• Title, Summary, Keyword: Vector Space Model

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Development of a Daily Solar Major Flare Occurrence Probability Model Based on Vector Parameters from SDO/HMI

  • Lim, Daye;Moon, Yong-Jae;Park, Jongyeob;Lee, Kangjin;Lee, Jin-Yi
    • The Bulletin of The Korean Astronomical Society
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    • v.42 no.2
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    • pp.59.5-60
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    • 2017
  • We present the relationship between vector magnetic field parameters and solar major flare occurrence rate. Based on this, we are developing a forecast model of major flare (M and X-class) occurrence rate within a day using hourly vector magnetic field data of Space-weather HMI Active Region Patch (SHARP) from May 2010 to April 2017. In order to reduce the projection effect, we use SHARP data whose longitudes are within ${\pm}60$ degrees. We consider six SHARP magnetic parameters (the total unsigned current helicity, the total photospheric magnetic free energy density, the total unsigned vertical current, the absolute value of the net current helicity, the sum of the net current emanating from each polarity, and the total unsigned magnetic flux) with high F-scores as useful predictors of flaring activity from Bobra and Couvidat (2015). We have considered two cases. In case 1, we have divided the data into two sets separated in chronological order. 75% of the data before a given day are used for setting up a flare model and 25% of the data after that day are used for test. In case 2, the data are divided into two sets every year in order to reduce the solar cycle (SC) phase effect. All magnetic parameters are divided into 100 groups to estimate the corresponding flare occurrence rates. The flare identification is determined by using LMSAL flare locations, giving more numbers of flares than the NGDC flare list. Major results are as follows. First, major flare occurrence rates are well correlated with six magnetic parameters. Second, the occurrence rate ranges from 0.001 to 1 for M and X-class flares. Third, the logarithmic values of flaring rates are well approximated by two linear equations with different slopes: steeper one at lower values and lower one at higher values. Fourth, the sum of the net current emanating from each polarity gives the minimum RMS error between observed flare rates and predicted ones. Fifth, the RMS error for case 2, which is taken to reduce SC phase effect, are smaller than those for case 1.

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Power Module Bridge Type Auxiliary Resonant AC Link Snubber-Assisted Three-Phase Soft Switching Inverter

  • Hisashi Iyomori;Nagai, Shin-ichiro;Masanobu Yoshida;Eiji Hiraki;Mutsuo Nakaoka
    • Journal of Power Electronics
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    • v.4 no.2
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    • pp.77-86
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    • 2004
  • This paper presents a novel three-phase power module bridge type auxiliary resonant AC link snubber for the three-phase voltage-fed sinwave soft switching PWM inverter operating under specific instantaneous space voltage vector modulation. The operating principle of this resonant snubber is described for current source load model during one switching period, along with its design approach based on the simulation data. The performance evaluations of space vector modulation three-phase sinewave soft switching inverter with a new three-phase active auxiliary resonant AC link snubber are discussed as compared with those of three-phase voltage source-fed sinewave hard switching PWM inverter with a standard space voltage vector modulation strategy. The power loss analysis and conventional efficiency estimation of three-phase soft switching PWM inverter using ICBT modules are carried out including all the conduction power losses based upon the measured v-i characteristics of IGBT and its antiparallel diode as well as their switching losses.

Analysis and Control of NPC-3L Inverter Fed Dual Three-Phase PMSM Drives Considering their Asymmetric Factors

  • Chen, Jian;Wang, Zheng;Wang, Yibo;Cheng, Ming
    • Journal of Power Electronics
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    • v.17 no.6
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    • pp.1500-1511
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    • 2017
  • The purpose of this paper is to study a high-performance control scheme for neutral-point-clamping three-level (NPC-3L) inverter fed dual three-phase permanent magnet synchronous motor (PMSM) drives by considering some asymmetric factors such as the non-identical parameters in phase windings. To implement this, the system model is analyzed for dual three-phase PMSM drives with asymmetric factors based on the vector space decomposition (VSD) principle. Based on the equivalent circuits, PI controllers with feedforward compensation are used in the d-q subspace for regulating torque, where the cut-off frequency of the PI controllers are set at the twice the fundamental frequency for compensating both the additional DC component and the second order component caused by asymmetry. Meanwhile, proportional resonant (PR) controllers are proposed in the x-y subspace for suppressing the possible unbalanced currents in the phase windings. A dual three-phase space vector modulation (DT-SVM) is designed for the drive, and the balancing factor is designed based on the numerical fitting surface for balancing the DC link capacitor voltages. Experimental results are given to demonstrate the validity of the theoretical analysis and the proposed control scheme.

