• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1666-1672
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• 2016

In recent years, smart technology has been applied to various information system fields. Especially, traditional library service area is changing to Smart-Library from Digital-Library. In this environment are need to library service software platform for supporting variety content, library services, users and smart-devices. Due to this, existing library service has a limitation that inhibits semantic interoperability between different heterogeneous library systems. In this paper, we propose Linked-Open-Data based smart library as an archetype of future-library system that provide a variety content and system interaction and integration of services. It is an innovative system of the cutting-edge information intensive. Therefore, we designed system environments according to various integration requirements for smart library based on Linked-Open-Data. And, we describe the functional requirements of smart-library systems by considering the users' demands and the eco-systems of information technology. In addition, we show the reference framework, which can accommodate the functional requirements and provide smart knowledge service to user through a variety of smart-devices.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.183-189
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• 2021

In recent years, active research has been devoted toward developing a monitoring system using ambient vibration data in order to quantitatively determine the deterioration occurring in a structure over a long period of time. This study developed a low-cost edge computing system that detects the abnormalities in structures by utilizing the dynamic characteristics acquired from the structure over the long term for ensemble learning. The system hardware consists of the Raspberry Pi, an accelerometer, an inclinometer, a GPS RTK module, and a LoRa communication module. The structural abnormality detection afforded by the ensemble learning using dynamic characteristics is verified using a laboratory-scale structure model vibration experiment. A real-time distributed processing algorithm with dynamic feature extraction based on the experiment is installed on the Raspberry Pi. Based on the stable operation of installed systems at the Community Service Center, Pohang-si, Korea, the validity of the developed system was verified on-site.

• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.123-134
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• 2009

The CRM(Customer Relationship Management) is a business strategy model which can reap higher profits and can provide a competitive edge to an enterprise in today's new business environments. Early next year (2009), the Capital Market Consolidation Act will be in effect in South Korea. This is required for a qualitative growth to provide QoS (Quality of Service) and ensure growth in finance, IT industry & service. Accordingly, the securities and insurance companies, banks and other financial institutions make efforts to improve their derivative financial product and also enhance their services. In this paper we design and implement a Channel Server model for a Scalable Service Channel Server to efficiently manage the high volumes of inbound customer interactions based on the requirements of a CRM center. The proposed Scalable Service Channel Server supports integration with other third party service and standardization of multiple inbound service channels. The proposed model can be efficiently used in an inbound CRM center of any banking, finance, securities and insurance establishments.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.1-34
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• 2016

Gene identification is at the center of genomic studies. Although the first phase of the Encyclopedia of DNA Elements (ENCODE) project has been claimed to be complete, the annotation of the functional elements is far from being so. Computational methods in gene identification continue to play important roles in this area and other relevant issues. So far, a lot of work has been performed on this area, and a plethora of computational methods and avenues have been developed. Many review papers have summarized these methods and other related work. However, most of them focus on the methodologies from a particular aspect or perspective. Different from these existing bodies of research, this paper aims to comprehensively summarize the mainstream computational methods in gene identification and tries to provide a short but concise technical reference for future studies. Moreover, this review sheds light on the emerging trends and cutting-edge techniques that are believed to be capable of leading the research on this field in the future.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.6
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• pp.464-468
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• 2009

In this paper we propose an image-based approach, which is different from the traditional flow cytometric method to detect shape of apoptosis cells. This method can overcome the defects of cytometry and give precise recognition of apoptosis cells. In this work K-means clustering was used to do the rough segmentation and an active contour model, called 'snake' was used to do the precise edge detection. And then some features were extracted including physical feature, shape descriptor and texture features of the apoptosis cells. Finally a Mahalanobis distance classifier classifies the segmentation images as apoptosis and non-apoptosis cell.

• Journal of Internet Computing and Services
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• v.9 no.2
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• pp.51-59
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• 2008

As the innovative IT are being developed and applied in the e-business environment, firms are recognizing the fact that amount of customer information is providing care competitive edge. However, sensitive privacy information are abused and misused, and it is affecting the firms to require appropriate measures to protect privacy information and implement security techniques to safeguard carparate resources. This research analyzes the threat of privacy information exposure in the e-business environment, suggest the IPM-Trusted Privacy Policy Model in order to resolve the related problem, and examines 4 key mechanisms (CAM, SPM, RBAC Controller, OCM) focused on privacy protection. The model is analyzed and designed to enable access management and control by assigning user access rights based on privacy information policy and procedures in the e-business environment. Further, this research suggests practical use areas by applying TPM to CRM in e-business environment.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.11
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• pp.841-845
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• 2009

The independent cascade model has been widely used to analyze information diffusion in the blog world. In this paper, we propose a new method to construct a blog network for applying the independent cascade model to analyzing of information diffusion in a blog world. To construct a blog network, the proposed method establishes the edge between two users and calculates diffusion probabilities between them by analyzing the activities happened between two users. To calculate diffusion probabilities, the method exploits the ratio of the number of documents actually diffused to a specific user to that of documents written for the purpose of being diffused to other blogs. The experimental result using a real world blog data demonstrates that our method reflects actual information diffusion in a blog world better than existing ones.

• Journal of Korean Society for Geospatial Information Science
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• v.4 no.1 s.6
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• pp.93-102
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• 1996

For an atomatic generation of DEM(Digital Elevation Model) by computer, it is a time-consumed work to determine adquate matches from stereo images. Correlation and evenly distributed area-based method is generally used for matching operation. In this paper, we propose a new approach that computes matches efficiantly by changing the size of mask window and search area according to the given terrain information. For image segmentation, at first edge-preserving smoothing filter is used for preprocessing, and then region growing algorithm is applied for the filterd images. The segmented regions are classifed into mountain, plain and water area by using MRF(Markov Random Filed) model. Maching is composed of predicting parallex and fine matching. Predicted parallex determines the location of search area in fine matching stage. The size of search area and mask window is determined by terrain information for each pixel. The execution time of matching is reduced by lessening the size of search area in the case of plain and water. For the experiments, four images which are covered $10km{\times}10km(1024{\times}1024\;pixel)$ of Taejeon-Kumsan in each are studied. The result of this study shows that the computing time of the proposed method using terrain information for matching operation can be reduced from 25% to 35%.

• KIISE Transactions on Computing Practices
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• v.21 no.2
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• pp.109-114
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• 2015

This paper presents a computational model on the transfer of airborne fine particles to analyze the similarities and influences among the 25 districts in Seoul by quantifying a time series data collected from each district. The properties of each district are driven with the model of a time series of the fine particle concentrations, and the calculation of edge-based weights are carried out with the transfer entropies between all pairs of the districts. We applied a modularity-based graph clustering technique to detect the communities among the 25 districts. The result indicates the discovered clusters correspond to a high transfer-entropy group among the communities with geographical adjacency or high in-between traffic volumes. We believe that this approach can be further extended to the discovery of significant flows of other indicators causing environmental pollution.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.110-114
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• 2010

We describe a new method for text categorization using TextRank algorithm. Text categorization is a problem that over one pre-defined categories are assigned to a text document. TextRank algorithm is a graph-based ranking algorithm. If we consider that each word is a vertex, and co-occurrence of two adjacent words is a edge, we can get a graph from a document. After that, we find important words using TextRank algorithm from the graph and make feature which are pairs of words which are each important word and a word adjacent to the important word. We use classifiers: SVM, Na$\ddot{i}$ve Bayesian classifier, Maximum Entropy Model, and k-NN classifier. We use non-cross-posted version of 20 Newsgroups data set. In consequence, we had an improved performance in whole classifiers, and the result tells that is a possibility of TextRank algorithm in text categorization.