• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.71-76
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• 2006

With the rapid development of spatial infrastructure in US, Europe, Japan, China and India, there is no doubt that the next generation Global Navigation Satellite System (GNSS) will improve the integrity, accuracy, reliability and availability of the position solution. GNSS is becoming an essential element of personal, commercial and public infrastructure and consequently part of our daily lives. However, the applicability of GPS in supporting a range of location-sensitive applications such as location based services in an urban environment is severely curtailed by the interference of the 3D urban settings. To characterize and gain in-depth understanding of such interferences and to be able to provide location-based optimization alternatives, a high-fidelity 3D urban model of Melbourne CBD built with ArcGIS and large scale high-resolution spatial data sets is used in this study to support a comprehensive simulation of current and future GNSS signal performance, in terms of signal continuity, availability, strength, geometry, positioning accuracy and reliability based on a number of scenarios. The design, structure and major components of the simulator are outlined. Useful time-stamped spatial patterns of the signal performance over the experimental urban area have been revealed which are valuable for supporting location based services applications, such as emergency responses, the optimization of wireless communication infrastructures and vehicle navigation services.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.1
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• pp.46-55
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• 2006

The purpose of this study is a development of an automatic mosaicing for applying to large number of airborne multispectral images, which reduces manual operation by human. 2436 airborne multispectral images were acquired from DuncanTech MS4100 camera with three bands; green, red and near infrared. LIDAR(LIght Detection And Ranging) data and GPS/INS(global positioning system/inertial navigation system) data were collected with the multispectral images. First, the multispectral images were converted to image patterns by unsupervised classification. Their patterns were compared with those of adjacent images to derive relative spatial position between images. Relative spatial positions were derived for 80% of the whole images. Second, it accomplished an automatic mosaicing using GPS/INS data and unsupervised classification. Since the time of GPS/INS data did not synchronized the time of readout images, synchronized GPS/INS data with the time of readout image were selected in consecutive data by comparing unsupervised classified images. This method realized mosaicing automatically for 96% images and RMSE (root mean square error) for the spatial precision of mosaiced images was only 1.44 m by validation with LIDAR data.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.6
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• pp.565-570
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• 2015

Supervised machine learning has become popular in discovering context descriptions from sensor data. However, collecting a large amount of labeled training data in order to guarantee good performance requires a great deal of expense and time. For this reason, semi-supervised learning has recently been developed due to its superior performance despite using only a small number of labeled data. In the existing semi-supervised learning algorithms, unlabeled data are used to build a graph Laplacian in order to represent an intrinsic data geometry. In this paper, we represent the unlabeled data as the spatial-temporal dataset by considering smoothly moving objects over time and space. The developed algorithm is evaluated for position estimation of a smartphone-based robot. In comparison with other state-of-art semi-supervised learning, our algorithm performs more accurate location estimates.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.3
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• pp.382-392
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• 1999

Industrial robots have increased in both the number and applications in today's material handling systems. However, traditional approaches to robot controling have had limited success in complicated environment, especially for real time applications. One of the main reasons for this is that most traditional methods use a set of kinematic equations to figure out the physical environment of the robot. In this paper, a neural network model to solve robot manipulator's inverse kinematics problem is suggested. It is composed of two Self-Organizing Feature Maps by which the workspace of robot environment and the joint space of robot manipulator is inter-linked to enable the learning of the inverse kinematic relationship between workspace and joint space. The proposed model has been simulated with two robot manipulators, one, consisting of 2 links in 2-dimensional workspace and the other, consisting of 3 links in 2-dimensional workspace, and the performance has been tested by accuracy of the manipulator's positioning and the response time.

