• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.10
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• pp.89-100
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• 2007

Consumer Portal is defined as "a combination of hardware and software that enables two-way communication between energy service provider(ESP, like KEPCO) and equipment within the consumer's premises". The portal provides both a physical link(between wires, radio waves, and other media) and a logical link(translating among language-like codes and etiquette-like protocols) between in-building and wide-area access networks. Thus, the consumer portal is an important, open public shared infrastructure in the future vision of energy services. In this paper, we describe a new methodology for local communication and computer network capacity design of consumer portal, and also presents capacity calculation method using a network system limitation factors. By the approach, we can check into the limitations of existing methods, and propose an improved data processing algorithm that can expand the maximum number of the networked end-use devices up to $30{\sim}40$ times. For validation, we applies the proposed methode to our real system design. Our contribution will help electrical power information network design.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.3
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• pp.57-67
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• 2007

Researchers in mobile AR systems have so far put the value on the technical challenges involved in the limitations imposed from mobility. Beyond such immediate technical questions, however, are questions regarding the possible contents that are to be used for the user’s interaction in ubiquitous computing environment. Various aspects of context of user and environment can be utilized easily as well as effectively. Moreover, the environment will be equipped with lots of pervasive but invisible computing resources. However, it is difficult for users to have access to those computing resources. At the same time, as the smart appliances get to have more features, their user interfaces tend to become harder to use. Thus, in this paper, we propose Context-Aware Mobile Augmented Reality (CAMAR) system. In our system, users only need to take a picture of smart appliances with a built-in camera in a mobile device when they intend to control the appliances. It lets users interact with the smart appliances through personalized control interfaces on their mobile devices. Also, it supports enabling contents to be not only personalized but also shared selectively and interactively among user communities.

• Journal of Broadcast Engineering
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• v.13 no.5
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• pp.651-661
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• 2008

This paper proposes a novel contrast enhancement (CE) algorithm for color images using the modified error diffusion (ED). After conventional color histogram equalization (HE), artifacts such as false contours are produced in the contrast enhanced image. The proposed CE algorithm using the modified ED consists of two parts: CE and ED. In the first part, a low-contrast input image is enhanced by the conventional HE method. In the second part, we use the modified ED algorithm. The inputs of the second part are the average and scaled difference images of the original color input image and the HE image, in which the scaled color difference image is diffused by the ED algorithm. In the proposed algorithm, the modified ED algorithm reduces the artifacts produced in the HE image, and increases the number of color levels. Computer simulations with a number of low-contrast color images show the effectiveness of the proposed CE method in terms of the visual quality as well as the probability mass function. It can be used as a post-processing for CE with simultaneous artifact reduction in various display devices.

• The Journal of the Korea Contents Association
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• v.12 no.9
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• pp.500-511
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• 2012

Recently, use frequency for traffic information which provides shortest paths and traffic condition is increasing. Specially, in the survey, it is shown that users prefer internet portal sites which can be used the most easily among traffic information media. But, there are not many verification systems for traffic information contents of internet portal sites which collect and provide information than traffic information contents which are provided by public service. The purpose of this study is to investigate real accuracy and accuracy felt by users about information provided by portal sites. Therefore, in this research we verified accuracy of information by portal site with real field data and investigate real usage about contents and experienced accuracy by users through survey. Also, users' expectation and satisfaction were surveyed and the contents to be improved were selected by using IPA technique. By the result of accuracy verification by field data using portable DSRC(Dedicated Short Range Communication) devices, it is shown that average error was 14~32% and sometimes very high rate. Also, it is shown that 28.3 % of total respondents prefers the information by portal sites and 50 % of total respondents felt that contents of traffic information by portal sites are not accurate. Real-time traffic condition was selected as the most inaccurate one among all contents of traffic information and it was analyzed that intensive efforts for improving information about real-time traffic condition are needed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.5
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• pp.914-923
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• 2015

Recently, as the number of smart devices (such as smart TV or Web based IPTV) increases, the way of digital broadcast contents is changed. This change leads that conventional broadcast media accepts Web platform and its services to provide more quality contents. Based on this change, in education field, education broadcasting also follows the trend. The traditional education broadcasting platforms, which just delivered the lecture in one-way, are utilized the Web technology to make interaction between teacher and student. Current education platforms, however, are insufficient to satisfy users' demands for two-way interactions. This paper proposes a new remote collaborative learning platform which able to provide high interactivity among users. Based on new functional requirements from original use case, the platform provides collaborative contents sharing and collaborative video streaming techniques by utilizing WebRTC (Web Real-Time Communication) technology. The implementation demonstrates the operability of proposed system.

