• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.5
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• pp.93-106
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• 2006

With the rapid growth of the wireless Internet communication, a new generation of mobile devices have made possible the broad distribution of mobile digital contents, such as image, music, video, games and applications over the wireless Internet. Mobile devices are rapidly becoming the major means to extend communication channels without copy Protection, usage rule controlling and authentication. As a result, mobile digital contents may be illegally altered, copied and distributed among unauthorized mobile devices. In this paper, we take a look at Open Mobile Alliance (OMA) DRM v2.0 in general, its purpose and function. The OMA is uniquely the focal point for development of an open standard for mobile DRM. Next we introduces features for an active content and illustrates the difference between an active content and an inactive content. Enabling fast rendering of an active content, we propose an OMA-based DRM framework. This framework include the following: 1) Extending DCF Header for supporting an selective encryption, 2) Content encryption key management, 3) Rendering API for an active content. Experimental results show that the proposed framework is able to render an active content fast enough to satisfy Quality of Experience. %is framework has been proposed for a mobile device environment, but it is also applicable to other devices, such as portable media players, set-top boxes, or personal computer.

• Design & Manufacturing
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• v.15 no.2
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• pp.23-29
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• 2021

The specific strength of magnesium alloy is four times that of iron and 1.5 times that of aluminum. For this reason, its use is increasing in the transportation industry which is promoting weight reduction. At room temperature, magnesium alloy has low formability due to Hexagonal closed packed (HCP) structure with relatively little slip plane. However, as the molding temperature increases, the formability of the magnesium alloy is greatly improved due to the activation of other additional slip systems, and the flow stress and elongation vary greatly depending on the temperature. In addition, magnesium alloys exhibit asymmetrical behavior, which is different from tensile and compression behavior. In this study, a jig was developed that can measure the plane deformation behavior on the surface of a material in tensile and compression tests of magnesium alloys in warm temperature. A jig was designed to prevent buckling occurring in the compression test by applying a certain pressure to apply it to the tensile and compression tests. And the tensile and compressive behavior of magnesium at each temperature was investigated with the developed jig and DIC equipment. In each experiment, the strain rate condition was set to a quasi-static strain rate of 0.01/s. The transformation temperature is room temperature, 100℃. 150℃, 200℃, 250℃. As a result of the experiment, the flow stress tended to decrease as the temperature increased. The maximum stress decreased by 60% at 250 degrees compared to room temperature. Particularly, work softening occurred above 150 degrees, which is the recrystallization temperature of the magnesium alloy. The elongation also tended to increase as the deformation temperature increased and increased by 60% at 250 degrees compared to room temperature. In the compression experiment, it was confirmed that the maximum stress decreased as the temperature increased.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1711-1717
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• 2020

X-ray of up to 9MeV are used for container inspection. X-ray intensity must be maintained stably regardless of changes in time. If dose is not constant, it may affect the image quality, and as a result, may affect the inspection of abnormal cargo. Therefore, to acquire high-quality images, continuous dose monitoring is required. In this study, the ion-chamber based device was developed for monitoring the dose change in high-energy x-ray. And to estimate the performance of signal-processing device change according to the environmental change, the output changing due to the change of temperature and humidity was observed. In addition, verification of the device was performed by measuring the output change. As a result of the measurement, there was no significant difference in performance due to changes in temperature and humidity, and the change in output according to the change in exposure was linear. Therefore, it was found that the developed device is suitable for the dose monitoring of high-energy x-ray.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.25-34
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• 2020

In this paper, we empirically compare the effectiveness of training models to recognize beauty-related skin disease using supervised deep learning algorithms. Recently, deep learning algorithms are being actively applied for various fields such as industry, education, and medical. For instance, in the medical field, the ability to diagnose cutaneous cancer using deep learning based artificial intelligence has improved to the experts level. However, there are still insufficient cases applied to disease related to skin beauty. This study experimentally compares the effectiveness of identifying beauty-related skin disease by applying deep learning algorithms, considering CNN, ResNet, and SE-ResNet. The experimental results using these training models show that the accuracy of CNN is 71.5% on average, ResNet is 90.6% on average, and SE-ResNet is 95.3% on average. In particular, the SE-ResNet-50 model, which is a SE-ResNet algorithm with 50 hierarchical structures, showed the most effective result for identifying beauty-related skin diseases with an average accuracy of 96.2%. The purpose of this paper is to study effective training and methods of deep learning algorithms in consideration of the identification for beauty-related skin disease. Thus, it will be able to contribute to the development of services used to treat and easy the skin disease.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.51-58
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• 2021

