• The KIPS Transactions:PartC
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• v.13C no.3 s.106
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• pp.317-322
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• 2006

This paper presents an efficient user-cooperation protocol associated with cyclic coding for WSNs (Wireless Sensor Networks) using LEACH(Low-Energy Adaptive Clustering Hierarchy). Since the proposed user-cooperation requires no CSI(Channel State Information) at both transmitter and receiver, and encoding and decoding of cyclic codes are simple, the processing complexity of sensor nodes is significantly reduced. Simulation results reveal such a combination can save the network energy up to 10dB over single-hop transmission at BER of $10^{-4}$.

• The Journal of Information Technology
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• v.1 no.1
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• pp.13-26
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• 1998

In general Information retrieval systems, improper keywords are often extracted and different search results are offered comparing to user's aim bacause the systems use only term frequency informations for selecting keywords and don't consider their meanings. It represents that improving precision is limited without considering semantics of keywords because recall ratio and precision have inverse proportion relation. In this paper, a system which is able to improve precision without decreasing recall ratio is designed and implemented, as client user module is introduced which can send feedbacks to server with user's intention. For this purpose, keywords are selected using relative term frequency and inverse document frequency and co-occurrence words are extracted from original documents. Then, the keywords are clustered by their semantics using calculated mutual informations. In this paper, the system can reject inappropriate documents using segmented semantic informations according to feedbacks from client user module. Consequently precision of the system is improved without decreasing recall ratio.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.59-69
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• 2018

In this paper, we present a robust beamforming design to tackle the weighted sum-rate maximization (WSRM) problem in a multicell multiple-input multiple-output (MIMO) - non-orthogonal multipleaccess (NOMA) downlink system for 5G wireless communications. This work consider the imperfectchannel state information (CSI) at the base station (BS) by adding uncertainties to channel estimation matrices as the worst-case model i.e., singular value uncertainty model (SVUM). With this observation, the WSRM problem is formulated subject to the transmit power constraints at the BS. The objective problem is known as on-deterministic polynomial (NP) problem which is difficult to solve. We propose an robust beam forming design which establishes on majorization minimization (MM) technique to find the optimal transmit beam forming matrix, as well as efficiently solve the objective problem. In addition, we also propose a joint user clustering and power allocation (JUCPA) algorithm in which the best user pair is selected as a cluster to attain a higher sum-rate. Extensive numerical results are provided to show that the proposed robust beamforming design together with the proposed JUCPA algorithm significantly increases the performance in term of sum-rate as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA) scheme.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.13-18
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• 2016

In Long Term Evolution-Advanced (LTE-A), small cell enhancement(SCE) has been developed as a cost-effective way of supporting exponentially increasing demand of wireless data services and satisfying the user quality of service(QoS). However, there are many problems such as the transmission rate and transmission quality degradation due to the dense and irregular distribution of a large number of small cells. In this paper, we propose a clustering based interference management scheme in dense small cell network. We divide the small cells into different clusters according to the reference signal received power(RSRP) from user equipment(UE). Within a cluster, an almost blank subframe(ABS) is implemented to mitigate interference between the small cells. In addition, we apply the power control to reduce the interference between the clusters. Simulation results show that proposed scheme can improve Signal to Interference plus Noise Ratio(SINR), throughput, and spectral efficiency of small cell users. Eventually, proposed scheme can improve overall cell performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.8
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• pp.1155-1160
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• 2013

The rapid growth of smart phones has made location-based services (LBSs) widely available. However, the use of LBS can raise privacy issues, as LBS can allow adversaries to violate the location privacy of users. There has been a considerable amount of research on preserving user location privacy. Most of these studies try to preserve location privacy by achieving what is known as location K-anonymity. In this paper, we propose a hierarchical clustering-based spatial cloaking algorithm for LBSs. The proposed algorithm constructs a tree using a modified version of agglomerative hierarchical clustering. The experimental results show, in terms of the ASR size, that the proposed algorithm is better than Hilbert Cloak and comparable to RC-AR (R-tree Cloak implementation of Reciprocal with an Asymmetric R-tree split). In terms of the ASR generation time, the proposed algorithm is much better in its performance than RC-AR and similar in performance to Hilbert Cloak.

