• The Journal of the Korea Contents Association
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• v.12 no.6
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• pp.48-55
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• 2012

The number of digital content used in mobile devices such as smartphones or tablet PCs has been rapidly increasing along with the development of IT industry. At the same time, studies on digital content management are actively conducted. One of the previous studies defined CTI(Content Tracking Information) for efficient management of the information on digital content usage between mobile devices in an area in which the internet is inaccessible, and suggested ways to deal with CTI management, reduction of the overhead and a technique of collecting as many CTIs as possible within a short time. This paper presents the design and implementation of a simulator capable of verifying the logic and the performance of the CTI management method. This tool simulates a mobile environment where devices move around in the internet inaccessible area and propagates digital contents each other. It shows several advantages including the efficient synchronization of CTI management and overhead reduction.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.341-344
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• 2007

A major obstacle to classify and validate Land Cover maps is the high cost of generating reference data or multiple thematic maps for subsequent comparative analysis. In case of inaccessible area such as North Korea, the high resolution satellite imagery may be used as in situ data so as to overcome the lack of reliable reference data. The objective of this paper is to investigate the possibility of utilizing QuickBird (0.6m) of North Korea obtained from Google Earth data provided thru internet. Monthly NDVI images of nine months from the summer of 2004 were classified into L=54 cluster using ISODATA algorithm, and these L clusters were assigned to 7 classes; coniferous forest, deciduous forest, mixed forest, paddy field, dry field, water and built-up area. The overall accuracy and Kappa index were 85.98% and 0.82, respectively, which represents about 10% point increase of classification accuracy than our previous study based on GCP point data around North Korea. Thus we can conclude that Google Earth may be used to substitute the traditional in situ data collection on the site where the accessibility is severely limited.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.37-47
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• 2024

This study presents the results of designing a system that determines the location of a person in an indoor environment based on a single IMU sensor attached to the tip of a person's shoe in an area where GPS signals are inaccessible. By adjusting for human footfall, it is possible to accurately determine human location and trajectory by correcting errors originating from the Inertial Measurement Unit (IMU) combined with advanced machine learning algorithms. Although there are various techniques to identify stepping, our study successfully recognized stepping with 98.7% accuracy using an artificial intelligence model known as Long Short-Term Memory (LSTM). Drawing upon the enhancements in our methodology, this article demonstrates a novel technique for generating a 200-meter trajectory, achieving a level of precision marked by a 2.1% error margin. Indoor pedestrian navigation systems, relying on inertial measurement units attached to the feet, have shown encouraging outcomes.