• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1752-1761
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• 2007

Location-based services are a recent concept that integrates a mobile device's location with other information in order to provide added value to a user. Although Location-based Services provide users with comfortable information, it is a complex task to manage and share heterogeneous and numerous data in decentralized environments. In this paper, we propose the Semantic LBS Model as one of the solution to resolve the problem. The Semantic LBS Model is a LBS middleware model that includes an ontology-based data model for LBS POI information and its processing mechanism based on Semantic Web technologies. Our model enables POI information to be described and retrieved over various domain-specific ontologies based on our proposed POIDL ontology. This mechanism provide rich expressiveness, interoperability, flexibility in describing and using information about POls, and it can enhance POI retrieval services.

• International Journal of Contents
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• v.13 no.1
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• pp.22-30
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• 2017

The diffusion of advanced mobile technology has introduced new types of personal information or 'location data'. These new data mean new opportunities for businesses, such as location-based services (LBS), but have resulted in new consumer anxieties regarding disclosure of personal information. This study examines the effects of the consumers' perceived control over "time-andplace" information in location-aware services on their perceived privacy risk. A total of 270 respondents participated in this study. Conditions of perceived privacy control were operationalized over time-and-place information, in a $2{\times}2$ factorial design. Results indicate that the perceived control over time-and-place personal information is a significant predictor of perceived risk, and control assurances over time-and-place information enhances the perception of control, thus alleviating the perceived risk. In addition, the effect is much more significant when time and place were combined.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.3
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• pp.169-173
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• 2020

Location-based services are used as core services in various fields. In particular, in the field of public services such as emergency rescue, accurate location estimation technology is very important. Recently, the technology of tracking the location of self-isolation subjects for COVID-19 has become a major issue. Therefore, location estimation technology using personal smart devices is being studied in various ways, and the most widely used method is to use GPS. Other representative methods are using Wi-Fi, Pedestrian Dead Reckoning (PDR), Bluetooth Low Energy (BLE) beacons, and LTE signals. In this paper, we introduced a positioning technology using deep learning based on LTE Channel State Information-Reference Signal (CSI-RS) data, and confirmed the possibility through an outdoor location estimation experiment using a commercial LTE signal.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.241-243
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• 2017

이동 단말기에서 실시간 데이터 제공을 위하여 대부분의 객체 위치 추적 시스템은 GPS 기반의 추적 기법을 사용하고 있으나, 본 논문에서는 위치 트리거 모델을 제안하여 객체의 이동 위치에 따른 시점과 위치 특성과 같은 지능적 정보를 통한 효율적 저비용의 위치 추적 기법을 제시한다. 본 논문에서 제안하는 위치 트리거 모델은 객체 정보의 흐름에 대한 실시간 모니터링과 예외상황 발생 시 지능화된 경고/조치, 최적화된 이동 경로 수립 및 계획의 동적/지능적 재조정을 위한 객체추적 및 이동의 최적화를 목표로 하는 시스템을 구성하기 위해 사용될 수 있다.

• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.6
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• pp.475-482
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• 2005

The iterative calculation by least square method was used to determine the source location of acoustic emission in rock, as so called "traditional method". The results were compared with source coordinates infered from the application of neural network system for new input data, as so called "new method". Input data of the neural network were based on the time differences of longitudinal waves arrived from acoustic emission events at each transducer, the variation of longitudinal velocities at each stress level, and the coordinates of transducer as in the traditional method. The momentum back propagation neural network system adopted to determine source location, which consists of three layers, and has twenty-seven input processing elements. Applicability of the new method were identified, since the results of source location by the application of two methods were similarly concordant.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.41-47
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• 2019

A BIS(Bus Information System) collects, processes and analyzes information such as real-time location and operation status during bus operation. And It is a system that provides valid information to citizens, drivers, traffic centers and bus companies. Transport information system sent by an each bus is collected through GPS(Global Positioning System), DSRC(Dedicated Short Range Communications), Beacon and passed to transport information center. BIS data by collected is handled and analyzed. Next, it is transmitted to citizen, drivers and bus companies in real time. The result of 5 times simulation satisfied the test criteria(error range ${\pm}10m$) with an average error range of 3.306m, and the reliability is increased. In this paper, we propose a improved location transfer component that can provide users to quicker and more accurate location information than existing BIS using GPS of smart phone. It can be seen that reliability is improved by securing improved bus position data.

• Spatial Information Research
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• v.23 no.2
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• pp.69-81
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• 2015

If one can infer the residential area of SNS users by analyzing the SNS big data, it can be an alternative by replacing the spatial big data researches which result from the location sparsity and ecological error. In this study, we developed the way of utilizing the daily life activity pattern, which can be found from timeline data of tweet users, to infer the residential areas of tweet users. We recognized the daily life activity pattern of tweet users from user's movement pattern and the regional cognition words that users text in tweet. The models based on user's movement and text are named as the daily movement pattern model and the daily activity field model, respectively. And then we selected the variables which are going to be utilized in each model. We defined the dependent variables as 0, if the residential areas that users tweet mainly are their home location(HL) and as 1, vice versa. According to our results, performed by the discriminant analysis, the hit ratio of the two models was 67.5%, 57.5% respectively. We tested both models by using the timeline data of the stress-related tweets. As a result, we inferred the residential areas of 5,301 users out of 48,235 users and could obtain 9,606 stress-related tweets with residential area. The results shows about 44 times increase by comparing to the geo-tagged tweets counts. We think that the methodology we have used in this study can be used not only to secure more location data in the study of SNS big data, but also to link the SNS big data with regional statistics in order to analyze the regional phenomenon.

• Journal of the Korean Data and Information Science Society
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• v.5 no.2
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• pp.11-18
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• 1994

Sample sizes for experiments concerned with inference on R=Pr(X

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.153-160
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• 2004

In recent years, the size of data set which we usually handle is enormous, so a lot of outliers could be included in data set. Therefore the robust procedures that automatically handle outliers become very importance issue. We consider the robust estimation problem of location parameter in the univariate case. In this paper, we propose a new method for defining robustness weights for the weighted mean based on the median distance of observations and compare its performance with several existing robust estimators by a simulation study. It turns out that the proposed method is very competitive.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.15-23
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• 2004

When X and Y have independent two parameter exponential distributions, we develop a Bayesian testing procedures for the equality of two location parameters. Under the noninformative prior, we propose a Bayesian test procedures for the equality of two location parameters using fractional Bayes factor and intrinsic Bayes factor. Simulation study and some real data examples are provided.