• Title, Summary, Keyword: Vector Space Model

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Speed Sensorless Vector Control of Induction Motor Using MATLAB/SIMULINK and dSPACE DS1104 (MATLAB/SIMULINK와 dSPACE DS1104를 이용한 유도 전동기의 속도 센서리스 벡터제어)

  • Lee, Dong-Min;Lee, Yong-Suk;Ji, Jun-Keun
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.8 no.2
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    • pp.212-218
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    • 2007
  • This paper presents a implementation of speed sensorless vector control of induction motor using MATLAB/SIMULINK and dSPACE DS1104. Proposed flux estimation algorithm, which utilize the combination of the voltage model based on stator equivalent model and the current model based on rotor equivalent model, enables stable estimation of rotor flux. Proposed rotor speed estimation algorithm utilizes the estimated flux. And the estimated rotor speed is used to speed control of induction motor. Overall system consists of speed controller, current controller, and flux controller using the most general PI controller. Speed sensorless vector control algorithm is implemented as block diagrams using MATLAB/SIMULINK. And realtime control is performed by dSPACE DS1104 control board and Real-Time-Interface(RTI).

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Speed Sensorless Vector Control of Induction Motor using dSPACE (dSPACE를 이용한 유도전동기의 속도센서리스 벡터제어)

  • Lee, Dong-Min;Ji, Jun-Keun
    • Proceedings of the KIEE Conference
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    • pp.163-165
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    • 2006
  • This paper presents a implementation of speed sensorless vector control algorithm of induction motor using MATLAB/SIMULINK amd dSPACE DSl104 R&D board. The estimation of rotor flux linkage and rotor speed is carried out using model reference adaptive system(MRAS) method. Estimated rotor speed is used to speed controller of induction motor. Simulation results are presented to confirm speed sensorless vector control algorithm.

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Word Sense Classification Using Support Vector Machines (지지벡터기계를 이용한 단어 의미 분류)

  • Park, Jun Hyeok;Lee, Songwook
    • KIPS Transactions on Software and Data Engineering
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    • v.5 no.11
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    • pp.563-568
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    • 2016
  • The word sense disambiguation problem is to find the correct sense of an ambiguous word having multiple senses in a dictionary in a sentence. We regard this problem as a multi-class classification problem and classify the ambiguous word by using Support Vector Machines. Context words of the ambiguous word, which are extracted from Sejong sense tagged corpus, are represented to two kinds of vector space. One vector space is composed of context words vectors having binary weights. The other vector space has vectors where the context words are mapped by word embedding model. After experiments, we acquired accuracy of 87.0% with context word vectors and 86.0% with word embedding model.

A Study on the Deep Neural Network based Recognition Model for Space Debris Vision Tracking System (심층신경망 기반 우주파편 영상 추적시스템 인식모델에 대한 연구)

  • Lim, Seongmin;Kim, Jin-Hyung;Choi, Won-Sub;Kim, Hae-Dong
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.45 no.9
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    • pp.794-806
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    • 2017
  • It is essential to protect the national space assets and space environment safely as a space development country from the continuously increasing space debris. And Active Debris Removal(ADR) is the most active way to solve this problem. In this paper, we studied the Artificial Neural Network(ANN) for a stable recognition model of vision-based space debris tracking system. We obtained the simulated image of the space environment by the KARICAT which is the ground-based space debris clearing satellite testbed developed by the Korea Aerospace Research Institute, and created the vector which encodes structure and color-based features of each object after image segmentation by depth discontinuity. The Feature Vector consists of 3D surface area, principle vector of point cloud, 2D shape and color information. We designed artificial neural network model based on the separated Feature Vector. In order to improve the performance of the artificial neural network, the model is divided according to the categories of the input feature vectors, and the ensemble technique is applied to each model. As a result, we confirmed the performance improvement of recognition model by ensemble technique.

A New Space Vector Random Position PWM Scheme (새로운 공간벡터 Random Position PWM기법)

  • Kim, Hoe-Geun;Lim, Young-Cheol;Na, Seok-Hwan;Jung, Young-Gook
    • Proceedings of the KIEE Conference
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    • pp.168-174
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    • 2001
  • In this paper, a new space vector RPPWM (Random Position PWM) is proposed. In the proposed RPPWM, each of three phase pulses is located randomly in each switching interval. Based on the space vector modulation technique, the duty ratio of the pulses is calculated. Along with the randomization of the PWM pulses, we can obtain the effects of spread spectra of voltage, current as in the case of randomly changed switching frequency. To verify the validity of the proposed RPPWM, simulation study was tried using Matlab/simulink. The main model described in Simulink block diagrams includes the space vector modulation block, pulse position randomization block, inverter block, 3 phase induction motor block, and so on. By the simulation study, the harmonics of the output voltage, and the current of inverter are predicted in different PWM methods- SVPWM, LLPWM, proposed RPPWM.

