• Title/Summary/Keyword: Eigenvector Method

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Eigen-constraint minimum variance beamformer for correlated interferences (상관관계가 있는 간섭신호를 위한 고유벡터 제한 MV 빔형성 기법)

  • Kim Seungil;Lee Chungyong
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.42 no.1
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    • pp.59-64
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    • 2005
  • To achieve a goal of minimum output power, the beamformer tends to cancel the desired signal if there exists correlated interference sources such as multipaths of the desired signal. In this paper, we propose a new method which overcomes the signal cancellation problem for correlated interferences. Instead of decorrelating the correlated interferences, the proposed bramformer regards them as replicas of the desired signal and coherently combines them with desired signal. This method uses an eigenvector constraint that suppresses a noise and uncorrelated interferences but keeps the desired signal and correlated interferences. Indisputably, the beamformer does not require any preliminary information on correlated interferences. Simulation results show that the proposed beamformer overcomes the signal cancellation problem and improves signal-to-noise ratio (SNR) of the array output when the correlated interferences exist.

On-line Nonlinear Principal Component Analysis for Nonlinear Feature Extraction (비선형 특징 추출을 위한 온라인 비선형 주성분분석 기법)

  • 김병주;심주용;황창하;김일곤
    • Journal of KIISE:Software and Applications
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    • v.31 no.3
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    • pp.361-368
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    • 2004
  • The purpose of this study is to propose a new on-line nonlinear PCA(OL-NPCA) method for a nonlinear feature extraction from the incremental data. Kernel PCA(KPCA) is widely used for nonlinear feature extraction, however, it has been pointed out that KPCA has the following problems. First, applying KPCA to N patterns requires storing and finding the eigenvectors of a N${\times}$N kernel matrix, which is infeasible for a large number of data N. Second problem is that in order to update the eigenvectors with an another data, the whole eigenspace should be recomputed. OL-NPCA overcomes these problems by incremental eigenspace update method with a feature mapping function. According to the experimental results, which comes from applying OL-NPCA to a toy and a large data problem, OL-NPCA shows following advantages. First, OL-NPCA is more efficient in memory requirement than KPCA. Second advantage is that OL-NPCA is comparable in performance to KPCA. Furthermore, performance of OL-NPCA can be easily improved by re-learning the data.

Spectral Analysis Method for Classification of Liquid Characteristics (액체의 특성 분류를 위한 스펙트럼 분석 방법)

  • Lee, Jonggil
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.20 no.12
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    • pp.2206-2212
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    • 2016
  • It is necessary to find characteristic phenomena related with permittivity differences for classification of liquid characteristics. If these phenomena can be remotely detected and characteristics can be extracted, it will be very useful in finding flammable liquid materials and classifying substances of these liquids. Therefore, in this paper, reflection and transmitted signals were analyzed from three receiving antennas with one transmitting antenna using wideband electromagnetic wave signals. Frequency response characteristics of reflected or transmitted signals are different according to characteristics of liquid materials. However, conventional FFT methods cannot be applied due to problems of low resolution caused by data windowing distortion. To minimize these problems, eigenvector analysis method was applied for high resolution spectrum estimation of received signals. From these results, it can be shown that classification of many kinds of liquids are possible using peak frequencies and corresponding peak power values of spectrum estimates obtained from various liquid materials.

Self-Calibration for Direction Finding in Multi-Baseline Interferometer System (멀티베이스라인 인터페로미터 시스템에서의 자체 교정 방향 탐지 방법)

  • Kim, Ji-Tae;Kim, Young-Soo;Kang, Jong-Jin;Lee, Duk-Yung;Roh, Ji-Hyun
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.21 no.4
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    • pp.433-442
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    • 2010
  • In this paper, self-calibration algorithm based on covariance matrix is proposed for compensating amplitude/phase mismatch in multi-baseline interferometer direction finding system. The proposed method is a solution to nonlinear constrained minimization problem which dramatically calibrate mismatch error using space sector concept with cost function as defined in this paper. This method, however, has a drawback that requires an estimated initial angle to determine the proper space sector. It is well known that this type of drawback is common in nonlinear optimization problem. Superior calibration capabilities achieved with this approach are illustrated by simulation experiments in comparison with interferometer algorithm for a varitiety of amplitude/phase mismatch error. Furthermore, this approach has been found to provide an exceptional calibration capabilities even in case amplitude and phase mismatch are more than 30 dB and over $5^{\circ}$, respectively, with sector spacing of less than $50^{\circ}$.

A Boundary-layer Stress Analysis of Laminated Composite Beams via a Computational Asymptotic Method and Papkovich-Fadle Eigenvector (전산점근해석기법과 고유벡터를 이용한 복합재료 보의 경계층 응력 해석)

  • Sin-Ho Kim;Jun-Sik Kim
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.37 no.1
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    • pp.41-47
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    • 2024
  • This paper utilizes computational asymptotic analysis to compute the boundary layer solution for composite beams and validates the findings through a comparison with ANSYS results. The boundary layer solution, presented as a sum of the interior solution and pure boundary layer effects, necessitates a mathematically rigorous formalization for both interior and boundary layer aspects. Computational asymptotic analysis emerges as a robust technique for addressing such problems. However, the challenge lies in connecting the boundary layer and interior solutions. In this study, we systematically separate the principles of virtual work and the principles of Saint-Venant to tackle internal and boundary layer issues. The boundary layer solution is articulated by calculating the Papkovich-Fadle eigenfunctions, representing them as linear combinations of real and imaginary vectors. To address warping functions in the interior solutions, we employed a least squares method. The computed solutions exhibit excellent agreement with 2D finite element analysis results, both quantitatively and qualitatively. This validates the effectiveness and accuracy of the proposed approach in capturing the behavior of composite beams.

