• Journal of KIISE
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• v.43 no.2
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• pp.163-176
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• 2016

The contention overhead on the same channel in an NoC architecture can significantly increase a communication delay due to the simultaneous communication requests that occur. To reduce the overall overhead, we propose task-to-tile binding techniques for an NoC-based manycore platform, whereby it is assumed that the task mapping decision has already made. Since the NoC architecture may have multiple memory tiles as its size grows, memory clustering is used to balance the load of memory by making applications access different memory tiles. We assume that the information on the communication overhead of each application is known since it is specified in a dataflow task graph. Using this information, this paper proposes two heurisitics that perform binding of multiple tasks at once based on a proper memory clustering method. Experiments with an NoC simulator prove that the proposed heurisitic shows performance gains that are 25% greater than that of the previous binding heuristic.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.319-326
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• 2002

In this paper, we have designed and implemented cluttering classification systems- unsupervised classifiers-for the processing of satellite remote sensing images. Implemented systems adopt various design patterns which include a factory pattern and a strategy pattern to support various satellite images'formats and to design compatible systems. The clustering systems consist of sequential clustering, K-Means clustering, ISODATA clustering and Fuzzy C-Means clustering classifiers. The systems are tested by using a Landsat TM satellite image for the classification input. As results, these clustering systems are well designed to extract sample data for the classification of satellite images of which there is no previous knowledge. The systems can be provided with real-time base clustering tools, compatibilities and components' reusabilities as well.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.431-434
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• 2003

In this paper we introduce a modified objective function for fuzzy c-means clustering with logistic regression model in the presence of noise cluster. The logistic regression model is commonly used to describe the effect of one or several explanatory variables on a binary response variable. In real application there is very often no sharp boundary between clusters so that fuzzy clustering is often better suited for the data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.89-96
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• 2022

This paper suggests heuristic algorithm for single-model cross-over assembly line balancing problem that is a kind of NP-hard problem. The assembly line balance problem is mainly applied with metaheuristic methods, and no algorithm has been proposed to find the exact solution of polynomial time, making it very difficult to apply in practice. The proposed bi-directional clustering algorithm computes the minimum number of worker m^* = ⌈W/c⌉ and goal cycle time c^* = ⌈W/m^*⌉ from the given total assembling time W and cycle time c. Then we assign each workstation i=1,2,…,m^* to T_i=c^* ±α≤ c using bi-directional clustering method. For 7 experimental data, this bi-directional clustering algorithm same performance as other methods.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.159-168
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• 2004

This paper presents hybrid classification method to improve the performance of satellite images classification by combining Bayesian maximum likelihood classifier, ISODATA clustering and fuzzy C-Means algorithm. In this paper, the training data of each class were generated by separating the spectral signature using ISODATA clustering. We can classify according to pixel's membership grade followed by cluster center of fuzzy C-Means algorithm as the mean value of training data for each class. Bayesian maximum likelihood classifier is performed with prior probability by result of fuzzy C-Means classification. The results shows that proposed method could improve performance of classification method and also perform classification with no concern about spectral signature of the training data. The proposed method Is applied to a Landsat TM satellite image for the verifying test.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.627-631
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• 2004

An efficient algorithm for clustering of GPDFs(Gaussian Probability Density Functions) in a speech recognition model is proposed in this paper. The proposed algorithm is based on CNN with the divergence as its distance measure and is applied to a speech recognition. The algorithm is compared with conventional Dk-means(Divergence-based k-means) algorithm in CDHMM(Continuous Density Hidden Markov Model). The results show that it can reduce about 31.3％ of GPDFs over Dk-means algorithm without suffering any recognition performance. When compared with the case that no clustering is employed and full GPDFs are used, the proposed algorithm can save about 61.8％ of GPDFs while preserving the recognition performance.

• Korean Journal of Plant Taxonomy
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• v.49 no.2
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• pp.118-126
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• 2019