• Journal of the Korean Professional Engineers Association
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• v.37 no.2
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• pp.47-53
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• 2004

Space-based radio-positioning and time transfer system, Provide accurate position, velocity, and time(PVT) information \longleftarrowunlimited number of suitably equipped ground, sea, air and space users, by the U.S Department of Defense(DoD), Comprises major system segments\longrightarrowSpace, Control and User

• Journal of Astronomy and Space Sciences
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• v.34 no.1
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• pp.7-17
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• 2017

In South Korea, there are about 80 Global Positioning System (GPS) monitoring stations providing total electron content (TEC) every 10 min, which can be accessed through Korea Astronomy and Space Science Institute (KASI) for scientific use. We applied the computerized ionospheric tomography (CIT) algorithm to the TEC dataset from this GPS network for monitoring the regional ionosphere over South Korea. The algorithm utilizes multiplicative algebraic reconstruction technique (MART) with an initial condition of the latest International Reference Ionosphere-2016 model (IRI-2016). In order to reduce the number of unknown variables, the vertical profiles of electron density are expressed with a linear combination of empirical orthonormal functions (EOFs) that were derived from the IRI empirical profiles. Although the number of receiver sites is much smaller than that of Japan, the CIT algorithm yielded reasonable structure of the ionosphere over South Korea. We verified the CIT results with NmF2 from ionosondes in Icheon and Jeju and also with GPS TEC at the center of South Korea. In addition, the total time required for CIT calculation was only about 5 min, enabling the exploration of the vertical ionospheric structure in near real time.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.455-458
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• 2006

The Real Time Kinematic (RTK) technique is the most productive and accurate GPS positioning method today, as it can be determinate position within few centimeters instantly. This method is widely used for applications such as surveying, structure monitoring and machine guidance etc. In order to perform RTK processing for large scale systems (i.e. precise vehicle monitoring with many rovers), many expensive RTK receivers and same number of bidirectional communication units have to be installed to collect observation data communicate with the reference site and monitor its RTK solutions. Moreover, if applications require remote control or apply sensing instruments, we have to install computers at each rover. To limit expense and complexity of system management with a large number of rovers, we have developed server based RTK processing platform to share RTK function for all rovers. The system can be process many GPS stations with a single personal computer. we have also developed a specialized dual frequency GPS receiver unit without on-board RTK processing capability to reduce receiver cost in order to demonstrate the advantage of our server based RTK platform. This paper describes the concept of our server based RTK platform and specialized GPS receiver unit with existing applications in Japan.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.119-125
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• 2018

Various techniques for indoor positioning using a smart phone have been studied. Among them, the positioning technology using the acceleration sensor and the gyro sensor built in the smartphone is widely used in conjunction with the WiFi fingerprint technology. The location tracking technology using sensors has been used for a long time, but the performance environment of the smartphone is poor and the user is moving with the smartphone in a certain posture. Therefore, in order to improve the accuracy of location tracking in a smartphone environment, it is necessary to study and develop appropriate algorithms in a mobile environment. In this paper, we analyze the performances of frequency analysis method, maximum sum of minimum acceleration method and adaptive threshold method, which are the user's moving step count detection algorithms, and determine the most accurate method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.4
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• pp.76-86
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• 2013

As the basic Kalman filter is limited to be used for indoor navigation, and particle filters incur serious computational overhead, especially in mobile devices, we propose an adaptive hybrid filter for WiFi-based indoor positioning systems. The hybrid filter utilizes the same prediction framework of the basic Kalman filter, and it adopts the notion of particle filters only using a small number of particles. Restricting the predicts of a moving object to a small number of particles on a way network and substituting a dynamic weighting scheme for Kalman gain are the key features of the filter. The adaptive hybrid filter showed significantly better accuracy than the basic Kalman filter did, and it showed greatly improved performance in processing time and slightly better accuracy compared with a particle filter.

• Journal of Korea Multimedia Society
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• v.20 no.4
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• pp.649-659
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• 2017

Typically, a smart car is equipped with access to the Internet and a wireless local area network. Moreover, a smart car is equipped with a global positioning system (GPS) based navigation system that presents a map to a user for recommending the shortest path to a desired destination. This paper presents the design and implementation of a real-time shortest path search system for directed and dynamic roads. Herein, we attempt to simulate real-world road environments, while considering changes in the ratio of directed roads and in road conditions, such as traffic accidents and congestions. Further, we analyze the effect of the ratio of directed roads and road conditions on the communication cost between the server and vehicles and the arrival times of vehicles. In this study, we compare and analyze distance-based shortest path algorithms and driving time-based shortest path algorithms while varying the number of vehicles to search for the shortest path, road conditions, and ratio of directed roads.