• Korean Journal of Optics and Photonics
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• v.16 no.1
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• pp.27-33
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• 2005

In this paper, a new wavelength converter using an SOA(Semiconductor Optical Amplifier)-loop-mirror for NRZ(NonReturn to Zero) optical data has been proposed and experimentally demonstrated. Conventional nonlinear fiber-loop-mirror methods can perform RZ-to-RZ, NRZ-to-RZ, and RZ-to-NRZ data format conversion, but NRZ-to-NRZ conversion has not been demonstrated until now. The experiment for the conversion from a 1300 nm NRZ data signal at 1.5 Gbps to a 1550 nm NRZ data one is successfully performed using a fiber-loop-mirror with 1300 nm-SOA.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.06a
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• pp.317-318
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• 2013

International Hydrography Organization has published S-100 Universal Hydrographic Data Model to support use of various hydrographic data for navigational safety. In the S-100 standards, it is possible to manage hydrographic data and apply various application field by introducing the concept of registry and its register. In this study, the S-100 standard based product specification in the field of maritime safety is developed by designing application schema according to general feature model defined in the S-100 standard, and feature catalogue is produced through simple registry.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.23-25
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• 2019

낸드 플래시 메모리(NAND Flash Memory)는 컴퓨터 시스템의 대용량 저장장치를 위한 소자로써, 대용량화의 주요 원인으로는 메모리 셀(Cell) 당 저장할 수 있는 비트 수를 증가시킴으로써 집적도를 증가시킨 것이다. 그러나, 이러한 집적도의 증가는 에러의 증가를 가져와서 저장장치에서 가장 중요한 신뢰성이 급격하게 저하하는 요인이며, 저장장치의 생명주기(Lifetime)을 감소시키게 된다. 기존에 낸드 플래시 메모리 저장장치의 Lifetime을 증대시키기 위해서 P/E cycle을 고려하여 데이터 영역의 일부를 점점 더 ECC 영역으로 변경시키는 방식을 적용한 바가 있다. 이러한 방식은 데이터 영역의 감소로 인한 저장장치 내에서 관리되는 호스트-플래시 간 데이터 관리 크기의 미스매치로 인한 여러 가지 오버 헤드를 생성한다. 본 연구에서는 P/E cycle에 따른 데이터 영역의 ECC 영역으로의 전환을 통한 Lifetime을 증가시키는 방식에 있어서, 오버헤드를 줄이기 위한 캐쉬 관리 구조 및 매핑 관리 구조에 대한 설계를 진행하였다. 이러한 설계를 낸드 플래시 메모리 기반 저장장치에 적용할 경우, LifeTime을 증대시키기 위해서 ECC를 데이터 영역으로 확장하는 방식을 사용할 때 저하될 수 있는 일반 읽기 및 쓰기의 성능 저하를 어느 정도 감소시켜줄 수 있을 것으로 기대한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.1-6
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• 2009

One of the most important problems in bioinformatics is how to extract the useful information from a huge amount of data, and make a decision in diagnosis, prognosis, and medical treatment applications. This paper proposes a weighted kernel function for support vector machine and its learning method with a fast convergence and a good classification performance. We defined the weighted kernel function as the weighted sum of a set of different types of basis kernel functions such as neural, radial, and polynomial kernels, which are trained by a learning method based on genetic algorithm. The weights of basis kernel functions in proposed kernel are determined in learning phase and used as the parameters in the decision model in classification phase. The experiments on several clinical datasets such as colon cancer indicate that our weighted kernel function results in higher and more stable classification performance than other kernel functions.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.195-208
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• 2005

Recently, amount of the data such as sequences is being increased rapidly due to deploying computational technique and advance of experiment tools in the biological areas. In bioinformatics, it is very significant to extract the knowledge from such huge biological data. Sequence comparisons are most frequently used to predict the function of the genes or proteins. However it takes so much time to process the persistently increasing data In this paper, we propose hardware-based grid, CGRID(Chungbuk National University GRID), to improve performance and complement existing middleware-only approach and apply it in the sequence comparison. Hardware-based approach is easy to construct, maintain, and manage the grid as not requiring the software installation individually for every node. We reduce orthologous database construction time from 33 weeks to just a week. Furthermore, CGRID guarantees that the performance increases proportionally as adding the nodes.

