducted for accurate services in indoor location-based services. Among the deep learning models, an RNN model that can store information from the past can store continuous movements in indoor positioning, thereby reducing positioning errors. At this time, continuous sequential data is required as training data. However, since Wi-Fi fingerprint data is generally managed only with signals for a specific location, it is inappropriate to use it as training data for an RNN model. This paper proposes a path generation method through prediction of a moving path based on Wi-Fi fingerprint data extended to region data through clustering to generate sequential input data of the RNN model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.264-266
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• 2022

In order to apply the RNN model to the radio fingerprint-based indoor path generation technology, the data set must be continuous and sequential. However, Wi-Fi radio fingerprint data is not suitable as RNN data because continuity is not guaranteed as characteristic information about a specific location at the time of collection. Therefore, continuity information of sequential positions should be given. For this purpose, clustering is possible through classification of each region based on signal data. At this time, the continuity information between the clusters does not contain information on whether actual movement is possible due to the limitation of radio signals. Therefore, correlation information on whether movement between adjacent clusters is possible is required. In this paper, a deep learning network, a recurrent neural network (RNN) model, is used to predict the path of a moving object, and it reduces errors that may occur when predicting the path of an object by generating continuous location information for path generation in an indoor environment. We propose a method of giving correlation between clustering for generating an improved moving path that can avoid erroneous path prediction that cannot move on the predicted path.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.456-457
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• 2021

Recently, in order to more accurately provide indoor location-based services, technologies using Wi-Fi fingerprints and deep learning are being studied. Among the deep learning models, an RNN model that can store information from the past can store continuous movements in indoor positioning, thereby reducing positioning errors. When using an RNN model for indoor positioning, the collected training data must be continuous sequential data. However, the Wi-Fi fingerprint data collected to determine specific location information cannot be used as training data for an RNN model because only RSSI for a specific location is recorded. This paper proposes a region clustering technique for sequential input data generation of RNN models based on Wi-Fi fingerprint data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.223-228
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• 2007

In this paper, Wideband antenna using E-shaped stacked patches has been designed and fabricated for 4th generation mobile communication(IMT-Advanced) AccessPoint application. E-shaped patch was miniature and brodband by made the movement route of current long. And inductive of coaxial probe compensates capacitive by slot. Therefore we fabricated to improve the bandwidth of proposed antenna. The E-shaped single patch antenna has an impedance bandwidth of about 13%(510[MHz]), By adding a second patch at the top of the first patch a bandwidth of 56%(2060[MHz]). The final fabricated antenna could have a good return loss(Return loss ${\leq}-10dB$) and a high gain(over 9.6dBi) at the range of 3.23 ${\sim}$ 5.29 [GHz].

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.552-557
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• 2015

Recent performed by recognizing a user's location, various services have been tailored focus on short-range wireless communication technology. A beacon in which is attracting attention as next-generation technology. Beacon is a terminal by utilizing the frequency of the non-audible area can not be bluetooth and human sending and receiving terminals and information, Apple recently iBeacon like a low-power Bluetooth (BLE; bluetooth low energy) based beacon showing a tendency to rise into the mainstream there. Services using a beacon is basically installs the terminal in a certain place indoor. It is characterized by providing the user the services to catch the user's position, even automatically take a separate action. Various types of location-based service provided by the target interior space began to attract attention. A variety of location-based services are provided in the interior space in order to be successfully deployed and provide guidance to the interior space, the movement route and the like are essentially required to build various types of information. In this paper, for efficient indoor positioning by varying the signal strength of the beacon in such areas were measured and analyzed.