• Journal of Korea Multimedia Society
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• v.13 no.10
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• pp.1534-1546
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• 2010

In sensor network systems, various hierarchical clustering schemes have been proposed in order to efficiently maintain the energy consumption of sensor nodes. Most of these schemes, however, are hardly applicable in practice since these schemes might produce unbalanced clusters or randomly distributed clusters without taking into account of the distribution of sensor nodes. To overcome the limitations of such hierarchical clustering schemes, we propose a novel scheme called CSM(Clustering using Split & Merge algorithm), which exploits node split and merge algorithm of tree-based indexing structures to efficiently construct clusters. Our extensive performance studies show that the CSM constructs highly balanced clustering in a energy efficient way and achieves higher performance up to 1.6 times than the previous clustering schemes, under various operational conditions.

• Molecules and Cells
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• v.41 no.7
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• pp.622-630
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• 2018

Leukocyte common antigen-related protein tyrosine phosphatases (LAR-RPTPs) are cellular receptors of heparan sulfate (HS) and chondroitin sulfate (CS) proteoglycans that regulate neurite outgrowth and neuronal regeneration. LAR-RPTPs have also received particular attention as the major presynaptic hubs for synapse organization through selective binding to numerous postsynaptic adhesion partners. Recent structural studies on LAR-RPTP-mediated trans-synaptic adhesion complexes have provided significant insight into the molecular basis of their specific interactions, the key codes for their selective binding, as well as the higher-order clustering of LAR-RPTPs necessary for synaptogenic activity. In this review, we summarize the structures of LAR-RPTPs in complex with various postsynaptic adhesion partners and discuss the molecular mechanisms underlying LAR-RPTP-mediated synaptogenesis.

• Journal of the military operations research society of Korea
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• v.26 no.2
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• pp.8-27
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• 2000

This paper evaluates the performance of clustering algorithms in the determination of combat fatigues. Among clustering algorithms, GLVQ in neural clustering methods. and ISODATA algorithm are evaluated in this paper. In order to evaluate, this study used the data sets which showed the body measurement of Korean military personnel. For the comparison of the three methods, the coverage rate and fitness for combat fatigues are evaluated and the convergence of final standard sizes are considered. As a result, the mean of the coverage rate and fitness for combat fatigues obtained with the GLVQ are higher than those obtained with -Means and ISODATA algorithms.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.6
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• pp.403-406
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• 2015

In this paper, we propose a supervoxel clustering-based staircase extraction algorithm to obtain poses and dimensions of staircases from a point cloud. In order to effectively reduce the candidate points and accelerate supervoxel clustering, large planes in the scene, such as walls, floors, and ceilings, are eliminated while scanning the environment. Next, staircase candidates with small planes are initially estimated using supervoxel clustering. Then, parameter values for the staircases are refined, and higher staircases that remain undetected due to occlusion are predicted and generated virtually. Experimental results show that staircases are detected accurately and predicted successfully.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.115-120
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• 2005

In this paper, we propose a new robust content-based musical genre classification algorithm using multi-feature clustering(MFC) method. In contrast to previous works, this paper focuses on two practical issues of the system dependency problem on different input query patterns(or portions) and input query lengths which causes serious uncertainty of the system performance. In order to solve these problems, a new approach called multi-feature clustering(MFC) based on k-means clustering is proposed. To verify the performance of the proposed method, several excerpts with variable duration were extracted from every other position in a queried music file. Effectiveness of the system with MFC and without MFC is compared in terms of the classification accuracy. It is demonstrated that the use of MFC significantly improves the system stability of musical genre classification performance with higher accuracy rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1512-1518
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• 2021

Since single cell RNA sequencing provides the expression profiles of individual cells, it provides higher cellular differential resolution than traditional bulk RNA sequencing. Using these single cell RNA sequencing data, clustering analysis is generally conducted to find cell types and understand high level biological processes. In order to effectively process the high-dimensional single cell RNA sequencing data fir the clustering analysis, this paper uses a variational autoencoder to transform a high dimensional data space into a lower dimensional latent space, expecting to produce a latent space that can give more accurate clustering results. By clustering the features in the transformed latent space, we compare the performance of various classical clustering methods for single cell RNA sequencing data. Experimental results demonstrate that the proposed framework outperforms many state-of-the-art methods under various clustering performance metrics.

