• Title/Summary/Keyword: vector decomposition

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Spectrum Sensing based on Support Vector Machine using Wavelet Packet Decomposition in Cognitive Radio Systems (인지 무선 시스템에서 웨이블릿 패킷 분해를 이용한 서포트 벡터 머신 기반 스펙트럼 센싱)

  • Lee, Gyu-Hyung;Lee, Young-Doo;Koo, In-Soo
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.18 no.2
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    • pp.81-88
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    • 2018
  • Spectrum sensing, the key technology of the cognitive radio networks, is used by a secondary user to determine the frequency state of a primary user. The energy detection in the spectrum sensing determines the presence or absence of a primary user according to the intensity of the allocated channel signal. Since this technique simply uses the strength of the signal for spectrum sensing, it is difficult to detect the signal of a primary user in the low SNR band. In this paper, we propose a way to combine spectrum sensing and support vector machine using wavelet packet decomposition to overcome performance degradation in low SNR band. In our proposed scheme, the sensing signals were extracted by wavelet packet decomposition and then used as training data and test data for support vector machine. The simulation results of the proposed scheme are compared with the energy detection using the AUC of the ROC curve and the accuracy according to the SNR band. With simulation results, we demonstrate that the proposed scheme show better determining performance than one of energy detection in the low SNR band.

An Estimating Method for Priority Vector in AHP, Using the Eigen-Decomposition of a Skew-Symmetric Matrix (AHP에서 왜대칭행렬의 고유분해를 이용한 중요도 추정법의 제안)

  • 이광진
    • The Korean Journal of Applied Statistics
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    • v.17 no.1
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    • pp.119-134
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    • 2004
  • Generally to estimate the priority vector in AHP, an eigen-vector method or a log-arithmic least square method is applied to pairwise comparison matrix itself. In this paper an estimating method is suggested, which is applied to pairwise comparison matrix adjusted by using the eigen-decomposition of skew-symmetric matrix. We also show theoretical background, meanings, and several advantages of this method by example. This method may be useful in case that pairwise comparison matrix is quite inconsistent.

An Watermarking Method Based on Singular Vector Decomposition and Vector Quantization Using Fuzzy C-Mean Clustering (특이치 분해와 Fuzzy C-Mean(FCM) 군집화를 이용한 벡터양자화에 기반한 워터마킹 방법)

  • Lee, Byung-Hee;Jang, Woo-Seok;Kang, Hwan-Il
    • Journal of the Korean Institute of Intelligent Systems
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    • v.17 no.7
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    • pp.964-969
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    • 2007
  • In this paper, we propose the image watermarking method for good compression ratio and satisfactory image quality of the cover image and the embedding image. This method is based on the singular value decomposition and the vector quantization using fuzzy c-mean clustering. Experimental results show that the embedding image has invisibility and robustness to various serious attacks. The advantage of this watermarking method is that we can achieve both the compression and the watermarking method for the copyright protection simultaneously.

A Form-finding of Planar Tensegrity Structures

  • Lee, Sang Jin
    • Architectural research
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    • v.14 no.4
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    • pp.143-152
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    • 2012
  • A form-finding procedure is presented for planar tensegrity structures. Notably, a simple decision criteria is proposed to select the desirable candidate position vector from the unitary matrix produced by the eigenvalue decomposition of force density matrix. The soundness of the candidate position vector guarantees faster convergence and produces a desirable form of tensegrity without any member having zero-length. Several numerical examples are provided to demonstrate the capability of the proposed form-finding process.

An Equivalent Carrier-based Implementation of a Modified 24-Sector SVPWM Strategy for Asymmetrical Dual Stator Induction Machines

  • Wang, Kun;You, Xiaojie;Wang, Chenchen
    • Journal of Power Electronics
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    • v.16 no.4
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    • pp.1336-1345
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    • 2016
  • A modified space vector pulse width modulation (SVPWM) strategy based on vector space decomposition and its equivalent carrier-based PWM realization are proposed in this paper, which is suitable for six-phase asymmetrical dual stator induction machines (DSIMs). A DSIM is composed of two sets of symmetrical three-phase stator windings spatially shifted by 30 electrical degrees and a squirrel-cage type rotor. The proposed SVPWM technique can reduce torque ripples and suppress the harmonic currents flowing in the stator windings. Above all, the equivalent relationship between the proposed SVPWM technique and the carrier-based PWM technique has been demonstrated, which allows for easy implementation by a digital signal processor (DSP). Simulation and experimental results, carried out separately on a simulation system and a 3.0 kW DSIM prototype test bench, are presented and discussed.

