• Journal of the Korea Convergence Society
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• v.6 no.3
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• pp.103-109
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• 2015

TOLED(Transparent, Organic Light Emitting Diodes) based edutainment system has been studied to solve the actual feeling training and educational experience problem of e-learning. However, edutainment system using TOLED has a problem for the non-detection of multi marker array and rotate marker array, and it has problem for the dissonance phenomena caused by Illumination Environment between real world and virtual object. It also has a do not provide services through a variety of devices problem. Therefore, in this paper, we designed a system that provides a realistic actual feeling edutainment contents by recognizes the marker array rotation and a plurality of marker arrangement via an improved marker detection technique. And to unify the real space and virtual space of the lighting environment through a nested block layer.

• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.72-79
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• 2006

Recently, a MMORPG(Massively Multi-play Online Role Playing Game) has built distribute server by Seamless world. This paper proposes an efficient collision detection method. DLS is used to dynamically adjust spatial subdivisions in each the boundary regions of distribute server We use an index table to effectively utilize the relationships between in the nodes and can perform the collision detection efficiently by reconstructing nodes of the tree. Also, we maintain the information for the boundary region to efficiently detect the collections and adjust the boundary regions between distributed servers by using DLS. As the DLS uses pointers, the information for each server is not needed and the boundary regions between the distributed servers are efficiently searched. Using node index points, the construction table can be made to find between ray and neighborhood node, In addition, processes for Network traffic reduce because a copy of the boundary regions is not needed when a object moves with realtime.

• Journal of Information Technology Services
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• v.10 no.1
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• pp.105-116
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• 2011

With recent spotlight on the, uniquitous computing technology, the need for object of indentification and location infrastructure has increased. Such GPS technolgy must utilize IEEE 802.15.4 Zigbee used for existing wireless sensor network infra as a basice element for user's context-awareness in a uniquitous environement, for effectiveness.Such real-time GPS service is provided in the internal environment where the user would actually are and most high-rise buildlings apply. Underthe assumption, the real-time GPS technology is seperated by each floor, and signals do not get transmitted to other floors, the application on one floor within the high-rise buildling was conducted. This study intends to suggest a floor detection algorithm using IEE 802.15.3/Zigbee's RSSI which supports the accuracy within a couple of meters for the user's the movement between the floors in high-rise buildings in a complex environment. It proposes an floor detection algorithm using IEEE 802.15.4/Zigbee's RSSI which provides accuracy within a radius of few meters for the users movement between the floors for real-time location tracking within high-rise building in a cmoplex environment. Furthermore, for more accurate real-time location tracking, it suggests an algorithm for real-time location tracking using IEEE 802.15.4a/Zigbee's CSS technology based on triangulation. Based on the suggested algorithm, it designs a hybrid real-time location tracking service system in a high-rise buildling and test its functions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4290-4309
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• 2020

In recent years, advancements in machine learning capabilities have allowed it to see widespread adoption for tasks such as object detection, image classification, and anomaly detection. However, despite their promise, a limitation lies in the fact that a network's performance quality is based on the data which it receives. A well-trained network will still have poor performance if the subsequent data supplied to it contains artifacts, out of focus regions, or other visual distortions. Under normal circumstances, images of the same scene captured from differing points of focus, angles, or modalities must be separately analysed by the network, despite possibly containing overlapping information such as in the case of images of the same scene captured from different angles, or irrelevant information such as images captured from infrared sensors which can capture thermal information well but not topographical details. This factor can potentially add significantly to the computational time and resources required to utilize the network without providing any additional benefit. In this study, we plan to explore using image fusion techniques to assemble multiple images of the same scene into a single image that retains the most salient key features of the individual source images while discarding overlapping or irrelevant data that does not provide any benefit to the network. Utilizing this image fusion step before inputting a dataset into the network, the number of images would be significantly reduced with the potential to improve the classification performance accuracy by enhancing images while discarding irrelevant and overlapping regions.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.47-57
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• 2024

This paper proposes a system to efficiently manage delays caused by unmanaged and congested queues in crowded environments. Such queues not only cause inconvenience but also pose safety risks. Existing systems, relying on single-camera feeds, are inadequate for complex scenarios requiring multiple cameras. To address this, we developed a multi-vision long queue detection system that integrates multiple vision algorithms to accurately detect various types of queues. The algorithm processes real-time video data from multiple cameras, stitching overlapping segments into a single panoramic image. By combining object detection, tracking, and position variation analysis, the system recognizes long queues in crowded environments. The algorithm was validated with 96% accuracy and a 92% F1-score across diverse settings.

