• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.276-278
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• 2018

급격한 전파를 이용하는 기기의 다양화와 대중화로 인해 많은 전파 관련 보안 문제들이 일어나고 있다. 전파와 생활에서의 안전은 매우 밀접한데 전파의 방해와 교란은 단순 생활의 불편뿐 아니라 신체의 직접적인 피해를 입힐 수도 있기 때문에 전파보호는 매우 중요한 과제이다. 본 내용에서는 그 대안으로 본문의 전파 교란과 교섭을 막기 위한 방안으로 생체정보인 지문을 이용한 암호화된 토큰을만들어 토큰링을 통한 정보의 수신여부를 결정 하여 인증 강도, 호출자의 정보 등이 포함된 동적 보안 속성을 가진 수평 전파를 전송하고 java직렬화와 직렬화 해제 기능을 이용하여 토큰의 고유성을 확인수평전파를 송 수신 하여 해당 문제점을 해결 하고자 제안하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.639-640
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• 2018

Currently, the technique of identifying location using radio wave fingerprint is widely used in indoor positioning field. At this time, in order to confirm a successful position, it is necessary to construct the data necessary for learning and testing and to construct the multidimensional data. That is, location data collection and data management technology capable of responding to environmental changes that may occur due to various changes in peripheral radio wave fingerprint such as wireless AP, BLE iBeacon, and mobile terminal are required. Therefore, this paper proposes a technique to construct and manage multidimensional data which is less sensitive to environmental changes of radio wave fingerprinting required for positioning.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.641-642
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• 2018

Currently, the performance of positioning technology based on radio wave fingerprint is greatly influenced by the selection of data comparison algorithm. In this case, the accuracy of the indoor positioning can be greatly improved by the data expansion technique necessary for the learning structure. In this paper, we discuss the importance of learning structure that can be applied to actual positioning through classification and extension of learning data to construct learning structure based on Wi-Fi radio fingerprint.

• Journal of the Korean Society of Safety
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• v.14 no.2
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• pp.192-199
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• 1999

We propose a rotation invariant fingerprint identification system based on the circular harmonic filter(CHF) and binary phase extraction joint transform correlator(BPEJTC) for validation and security verification. It is shown that this system has the shift and rotation robust properties and can recognize the fingerprint in real-time. The complex circular harmonic filter, which is used to obtain the rotation invariance, is converted into the real-valued filter for real-time implementation. Experimental results show that this system has a good performance in the rotated fingerprints.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7B
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• pp.586-594
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• 2012

The drastic growth of mobile communication and spreading of smart phone make the significant attention on Location Based Service. The one of most important things for vitalization of LBS is the accurate estimating position for mobile object. Focusing on AP's probabilistic position estimation, we develop an AP distribution map and new pattern matching algorithm for position estimation. The developed approaches can strengthen the advantages of Radio fingerprint based Wi-Fi Positioning System, especiall on the algorithms and data handling. Compared on the existing approaches of fingerprint pattern matching algorithm, we achieve the comparable higher performance on both of average error of estimation and deviation of errors. Furthermore all fingerprint data have been harvested from the actual measurement of radio fingerprint of Seoul, Kangnam area. This can approve the practical usefulness of proposed methodology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1528-1533
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• 2020

Recently, indoor location recognition technology using Wi-Fi fingerprints has been applied and operated in various industrial fields and public services. Along with the interest in machine learning technology, location recognition technology based on machine learning using wireless signal data around a terminal is rapidly developing. At this time, in the process of collecting radio signal data required for machine learning, the accuracy of location recognition is lowered due to distorted or unsuitable data for learning. In addition, when location recognition is performed based on data collected at a specific location, a problem occurs in location recognition at surrounding locations that are not included in the learning. In this paper, we propose a learning data preprocessing technique to obtain an improved position recognition result through the preprocessing of the collected learning data.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.7
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• pp.774-781
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• 2014

This paper presents a fingerprint database construction method for WLAN RSSI (Received Signal Strength Indicator)-based indoor positioning. When RSSI is used for indoor positioning, the fingerprint method can achieve more accurate positioning than trilateration and centroid methods. However, a FD (Fingerprint Database) must be constructed before positioning. This step is a very laborious process. To reduce the drawbacks of the fingerprint method, a radio propagation model-based FD construction method is presented. In this method, an FD can be constructed by a simulator. Experimental results show that the constructed FD-based positioning has a 3.17m (CEP) error. In this paper, a spatial correlation method is presented to estimate the NLOS(Non-Line of Sight) error included in the FD constructed by a simulator. As a result, the NLOS error of the FD is reduced and the performance of the error compensated FD-based positioning is improved. The experimental results show that the enhanced FD-based positioning has a 2.58m (CEP) error that is a reasonable performance for indoor LBS (Location Based Service).

• Tuberculosis and Respiratory Diseases
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• v.60 no.3
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• pp.290-296
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• 2006

Background : IS6110 DNA fingerprint is a very useful tool for investigating the transmission of tuberculosis. The aim of this study was to identify the epidemiological situations within a given area (one province). Methods : The 681 Mycbobacterium tuberculosis isolates from patients, who were registered at health centers in Gyeonggi Province from May to December in 2004, were subjected to IS6110 DNA fingerprinting. Patients belonging to clusters were interviewed by health-workers to determine their previous contacts or household TB history. Results : The number of IS6110 copies of the 681 isolates showed diverse fingerprint patterns from 0 to 21 of which the most prevalent copy number was 10 from 120 isolates (17.6%). Thirty-three isolates (4.8%) belonged to the K strain, and 128 isolates (18.8%) belonged to the K family. There were 180 (26.4%) isolates belonged belonging to fifty clusters, of which two clusters were within household transmission. Forty-three (23.9%) out of 180 patients resided in an area under the same health center control. The rate of clusters in those aged 60-70 was higher than in any other age group ( 95% CI of RR : 1.072 ~ 1.988). Conclusion : This is the first report of an epidemiological survey based on a whole province using a DNA fingerprinting technique for M. tuberculosis. These results will be helpful in developing a program or policies to prevent the transmission of TB.

• 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.