• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.43-53
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• 2007

e-Learning education system as the next educational trend supporting remote and multimedia education. However, the students stay mainly at remote place and it is hard to certificate whether he is really studying now or not. To solve this problem, some solutions were proposed such as instructor's supervision by real time motion picture or message exchanging. Unhappily, as you can see, it needs much cost to establish the motion exchanging system and trampling upon human rights could occasion to reduce the student's will. Accordingly, we propose the new intelligent system based on face recognition to reduce the system cost. The e-Learning system running on the web page can check the student's status by motion image, and the images transfer to the instructor. For this study, 20 students and one instructor takes part in capturing and recognizing the face images. And the result produces the prevention the leave of students from lecture and improvement of attention.

• Korean Journal of Remote Sensing
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• v.15 no.3
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• pp.227-238
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• 1999

In advance of launch, simulated radiances of the Ocean Scanning Multispectral Imager (OSMI) will be very useful to guess the real imagery of OSMI and to prepare for data processing of OSMI. The data processing system for OSMI which is one of sensors aboard Korea Multi-Purpose Satellite (KOMPSAT) scheduled for launch in 1999 is developed based on the SeaWiFS Data Analysis System (SeaDAS). Simulation of radiances requires information on the spectral band, orbital and scanning characteristics of the OSMI and KOMPSAT spacecraft. This paper also describes a method to create simulated radiances of the OSMI over the oceans. Our method for constructing a simulated OSMI imagery is to propagate a KOMPSAT orbit over a field of Coastal Zone Color Scanner (CZCS) pigment concentrations and to use the values and atmospheric components for calculation of total radiances. A modified Brouwer-Lyddane model with drag was used for the realistic orbit prediction, the CZCS pigment concentrations were used to compute water-leaving radiances, and a variety of radiative transfer models were used to calculate atmospheric contributions to total radiances detected by OSMI. Imagery of the simulated OSMI radiances for 412, 443, 490, 555, 765, 865nm was obtained. As expected, water-leaving radiances were only a small fraction (below 10%) of total radiances and sun glint contaminations were observed near the solar declination. Therefore, atmospheric correction is critical in the calculation of pigment concentration from total radiances. Because the imagery near the sun's glitter pattern is virtually useless and must be discarded, more advanced data collection planning will be required to succeed in the mission of OSMI which is consistent monitoring of global oceans during three year mission lifetime.

• Journal of Aerospace System Engineering
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• v.14 no.3
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• pp.42-50
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• 2020

For calibration of the non-uniformity characteristics of the space-borne infrared (IR) sensor, a black body system shall provide estimated representative surface temperature at various reference temperatures by using the limited number of temperature sensors. The black body system proposed in this study has an I/F flange integrated on the rear side of the black body for installation of the heat pipe to transfer the residual heat after the black body heat-up. This design allows for obtaining a circular symmetric thermal contour of black body with low surface temperature gradient, leading to much easier representative temperature estimation. Additionally, this provides mechanically stable thermal I/F under launch and on-orbit environmental loads, as well as allowing a fail safe design by using the two heat pipes. Also, a highly accurate temperature estimation is possible even if the temperature sensors are attached on the surface on the rear side of the black body. The effectiveness of the thermal design of the proposed black body has been verified through the on-orbit thermal analysis. Based on the results, the representative surface temperature was estimated according to the number and position of the temperature sensors.

• Korean Journal of Optics and Photonics
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• v.32 no.2
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• pp.68-78
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• 2021

A subminiature catadioptric omnidirectional optical system (SCOOS) with 2 mirrors, 6 plastic aspherical lenses, and an illumination system of 6 light emitting diodes, to observe the 360° panoramic image of the inner intestine, is optically designed and evaluated for a capsule endoscope. The total length, overall length, half field of view (HFOV), and F-number of the SCOOS are 14.3 mm, 8.93 mm, 51°~120°, and 3.5, respectively. The optical system has a complementary metal-oxide-semiconductor sensor with 0.1 megapixels, and an illumination system of 6 light-emitting diodes (LEDs) with 0.25 lm to illuminate on the 360° side view of the intestine along the optical axis. As a result, the spatial frequency at the modulation transfer function (MTF) of 0.3, the depth of focus, and the cumulative probability of tolerance at the Nyquist frequency of 44 lp/mm and MTF of 0.3 of the optimized optical system are obtained as 130 lp/mm, -0.097 mm to +0.076 mm, and 90.5%, respectively. Additionally, the simulated illuminance of the LED illumination system at the inner surface of the intestine within HFOV, at a distance of 15.0 mm from the optical axis, is from a minimum of 315 lx to a maximum of 725 lx, which is a sufficient illumination and visibility.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.4
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• pp.859-866
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• 2019

Cybersecurity attack targeting a specific user is rising in number, even enterprises are trying to strengthen their cybersecurity. Network segmentation environment where public network and private network are separated could block information coming from the outside, however, it is unable to control outside information for business efficiency and productivity. Even if enterprises try to enhance security policies and introduce the network segmentation system and a solution incorporating CDR technology to remove unnecessary data contained in files, it is still exposed to security threats. Therefore, we suggest a system that uses file format conversion to transmit a secure file in the network separation environment. The secure file is converted into an image file from a document, as it reflects attack patterns of inserting malicious code into the document file. Additionally, this paper proposes a system in the environment which functions that a document file can keep information for incident response, considering forensic readiness.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.203-215
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• 2019