KMSVOD: Support Vector Data Description using K-means Clustering (KMSVDD: K-means Clustering을 이용한 Support Vector Data Description)

  • Kim, Pyo-Jae;Chang, Hyung-Jin;Song, Dong-Sung;Choi, Jin-Young
    • Proceedings of the KIEE Conference
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    • 2006.04a
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    • pp.90-92
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    • 2006
  • 기존의 Support Vector Data Description (SVDD) 방법은 학습 데이터의 개수가 증가함에 따라 학습 시간이 지수 함수적으로 증가하므로, 대량의 데이터를 학습하는 데에는 한계가 있었다. 본 논문에서는 학습 속도를 빠르게 하기 위해 K-means clustering 알고리즘을 이용하는 SVDD 알고리즘을 제안하고자 한다. 제안된 알고리즘은 기존의 decomposition 방법과 유사하게 K-means clustering 알고리즘을 이용하여 학습 데이터 영역을 sub-grouping한 후 각각의 sub-group들을 개별적으로 학습함으로써 계산량 감소 효과를 얻는다. 이러한 sub-grouping 과정은 hypersphere를 이용하여 학습 데이터를 둘러싸는 SVDD의 학습 특성을 훼손시키지 않으면서 중심점으로 모여진 작은 영역의 학습 데이터를 학습하도록 함으로써, 기존의 SVDD와 비교하여 학습 정확도의 차이 없이 빠른 학습을 가능하게 한다. 다양한 데이터들을 이용한 모의실험을 통하여 그 효과를 검증하도록 한다.

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Reduction in Computational Complexity of KLT-CVQ using UTV Decomposition (UTV 분해를 이용한 KLT-CVQ 코더의 계산량 개선)

  • Ju, Hyunho;Kim, Moo Young
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2012.07a
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    • pp.176-177
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    • 2012
  • 사람의 음성을 압축하는 방법으로 Code Excited Linear Prediction (CELP) 코더가 주로 사용되어 왔다. CELP 코더의 수신단에서는 양자화 된 여기신호를 LPC 필터로 합성하여 신호를 복원한다. LPC 합성필터의 영향으로 양자화 된 여기신호의 보로노이 셀 모양이 변형되는 문제점이 있기 때문에 이런 문제점을 해결하기 위해서 Karhunen-Loeve-Transform based Classify vector Quantization (KLT-CVQ) 코더가 제안되었다. 기존 KLT-CVQ 코더는 KLT 변환과 class 선택을 위해서 Eigen Value Decomposition (EVD)을 이용해서 eigen vector와 eigen value를 계산한다. 본 논문에서는 EVD 대신에 UTV Decomposition (UTVD)을 이용하여 KLT-CVQ의 계산량 문제점을 개선하는 방법을 제안한다.

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Strain Decomposition Method in Hull Stress Monitoring System for Container Ship

  • Park, Jae-Woong;Kang, Yun-Tae
    • Journal of Ship and Ocean Technology
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    • v.7 no.3
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    • pp.56-65
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    • 2003
  • The hull monitoring systems of container ships with four long-base gages give enough information for identifying the hull girder loads such as bending and torsional moments. But such a load-identification for container ships has not been known. In this paper, a load-identification method is suggested in terms of a linear matrix equation that the measured strain vector equals to the multiplication of the transformation matrix and the desired strain component vector. The equation is proved to be mathematically complete by the property of positive-definite determinant of the transformation matrix. The method is applied to a hull stress monitoring system for 8100TED container ship during sea trial, and the estimated external loads illustrate reasonable results in comparison with the pre-estimated results. This moment decomposition concept has also been tested in real operation conditions. The typical phenomena over the Suez Canal illustrated very suitable results comparing with the physical understandings. Henceforth, one can effectively use the proposed concept to monitor the hull girder loads such as bending and torsional moments.

A Text Similarity Measurement Method Based on Singular Value Decomposition and Semantic Relevance

  • Li, Xu;Yao, Chunlong;Fan, Fenglong;Yu, Xiaoqiang
    • Journal of Information Processing Systems
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    • v.13 no.4
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    • pp.863-875
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    • 2017
  • The traditional text similarity measurement methods based on word frequency vector ignore the semantic relationships between words, which has become the obstacle to text similarity calculation, together with the high-dimensionality and sparsity of document vector. To address the problems, the improved singular value decomposition is used to reduce dimensionality and remove noises of the text representation model. The optimal number of singular values is analyzed and the semantic relevance between words can be calculated in constructed semantic space. An inverted index construction algorithm and the similarity definitions between vectors are proposed to calculate the similarity between two documents on the semantic level. The experimental results on benchmark corpus demonstrate that the proposed method promotes the evaluation metrics of F-measure.

The development of solar tracking sensor and controller for improvement of generation efficiency (발전 효율향상을 위한 태양광추적 센서 및 제어기 개발)

  • Han, Ki-Bong;Han, Tae-Hee;Lee, Shin-Won;Han, Seung-Woo
    • Journal of the Korean Solar Energy Society
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    • v.32 no.6
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    • pp.29-36
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    • 2012
  • The existing solar tracking sensor for 2 axial control system to trace latitude and longitude is made of four phototransistor. The phototransistor-making is difficult and it's manufacturing is more high-priced than a wide use phototransistor because they have to the same characteristics of each phototransistor output signal. This paper described the algorithm for supplement these weakness. The algorithm applied to signal normalizing method and vector decomposition law. The deviations of each a wide use phototransistor output signal are resolved by signal normalizing method and it is able to make a solar tracking sensor using three phototransistor by vector decomposition law. Therefore, in this paper, it is reduced the number of phototransistor that is composed of solar tracking sensor and possible to use a wide use phototransistor by the proposed algorithm.