• Journal of Korea Multimedia Society
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• v.14 no.12
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• pp.1613-1620
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• 2011

Roof structure of the traditional buildings in the Northeast Asia region including Korea contains the most complicated and crucial components of the building and therefore the issues such as cost down, productivity and the attempt to combine the traditional building methodology with contemporary building technology turn out to be vital to the survival of the old yet disconnected traditional building industry. One of the distinctive modern building technologies is handling life-cycle building information by constructing virtual buildings using BIM, building information modeling tools. In this paper we follow a procedure to implement some of the design modules to be applied in BIM tools which are platforms for constructing virtual building models. We focus on Gong-po components namely the bracket-sets which are the essential part that connects the middle parts to the top parts (the roof structure) which are considered to be the most elaborate parts of the traditional buildings. The target building to work with in this paper is the Sung-Rye-Moon which has special cultural and social meanings nowadays and we tested our understanding and the design modules such as the bracket-sets by constructing a virtual building model of Sung-Rye-Moon.

• Journal of Korea Multimedia Society
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• v.12 no.1
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• pp.122-129
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• 2009

Incorporating proprietary existing it solutions like legacy systems or vendor specific with new technologies is an expensive and time consuming task. Such situations take place due to the lack of ability of proprietary software to cooperate with other parties or to cooperation only with specific vendor products. Such a situation is undesirable and causes a prolonged adaptation period for new applications. This thesis is to show the new approach to creation of Internet applications in Service Oriented Architecture through loose coupling, introduces fare more flexibility into a system composed of connected applications. This approach allows one to integrate through XML based Web Service and reuse a number of arbitrary services available over the Internet in a complex processes specified as a workflow model.

• Journal of Korea Multimedia Society
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• v.22 no.11
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• pp.1269-1279
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• 2019

In medical image analysis, segmentation is considered as a vital process since it partitions an image into coherent parts and extracts interesting objects from the image. In this paper, we consider automatic segmentations of OCT retinal images to find six layer boundaries using convolutional neural networks. Segmenting retinal images by layer boundaries is very important in diagnosing and predicting progress of eye diseases including diabetic retinopathy, glaucoma, and AMD (age-related macular degeneration). We applied well-known CNN architecture for general image segmentation, called Segnet, U-net, and CNN-S into this problem. We also proposed a shortest path-based algorithm for finding the layer boundaries from the outputs of Segnet and U-net. We analysed their performance on public OCT image data set. The experimental results show that the Segnet combined with the proposed shortest path-based boundary finding algorithm outperforms other two networks.

• Journal of Multimedia Information System
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• v.1 no.1
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• pp.11-22
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• 2014

This work discusses and evaluates the use of wireless and multi-sensor based technologies to develop UbiqBIOPARC, a new generation zoological park that has been created based on the zoo-immersion concept. It offers appropriate contextual information to zoo visitors, depending on their preferences and the environment in which they are positioned. It combines the flexibility of the iPhone SDK, the connectivity provided by 3G technologies, the location capabilities of GPS, and the orientation offered by a digital compass integrated in the device. In this document the overall architecture and the implementation steps followed to create this context-aware application are presented. We compare our system with respect to previous ones and demonstrate that UbiqBIOPARC is an example of how innovative context-aware applications can be built with the aid of GPS and compass features. Several real experiments have been carried out in order to evaluate performance and system behavior, and numerical results demonstrate the practicality offered by our application, while providing a quite reasonable performance in terms of delay, usability, and energy efficiency.

