• Architectural research
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• v.12 no.2
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• pp.15-22
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• 2010

Our prediction model is based on the development of "Semantic Location Model." It embodies geometrical and topological information which can increase the efficiency in prediction and make it easy to manipulate the prediction model. Data mining is being implemented to extract the inhabitant's location patterns generated day by day. As a result, the self-learning system will be able to semantically predict the inhabitant's location in advance. This context-aware system brings about the key component of the ubiquitous computing environment. First, we explain the semantic location model and data mining methods. Then the location prediction model for the ubiquitous computing system is described in details. Finally, the prototype system is introduced to demonstrate and evaluate our prediction model.

• 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 Information Technology Services
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• v.10 no.4
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• pp.243-258
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• 2011

With increasing interest in ubiquitous computing technology, an infrastructure for the short-distance wireless communication has been extended socially, bringing spotlight to the security system using the image or location. In case of existing security system, there have been issues such as the occurrences of blind spots, difficulty in recognizing multiple objects and storing of the unspecified objects. In order to solve this issue, zone-based location-estimation search system for the image have been suggested as an alternative based on the real-time location determination technology combined with image. This paper intends to suggest the search service for the image zone-based location-estimation. For this, it proposed the location determination algorism using IEEE 802.15.4/ZigBee's RSSI and for real-time image service, the RTP/RTCP protocol was applied. In order to combine the location and image, at the event of the entry of the specified target, the record of the time for image and the time of occurrence of the event on a global time standard, it has devised a time stamp, applying XML based meta data formation method based on the media's feature data based in connection with the location based data for the events of the object. Using the proposed meta data, the service mode which can search for the image from the point in time when the entry of the specified target was proposed.

• Aerospace Engineering and Technology
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• v.1 no.2
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• pp.141-148
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• 2002

The purpose of this study is to enhance geo-location accuracy of the image data acquired by the Electro-Optical Camera(EOC) onboard KOMPSAT-1. EOC image data were analyzed to verify geo-location error. It was found that the major contribution was the time mark inaccuracy and attitude knowledge error. This study shows that the geo-location accuracy can be enhanced by modifying the time and attitude data of the ancillary data.

• Health Policy and Management
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• v.9 no.3
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• pp.21-32
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• 1999

The purpose of this study is to assess individual decisive factors for distribution of medical specialists in Korea. A data set was constructed using several published data sources. including the Korean Medical Association's physician master file as a principal source for physician information. Linear logistic regression analysis was performed to assess the relationship between the location of private specialist clinic for practice with six variables related with individual characteristics: age. sex. location of postgraduate training hospital. location of medical school graduated, size of hospital for training, and specialty. Analysis showed that location of practice. classified into urban and rural areas, was significantly associated with the variables of sex. location of postgraduate training hospital. location of medical school. In addition, significant association was found between the location of practice which was categorized into "near-Seoul area" and others, and sex, location of postgraduate training hospital. and location of medical school. We could conclude that to improve area maldistribution of physicians locations of hospitals for training and medical schools have to have the highest priority in the policymaking.icymaking.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.88-95
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• 2022

Detecting anomalies in human movement is an important task in industrial applications, such as monitoring industrial disasters or accidents and recognizing unauthorized factory intruders. In this paper, we propose an abnormal worker movement detection system based on data stream processing and hierarchical clustering. In the proposed system, Apache Spark is used for streaming the location data of people. A hierarchical clustering-based anomalous trajectory detection algorithm is designed for detecting anomalies in human movement. The algorithm is integrated into Apache Spark for detecting anomalies from location data. Specifically, the location information is streamed to Apache Spark using the message queuing telemetry transport protocol. Then, Apache Spark processes and stores location data in a data frame. When there is a request from a client, the processed data in the data frame is taken and put into the proposed algorithm for detecting anomalies. A real mobility trace of people is used to evaluate the proposed system. The obtained results show that the system has high performance and can be used for a wide range of industrial applications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.449-452
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• 2007

An intelligent robot with RSSI based indoor location estimation function was designed and implemented. A wireless sensor node was attached to the robot to received the location data from the indoor location estimation function. Spartan III was used as the main control device in the mobile robot. The current location data collected from the indoor location estimation system was transferred to the mobile robot and server through Zigbee/IEEE 802.15.4 wireless communication of the sensor node. Once the location data is received, the sensor node senses the direction of the robot head and directs the robot to move to its destination. Indoor location estimation intelligent robot is able to move efficiently and actively to the user appointed location by implementing the proposed obstacles avoidance algorithm. This system is able to monitor real-time environmental data and location of the robot using PC program. Indoor location estimation intelligent robot also can be controlled by executing the instructions sent from the PC program.

• Convergence Security Journal
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• v.8 no.4
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• pp.97-104
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• 2008

Recently, the wireless positioning techniques and mobile computing techniques have rapidly developed to use location data of moving objects. The more the number of moving objects is numerous and the more periodical sampling of locations is frequent, the more location data of moving objects become very large. Hence the system should be able to efficiently manage mass location data, support various spatio-temporal queries for LBS, and solve the uncertainty problem of moving objects. Therefore, in this paper, innovating the location data of moving object effectively, we propose Rend 3DR-tree method to decrease the dead space and complement the overlapping of nodes by utilizing 3DR-tree with the indexing structure to support indexing of current data and history data.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.19-27
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• 2021

In this paper, we present a method for effectively predicting traffic volume based on vehicle location data that are collected by using LDP (Local Differential Privacy). The proposed solution in this paper consists of two phases: the process of collecting vehicle location data in a privacy-presering manner and the process of predicting traffic volume using the collected location data. In the first phase, the vehicle's location data is collected by using LDP to prevent privacy issues that may arise during the data collection process. LDP adds random noise to the original data when collecting data to prevent the data owner's sensitive information from being exposed to the outside. This allows the collection of vehicle location data, while preserving the driver's privacy. In the second phase, the traffic volume is predicted by applying deep learning techniques to the data collected in the first stage. Experimental results with real data sets demonstrate that the method proposed in this paper can effectively predict the traffic volume using the location data that are collected in a privacy-preserving manner.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.161-169
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• 2022

With the development of mobile devices and global positioning systems, various location-based services can be utilized, which collects user's location information and provides services based on it. In this process, there is a risk of personal sensitive information being exposed to the outside, and thus Geo-indistinguishability (Geo-Ind), which protect location privacy of LBS users by perturbing their true location, is widely used. However, owing to the data perturbation mechanism of Geo-Ind, it is hard to accurately obtain the density distribution of LBS users from the collection of perturbed location data. Thus, in this paper, we aim to develop a novel method which enables to effectively compute the user density distribution from perturbed location dataset collected under Geo-Ind. In particular, the proposed method leverages Expectation-Maximization(EM) algorithm to precisely estimate the density disribution of LBS users from perturbed location dataset. Experimental results on real world datasets show that our proposed method achieves significantly better performance than a baseline approach.