As hyperspectral sensors that can be mounted on drones are developed, it is possible to acquire hyperspectral imagery with high spatial and spectral resolution. Although the importance of atmospheric correction has been reduced since imagery of drones were acquired at a low altitude,studies on the conversion process from raw data to spectral reflectance should be done for studies such as estimating the concentration of surface materials using hyperspectral imagery. In this study, a vicarious radiometric calibration and an atmospheric correction algorithm based on atmospheric radiation transfer model were applied to hyperspectral data of drone and the results were compared and analyzed. The vicarious calibration method was applied to an empirical line calibration using the spectral reflectance of a tarp made of uniform material. The atmospheric correction algorithm used ATCOR-4 based Modran-5 that was widely used for the atmospheric correction of aerial hyperspectral imagery. As a result of analyzing the RMSE of the difference between the reference reflectance and the correction, the vicarious calibration using the tarp in a single period of hyperspectral image was the most accurate, but the atmospheric correction was possible according to the application purpose of using hyperspectral imagery. If the correction process of normalized spectral reflectance is carried out through the additional vicarious calibration for imagery from multiple periods in the future, accurate analysis using hyperspectral drone imagery will be possible.

• The Journal of Korean Academy of Prosthodontics
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• v.59 no.1
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• pp.146-152
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• 2021

Accurate transfer of the maxillo-mandibular relationship to an articulator (i.e., mounting) is critical in prosthetic treatment procedures. In the current study, a PubMed search was performed to review the influencing factors for the maxillo-mandibular relationship's accuracy. The search included digital mounting as well as conventional gypsum cast mounting. The results showed that a greater amount of displacement was introduced during positioning the maxillary and mandibular models to interocclusal records rather than the dimensional change of registration material. Most intraoral scanners resulted in an accurate reproduction of the maxillo-mandibular relationship for posterior quadrant scanning; however, the accuracy was declined as the scan area increased to a complete arch scan. The digital mounting accuracy was also influenced by the image processing algorithms and software versions, especially for complete arch scans.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1137-1144
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• 2022

In this study, an artificial intelligence(AI) was developed to help with facial expression practice in order to express emotions. The developed AI used multimodal inputs consisting of sentences and facial images for deep neural networks (DNNs). The DNNs calculated similarities between the emotions predicted by the sentences and the emotions predicted by facial images. The user practiced facial expressions based on the situation given by sentences, and the AI provided the user with numerical feedback based on the similarity between the emotion predicted by sentence and the emotion predicted by facial expression. ResNet34 structure was trained on FER2013 public data to predict emotions from facial images. To predict emotions in sentences, KoBERT model was trained in transfer learning manner using the conversational speech dataset for emotion classification opened to the public by AIHub. The DNN that predicts emotions from the facial images demonstrated 65% accuracy, which is comparable to human emotional classification ability. The DNN that predicts emotions from the sentences achieved 90% accuracy. The performance of the developed AI was evaluated through experiments with changing facial expressions in which an ordinary person was participated.

• Journal of Christian Education in Korea
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• v.68
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• pp.175-216
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• 2021

This essay aims to seek an alternative model of catechesis, as this form of education faces various challenges from the Korean Church especially during COVID-19 pandemic. For a long time, catechesis in the Korean Church narrowly focused on the act of producing Christians who would be loyal to the local church, rather than focusing on nurturing members loyal to Christ, an issue that has been problematized in recent publications on catechesis. Thus, the loss of social trust in the Korean Church and the decline of its public image exemplify how this type of catechesis as disciple-making for local church's benefit, mostly nurtures a vertical dimension of faith. The current teaching and learning method mostly employs a unilateral transfer of doctrine from the teacher to the learner and emphasizes the memorization of doctrine. This type of instruction renders the catechesis as the most lackluster and outdated form of Christian education. This essay aims to reconceptualize the traditional model of catechesis. This essay first critically evaluates current situations of catechesis and presents several alternative meanings on the concept of doctrine. Then it explores the theories of catechesis through different models posed by Christian educators such as John Westerhoff III and Richard Osmer. The final section is devoted to presenting an alternative form of catechesis that focuses on seeking holistic faith.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.199-205
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• 2024

Purpose: This study aims to improve the recognition rate of Auto People Counting (APC) in accurately identifying and providing information on remaining evacuees in disaster-vulnerable facilities such as nursing homes to firefighting and other response agencies in the event of a disaster. Methods: In this study, a baseline model was established using CNN (Convolutional Neural Network) models to improve the algorithm for recognizing images of incoming and outgoing individuals through cameras installed in actual disaster-vulnerable facilities operating APC systems. Various algorithms were analyzed, and the top seven candidates were selected. The research was conducted by utilizing transfer learning models to select the optimal algorithm with the best performance. Results: Experiment results confirmed the precision and recall of Densenet201 and Resnet152v2 models, which exhibited the best performance in terms of time and accuracy. It was observed that both models demonstrated 100% accuracy for all labels, with Densenet201 model showing superior performance. Conclusion: The optimal algorithm applicable to APC among various artificial intelligence algorithms was selected. Further research on algorithm analysis and learning is required to accurately identify the incoming and outgoing individuals in disaster-vulnerable facilities in various disaster situations such as emergencies in the future.