• Geophysics and Geophysical Exploration
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• v.5 no.4
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• pp.309-315
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• 2002

Conventional electromagnetic (EM) method using small loops as a source and receiver has been used in detection of conductive buried objects like a metal detector or in qualitative estimation of the subsurface conductivity variation. Recently, however, since detection of buried objects and imaging of the subsurface conductivity distribution in a relatively conductive area are in a high demand for environmental and engineering purposes, the quantitative interpretation technique of EM data is actively studied. In this regard, we introduce a brief principle of EM survey and show an example of the detection of buried conductive material and imaging of the subsurface conductivity distribution based on data measured at a test survey area. Through this study, we show that multi-frequency EM surveys using small loops may be a good solution to give quick and detail information of subsurface in a conductive survey area.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.575-579
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• 2006

The land use in suburban area has been changed rapidly due to the urban expansion in Korea during the last few decades. And such land use changes result in various environmental problems such as biodiversity decrease, habitat fragmentation, air pollution and urban heat island. Remote Sensing (RS) and Geographical Information Systems (GIS) can be used for land cover change detection to understand the impact and trend of the land use change. Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times and it can provide quantitative and comparative information for the land use/cover change. RS is less expansive than field survey for producing land use maps, and can be accessed quickly and repetitively for large area. Also it can be used for change detection using multi-temporal land use/cover by accumulated data. Therefore, the purpose of this study is to detect and quantitatively evaluate urban land cover change in urban stream watershed area for the last few decades and ultimately to provide the basic data for urban land use planning and management.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.245-247
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• 2022

Backscattered x-ray imaging is a technology capable of acquiring an image inside an irradiated object by measuring X-rays scattered from an object. For image acquisition, the system must include an X-ray generator and a detection system for measuring scattered x-rays. The imaging device must acquire a real-time signal at sampling intervals for x-rays generated by passing through a high-speed rotating collimator, and for this purpose, a high-speed signal acquisition device is required. We developed a high-speed multi-channel signal acquisition device for converting and transmitting signals generated by the sensor unit composed of a large-area plastic scintillator and a photomultiplier tube. The developed detector is a system capable of acquiring signals at intervals of at least 15u seconds and converting and transmitting signals of up to 6 channels. And a system includes remote control functions such as high voltage, signal gain, and low level discrimination for individual calibration of each sensor. Currently, we are conducting an application test for image acquisition under various conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.546-554
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• 2019

Typically, closed-circuit television (CCTV) monitoring is mainly used for post-processes (i.e. to provide evidence after an incident has occurred), but by using a streaming video feed, machine-based learning, and advanced image recognition techniques, current technology can be extended to respond to crimes or reports of missing persons in real time. The multi-CCTV cooperation technique developed in this study is a program model that delivers similarity information about a suspect (or moving object) extracted via CCTV at one location and sent to a monitoring agent to track the selected suspect or object when he, she, or it moves out of range to another CCTV camera. To improve the operating efficiency of local government CCTV control centers, we describe here the partial automation of a CCTV control system that currently relies upon monitoring by human agents. We envisage an integrated crime prevention service, which incorporates the cooperative CCTV network suggested in this study and that can easily be experienced by citizens in ways such as determining a precise individual location in real time and providing a crime prevention service linked to smartphones and/or crime prevention/safety information.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.91-108
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• 2022

In most cases, optical images have been used as training data of DL (Deep Learning) models for object detection, recognition, identification, classification, semantic segmentation, and instance segmentation. However, properties of 3D objects in the real-world could not be fully explored with 2D images. One of the major sources of the 3D geospatial information is DSM (Digital Surface Model). In this matter, characteristic information derived from DSM would be effective to analyze 3D terrain features. Especially, man-made objects such as buildings having geometrically unique shape could be described by geometric elements that are obtained from 3D geospatial data. The background and motivation of this paper were drawn from concept of the intrinsic image that is involved in high-level visual information processing. This paper aims to extract buildings after classifying terrain features by training DL model with DSM-derived information including slope, aspect, and SRI (Shaded Relief Image). The experiments were carried out using DSM and label dataset provided by ISPRS (International Society for Photogrammetry and Remote Sensing) for CNN-based SegNet model. In particular, experiments focus on combining multi-source information to improve training performance and synergistic effect of the DL model. The results demonstrate that buildings were effectively classified and extracted by the proposed approach.