• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.9 no.2
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• pp.48-55
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• 2010

This paper proposes a realtime tracking algorithm of mobile object in indoor or outdoor environment. For this purpose the proposed system selects location data closer to mobile objects in real time using Triangulation method and DCM (Database Correlation Method). Also, this system utilizes the adjusted location data selected by using Kalman filter to improve the location accuracy of transfer object. Be studied in existing the Kalman filter have unstable location data until its settlement because of it extracts current values by using the past the information. However, proposed location tracking system don't apply existent Kalman filter to this system and it permits precisional tracking location by using more effective methods.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.921-929
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• 2014

A bivariate exponential distribution with a location parameter is proposed as a model for a two-component shared load system with a guarantee time. Some statistical properties of the proposed model are investigated. The maximum likelihood estimators and uniformly minimum variance unbiased estimators of the parameters, mean time to failure, and the reliability function of system are obtained with unknown guarantee time. Simulation studies are given to illustrate the results.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1095-1106
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• 2005

When X and Y have independent two parameter exponential distributions, we develop a Bayesian testing procedures for the equality of two location parameters. The reference prior in non-regular exponential model is derived. Under this reference 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.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.11
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• pp.1150-1156
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• 2009

The spread of wireless Internet technology development and applications with location information in the form of location-based services are more varied. In particular, where you recognize the location of objects such as people and things, and to provide valuable services based on ubiquitous, location-based services (Ubiquitous Location Based Services: u-LBS) is emerging as an important service. In this paper precise location data to the car crash through the location and offers related service system. In this paper the precise location tracking proposed by the concept of the Rail, road, to extract the location Data Matching Data and the current location is obtained. System used in GPS Packet and information about the location of the vehicle collision and the collision time, the vehicle consists of NodeID is about. Using these data, a packet is to be created when the conflict between vehicles in the vehicle will be sent to Gateway. Gateway to the packets that were sent from the Server to determine whether the conflict is that in an emergency situation, Emergency Center for location information and let me know whether or not the conflict will be measured. Also, for those on the outside of an emergency such as a family related to the wireless terminal wireless (PDA, cell phone) is to let me know. Server get into the conflict that was configured to store information on the Database. Additionally, the proposed u-LBS system to verify the validity of the experiment was performed.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.97-104
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• 2017

Location prediction has been successfully utilized to provide high quality of location-based services to customers in many applications. In its usual form, the conventional type of location prediction is to predict future locations based on user's past movement history. However, as location prediction needs are expanded into much complicated cases, it becomes necessary quite frequently to make inference on the locations that target user visited in the past. Typical cases include the identification of locations that infectious disease carriers may have visited before, and crime suspects may have dropped by on a certain day at a specific time-band. Therefore, primary goal of this study is to predict locations that users visited in the past. Information used for this purpose include user's demographic information and movement histories. Data mining classifiers such as Bayesian network, neural network, support vector machine, decision tree were adopted to analyze 6868 contextual dataset and compare classifiers' performance. Results show that general Bayesian network is the most robust classifier.

• Journal of Internet Computing and Services
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• v.11 no.4
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• pp.85-94
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• 2010

In this paper, we propose a location-prediction structure that can provide user service in advance. It consists of seven steps and supplies intelligent services which can forecast user's location. Context information collected from physical sensors and a history database is so difficult that it can't present importance of data and abstraction of data because of heterogeneous data type. Hence, we offer the location-prediction that change data type from heterogeneous data to homogeneous data. Extracted data is clustered by SOFM, then it gets user's location information by ARIMA and realizes the services by a reasoning engine. In order to validate the proposed location-prediction, we built a test-bed and test it by the scenario.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.1951-1966
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• 2020

Recent advancements in mobile device technology have enabled real-time positioning so that mobile patterns of people and favorable locations can be identified and related researches have become plentiful. One of the fields of research is the relationship between the object properties and the favored location to visit. The object properties of a person include personality, which is a major property jobs, income, gender, and age. In this study, we analyzed the relationship between the human personality and the preference of the location to visit. We used Spearman's Rank correlation coefficient, one of the many methods that can be used to determine the correlation between two variables. Instead of using actual data values, Spearman's Rank correlation coefficient deals with the ranks of the two data sets. In our research, the personality and the location data sets are used. Our personality data is ranked in five ranks and the location data is ranked in 8 ranks. Spearman's Rank correlation coefficient showed better results compared to Pearson linear correlation coefficient and Kendall rank correlation coefficient. Using Spearman's correlation coefficient, the degree of the relationship between the personality and the location preference is found to be 43%.

• Journal of the Korea Computer Industry Society
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• v.6 no.3
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• pp.495-508
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• 2005

Moving objects are spatiotemporal data that change their location or shape continuously over time. Their location coordinates are periodically measured and stored i3l the application systems. The linear function is mainly used to estimate the location information that is not in the system at the query time point. However, a new method is needed to improve uncertainties of the location representation, because the location estimation by linear function induces the estimation error. This paper proposes an application method of the cubic spline interpolation in order to reduce deviation of the location estimation by linear function. First, we define location information of the moving object on the two-dimensional space. Next, we apply the cubic spline interpolation to location estimation of the proposed data model and describe algorithm of the estimation operation. Finally, the precision of this estimation operation model is experimented. The experimentation comes out more accurate results than the method by linear function.

• Journal of Communications and Networks
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• v.7 no.4
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• pp.478-488
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• 2005

To conserve the usage of energy, indexing techniques have been developed in a wireless mobile environment. However, the use of interleaved index segments in a broadcast cycle increases the average access latency for the clients. In this paper, we present the broadcast-based location dependent data delivery scheme (BBS) for dynamic range queries. In the BBS, broadcasted data objects are sorted sequentially based on their locations, and the server broadcasts the location dependent data along with an index segment. Then, we present a data prefetching and caching scheme, designed to reduce the query response time. The performance of this scheme is investigated in relation to various environmental variables, such as the distributions of the data objects, the average speed of the clients, and the size of the service area.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.310-316
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• 2022

The smart tourism service provides services such as travel planning and tour guides to tourists using key technologies of the 4th industrial revolution, such as the Internet of Things, communication infrastructure, big data, artificial intelligence, AR/VR, and drones. We are developing smart tourism services such as recommended travel products, my travel itinerary, tourism information, and chatbots for tourists through the smart tourism app. In this paper, we develop a smart tourism service system that provides real-time location-based tourism information and weather information to tourists. The smart tourism service system consists of a smart tourism app, a smart tourism information system, and an IoT data collection device. The smart tourism information system receives weather information from the IoT data collection device installed in the tourist destination. The location-based smart tourism service is provided as a smart tourism app in the smart tourism information system according to the Beacon's UUID in the IoT data collection device. The smart tourism information system stores the Beacon's UUIDs received from tourists and provides a safe hiking service for tourists.