The taxonomic rank of the tiny-leaved terrestrial orchid Cephalanthera subaphylla Miyabe & $Kud{\hat{o}}$ has been somewhat controversial, as it has been treated as a species or as an infraspecific taxon, under C. erecta (Thunb.) Blume [C. erecta var. subaphylla (Miyabe & $Kud{\hat{o}}$) Ohwi and C. erecta f. subaphylla (Miyabe & $Kud{\hat{o}}$) M. Hiro]. Allozyme markers, traditionally employed for delimiting species boundaries, are used here to gain information for determining the taxonomic status of C. subaphylla. To do this, we sampled three populations of five taxa (a total of 15 populations) of Cephalanthera native to the Korean Peninsula [C. erecta, C. falcata (Thunb.) Blume, C. longibracteata Blume, C. longifolia (L.) Fritsch, and C. subaphylla]. Among 20 putative loci resolved, three were monomorphic (Dia-2, Pgi-1, and Tpi-1) across the five species. Apart from C. longibracteata, there was no allozyme variation within the remaining four species. Of the 51 alleles harbored by these 17 polymorphic loci, each of the 27 alleles at 14 loci was unique to a single species. Accordingly, we found low average values of Nei's genetic identities (I) between ten species pairs (from I = 0.250 for C. erecta versus C. longifolia to I = 0.603 for C. falcata vs. C. longibracteata), with C. subaphylla being genetically clearly differentiated from the other species (from I = 0.349 for C. subaphylla vs. C. longifolia to 0.400 for C. subaphylla vs. C. falcata). These results clearly indicate that C. subaphylla is not genetically related to any of the other taxa of Cephalanthera that are native to the Korean Peninsula, including C. erecta. In a principal coordinate analysis (PCoA), C. subaphylla was positioned distant not only from C. falcata, C. longibracteata, and C. longifolia, but also from C. erecta. Finally, K = 5 was the best clustering scheme using a Bayesian approach, with five clusters precisely corresponding to the five taxa. Thus, our allozyme results strongly suggest that C. subaphylla merits the rank of species.

• Asian Journal of Business Environment
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• v.12 no.1
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• pp.11-16
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• 2022

Purpose: This study is aimed to map the provinces in Indonesia based on the education and ICT indicators using several unsupervised learning algorithms. Research design, data, and methodology: The education and ICT indicators such as student-teacher ratio, illiteracy rate, net enrolment ratio, internet access, computer ownership, are used. Several approaches to get deeper understanding on provincial strength and weakness based on these indicators are implemented. The approaches are Ensemble K-Mean and Fuzzy C Means clustering. Results: There are at least three clusters observed in Indonesia the education quality, participation, facilities and ICT Access. Cluster with high education quality and ICT access are consist of DKI Jakarta, Yogyakarta, Riau Islands, East Kalimantan and Bali. These provinces show rapid economic growth. Meanwhile the other cluster consisting of six provinces (NTT, West Kalimantan, Central Sulawesi, West Sulawesi, North Maluku, and Papua) are the cluster with lower education quality and ICT development which impact their economic growth. Conclusions: The provinces in Indonesia are clustered into three group based on the education attainment and ICT indicators. Some provinces can directly implement e-learning; however, more provinces need to improve the education quality and facilities as well as the ICT infrastructure before implementing the e-learning.

• Journal of Korea Port Economic Association
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• v.33 no.4
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• pp.17-36
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• 2017

In the modern economic context, it is apparent that there is a strong focus on the importance of global shipping industry. Recently, the world economic crisis has negatively influenced the industry with regard to both supply and demand, which has seen almost no sign of recovery. The fact that the entire industry is operating with low efficiency and at a low profit state has made all stakeholders anxious. This research examines the financial performance of the world's major shipping lines in order to give maritime stakeholders a closer look into the industry behind the ranking. Besides, the research evaluates the competitiveness of shipping companies in terms of financial ability and suggestions for strategic actions to stakeholders are provided. For these purposes, Fuzzy-C Means is used to cluster the selected lines into different groups based on their financial indices, namely liquidity, asset management, debt management and profitability. Levene's tests which are then followed by ANOVA tests are also utilized to assess the robustness of the clustering outcomes. The results indicate that liquidity, solvency and profitability act as the main criteria in the classification problem.

• Journal of the Korean Electrochemical Society
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• v.14 no.1
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• pp.9-15
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• 2011

The composite membranes comprising of sulfonated polymers as matrix and ionic liquids as ion-conducting medium in replacement of water are studied to investigate the effect of annealing of the sulfonated polymers. The polymeric membranes are prepared on recast Nafion containing the ionic liquid, 1-ethyl-3-methylimidazolium tetrafluoroborate ($EMIBF_4$). The composite membranes are characterized by thermogravitational analyses, ion conductivity and small-angle X-ray scattering. The composite membranes annealed at $190^{\circ}C$ for 2 h after the fixed drying step showed better ionic conductivity, but no significant increase in thermal stability. The mean Bragg distance between the ionic clusters, which is reflected in the position of the ionomer peak (small-angle scattering maximum), is larger in the annealed composite membranes containing $EMIBF_4$ than the non-annealed ones. It might have been explained to be due to the different level of ion-clustering ability of the hydrophilic parts (i.e., sulfonic acid groups) in the non- and annealed polymer matrix. In addition, the ionic conductivity of the membranes shows higher for the annealed composite membranes containing $EMIBF_4$. It can be concluded that the annealing of the composite membranes containing ionic liquids due to an increase in ion-clustering ability is able to bring about the enhancement of ionic conductivity suitable for potential use in proton exchange membrane fuel cells (PEMFCs) at medium temperatures ($150-200^{\circ}C$) in the absence of external humidification.