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Cointegration Analysis with Mixed-Frequency Data of Quarterly GDP and Monthly Coincident Indicators

  • Seong, Byeongchan
    • The Korean Journal of Applied Statistics
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    • v.25 no.6
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    • pp.925-932
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    • 2012
  • The article introduces a method to estimate a cointegrated vector autoregressive model, using mixed-frequency data, in terms of a state-space representation of the vector error correction(VECM) of the model. The method directly estimates the parameters of the model, in a state-space form of its VECM representation, using the available data in its mixed-frequency form. Then it allows one to compute in-sample smoothed estimates and out-of-sample forecasts at their high-frequency intervals using the estimated model. The method is applied to a mixed-frequency data set that consists of the quarterly real gross domestic product and three monthly coincident indicators. The result shows that the method produces accurate smoothed and forecasted estimates in comparison to a method based on single-frequency data.

The consistency estimation in nonlinear regression models with noncompact parameter space

  • Park, Seung-Hoe;Kim, Hae-Kyung;Jang, Sook-Hee
    • Bulletin of the Korean Mathematical Society
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    • v.33 no.3
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    • pp.377-383
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    • 1996
  • We consider in this paper the following nonlinear regression model $$ (1.1) y_t = f(x_t, \theta_o) + \in_t, t = 1, \ldots, n, $$ where $y_t$ is the tth response, $x_t$ is m-vector imput variable, $\theta_o$ is a p-vector of unknown parameter belong to a parameter space $\Theta, f:R^m \times \Theta \ to R^1$ is a nonlinear known function, and $\in_t$ are independent unobservable random errors with finite second moment.

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On Error Modeling and Compensation of Machine Tools (공작기계 오차 모델링과 보정에 관한 연구)

  • Song, Il-Gyu;Choi, Young
    • Journal of the Korean Society for Precision Engineering
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    • v.13 no.1
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    • pp.98-107
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    • 1996
  • The use of composite hyperpatch model is proposed to predict a machine tool positional error over the entire work space. This is an appropriate representation of the distorted work space. This model is valid for any configuration of 3-axis machine tool. Tool position, which is given NC data or CL data, contains error vector in actual work space. In this study, off-line compensation scheme was investigated for tool position error due to inaccuracy in machine tool structure. The error vector in actual work space is corrected by the error model using Newton-Raphson method. The proposed error compensation method shows the possibility of improving machine accuracy at a low cost.

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A New Space Vector Random PWM Scheme for Inverter Fed Drive Systems (인버터 구동 시스템을 위한 새로운 공간벡터 Random PWM기법)

  • 나석환;정영국;임영철
    • The Transactions of the Korean Institute of Power Electronics
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    • v.6 no.6
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    • pp.525-537
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    • 2001
  • In this paper a new space vector RPPWM(Random Position PWM) is proposed. In the propsed RPPWM each of three phase pulses is located randomly in each switching interval. Based on the Space vector modulation technique the duty ratio of the pulses is calculated Along with the randomization of the PWM pulses. we can obtain the effects of spread spectra of votlage, current as in the case of randomly changed switching frequency, To verify the validity of the proposed RPPWM simulation study was tried using Matlab/Simulink The main model described in Simulink block diagrams includes the space vector modulation block pulse position randomization block inverter block 3 phase induction motor block and so on By the simulation study, the harmonics of the output voltage and the current of inverter are predicted in different PWM methods- SVPWM, LLPWM proposed RPPWM.

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Implementation of Vector Control for SPMSM Using Model Based Controller Design in MATLAB/SIMULINK (MATLAB/SIMULINK의 모델기반 제어기 설계를 이용한 표면부착형 영구자석 동기전동기의 벡터제어 구현)

  • Ji, Jun-Keun;Lee, Yong-Seok;Cha, Guee-Soo
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.57 no.8
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    • pp.1383-1391
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    • 2008
  • This paper presents an implementation of vector control for SPMSM using model based controller design in MATLAB/SIMULINK. The model based controller design enables fast development of control system for motor by designing controllers and performing simulation on the GUI (Graphic User Interface) platform, converting program code directly into real-time programs, and then performing tests for the responses from controllers. The controllers designed in this paper are PI speed controller and decoupling PI current controller. Also space vector modulation method using offset voltage is used in PWM scheme. And system stability is also secured by close magnitude overmodulation method, maintaining dynamics of load when overmodulation occurs. The validity of vector control implemented is verified through simulations and experiments.