• Proceedings of the Korean Society of Computer Information Conference
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• 2008.06a
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• pp.17-24
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• 2008

위치기반 서비스(LBS: Location Based Service)는 사용자의 현재 위치를 파악하여, 사용자의 위치를 고려한 유용한 정보를 제공하는 서비스를 일컫는다. 위치기반 서비스에는 디렉토리 서비스, 게이트웨이 서비스, 유틸리티 서비스, 표현 서비스, 경로 서비스 등이 있는데, 이러한 서비스를 개발하려면 필수적으로 사용자의 위치를 파악해야 한다. 본 논문에서는 옥내 무선근거리통신망 환경에서 Fingerprint 방식으로 의사결정트리를 이용한 옥내 측위 방법을 소개한다. 또한 이 측위 방법으로 실험 데이터를 수집하고, 이를 이용한 이동 패턴 정보 추출에 대하여 살펴본다. 실험을 위해 의사결정트리를 생성하는 알고리즘과 현재 위치를 판정하는 알고리즘을 소개하고, 이 알고리즘을 적용한 옥내 측위 프로그램을 이용한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2961-2975
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• 2022

The Location-Based Service (LBS) is one of the most well-known services on the Internet. Positioning is the primary association with LBS services. This study proposes an intelligent LoRa-based positioning system, called AI@LBS, to provide accurate location data. The fingerprint mechanism with the clustering algorithm in unsupervised learning filters out signal noise and improves computing stability and accuracy. In this study, data noise is filtered using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, increasing the positioning accuracy from 95.37% to 97.38%. The problem of data imbalance is addressed using the SMOTE (Synthetic Minority Over-sampling Technique) technique, increasing the positioning accuracy from 97.38% to 99.17%. A field test in the NTUST campus (www.ntust.edu.tw) revealed that AI@LBS system can reduce average distance error to 0.48m.

• The KIPS Transactions:PartB
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• v.10B no.5
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• pp.497-502
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• 2003

Fingerprint classification plays a role of reduction of precise joining time and improvement of the accuracy in a large volume of database. Patterns of fingerprint are classified as 5 patterns : left loop, right loop, arch, whorl, and tented arch by numbers and the location of core point and delta point. The existing fingerprint classification is useful in a captured fingerprint image of core point and delta point using paper and ink. However, this system is unapplicable in modern Automatic Fingerprint Identification System (AFIS) because of problems such as size of input and way of input. To solve the problem, this study is to suggest the way of being able to improve accuracy of fingerprint by fingerprint classification based on the entropy of ridges using fingerprint captured mage of core point and prove this through the experiment.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.1
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• pp.49-54
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• 2021

Indoor positioning system becomes of increasing interests due to the demands for accurate indoor location information where Global Navigation Satellite System signal does not approach. Wi-Fi access points (APs) built in many construction in advance helps developing a Wi-Fi Received Signal Strength Indicator (RSSI) based indoor localization. This localization method first collects pairs of position and RSSI measurement set, which is called fingerprint database, and then estimates a user's position when given a query measurement set by comparing the fingerprint database. The challenge arises from nonlinearity and noise on Wi-Fi RSSI measurements and complexity of handling a large amount of the fingerprint data. In this paper, machine learning techniques have been applied to implement Wi-Fi based localization. However, most of existing indoor localizations focus on single position estimation. The main contribution of this paper is to develop multi-target localization by using deep neural, which is beneficial when a massive crowd requests positioning service. This paper evaluates the proposed multilocalization based on deep learning from a multi-story building, and analyses its learning effect as increasing number of target positions.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.113-118
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• 2023

The WiFi fingerprint method for location estimation within the home has the advantage of using the existing infrastructure and estimating absolute coordinates, so many studies are being conducted. Existing studies have mainly focused on the study of localization algorithms, but the improvement of accuracy has reached its limits. However, since a wireless LAN receiver such as a smartphone cannot measure signals smaller than the reception sensitivity of radio signals, the position estimation error varies depending on the method of processing these values. In this paper, we proposed a method to increase the location estimation accuracy by pre-processing the received signal data of the measured wireless LAN router in various ways and applying it to the existing algorithm, and greatly improved accuracy was obtained. In addition, the preprocessed data was applied to the KNN method and the CNN method and the performance was compared.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.594-596
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• 2019

In this paper, we propose an indoor logistic tracking system that identifies the location and inventory of the logistics in the room based on fingerprints. Through this, we constructed the actual infrastructure of the logistics center and designed and implemented the logistics management system. The proposed system collects the signal strength through the location terminal and generates the signal map to locate the goods. The location terminal is composed of a UHF RFID reader and a wireless LAN card, reads the peripheral RFID signal and the signal of the wireless AP, and transmits it to the web server. This allows the user to communicate with the server through the smartphone app and get information and location of nearby items.

• The KIPS Transactions:PartB
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• v.9B no.1
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• pp.29-34
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• 2002

Fingerprint recognition technology was studied by classification and matching. In general, there are five different classifications left loop, right loop, whore, arch, and tented-arch. These classifications are used to determine which class an individual's fingerprint belong to, thereby identifying the individual's fingerprint pattern. The result of this classification, which is sent to the large fingerprint database as an index, helps reduce the matching time and enhance the accuracy of fingerprint matching. The existing fingerprint classification method relies on the number and location of cores and delta points called singular points. The drawback of this method is the lack of accuracy stemming from the classification difficulty involving unclear and/or partially-erased fingerprints. The current paper presents an efficient classification method to rectify the problem associated with identifying Singular points from unclear fingerprints. This method, which is based on Gabor filter's unique characteristics for magnifying directional patterns and frequency range selections, improves fingerprint classification accuracy significantly. In this paper, this method is described and its test result is presented for verification.

• 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).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.1951-1972
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• 2018

Wi-Fi Access Point (AP) optimization approaches are used in indoor positioning systems for signal coverage enhancement, as well as positioning precision improvement. Although the huge power consumption of the AP optimization forms a serious problem due to the signal coverage requirement for large-scale indoor environment, the conventional approaches treat the problem of power consumption independent from the design of indoor positioning systems. This paper proposes a new Fast Water-filling algorithm Group Power Constraint (FWA-GPC) based Wi-Fi AP optimization approach for indoor positioning in which the power consumed by the AP optimization is significantly considered. This paper has three contributions. First, it is not restricted to conventional concept of one AP for one candidate AP location, but considered spare APs once the active APs break off. Second, it utilizes the concept of water-filling model from adaptive channel power allocation to calculate the number of APs for each candidate AP location by maximizing the location fingerprint discrimination. Third, it uses a fast version, namely Fast Water-filling algorithm, to search for the optimal solution efficiently. The experimental results conducted in two typical indoor Wi-Fi environments prove that the proposed FWA-GPC performs better than the conventional AP optimization approaches.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1321-1323
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• 2007

A new reference point location method based on an edge map is proposed, where an orientation map is defined and used to find the edge map. Experimental results show that the proposed method can effectively detect the core point in poor quality and arch-type fingerprint images and produces better results in terms of the detection rate and accuracy than the sine map-based method.