A Study on the Collaboration Network Analysis of Document Delivery Service in Science and Technology (과학기술분야 원문제공서비스의 협력 네트워크 분석)

  • Kim, Ji-Young;Lee, Seon-Hee
    • Journal of Korean Library and Information Science Society
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    • v.44 no.4
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    • pp.443-463
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    • 2013
  • Korea Institute of Science and Technology Information(KISTI) provides domestic researchers with science and technology information through NDSL Information Document Service(NIDS) network to improve research productivity in Korea. University libraries and information centers of research institutes are playing a major role in the NIDS collaboration network. In this study, we examined the relationship among the participating organizations for document delivery service using the social network analysis(SNA) method. Centrality of each organization in the NIDS network was analyzed with the indexes such as degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality. The research results show that KISTI, KAIST, POSTECH, and FRIC are located at the center of the NIDS network. Based on the research results, this paper suggests several directions for improvement of document delivery service.

The Analysis of Knowledge Structure using Co-word Method in Quality Management Field (동시단어분석을 이용한 품질경영분야 지식구조 분석)

  • Park, Man-Hee
    • Journal of Korean Society for Quality Management
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    • v.44 no.2
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    • pp.389-408
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    • 2016
  • Purpose: This study was designed to analyze the behavioral change of knowledge structures and the trends of research topics in the quality management field. Methods: The network structure and knowledge structure of the words were visualized in map form using co-word analysis, cluster analysis and strategic diagram. Results: Summarizing the research results obtained in this study are as follows. First, the word network derived from co-occurrence matrix had 106 nodes and 5,314 links and its density was analyzed to 0.95. Average betweenness centrality of word network was 2.37. In addition, average closeness centrality and average eigenvector centrality of word network were 0.01. Second, by applying optimal criteria of cluster decision and K-means algorithm to word co-occurrence matrix, 106 words were grouped into seven clusters such as standard & efficiency, product design, reliability, control chart, quality model, 6 sigma, and service quality. Conclusion: According to the results of strategic diagram analysis over time, the traditional research topics of quality management field related to reliability, 6 sigma, control chart topics in the third quadrant were revealed to be declined for their study importance. Research topics related to product design and customer satisfaction were found to be an important research topic over analysis periods. Research topic related to management innovation was emerging state and the scope of research topics related to process model was extended to research topics with system performance. Research topic related to service quality located in the first quadrant was analyzed as the key research topic.

A Computation Reduction Technique of MUSIC Algorithm for Optimal Path Tracking (최적경로 추적을 위한 MUSIC 알고리즘의 계산량 감소 기법)

  • Kim, Yongguk;Park, Hae-Guy;Ryu, Heung-Gyoon
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.39A no.4
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    • pp.188-194
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    • 2014
  • V2I(Vehicular to Infrastructure) is a one kind of communication systems which is used between the base stations and mobile objects. In V2I communication system, it is difficult to obtain the desired communication performance. Beamforming technology is to find the optimal path. and it can be improved the communication performance. MUSIC algorithm can be estimated the direction of arrival. The directional vector of received signals and the eigenvector has orthogonal property. MUSIC algorithm uses this property. In V2I communication environment, real time optimal path is changed. By the high computational complexity of the MUSIC algorithm, the optimal path estimation error is generated. In this paper, we propose a method of computation reduction algorithm for MUSIC algorithm.

Movie Popularity Classification Based on Support Vector Machine Combined with Social Network Analysis

  • Dorjmaa, Tserendulam;Shin, Taeksoo
    • Journal of Information Technology Services
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    • v.16 no.3
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    • pp.167-183
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    • 2017
  • The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

Robust Feature Normalization Scheme Using Separated Eigenspace in Noisy Environments (분리된 고유공간을 이용한 잡음환경에 강인한 특징 정규화 기법)

  • Lee Yoonjae;Ko Hanseok
    • The Journal of the Acoustical Society of Korea
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    • v.24 no.4
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    • pp.210-216
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    • 2005
  • We Propose a new feature normalization scheme based on eigenspace for achieving robust speech recognition. In general, mean and variance normalization (MVN) is Performed in cepstral domain. However, another MVN approach using eigenspace was recently introduced. in that the eigenspace normalization Procedure Performs normalization in a single eigenspace. This Procedure consists of linear PCA matrix feature transformation followed by mean and variance normalization of the transformed cepstral feature. In this method. 39 dimensional feature distribution is represented using only a single eigenspace. However it is observed to be insufficient to represent all data distribution using only a sin91e eigenvector. For more specific representation. we apply unique na independent eigenspaces to cepstra, delta and delta-delta cepstra respectively in this Paper. We also normalize training data in eigenspace and get the model from the normalized training data. Finally. a feature space rotation procedure is introduced to reduce the mismatch of training and test data distribution in noisy condition. As a result, we obtained a substantial recognition improvement over the basic eigenspace normalization.