• Bulletin of the Korean Chemical Society
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• v.28 no.11
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• pp.1996-2002
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• 2007

The redox behavior of a [Ni6(μ3-Se)2(μ4-Se)3(Fe(η 5-C5H4P-Ph2)2)3] (= [Ni-Se-dppf], dppf = 1,1-bis(diphenylphosphino) ferrocene) cluster was studied using platinum (Pt) and glassy carbon electrodes (GCE) in nonaqueous media. The cluster showed electrochemical activity at the potential range between +1.6 and ?1.6 V. In the negative region (0 to ?1.6 V), the cluster exhibited two-step reductions. The first step was one-electron reversible, while the second step was a five-electron quasi-reversible process. On the other hand, in the positive region (0 to +1.6 V), the first step involved one-electron quasi-reversible process. The applicability of the cluster was found towards the electrocatalytic reduction of CO2 and was evaluated by experiments using rotating ring disc electrode (RRDE). RRDE experiments demonstrated that two electrons were involved in the electrocatalytic reduction of CO2 to CO at the Se-Ni-dppf-modified electrode.

• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.80-84
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• 2006

Motivated by the fact that cluster reduction strategies such as inserting a vowel or deleting a consonant in resolving English complex clusters differ depending on studies, this paper investigates the repair strategies employed by Korean EFL students. A total of 60 college students participated in the present study and the participants' production of English voiceless word-initial and word-final clusters was measured using the materials designed for this study. It has been shown that prosodic positions such as onset and coda and the number of cluster sequences influenced cluster reduction strategies. The error rates of both insertion and deletion were noticeably higher in the coda position than in the onset position and both insertion and deletion error rates were higher in triconsonatal cluster than in biconsonantal cluster sequences. Overall, the insertion rate was higher than the deletion rate. However, the deletion rate was significantly higher than the insertion rate in triconsonantal coda cluster sequences. Because of this, the deletion rate was higher than the insertion rate for triconsonantal cluster sequences across onset and coda. Also, the high deletion rate of triconsonantal coda clusters contributed to the high deletion rate for the coda clusters in general.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1336-1341
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• 2009

In this paper, an EMI reduction effects using an EMC chamber is described and reduction methods is proposed. In the case of general electronic components a working frequency is low. But in this paper the vehicle cluster works 75MHz in the main clock frequency, becoming weak by noise because of being attached in TFT LCD. As the outer case installed in the vehicle is made up of plastic materials, the noise is radiated if not protecting noise in the PCB itself. Therefore, This paper will explain the theoretical basis and propriety with respect to the discussion and need about the guide for PCB design considering EMC, through the reduction of PCB noise.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.905-922
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• 2021

Cluster analysis is one of unsupervised learning techniques used for discovering clusters when there is no prior knowledge of group membership. K-means, one of the commonly used cluster analysis techniques, may fail when the number of variables becomes large. In such high-dimensional cases, it is common to perform tandem analysis, K-means cluster analysis after reducing the number of variables using dimension reduction methods. However, there is no guarantee that the reduced dimension reveals the cluster structure properly. Principal component analysis may mask the structure of clusters, especially when there are large variances for variables that are not related to cluster structure. To overcome this, techniques that perform dimension reduction and cluster analysis simultaneously have been suggested. This study proposes probabilistic reduced K-means, the transition of reduced K-means (De Soete and Caroll, 1994) into a probabilistic framework. Simulation shows that the proposed method performs better than tandem clustering or clustering without any dimension reduction. When the number of the variables is larger than the number of samples in each cluster, probabilistic reduced K-means show better formation of clusters than non-probabilistic reduced K-means. In the application to a real data set, it revealed similar or better cluster structure compared to other methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3714-3732
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• 2019

With the rapid development of network, Intrusion Detection System(IDS) plays a more and more important role in network applications. Many data mining algorithms are used to build IDS. However, due to the advent of big data era, massive data are generated. When dealing with large-scale data sets, most data mining algorithms suffer from a high computational burden which makes IDS much less efficient. To build an efficient IDS over big data, we propose a classification algorithm based on data clustering and data reduction. In the training stage, the training data are divided into clusters with similar size by Mini Batch K-Means algorithm, meanwhile, the center of each cluster is used as its index. Then, we select representative instances for each cluster to perform the task of data reduction and use the clusters that consist of representative instances to build a K-Nearest Neighbor(KNN) detection model. In the detection stage, we sort clusters according to the distances between the test sample and cluster indexes, and obtain k nearest clusters where we find k nearest neighbors. Experimental results show that searching neighbors by cluster indexes reduces the computational complexity significantly, and classification with reduced data of representative instances not only improves the efficiency, but also maintains high accuracy.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1167-1175
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• 2018