• The KIPS Transactions:PartB
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• v.17B no.3
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• pp.197-206
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• 2010

High-quality and low-price digital printing devices are nowadays abused to print or forge official documents and bills. Identifying color laser printers will be a step for media forensics. This paper presents a new method to identify color laser printers with printed color images. Since different printer companies use different manufactural systems, printed documents from different printers have little difference in visual. Analyzing this artifact, we can identify the color laser printers. First, high-frequency components of images are extracted from original images with discrete wavelet transform. After calculating the gray-level co-occurrence matrix of the components, we extract some statistical features. Then, these features are applied to train and classify the support vector machine for identifying the color laser printer. In the experiment, total 2,597 images of 7 printers (HP, Canon, Xerox DCC400, Xerox DCC450, Xerox DCC5560, Xerox DCC6540, Konica), are tested to classify the color laser printer. The results prove that the presented identification method performs well with 96.9% accuracy.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.125-131
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• 2021

In recent years, Convolutional Neural Networks (CNNs) have achieved outstanding performance in the fields of computer vision such as image classification, object detection, visual quality enhancement, etc. However, as huge amount of computation and memory are required in CNN models, there is a limitation in the application of CNN to low-power environments such as mobile or IoT devices. Therefore, the need for neural network compression to reduce the model size while keeping the task performance as much as possible has been emerging. In this paper, we propose a method to compress CNN models by combining matrix decomposition methods of LR (Low-Rank) approximation and CP (Canonical Polyadic) decomposition. Unlike conventional methods that apply one matrix decomposition method to CNN models, we selectively apply two decomposition methods depending on the layer types of CNN to enhance the compression performance. To evaluate the performance of the proposed method, we use the models for image classification such as VGG-16, RestNet50 and MobileNetV2 models. The experimental results show that the proposed method gives improved classification performance at the same range of 1.5 to 12.1 times compression ratio than the existing method that applies only the LR approximation.

• The Journal of the Korea Contents Association
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• v.19 no.8
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• pp.359-369
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• 2019

This research suggests video solutions and efficient implementations for low-budget performances. This study adopts Millumin as a mapping server, which reflects the character of small theatres with a lower budget that doesn't use more than four projectors in a show. By comparing pros and cons of media servers, the study discovers how to employ an appropriate server as well as to participate in a pre-video production stage, which increases the artistry of directing and reduces unnecessary graphics. Meanwhile, with the participation of an interpretive programmer, this study suggests a way to manage the rehearsal time and to increase the artistry of directing. In addition, this study analyses the relationship between the video's visual motive source in the story's development, crisis, climax, twist and the provided narrative based on "Trace U the musical (2018)", by this analyzation, the relationship between storytelling and the video is fully shown. A visual motive is related to the action of actors, the movement of dancers, the music, the lyrics and the lines. Furthermore, the provided narrative confirms that the existence of an actual relationship with the turning point of the plots, characters' emotion, suggestions of sub-plot and the twists of own story. In conclusion, it implies a video of small theatres can not be separated from the probability of the narrative.sh an efficient ad execution strategy that reflected the characteristics of mobile devices.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.483-497
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• 2022

Recently, due to COVID-19, non-face-to-face online lectures are being held all over the world. In higher education in the post-corona era, distance learning has become the main teaching and learning method. At this time, Metaverse is being proposed as a new alternative. Metaverse has basic elements such as avatars, 3D space, and activities accompanied by interaction, which can be seen as a difference compared to existing VR (Virtual Reality) contents. This study designed and built an educational metaverse platform that can be applied to actual lectures by reflecting the three elements of the metaverse.In addition, we implemented a cross-device-platform that supports various devices such as HMDs, smartphones, tablets, and PCs by reflecting user requirements through usability tests such as middle school, high school, college students, and parents, so that anyone can easily participate in Metaverse lectures. Currently, the metaverse platform is being developed and serviced in various ways, but there are hardly any services designed for education. Just as services such as Zoom, the existing video conferencing solution, were used for non-face-to-face education, some functions of the currently serviced metaverse are utilized for education and used in the form of a one-time event. The educational metaverse platform developed through this study is expected to be a reference in constructing the metaverse for education in the future.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.378-390
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• 2022

With the recent development of deep composite multiplication neural network learning, deep learning techniques applied to single-image super-resolution have shown good results, and the strong expression ability of deep networks has enabled complex nonlinear mapping between low-resolution and high-resolution images. However, there are limitations in applying it to real-time or low-power devices with increasing parameters and computational amounts due to excessive use of composite multiplication neural networks. This paper uses blocks that extract hierarchical characteristics little by little using information distillation and suggests the Recursive Distillation Super Resolution Network (RDSRN), a lightweight network that improves performance by making more accurate high frequency components through high frequency residual purification blocks. It was confirmed that the proposed network restores images of similar quality compared to RDN, restores images 3.5 times faster with about 32 times fewer parameters and about 10 times less computation, and produces 0.16 dB better performance with about 2.2 times less parameters and 1.8 times faster processing time than the existing lightweight network CARN.