• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.715-726
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• 2019

With the increasing prevalence of high-resolution satellite images, the need for technology to generate accurate 3D information from the satellite images is emphasized. In order to create a digital terrain model (DTM) that is widely used in applications such as change detection and object extraction, it is necessary to extract trees, buildings, etc. that exist in the digital surface model (DSM) and estimate the height of the ground. This paper presents a method for automatically generating DTM from DSM extracted from KOMPSAT-3A stereo images. The technique was developed to detect the non-ground area and estimate the height value of the ground by using the previously constructed low-resolution topographic data. The average vertical accuracy of DTMs generated in the four experimental sites with various topographical characteristics, such as mountainous terrain, densely built area, flat topography, and complex terrain was about 5.8 meters. The proposed technique would be useful to produce high-quality DTMs that represent precise features of the bare-earth's surface.

• The Journal of Bigdata
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• v.7 no.1
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• pp.29-36
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• 2022

The prevalence of stress and depression among emotional workers is increasing due to the rapid increase in emotional labor and service workers. However, the current mental health management of emotional workers is difficult to consider the emotional response at the time of stress situations, and the existing mental health management is limited because the individual's base state is not reflected. In this study, we present mental healthcare digital twin solution technology, a personalized stress risk management solution. For mental health risk management due to emotional labor, a solution simulation is performed to accurately predict stress risk through synchronization/modeling of dynamic objects in virtual space by extracting individual stress risk factors such as emotional/physical response and environment into various modalities. It provides a mental healthcare digital twin solution for predicting personalized mental health risks that can be configured with modalities and objects tailored to the environment of emotional workers and improved according to user feedback.

• Convergence Security Journal
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• v.22 no.2
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• pp.21-26
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• 2022

Wearing face masks is an effective measure to prevent COVID-19 infection. Infrared thermal image based temperature measurement and identity recognition system has been widely used in many large enterprises and universities in China, so it is totally necessary to research the face mask detection of thermal infrared imaging. Recently introduced MTCNN (Multi-task Cascaded Convolutional Networks)presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask MTCNN is an algorithm that extends MTCNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. It is easy to generalize the R-CNN to other tasks. In this paper, we proposed an infrared image detection algorithm based on R-CNN and detect heating elements which can not be distinguished by RGB images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.550-554
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• 2002

In this paper, we design the CORBA-based Mobile-Agent Model (CMAM) which has the capability of the efficient work processing in distributed environment through sensing network traffic and searching the optimal path for migration nodes of mobile agent. In case existing mobile agent model is given various works from user, the network overhead and traffic are increased by increasing of execution module size. Also, if it happens a large quantity of traffics due to migration of nodes according to appointment of the passive host(node) muting schedule by user, it needs much cost for node search time by traffic. Therefore, in this paper, we design a new mobile agent model that assures the reliability of agent's migration through dynamic act on the specific situation according to appointment of the active routing schedule and can minimize agent's work processing time through optimal path search. The proposed model assigns routing schedule of the migration nodes actively using an extended MAFFinder. Also, for decreasing overhead of network by agent's size, it separated by mobile agent including oかy agent calling module and push agent with work executing module based on distributed object type of CORBA. Also, it can reduce the required time for round works of mobile agent through the optimal path search of migration nodes.

• Journal of the Korean Society for Library and Information Science
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• v.32 no.2
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• pp.295-307
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• 1998

This article is an overview of the digital library project, indicating what roles Koreas diverse digital collections may play. Our digital library prototype has simple architecture, consisting of digital repositories, filters, indexing and searching, and clients. Digital repositories include various types of materials and databases. The role of filters is to recognize a format of a document collection and mark the structural components of each of its documents. We are using a database management system (ORACLE and ConText) supporting user-defined functions and access methods that allows us to easily incorporate new object analysis, structuring, and indexing technology into a repository. Clients can be considered browsers or viewers designed for different document data types, such as image, audio, video, SGML, PDF, and KORMARC. The combination of navigational tools supports a variety of approaches to identifying collections and browsing or searching for individual items. The search interface was implemented using HTML forms and the World Wide Web's CGI mechanism.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.2
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• pp.23-31
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• 2004

This paper focuses on a boundary surface based ray casting. In general the boundary surface based ray casting is processed in two stages. The first stage finds boundary surfaces and stores them into buffers. The second stage calculates a distance from a viewpoint to the voxels of the interested area by projecting boundary surfaces on the view plane, and then starts to traverse a volume data space with the distance. Our approach differs from the general boundary surface based ray casting in processing the first stage of it. Contrast to the typical boundary surface based ray casting where all boundary surfaces of volume data are stored into buffers, they are projected on the planes aligned to the axis of volume data coordinates and these projected data are stored into 6 buffers. Such maneuver shortens time for ray casting, and reduces memory usage because it can be carried out independently from the amount of the volume data.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.335-344
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• 2005

Recently open data mining methods focusing on a data stream that is a massive unbounded sequence of data elements continuously generated at a rapid rate are proposed actively. Knowledge embedded in a data stream is likely to be changed over time. Therefore, identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. This paper proposes a sliding window technique for finding recently frequent itemsets, which is applied efficiently in open data mining. In the proposed technique, its memory usage is kept in a small space by delayed-insertion and pruning operations, and its mining result can be found in a short time since the data elements within its target range are not traversed repeatedly. Moreover, the proposed technique focused in the recent data elements, so that it can catch out the recent change of the data stream.

• The KIPS Transactions:PartA
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• v.10A no.5
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• pp.439-452
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• 2003

Mobile agent is one of the good technologies for overcoming network load and latency in distributed applications, and it may be a promising way of base technology of distributed applications because of its high adaptability for various network environments. In this paper, a mobile agent programming system, called HUMAN, is designed and implemented efficient use in various distributed applications based on mobile agents. HUMAN supports such high level utilities as file searhing, addressing by groups of nodes, storing path information, storing search information, and thus it gives us high easiness in agent-based programming. And it provides various itinerary modes and flexible reply modes for easy adaptation to given network environment. It also provides a management server for registering and active agents. Thus it can be efficiently applied for such varous distributed applications as searching distributed information, remote control, and file sharing in networks. A simple electronic commerce system is designed is designed and implemented as a HUMAN based illustrative application.

• Journal of Korea Game Society
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• v.12 no.2
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• pp.53-62
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• 2012

Visualizing a volume dataset with ray-casting which of visualization methods provides high quality image. However it spends too much time for rendering because the size of volume data are huge. Recently, various researches have been proposed to accelerate GPU-based volume rendering to solve these problems. In this paper, we propose an efficient GPU-based empty space skipping to accelerate volume ray-casting using octree traversal. This method creates min-max octree and searches empty space using vertex splitting. It minimizes the bounding polyhedron by eliminating empty space found in the octree traveral step. The rendering results of our method are identical to those of previous GPU-based volume ray-casting, with the advantage of faster run-time because of using minimized bounding polyhedron.

• Journal of Korea Multimedia Society
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• v.15 no.9
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• pp.1149-1155
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• 2012

In this paper, we propose cause diagnosis method of semiconductor defects from semiconductor industrial images. Our method constructs feature database (DB) of defect images. Then, defect and input images are subdivided by uniform block. And the block similarity is measured using histogram kai-square distance after color histogram calculation. Then, searched blocks in each image are merged into connected objects using clustering. Finally, the most similar defect image from feature DB is searched with the defect cause by measuring cluster similarity based on features of each cluster. Our method was validated by calculating the search accuracy of n output images having high similarity. With n = 1, 2, 3, the search accuracy was measured to be 100% regardless of defect categories. Our method could be used for the industrial applications.