• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1638-1654
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• 2018

The heterogeneous network (HetNet) has been one of the key technologies in Long Term Evolution-Advanced (LTE-A) with growing capacity and coverage demands. However, the introduction of femtocells has brought serious co-layer interference and cross-layer interference, which has been a major factor affecting system throughput. It is generally acknowledged that the resource allocation has significant impact on suppressing interference and improving the system performance. In this paper, we propose a hybrid-clustering algorithm based on the $Mat{\acute{e}}rn$ hard-core process (MHP) to restrain two kinds of co-channel interference in the HetNet. As the impracticality of the hexagonal grid model and the homogeneous Poisson point process model whose points distribute completely randomly to establish the system model. The HetNet model based on the MHP is adopted to satisfy the negative correlation distribution of base stations in this paper. Base on the system model, the spectrum sharing problem with restricted spectrum resources is further analyzed. On the basis of location information and the interference relation of base stations, a hybrid clustering method, which takes into accounts the fairness of two types of base stations is firstly proposed. Then, auction mechanism is discussed to achieve the spectrum sharing inside each cluster, avoiding the spectrum resource waste. Through combining the clustering theory and auction mechanism, the proposed novel algorithm can be applied to restrain the cross-layer interference and co-layer interference of HetNet, which has a high density of base stations. Simulation results show that spectral efficiency and system throughput increase to a certain degree.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.453-462
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• 2016

Since the Internet provides a way of expressing and sharing Internet users' mindsets, corporate marketers want to acquire measurable and actionable insights from web data. In the past, companies used to analyze the attitude, satisfaction, and loyalty of consumers toward their brands using survey data, whereas nowadays this is done using the big data extracted from Social Network Services. In this study, we propose a framework for clustering brand names using the social metrics gathered on social media. We also conduct a case study of the automobile industry to verify the feasibility of the proposed framework. We calculate the brand name distance for each pair of brand names based on the total number of times that they are mentioned together. These distances are used to project the brand name onto a 3-dimensional space using multidimensional scaling. After the projection, we found the clusters of brand names and identified the characteristics of each cluster. Furthermore, we concluded this paper with a discussion of the limitations and future directions of this research.

• Safety and Health at Work
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• v.7 no.3
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• pp.201-207
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• 2016

Background: Korean Working Conditions Surveys (KWCS), referencing European Working Conditions Surveys, have been conducted three times in order to survey working condition and develop work-related policies. However, we found three limitations for managing the collected KWCS data: (1) there was no computerized system for managing data; (2) statistical KWCS data were provided by limited one-way communication; and (3) the concept of a one-time provision of information was pursued. We suggest a web-based public service system that enables ordinary people to make greater use of the KWCS data, which can be managed constantly in the future. Methods: After considering data characteristics, we designed a database, which was able to have the result of all pairwise combinations with two extracted data to construct an analysis system. Using the data of the social network for each user, the tailored analysis system was developed. This system was developed with three methods: clustering and classification for building a social network, and an infographic method for improving readability through a friendly user interface. Results: We developed a database including one input entity consisting of the sociodemographic characteristics and one output entity consisting of working condition characteristics, such as working pattern and work satisfaction. A web-based public service system to provide tailored contents was completed. Conclusion: This study aimed to present a customized analysis system to use the KWCS data efficiently, provide a large amount of data in a form that can give users a better understanding, and lay the ground for helping researchers and policy makers understand the characteristics.

• 전자공학회논문지 IE
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• v.47 no.3
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• pp.41-46
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• 2010

WSN, or Wireless Sensor Network, consists of a multitude of inexpensive micro-sensors. Because the batteries in sensor nodes can not be replaced once they are deployed, the life of a WSN is absolutely determined by the batteries. So, energy efficiency of a network is a critical factor for long-life operation. LEACH protocol which divides WSN into two groups is a typical routing protocol based on the clustering scheme for the efficient use of limited energy. It is composed of round units which are separated into set-up and steady state. In this paper we propose a power saving scheme to minimize set-up phase itself and to involve a data comparison algorithm. We evaluate the performance of the proposed scheme in comparison with original LEACH protocol. Simulation results validate our scheme has better performance in terms of the number of alive nodes as time evolves and average energy dissipated.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.633-650
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• 2013

All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes a computational intelligence based architecture for online data imputation and extended versions of an existing offline data imputation method as well. The proposed online imputation technique has 2 stages. In stage 1, Evolving Clustering Method (ECM) is used to replace the missing values with cluster centers, as part of the local learning strategy. Stage 2 refines the resultant approximate values using a General Regression Neural Network (GRNN) as part of the global approximation strategy. We also propose extended versions of an existing offline imputation technique. The offline imputation techniques employ K-Means or K-Medoids and Multi Layer Perceptron (MLP)or GRNN in Stage-1and Stage-2respectively. Several experiments were conducted on 8benchmark datasets and 4 bank related datasets to assess the effectiveness of the proposed online and offline imputation techniques. In terms of Mean Absolute Percentage Error (MAPE), the results indicate that the difference between the proposed best offline imputation method viz., K-Medoids+GRNN and the proposed online imputation method viz., ECM+GRNN is statistically insignificant at a 1% level of significance. Consequently, the proposed online technique, being less expensive and faster, can be employed for imputation instead of the existing and proposed offline imputation techniques. This is the significant outcome of the study. Furthermore, GRNN in stage-2 uniformly reduced MAPE values in both offline and online imputation methods on all datasets.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.10
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• pp.801-807
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• 2013

