• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1091-1110
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• 2016

Hybrid deployment under current cloud data centers is a combination of online and offline services, which improves the utilization of the cluster resources. However, the performance of the cluster is often affected by the online services in the hybrid deployment environment. To improve the response time of online service (e.g. search engine), an effective scheduling algorithm based on Storm is proposed. At the component level, the algorithm dispatches the component with more influence to the optimal performance node. Inside the component, a reasonable resource allocation strategy is used. By searching the compressed index first and then filtering the complete index, the execution speed of the component is improved with similar accuracy. Experiments show that our algorithm can guarantee search accuracy of 95.94%, while increasing the response speed by 68.03%.

• Plant Resources
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• v.2 no.2
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• pp.75-80
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• 1999

Twenty two varieties of pecan including wild types were classified based on 6 characters measured by principal component analysis score distance. The results are summarized as fellow. Twenty two varieties were classified into 5 groups based in PCA score distance. Five groups were distinctly characterized by many morphological characters. Total variation could be explained by 51%, 95%, 99% with first, third and fifth principal components respectively. Varimax rotation of the factor loading of the first factors indicated that the first component was highly loaded with leaf characters, the second component with fruit characters, but fruit length was negative loaded. The second, the third and the fourths groups of cultivars had very close genetic parentage similarity.

• Bulletin of the Korean Chemical Society
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• v.24 no.9
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• pp.1345-1350
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• 2003

Principal component analysis based two-dimensional (PCA-2D) correlation analysis is applied to FTIR spectra of polystyrene/methyl ethyl ketone/toluene solution mixture during the solvent evaporation. Substantial amount of artificial noise were added to the experimental data to demonstrate the practical noise-suppressing benefit of PCA-2D technique. 2D correlation analysis of the reconstructed data matrix from PCA loading vectors and scores successfully extracted only the most important features of synchronicity and asynchronicity without interference from noise or insignificant minor components. 2D correlation spectra constructed with only one principal component yield strictly synchronous response with no discernible a asynchronous features, while those involving at least two or more principal components generated meaningful asynchronous 2D correlation spectra. Deliberate manipulation of the rank of the reconstructed data matrix, by choosing the appropriate number and type of PCs, yields potentially more refined 2D correlation spectra.

• Phonetics and Speech Sciences
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• v.1 no.1
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• pp.69-73
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• 2009

This paper proposes an efficient global covariance-based principal component analysis (GCPCA) for speaker identification. Principal component analysis (PCA) is a feature extraction method which reduces the dimension of the feature vectors and the correlation among the feature vectors by projecting the original feature space into a small subspace through a transformation. However, it requires a larger amount of training data when performing PCA to find the eigenvalue and eigenvector matrix using the full covariance matrix by each speaker. The proposed method first calculates the global covariance matrix using training data of all speakers. It then finds the eigenvalue matrix and the corresponding eigenvector matrix from the global covariance matrix. Compared to conventional PCA and Gaussian mixture model (GMM) methods, the proposed method shows better performance while requiring less storage space and complexity in speaker identification.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.25-29
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• 2008

The CORBA that supports FPGA has not been used generally and it is difficult to implement and to develop the CORBA for FPGA. In this paper we propose the way to design FPGA to support a CORBA component. For FPGA to support the CORBA component, embedded processor provided by FPGA and PCI based CORBA is utilized. The PCI based CORBA is for improving data transfer throughput. This paper will be organized as follows. In Chapter I, existing research trend and background are presented for why we propose design of FPGA that support the CORBA component. In Chapter II, FPGA design for supporting CORBA components is proposed and described in detail. In Chapter III, simple experiment is tested to confirm the proposed FPGA design. Finally session 4 is conclusion of this paper.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.434-440
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• 2010

In this paper, we define generalized induced linguistic aggregation operator called generalized induced linguistic ordered weighted harmonic mean(GILOWHM) operator. Each object processed by this operator consists of three components, where the first component represents the importance degree or character of the second component, and the second component isused to induce an ordering, through the first component, over the third components which are linguistic variables and then aggregated. It is shown that the induced linguistic ordered weighted harmonic mean(ILOWHM) operator and linguistic ordered weighted harmonic mean(LOWHM) operator are the special cases of the GILOWHM operator. Based on the GILOWHM and LWHM operators, we develop an approach to group decision making with linguistic preference relations. Finally, a numerical example is used to illustrate the applicability of the proposed approach.

• International Journal of Contents
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• v.14 no.4
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• pp.24-29
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• 2018

EEG-based brain-computer interfaces has focused on explicitly expressed intentions to assist physically impaired patients. For EEG-based-computer interfaces to function effectively, it should be able to understand users' implicit information. Since it is hard to gather EEG signals of human brains, we do not have enough training data which are essential for proper classification performance of implicit intention. In this paper, we improve the subject independent classification of implicit intention through the generation of additional training data. In the first stage, we perform the PCA (principal component analysis) of training data in a bid to remove redundant components in the components within the input data. After the dimension reduction by PCA, we train ICA (independent component analysis) network whose outputs are statistically independent. We can get additional training data by adding Gaussian noises to ICA outputs and projecting them to input data domain. Through simulations with EEG data provided by CNSL, KAIST, we improve the classification performance from 65.05% to 66.69% with Gamma components. The proposed sample generation method can be applied to any machine learning problem with fewer samples.

• Journal of Power Electronics
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• v.19 no.4
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• pp.1045-1053
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• 2019

A symmetrical component decomposition scheme utilizing the characteristics of the asynchronous rotational reference frame transformation is proposed in this paper for the extraction of the positive and negative sequence components of distorted three-phase grid voltages. The undesired frequency component can be removed using a specially designed series coordinate transformation and half-cycle delays, where the delay can be controlled by adjusting the frequency of the rotating reference frame. The extracted symmetrical component can then be compensated based on the applied coordinated transformation. The dynamic response of the proposed algorithm is improved when compared to that of conventional methods. The effectiveness of the proposed algorithm is verified by simulation and experimental results.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2407-2418
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• 2023

Internal replaceable electronic module substitutions can impact EMC (ElectroMagnetic Compatibility) qualification testing and results if EMC testing is conducted at the cabinet level. The impact of component substitutions on EMC qualification results therefore should be evaluated. If a qualitative evaluation is not adequate to ensure that the modified product will not impact the cabinet level EMC qualification results, a new qualification testing should be conducted. Component level retesting should be conducted under electromagnetically equivalent conditions with the cabinet level test. This paper analyzes the propagation of conducted susceptibility test waveforms in a representative cabinet and evaluates the impact of component substitutions on cabinet level EMC qualification results according to the location of the replacement items. A guideline for a qualitative evaluation of the impact of component substitutions is described based on the propagation of the conducted susceptibility test waveforms. A module level test method is also described based on an analysis of the shielding effectiveness of the cabinet.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.653-656
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• 2007

과거의 기능 중심의 소프트웨어 개발방법론에서 벗어나 최근에는 서비스 중심의 컴포넌트 기반 소프트웨어 개발 (CBD: Component Based Development) 방법론이 새로운 개발 패러다임으로 각광을 받고 있다. 본 논문은 CBD 방법론 중 마이크로 소프트사의 MSF/CD 방법론을 기반으로 한 물리적인 설계를 할 때 기존의 CID (Component Interaction Diagram)에 Dispatch Agent 를 제안하였다. 실험 결과를 통해 제안된 기법이 component 서비스 속도를 향상시키고 보다 신속한 트랜잭션 처리를 제공함을 확인하였다.