Microarray data plays an essential role in diagnosing and detecting cancer. Microarray analysis allows the examination of levels of gene expression in specific cell samples, where thousands of genes can be analyzed simultaneously. However, microarray data have very little sample data and high data dimensionality. Therefore, to classify microarray data, a dimensional reduction process is required. Dimensional reduction can eliminate redundancy of data; thus, features used in classification are features that only have a high correlation with their class. There are two types of dimensional reduction, namely feature selection and feature extraction. In this paper, we used k-means algorithm as the clustering approach for feature selection. The proposed approach can be used to categorize features that have the same characteristics in one cluster, so that redundancy in microarray data is removed. The result of clustering is ranked using the Relief algorithm such that the best scoring element for each cluster is obtained. All best elements of each cluster are selected and used as features in the classification process. Next, the Random Forest algorithm is used. Based on the simulation, the accuracy of the proposed approach for each dataset, namely Colon, Lung Cancer, and Prostate Tumor, achieved 85.87%, 98.9%, and 89% accuracy, respectively. The accuracy of the proposed approach is therefore higher than the approach using Random Forest without clustering.

• The Journal of Bigdata
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• v.5 no.2
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• pp.135-143
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• 2020

This paper proposes a pre-processing method and a dimensional reduction method in the analysis of shopping carts where there are many correlations between variables when dividing the types of consumers in the agri-food consumer panel data. Cluster analysis is a widely used method for dividing observational objects into several clusters in multivariate data. However, cluster analysis through dimensional reduction may be more effective when several variables are related. In this paper, the food consumption data surveyed of 1,987 households was clustered using the K-means method, and 17 variables were re-selected to divide it into the clusters. Principal component analysis and factor analysis were compared as the solution for multicollinearity problems and as the way to reduce dimensions for clustering. In this study, both principal component analysis and factor analysis reduced the dataset into two dimensions. Although the principal component analysis divided the dataset into three clusters, it did not seem that the difference among the characteristics of the cluster appeared well. However, the characteristics of the clusters in the consumption pattern were well distinguished under the factor analysis method.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.10
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• pp.33-51
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• 1997

On ATM-based wired/wireless integrated network, we apply the connection re-routing method[1] which reduced the inter-cluster handoff delay by reserving VPI/VCLs for possible inter-cluster handoff calls in advance. Additionally, we propose wired resource reservation methods, which are ausiliary method and split method, for handoff QoS guarantee of various expected services. The characteristics of these methods reserve wired connection resources based on the information on the possible inter-cluster handoff calls. With mathematical analysis, we also propose each algorithm and cost function for deciding an optimal amount in reserving resources. With numberical examples, we can see that the auxiliary method effectively reduces the cost in all cases(.alpha.>.betha., .alpha.=.betha., and .alpha.<.betha.). The split method has a little cost-reduction effects, when handoffs call does not have priority over new calls (that is, .alpha..leq..betha.) and the total capacity is relatively large. In other cases, the split method, however, has effective cost-reduction effects. The numerical resutls show that these reservation methods ca flexibly cope with the time-variant environment and meet the QoS requriements on the inter-cluster handoff calls.

• Transactions of the Society of Information Storage Systems
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• v.6 no.1
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• pp.31-36
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• 2010

Data storage related with writing and retrieving requires high storage capacity, fast transfer rate and less access time. Today any data storage system cannot satisfy these conditions, however holographic data storage system can perform faster data transfer rate because it is a page oriented memory system using volume hologram in writing and retrieving data. System can be constructed without mechanical actuating part so fast data transfer rate and high storage capacity about 1Tb/cm3 can be realized. In this research, to correct errors of binary data stored in holographic data storage system, a new method for reduction errors is suggested. First, find cluster centers using subtractive clustering algorithm then reduce intensities of pixels around cluster centers. By using this error reduction method following results are obtained ; the effect of Inter Pixel Interference noise in the holographic data storage system is decreased and the intensity profile of data page becomes uniform therefore the better data storage system can be constructed.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.1
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• pp.51.1-51.1
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• 2017

The galaxy cluster Abell 115 shows ongoing merger features, which suggest that it might be in an intermediate phase of dynamical evolution. As merging clusters often show, the characteristic hints of A115's merging activities include radio relics, double X-ray peaks, and large offsets between the cluster member galaxies and the X-ray distributions. To constrain the exact stage of the merger, it is necessary to obtain its dark matter distribution. In this study, we carry out a precision weak lensing study of this interesting system based on Subaru images. We present our mass reconstruction together with descriptions on our core procedure of the analysis: Subaru data reduction, galaxy shape measurement, and source selection. We find that Abell 115 consists of two massive dark matter clumps, which closely follow the cluster galaxies. Our weak lensing mass estimate is a few factors lower than the published dynamical mass obtained from velocity dispersion. This large mass discrepancy may be attributed to a significant departure from dynamical equilibrium.