• Journal of Korea Multimedia Society
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• v.16 no.10
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• pp.1204-1213
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• 2013

With the development of digital satellite communication technology and the widespread use of smart devices, the demand for data communication in the maritime ship has increased. Recently, the communication between the maritime ship and the land is based on Inmarsat satellite service. The Inmarsat provides telephone, fax, data and telex service etc. However, since the satellite is payper-seconds billing service, the transmission of whole web contents to the maritime ship through the satellite causes high cost. In this paper, we propose web browsing system architecture that reduces the data traffic on the satellite link and retransmits the content selectively in order to solve these problems.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.233-238
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• 2017

Automatic identification of disease in plants from their leaves is one of the most challenging task to researchers. Diseases among plants degrade their performance and results into a huge reduction of agricultural products. Therefore, early and accurate diagnosis of such disease is of the utmost importance. The advancement in deep Convolutional Neural Network (CNN) has change the way of processing images as compared to traditional image processing techniques. Deep learning architectures are composed of multiple processing layers that learn the representations of data with multiple levels of abstraction. Therefore, proved highly effective in comparison to many state-of-the-art works. In this paper, we present a plant disease identification methodology from their leaves using deep CNNs. For this, we have adopted GoogLeNet that is considered a powerful architecture of deep learning to identify the disease types. Transfer learning has been used to fine tune the pre-trained model. An accuracy of 85.04% has been recorded in the identification of four disease class in Apple plant leaves. Finally, a comparison with other models has been performed to show the effectiveness of the approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.7B
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• pp.641-649
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• 2004

We propose a dynamic traffic management model which uses adaptive priority reassignment algorithm to deliver service differentiation in home networks, and implement adaptive priority reassignment algorithm using FPGA. The proposed architecture is designed to handle home network traffic without the need for signaling protocol. We categorize home network traffic into three kinds of traffic class: control data traffic class, the Internet data and non-real-time data traffic class, and multimedia data traffic class (include non-real-time and real-time multimedia data traffic). To support differential service about these kinds of traffic class, we designed and implemented a traffic management framework that dynamically change each traffic class priority depending on bandwidth utilization of each traffic class.

• Journal of Broadcast Engineering
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• v.20 no.5
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• pp.774-777
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• 2015

This paper introduces the world's first programmable video-processing platform for the enhancement of the video quality of the 8K(7680x4320) UHD(Ultra High Definition) TV operating up to 60 frames per second. In order to support required computing capacity and memory bandwidth, the proposed platform implemented several key features such as symmetric multi-cluster architecture for parallel data processing, a ring-data path between the clusters for data pipelining and hardware accelerators for computing filter operations. The proposed platform based on RP(Reconfigurable Processor) processes video quality enhancement algorithms and handles effectively new UHD broadcasting standards and display panels.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.2
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• pp.41-51
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• 2014

This paper describes a method to estimate the noise power using the minimum statistics approach, which was originally proposed for audio processing. The proposed minimum statistics-based method separates a noisy image into multiple frequency bands using the three-level discrete wavelet transform. By assuming that the output of the high-pass filter contains both signal detail and noise, the proposed algorithm extracts the region of pure noise from the high frequency band using an appropriate threshold. The region of pure noise, which is free from the signal detail part and the DC component, is well suited for minimum statistics condition, where the noise power can be extracted easily. The proposed algorithm reduces the computational load significantly through the use of a simple processing architecture without iteration with an estimation accuracy greater than 90% for strong noise at 0 to 40dB SNR of the input image. Furthermore, the well restored image can be obtained using the estimated noise power information in parametric image restoration algorithms, such as the classical parametric Wiener or ForWaRD image restoration filters. The experimental results show that the proposed algorithm can estimate the noise power accurately, and is particularly suitable for fast, low-cost image restoration or enhancement applications.

• Journal of Korea Multimedia Society
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• v.18 no.12
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• pp.1432-1438
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• 2015

We present a methodology to detect moving shadows in video sequences, which is considered as a challenging and critical problem in the most visual surveillance systems since 1980s. While most previous moving shadow detection methods used hand-crafted features such as chromaticity, physical properties, geometry, or combination thereof, our method can automatically learn features to classify whether image segments are shadow or foreground by using a deep learning architecture. Furthermore, applying Markov Random Field enables our system to refine our shadow detection results to improve its performance. Our algorithm is applied to five different challenging datasets of moving shadow detection, and its performance is comparable to that of state-of-the-art approaches.