In this paper, a multi-scale finite element (FE) modeling methodology for three-dimensional (3D) needle-punched (NP) C/SiC with a complex microstructure is presented. The variations of the material properties induced by the needle-punching process and complex geometrical features could pose challenges when estimating the material behavior. For considering these features of composites, a 3D microscopic FE approach is introduced based on micro-CT technology to produce a 3D high fidelity FE model. The image processing techniques of micro-CT are utilized to generate discrete-gray images and reconstruct the high fidelity model. Furthermore, a subcell modeling technique is developed for the 3D NP C/SiC based on the high fidelity FE model to expand to the macro-scale structural problem. A numerical homogenization approach under periodic boundary conditions (PBCs) is employed to estimate the equivalent behavior of the high fidelity model and effective properties of subcell components, considering geometry continuity effects. For verification, proposed models compare excellently with experimental results for the mechanical behavior of tensile, shear, and bending under static loading conditions.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.318-331
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• 2022

With the recent development of hardware computing devices and software based frameworks, machine tasks using deep learning networks are expected to be utilized in various industrial fields and personal IoT devices. However, in order to overcome the limitations of high cost device for utilizing the deep learning network and that the user may not receive the results requested when only the machine task results are transmitted from the server, Collaborative Intelligence (CI) proposed the transmission of feature maps as a solution. In this paper, an efficient compression method for feature maps with vast data sizes to support the CI paradigm was analyzed and presented through experiments. This method increases redundancy by applying feature map reordering to improve compression efficiency in traditional video codecs, and proposes a feature map method that improves compression efficiency and maintains the performance of machine tasks by simultaneously utilizing image compression format and video compression format. As a result of the experiment, the proposed method shows 14.29% gain in BD-rate of BPP and mAP compared to the feature compression anchor of MPEG-VCM.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.137-147
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• 2022

Diagnosis and management of skin condition is a very basic and important function in performing its role for workers in the beauty industry and cosmetics industry. For accurate skin condition diagnosis and management, it is necessary to understand the skin condition and needs of customers. In this paper, we developed SCIS, a big data-based skin care information system that supports skin condition diagnosis and management using social media big data for skin condition diagnosis and management. By using the developed system, it is possible to analyze and extract core information for skin condition diagnosis and management based on text information. The skin care information system SCIS developed in this paper consists of big data collection stage, text preprocessing stage, image preprocessing stage, and text word analysis stage. SCIS collected big data necessary for skin diagnosis and management, and extracted key words and topics from text information through simple frequency analysis, relative frequency analysis, co-occurrence analysis, and correlation analysis of key words. In addition, by analyzing the extracted key words and information and performing various visualization processes such as scatter plot, NetworkX, t-SNE, and clustering, it can be used efficiently in diagnosing and managing skin conditions.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.47 no.4
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• pp.297-304
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• 2021

In this study, we prepared a pigmented skin model, KeraSkin-M^TM for the in vitro evaluation of whitening agents. For the purpose of complementing the existing mono-layer cell culture testing method, KeraSkin-M^TM was produced through the co-culture of human skin-derived keratinocytes and melanocytes. The efficacy of four well-known whitening agents (arbutin, ascorbic acid, kojic acid, niacinamide) was evaluated in KeraSkin-M^TM in order to assess its usefulness in assessing whitening efficacy. As a result, it was possible to observe additional details such as the distribution of melanin granules and melanin capping in each skin layer through KeraSkin-M^TM, which was previously difficult to assess in the traditional 2D cell culture system. In addition, quantification through image analysis of KeraSkin-M^TM allowed for a statistical analysis of the whitening effects. These results suggest that the KeraSkin-M^TM can be used as a new evaluation method of evaluating whitening efficacy, as well as complement the traditional total melanin content and tyrosinase inhibition assays.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.149-155
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• 2021

In this study, we intend to develop a defective road surface object recognition model that automatically detects road surface defects that restrict the movement of the transportation handicapped using electric mobile devices with deep learning. For this purpose, road surface information was collected from the pedestrian and running routes where the electric mobility aid device is expected to move in five areas within the city of Busan. For data, images were collected by dividing the road surface and surroundings into objects constituting the surroundings. A series of recognition items such as the detection of breakage levels of sidewalk blocks were defined by classifying according to the degree of impeding the movement of the transportation handicapped in traffic from the collected data. A road surface object recognition deep learning model was implemented. In the final stage of the study, the performance verification process of a deep learning model that automatically detects defective road surface objects through model learning and validation after processing, refining, and annotation of image data separated and collected in units of objects through actual driving. proceeded.