• Journal of KIISE
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• v.45 no.1
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• pp.61-68
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• 2018

The identification of genes that contribute to the prediction of prognosis in patients with cancer is one of the challenges in providing appropriate therapies. To find the prognostic genes, several classification models using gene expression data have been proposed. However, the prediction accuracy of cancer prognosis is limited due to the heterogeneity of cancer. In this paper, we integrate microarray data with biological network data using a modified PageRank algorithm to identify prognostic genes. We also predict the prognosis of patients with 6 cancer types (including breast carcinoma) using the K-Nearest Neighbor algorithm. Before we apply the modified PageRank, we separate samples by K-Means clustering to address the heterogeneity of cancer. The proposed algorithm showed better performance than traditional algorithms for prognosis. We were also able to identify cluster-specific biological processes using GO enrichment analysis.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.75-83
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• 2019

Direct volume rendering is a widely used method for visualizing three-dimensional volume data such as medical images. This paper proposes a method for applying depth-of-field effects to volume ray-casting to enable more realistic depth-of-filed rendering in direct volume rendering. The proposed method exploits a camera model based on the human perceptual model and can obtain realistic images with a limited number of rays using jittered lens sampling. It also enables interactive exploration of volume data by on-the-fly calculating depth-of-field in the GPU pipeline without preprocessing. In the experiment with various data including medical images, we demonstrated that depth-of-field images with better depth perception were generated 2.6 to 4 times faster than the conventional method.

• The Journal of the Korea Contents Association
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• v.19 no.2
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• pp.135-146
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• 2019

This study was designed to analyze trends in the entire convergence research beyond academic research through social media big data analysis at a time when interdisciplinary convergence research is emphasized along with the fourth industrial revolution. For this purpose, about 150,000 cases of texts and titles were acquired for about 10 years from January 2009 to September 2018 in connection with the convergence research in social media, and word cloud and network analysis were conducted. As a results, the research fields that were actively conducted for each period were eco-tech in 2009 and 2010, smart technology in 2011 and 2012, information and communication in 2013 and 2014, robots in 2015 and 2016, and artificial intelligence in 2017 and 2018. Also, the research areas that have been consistently conducted for about 10 years are culture, design, chemistry, nanotechnology, biotechnology, robot, IT, and information and communication. Since this study identifies trends in convergence research over time, it can be helpful to researchers who are planning convergence research direction by understanding the trends of convergence research.

• The Journal of Bigdata
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• v.7 no.2
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• pp.195-204
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• 2022

The tourism industry is facing a crisis due to the recent COVID-19 pandemic, and it is vital to improving profitability to overcome it. In situations such as COVID-19, it would be more efficient to sell additional products other than guest rooms to customers who have visited to increase the unit price rather than adopting an aggressive sales strategy to increase room occupancy to increase profits. Previous tourism studies have used machine learning techniques for demand forecasting, but there have been few studies on cross-selling forecasting. Also, in a broader sense, a resort is the same accommodation industry as a hotel. However, there is no study specialized in the resort industry, which is operated based on a membership system and has facilities suitable for lodging and cooking. Therefore, in this study, we propose a cross-selling prediction model using various machine learning techniques with an actual resort company's accommodation data. In addition, by applying the explainable artificial intelligence XAI(eXplainable AI) technique, we intend to interpret what factors affect cross-selling and confirm how they affect cross-selling through empirical analysis.

• Journal of Service Research and Studies
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• v.5 no.2
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• pp.83-92
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• 2015

Many data on the web are present, put out by processing in the content in order to provide services by collecting the necessary data is not easy. One of the reasons is because there is no way to provide a standardized data. Therefore, it can be seen as a part or all of the contents of the site, the content distribution to be available for other services is very important. A syndication format that allows you to use a representative of some or all of the site's content for other services such as RSS and there are Atom, OPML-based XML. Throughout the links provided in this syndication format is called feed address. With a feed address to collect data faster than the conventional HTML parsing and data provider is the advantage of being able to easily provide the data to the outside. In this study, we feed the data obtained by collecting by implementing the web address based on the data acquisition system to propose a method for processing and utilizing the data as a background.