• The Journal of Society for e-Business Studies
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• v.10 no.2
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• pp.109-132
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• 2005

With the flood of multimedia contents over the digital TV channels, the internet, and etc., users sometimes have a difficulty in finding their preferred contents, spend heavy surfing time to find them, and are even very likely to miss them while searching. In this paper we suggests two-step clustering technique using time schema on how the system can recommend the user's preferred contents based on the collaborative filtering that has been proved to be successful when new users appeared. This method maps and recommends users' profile according to the gender and age at the first step, and then recommends a probabilistic item clustering customers who choose the same item at the same time based on time schema at the second stage. In addition, this has improved the accuracy of predictions in recommendation and the efficiency in time calculation by reflecting feedbacks of the result of the recommender engine and dynamically update customers' preference.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.1
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• pp.23-30
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• 2010

The H.264/AVC is increasingly used in broadcast video applications such as Internet Protocol television (IPTV), digital multimedia broadcasting (DMB) because of high compression performance. But the H.264/AVC coded video can be delivered to the widespread end-user equipment for MPEG-2 after transcoding between this video standards. This paper suggests a new transcoding algorithm for H.264/AVC to MPEG-2 transcoder that uses motion vector clustering in order to reduce the complexity without loss of video quality. The proposed method is exploiting the motion information gathered during h.264 decoding stage. To reduce the search space for the MPEG-2 motion estimation, the predictive motion vector is selected with a least distortion of the candidated motion vectors. These candidate motion vectors are considering the correlation of direction and distance of motion vectors of variable blocks in H.264/AVC. And then the best predictive motion vector is refined with full-search in ${\pm}2$ pixel search area. Compared with a cascaded decoder-encoder, the proposed transcoder achieves computational complexity savings up to 64% with a similar PSNR at the constant bitrate(CBR).

• The Journal of Society for e-Business Studies
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• v.16 no.4
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• pp.301-319
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• 2011

Blog plays an important role as new type of knowledge base distinguishing from traditional web resource. While information resources in their existing website dealt with a wide range of topics, information resources of the blog are concentrated in specific units of information depending on the user's interests and have the criteria of classification forresources published by tagging. In this research, we build a tag hierarchy utilizing title keywords and tags of the blog, and propose apost clustering methodology applying the tag hierarchy. We then generate the tag hierarchy reflected the relationship between tags and develop the tag clustering methodology according to tag similarity. In this paper, we analyze the possibility of applying the proposed methodology with real-world examples and evaluate its performances through developed prototype system.

• The KIPS Transactions:PartD
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• v.16D no.6
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• pp.845-850
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• 2009

Use of XML data has been increased with growth of Web 2.0 environment. XML is recognized its advantages by using based technology of RSS or ATOM for transferring information from blogs and news feed. Bitmap clustering is a method to keep index in main memory based on Relational DBMS, and which performed better than the other XML indexing methods during the evaluation. Existing method generates too many clusters, and it causes deterioration of result of searching quality. This paper proposes k-Bitmap clustering method that can generate user defined k clusters to solve above-mentioned problem. The proposed method also keeps additional inverted index for searching excluded terms from representative bits of k-Bitmap. We performed evaluation and the result shows that the users can control the number of clusters. Also our method has high recall value in single term search, and it guarantees the searching result includes all related documents for its query with keeping two indices.

• Journal of Digital Contents Society
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• v.14 no.1
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• pp.41-50
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• 2013

Social network analysis use the n nodes with l connections. About dozens or hundreds number of nodes are reasonable for social network analysis to the entire data. Beyond such number of nodes it will be difficult to analyze entire data. Therefore, it is necessary to separate the whole social networks, a method that can be used at this time is Clustering. You will be able to easily perform the analysis of the features of social networks and the relationships between nodes, if sub-group consists of all the nodes by Clustering. Clustering algorithm needs the interaction with the user and computer because it is need to pre-set the number of sub-groups. Sub-groups generated like this can not be guaranteed optimal results. In this paper, we propose dynamic sub-groups creating method using the external community association. We compared with previous studies by the number of sub-groups and sub-groups purity standards. Experimental results show the excellence of the proposed method.