• Journal of Communications and Networks
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• v.12 no.4
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• pp.358-365
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• 2010

We develop a low complexity cooperative diversity protocol for low energy adaptive clustering hierarchy (LEACH) based wireless sensor networks. A cross layer approach is used to obtain spatial diversity in the physical layer. In this paper, a simple modification in clustering algorithm of the LEACH protocol is proposed to exploit virtual multiple-input multiple-output (MIMO) based user cooperation. In lieu of selecting a single cluster-head at network layer, we proposed M cluster-heads in each cluster to obtain a diversity order of M in long distance communication. Due to the broadcast nature of wireless transmission, cluster-heads are able to receive data from sensor nodes at the same time. This fact ensures the synchronization required to implement a virtual MIMO based space time block code (STBC) in cluster-head to sink node transmission. An analytical method to evaluate the energy consumption based on BER curve is presented. Analysis and simulation results show that proposed cooperative LEACH protocol can save a huge amount of energy over LEACH protocol with same data rate, bit error rate, delay and bandwidth requirements. Moreover, this proposal can achieve higher order diversity with improved spectral efficiency compared to other virtual MIMO based protocols.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.171-178
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• 2008

Since the XML standard was introduced by the W3C in 1998, documents that have been written in XML have been gradually increasing. Accordingly, several systems have been developed in order to efficiently manage and retrieve massive XML documents. BitCube-a bitmap indexing system-is a representative system for this field of research. Based on the bitmap indexing technique, the path bitmap indexing system(LH06), which performs the clustering of similar paths, improved the problem that the existing BitCube system could not solve, namely, determining similar paths. The path bitmap indexing system has the advantage of a higher retrieval speed in not only exactly matched path searching but also similar path searching. However, the similarity calculation algorithm of this system has a few particular problems. Consequently, it sometimes cannot calculate the similarity even though some of two paths have extremely similar relationships; further, it results in an increment in the number of meaningless clusters. In this paper, we have proposed a novel method that clustering, the similarity between the paths in order to solve these problems. The proposed system yields a stable result for clustering, and it obtains a high score in clustering precision during a performance evaluation against LH06.

• Journal of Communications and Networks
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• v.12 no.2
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• pp.122-129
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• 2010

Energy conservation is one of the most important issues for evaluating the performance of wireless sensor network (WSN) applications. Generally speaking, hierarchical clustering protocols such as LEACH, LEACH-C, EEEAC, and BCDCP are more efficient in energy conservation than flat routing protocols. However, these typical protocols still have drawbacks of unequal and high energy depletion in cluster heads (CHs) due to the different transmission distance from each CH to the base station (BS). In order to minimize the energy consumption and increase the network lifetime, we propose a new hierarchical routing protocol, distance aware intelligent clustering protocol (DAIC), with the key concept of dividing the network into tiers and selecting the high energy CHs at the nearest distance from the BS. We have observed that a considerable amount of energy can be conserved by selecting CHs at the nearest distance from the BS. Also, the number of CHs is computed dynamically to avoid the selection of unnecessarily large number of CHs in the network. Our simulation results showed that the proposed DAIC outperforms LEACH and LEACH-C by 63.28% and 36.27% in energy conservation respectively. The distance aware CH selection method adopted in the proposed DAIC protocol can also be adapted to other hierarchical clustering protocols for the higher energy efficiency.

• Smart Media Journal
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• v.9 no.4
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• pp.126-133
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• 2020

The treatment policy and prognosis are determined based on the final stage of lung cancer patients. The final stage of lung cancer patients is determined based on the T, N, and M stage classification table provided by the American Cancer Society (AJCC). However, the final stage of AJCC has limitations in its use for various fields such as patient treatment, prognosis and survival days prediction. In this paper, clustering algorithm which is one of non-supervised learning algorithms was assessed in order to check whether using only T, N, M stages with a data science method is effective for classifying the group of patients in the aspect of survival days. The final stage groups and T, N, M stage clustering groups of lung cancer patients were compared by using the cox proportional hazard model. It is confirmed that the accuracy of prediction of survival days with only T, N, M stages becomes higher than the accuracy with the final stages of patients. Especially, the accuracy of prediction of survival days with clustering of T, N, M stages improves when more or less clusters are analyzed than the seven clusters which is same to the number of final stage of AJCC.