• Title/Summary/Keyword: Vector Reduce

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Support Vector Machine based on Stratified Sampling

  • Jun, Sung-Hae
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.9 no.2
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    • pp.141-146
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    • 2009
  • Support vector machine is a classification algorithm based on statistical learning theory. It has shown many results with good performances in the data mining fields. But there are some problems in the algorithm. One of the problems is its heavy computing cost. So we have been difficult to use the support vector machine in the dynamic and online systems. To overcome this problem we propose to use stratified sampling of statistical sampling theory. The usage of stratified sampling supports to reduce the size of training data. In our paper, though the size of data is small, the performance accuracy is maintained. We verify our improved performance by experimental results using data sets from UCI machine learning repository.

A New Study on Indirect Vector AC Current Control Method Using a Matrix Converter Fed Induction Motor

  • Lee Hong-Hee;Nguyen Hoang M.
    • Journal of Power Electronics
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    • v.6 no.1
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    • pp.67-72
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    • 2006
  • This paper introduces two different types of AC current control methods for an indirect vector controlled induction motor using a matrix converter. The proposed methods combine the advantages of matrix converters with the advantages of the indirect vector AC current control methods. The first proposed method explains the basic idea of the hysteresis current control method for matrix converters and shows its capability and stability in comparison to the conventional method usually used for VSI. With the aid of the special configuration of the matrix converter, we also propose another current method which is modified from the first one in order to reduce both current ripple and torque ripple. Simulation results have verified the feasibility and the effectiveness of the proposed methods.

Loss Minimizing Vector Control of Interior Permanent Magnet Synchronous Motor (매입형 영구자석 동기전동기의 최소 손실 벡터제어)

  • Chung, Euihoon;Lee, Yongjae;Ha, Jung-Ik
    • The Transactions of the Korean Institute of Power Electronics
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    • v.20 no.4
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    • pp.330-336
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    • 2015
  • This paper presents a loss-minimizing vector control method for interior permanent magnet synchronous motor (IPMSM). Conventionally, maximum torque per ampere (MTPA) control, which minimizes copper loss, has been widely used in industry. Iron loss, however, is not considered in MTPA control. In this paper, the loss model, including iron loss and copper loss, is derived to further reduce drive loss. The loss-minimizing vector controller is implemented based on the loss model. The controller generates optimal current vectors according to the operating conditions. The performance and validity of the proposed method are proved by experimental results through comparison with conventional methods.

Parallel Operation of Space Vector PWM Inverters for Large Capacity Inverter System (대용량 인버터 시스템을 위한 공간벡터 PWM 인버터의 병렬 운전)

  • Ji, Jun-Geun;Lee, Hyeon-Dong
    • The Transactions of the Korean Institute of Electrical Engineers B
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    • v.49 no.8
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    • pp.509-513
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    • 2000
  • This paper deals with the parallel operation of space vector PWM for large capacity inverter system. To enlarge the capacity of inverter system and to reduce the current ripples on inverter output side, two or more inverters are operated in parallel. In this paper, a new parallel operation strategy which minimizes the harmonic distortion of the output stage is described. The proposed method is developed on the basis of the space-vector PWM in order to increase the linearly controllable voltage range. With the help of the proposed voltage synthesis method, the total harmonic distortion of the output stage can be greatly reduced in compared with that of conventional method with sinusoidal PWM or that of the single inverter operation case. The experimental results with reduced scale test show the feasibility of the proposed voltage synthesis method.

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Category Factor Based Feature Selection for Document Classification

  • Kang Yun-Hee
    • International Journal of Contents
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    • v.1 no.2
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    • pp.26-30
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    • 2005
  • According to the fast growth of information on the Internet, it is becoming increasingly difficult to find and organize useful information. To reduce information overload, it needs to exploit automatic text classification for handling enormous documents. Support Vector Machine (SVM) is a model that is calculated as a weighted sum of kernel function outputs. This paper describes a document classifier for web documents in the fields of Information Technology and uses SVM to learn a model, which is constructed from the training sets and its representative terms. The basic idea is to exploit the representative terms meaning distribution in coherent thematic texts of each category by simple statistics methods. Vector-space model is applied to represent documents in the categories by using feature selection scheme based on TFiDF. We apply a category factor which represents effects in category of any term to the feature selection. Experiments show the results of categorization and the correlation of vector length.