WSN consists of hundreds to thousands of sensor nodes. In order to solve the problem of energy consumption imbalance cluster head is reelected in every round, while node authentication scheme is utilized for secure communication. However, re-clustering increases the overhead of sensor nodes and during the node authentication phase the frequent AREQ/AREP message exchange also increases the overhead. Therefore, a secure and energy efficient protocol, by which overhead of sensor nodes is reduced and long time communication is achieved, is required for wireless sensor network. In this paper, an energy efficient and reliable IDE-LEACH protocol for WSN is proposed. The proposed protocol is prolongs networks lifetime about average 20% compared to the LEACH-based protocols and all attending nodes in communication form BS authentication consisted of trusted nodes.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.186-198
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• 2021

The introduction of digital technologies affects most socio-economic processes and activities in the economy, from agriculture to public services. Even though the world is currently only in the early stages of digital transformation, the digital economy is growing rapidly, especially in developing countries. Shortly, digital platforms will be able to replace the "invisible hand" of the market and turn it into digital. Some digital platforms have already reached global reach in some sectors of the economy. The growing value of data and artificial intelligence is reflected in the high capitalization of these enterprises. Their growing role has far-reaching consequences for the organization of economic activity and integration into the field of e-business. However, their importance and level of development in different countries differ significantly. The main purpose of this article is an assessment of the level and trends of the digital economy in the world and the identification of homogeneous groups of states following the main trends in the development of its components from among the EU countries. The methodology of the conducted research is based on the use of general scientific research methods in the analysis of secondary sources and the application of statistical methods of correlation-regression and cluster analysis. Macroeconomic indicators and components of DESI (Digital Economy and Society Index) were used for the analysis. Results. Based on the analysis established that most developed countries have a medium level of digitalization of the business environment and a high level of digitalization of socially oriented public services, while countries with lower GDP focus their policies on building digital infrastructure and training qualified personnel. The study summarizes and analyzes current trends in digital technology, analyzes the level and dynamics of integration of digital technologies of the studied EU countries, the level of development of e-business and e-commerce. The conceptualization of mechanisms of creation of added value in the digital economy is offered and the possible consequences of digitalization of the economy of developing countries are generalized.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.89-98
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• 2024

In this paper, we propose a system for recommending lecture videos at the chapter level, addressing the balance between accuracy and processing speed in chapter-level video recommendations. Specifically, it has been observed that enhancing recommendation accuracy reduces processing speed, while increasing processing speed decreases accuracy. To mitigate this trade-off, a hybrid approach is proposed, utilizing techniques such as TF-IDF, k-means++ clustering, and Graph Neural Networks (GNN). The approach involves pre-constructing clusters based on chapter similarity to reduce computational load during recommendations, thereby improving processing speed, and applying GNN to the graph of clusters as nodes to enhance recommendation accuracy. Experimental results indicate that the use of GNN resulted in an approximate 19.7% increase in recommendation accuracy, as measured by the Mean Reciprocal Rank (MRR) metric, and an approximate 27.7% increase in precision defined by similarities. These findings are expected to contribute to the development of a learning system that recommends more suitable video chapters in response to learners' queries.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.8
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• pp.2868-2875
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• 2010

This study was carried out to present a co-brand strategies of medical equipment manufacturers in Wonju city. Based on the result of the analysis, followings were proposed. First, an adequate education and a publicity for the importance of a co-brand are required, and it needs to reinforce the recognition on quality differentiation through a technology development and the importance of quality control. Second, establishing a system to secure the brand reliability and operating a program to monitor the technology development and quality are required. Third, it needs to implement a joint participation with priority for establishing a sales network, advertize and a publicity. Fourth, an alternative may needed in order to maximize the synergy effect of the co-brand project and minimize any possible conflicts among participants. Fifth, securing a reliability is needed to acquire the ability to manage the brand and derive member's active participation.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.235-242
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• 2015

Social network technology has been increasing interest in the big data service and development. However, the data stored in the distributed server and not on the central server technology is easy enough to find and extract. In this paper, we propose a big data management techniques to minimize the processing time of information you want from the content server and the management server that provides big data services. The proposed method is to link the in-group data, classified data and groups according to the type, feature, characteristic of big data and the attribute information applied to a hash chain. Further, the data generated to extract the stored data in the distributed server to record time for improving the data index information processing speed of the data classification of the multi-attribute information imparted to the data. As experimental result, The average seek time of the data through the number of cluster groups was increased an average of 14.6% and the data processing time through the number of keywords was reduced an average of 13%.