• Bulletin of the Korean Chemical Society
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• 제28권11호
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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.

• 한국콘텐츠학회논문지
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• 제6권1호
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• pp.80-84
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• 2006

모음을 삽입시키거나 자음을 탈락시킴으로써 한 음절 내의 영어 연속음을 없애는 자음군 축약 전략이 기존의 연구들마다 다르다는 점에 동기를 부여받아서 한국 학생들의 영어 자음군 축약 전략을 탐구하게 되었다. 대학생 60명의 어두와 어말 자음군 발음을 조사한 결과, 초성이냐 종성이냐 하는 운율적 위치와 자음군이 몇 개로 구성되어 있느냐는 자음군 숫자에 따라 모음 삽입이냐 자음 탈락이냐 하는 자음군 축약 전략에 영향을 미치는 것으로 나타났다. 삽입과 탈락의 오류 비율은 초성보다는 종성에서 높았고 두 개의 자음군보다는 세 개의 자음군에서 높았다. 전반적으로 삽입 오류 비율이 탈락 오류 비율보다 높았으나, 종성 위치의 세 개의 자음군에서는 탈락 비율이 삽입 비율보다 중요하게 높았다. 종성 위치 세 개의 자음군에서 탈락 비율이 높은 것 때문에 운율 위치에 상관없이 세 개의 자음군에서 삽입보다 탈락 비율이 높게 나타났으며 전반적으로 종성에서 탈락 비율이 높이 나타났다.

• 전기학회논문지
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• 제58권7호
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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.

• 응용통계연구
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• 제34권6호
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• pp.905-922
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• 2021

라벨 없이 진행되는 비지도 학습 중 하나인 군집분석은 자료에 어떤 그룹이 내포되어 있는지 사전 지식이 없을 경우에 군집을 발굴하고, 군집 간의 특성 차이와 군집 안에서의 유사성을 분석하고자 할 때 유용한 방법이다. 기본적인 군집분석 중 하나인 K-means 방법은 변수의 개수가 많아질 때 잘 동작하지 않을 수 있으며, 군집에 대한 해석도 쉽지 않은 문제가 있다. 따라서 고차원 자료의 경우 주성분 분석과 같은 차원 축소 방법을 사용하여 변수의 개수를 줄인 후에 K-means 군집분석을 행하는 Tandem 군집분석이 제안되었다. 하지만 차원 축소 방법을 이용해서 찾아낸 축소 차원이 반드시 군집에 대한 구조를 잘 반영할 것이라는 보장은 없다. 특히 군집의 구조와는 상관없는 변수들의 분산 또는 공분산이 클 때, 주성분 분석을 통한 차원 축소는 오히려 군집의 구조를 가릴 수 있다. 이에 따라 군집분석과 차원 축소를 동시에 진행하는 방법들이 제안되어 왔다. 그 중에서도 본 연구에서는 De Soete와 Carroll (1994)이 제안한 방법론을 확률적인 모형으로 바꿔 군집분석을 진행하는 확률적 reduced K-means를 제안한다. 모의실험 결과 차원 축소를 배제한 군집분석과 Tandem 군집분석보다 더 좋은 군집을 형성함을 알 수 있었고 군집 당 표본 크기에 비해 변수의 개수가 많은 자료에서 기존의 비 확률적 reduced K-means 군집분석에 비해 우수한 성능을 확인했다. 보스턴 자료에서는 다른 군집분석 방법론보다 명확한 군집이 형성됨을 확인했다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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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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• 제14권5호
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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.

• 한국빅데이터학회지
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• 제5권2호
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• pp.135-143
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• 2020

본 논문은 농식품 소비자패널 데이터에서 소비자의 유형을 나눌 때에 변수간 연관성이 많은 장바구니 분석에서 전처리 방법과 차원축소의 방법을 제안한다. 군집분석은 다변량 자료에서 관측 개체를 몇 개의 군집으로 나눌 때 널리 사용되는 분석기법이다. 하지만 여러 개의 변수가 연관성을 가진 경우에는 차원축소를 통한 군집분석이 더 효과적일 수 있다. 본 논문은 1,987 가구를 대상으로 조사한 식품소비 데이터를 K-means 방법을 사용하여 군집화하였으며, 군집을 나누기 위해 17개의 변수를 선정하였고, 17개의 다중공선성 문제와 군집을 나누기 위한 차원축소의 방법 중 주성분 분석과 요인분석을 비교하였다. 본 연구에서는 주성분분석과 요인분석 모두 2개의 차원으로 축소하였으며 주성분분석에서는 3개의 군집으로 나뉘었지만 분석하고자 하였던 소비 패턴에 대한 군집의 특성이 잘 나타나지 않았으며 요인분석에서는 분석가가 보고자 하는 소비 패턴의 특징이 잘 나타났다.

• 전자공학회논문지S
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• 제34S권10호
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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.

• 정보저장시스템학회논문집
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• 제6권1호
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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.

• 천문학회보
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• 제42권1호
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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.