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A Singular Value Decomposition based Space Vector Modulation to Reduce the Output Common-Mode Voltage of Direct Matrix Converters

  • Guan, Quanxue;Yang, Ping;Guan, Quansheng;Wang, Xiaohong;Wu, Qinghua
    • Journal of Power Electronics
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    • v.16 no.3
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    • pp.936-945
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    • 2016
  • Large magnitude common-mode voltage (CMV) and its variation dv/dt have an adverse effect on motor drives that leads to early winding failure and bearing deterioration. For matrix converters, the switch states that connect each output line to a different input phase result in the lowest CMV among all of the valid switch states. To reduce the output CMV for matrix converters, this paper presents a new space vector modulation (SVM) strategy by utilizing these switch states. By this mean, the peak value and the root mean square of the CMV are dramatically decreased. In comparison with the conventional SVM methods this strategy has a similar computation overhead. Experiment results are shown to validate the effectiveness of the proposed modulation method.

A Study on the Enhancement of Image Distortion for the Hybrid Fractal System with SOFM Vector Quantizer (SOFM 벡터 양자화기와 프랙탈 혼합 시스템의 영상 왜곡특성 향상에 관한 연구)

  • 김영정;김상희;박원우
    • Journal of the Institute of Convergence Signal Processing
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    • v.3 no.1
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    • pp.41-47
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    • 2002
  • Fractal image compression can reduce the size of image data by the contractive mapping that is affine transformation to find the block(called as range block) which is the most similar to the original image. Even though fractal image compression is regarded as an efficient way to reduce the data size, it has high distortion rate and requires long encoding time. In this paper, we presented a hybrid fractal image compression system with the modified SOFM Vector Quantizer which uses improved competitive learning method. The simulation results showed that the VQ hybrid fractal using improved competitive loaming SOFM has better distortion rate than the VQ hybrid fractal using normal SOFM.

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Analysis of Frictional Contact Problems of Nonlinearly Deformable Bodies by Using Contact Error Vector (접촉 오차 벡터를 이용한 비선형 변형체의 마찰접촉 해석)

  • Lee, Kisu;Kim, Bang-Won
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.13 no.3
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    • pp.305-319
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    • 2000
  • Numerical solution lot frictional contact problems of nonlinearly deformable bodies having large deformation is presented. The contact conditions on the possible contact points are expressed by using the contact error vector, and the iterative scheme is used to reduce the contact error vector monotonically toward zero. At each iteration the solution consists of two steps : The first step is to revise the contact force by using the contact error vector given by the previous geometry, and the second step is to compute the displacement and the contact error vector by solving the equilibrium equation with the contact force given at the first step. Convergence of the iterative scheme to the correct solution is analyzed, and the numerical simulations we performed with a rigid-plastic membrane and a nonlinear elastic beam.

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Efficient Record Filtering In-network Join Strategy using Bit-Vector in Sensor Networks (센서 네트워크에서 비트 벡터를 이용한 효율적인 레코드 필터링 인-네트워크 조인 전략)

  • Song, Im-Young;Kim, Kyung-Chang
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.4
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    • pp.27-36
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    • 2010
  • The paper proposes RFB(Record Filtering using Bit-vector) join algorithm, an in-network strategy that uses bit-vector to drastically reduce the size of data and hence the communication cost. In addition, by eliminating data not involved in join result prior to actual join, communication cost can be minimized since not all data need to be moved to the join nodes. The simulation result shows that the proposed RFB algorithm significantly reduces the number of bytes to be moved to join nodes compared to the popular synopsis join(SNJ) algorithm.

New Fast Algorithm for the Estimation of Motion Vectors (움직임 벡터 추정을 위한 새로운 빠른 알고리즘)

  • 정수목
    • Journal of the Korea Computer Industry Society
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    • v.5 no.2
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    • pp.275-280
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    • 2004
  • In this paper, a very fast block matching scheme was proposed to reduce the computations of Block Sum Pyramid Algorithm for motion estimation in video coding. The proposed algorithm is based on Block Sum Pyramid Algorithm and Efficient Multi-level Successive Elimination Algorithm. The proposed algorithm can reduce the computations of motion estimation greatly with 100% motion estimation accuracy. The efficiency of the proposed algorithm was verified by experimental results.

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