Nonnegative estimates of variance components in a two-way random model

  • Choi, Jaesung
    • Communications for Statistical Applications and Methods
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    • v.26 no.4
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    • pp.337-346
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    • 2019
  • This paper discusses a method for obtaining nonnegative estimates for variance components in a random effects model. A variance component should be positive by definition. Nevertheless, estimates of variance components are sometimes given as negative values, which is not desirable. The proposed method is based on two basic ideas. One is the identification of the orthogonal vector subspaces according to factors and the other is to ascertain the projection in each orthogonal vector subspace. Hence, an observation vector can be denoted by the sum of projections. The method suggested here always produces nonnegative estimates using projections. Hartley's synthesis is used for the calculation of expected values of quadratic forms. It also discusses how to set up a residual model for each projection.

Implementation of Vector Control for SMPMSM Using Model Based Controller Design in MATLAB/SIMULINK (MATLAB/SIMULINK의 모델기반 제어기 설계를 이용한 표면 부착형 영구자석 동기 전동기의 벡터제어)

  • Lee, Yong-Seok;Ji, Jun-Keun;Cha, Gui-Soo
    • Proceedings of the KIPE Conference
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    • pp.145-147
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    • 2007
  • This paper presents an implementation of vector control for SMPMSM 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 controller is designed as PI controller for speed and decoupling PI controller for current. And PWM used space vector modulation method using offset voltage and system stability is also secured by close magnitude overmodulation method, maintaining dynamics of load when it overmodulation. The validity of vector control implemented is verified through simulations and experiments.

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Dynamic Nonlinear Prediction Model of Univariate Hydrologic Time Series Using the Support Vector Machine and State-Space Model (Support Vector Machine과 상태공간모형을 이용한 단변량 수문 시계열의 동역학적 비선형 예측모형)

  • Kwon, Hyun-Han;Moon, Young-Il
    • Journal of The Korean Society of Civil Engineers
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    • v.26 no.3B
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    • pp.279-289
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    • 2006
  • The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

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DEVELOPMENT OF A RECONFIGURABLE CONTROL FOR AN SP-100 SPACE REACTOR

  • Na Man-Gyun;Upadhyaya Belle R.
    • Nuclear Engineering and Technology
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    • v.39 no.1
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    • pp.63-74
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    • 2007
  • In this paper, a reconfigurable controller consisting of a normal controller and a standby controller is designed to control the thermoelectric (TE) power in the SP-100 space reactor. The normal controller uses a model predictive control (MPC) method where the future TE power is predicted by using support vector regression. A genetic algorithm that can effectively accomplish multiple objectives is used to optimize the normal controller. The performance of the normal controller depends on the capability of predicting the future TE power. Therefore, if the prediction performance is degraded, the proportional-integral (PI) controller of the standby controller begins to work instead of the normal controller. Performance deterioration is detected by a sequential probability ratio test (SPRT). A lumped parameter simulation model of the SP-100 nuclear space reactor is used to verify the proposed reconfigurable controller. The results of numerical simulations to assess the performance of the proposed controller show that the TE generator power level controlled by the proposed reconfigurable controller could track the target power level effectively, satisfying all control constraints. Furthermore, the normal controller is automatically switched to the standby controller when the performance of the normal controller degrades.

Near-real time Kp forecasting methods based on neural network and support vector machine

  • Ji, Eun-Young;Moon, Yong-Jae;Park, Jongyeob;Lee, Dong-Hun
    • The Bulletin of The Korean Astronomical Society
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    • v.37 no.2
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    • pp.123.1-123.1
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    • 2012
  • We have compared near-real time Kp forecast models based on neural network (NN) and support vector machine (SVM) algorithms. We consider four models as follows: (1) a NN model using ACE solar wind data; (2) a SVM model using ACE solar wind data; (3) a NN model using ACE solar wind data and preliminary kp values from US ground-based magnetometers; (4) a SVM model using the same input data as model 3. For the comparison of these models, we estimate correlation coefficients and RMS errors between the observed Kp and the predicted Kp. As a result, we found that the model 3 is better than the other models. The values of correlation coefficients and RMS error of the model 3 are 0.93 and 0.48, respectively. For the forecast evaluation of models for geomagnetic storms ($Kp{\geq}6$), we present contingency tables and estimate statistical parameters such as probability of detection yes (PODy), false alarm ratio (FAR), bias, and critical success index (CSI). From a comparison of these statistical parameters, we found that the SVM models (model 2 and model 4) are better than the NN models (model 1 and model 3). The values of PODy and CSI of the model 4 are the highest among these models (PODy: 0.57 and CSI: 0.48). From these results, we suggest that the NN models are better than the SVM models for predicting Kp and the SVM models are better than the NN models for forecasting geomagnetic storms.

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Sensorless Control of Induction Motor Drives Using an Improved MRAS Observer

  • Kandoussi, Zineb;Boulghasoul, Zakaria;Elbacha, Abdelhadi;Tajer, Abdelouahed
    • Journal of Electrical Engineering and Technology
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    • v.12 no.4
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    • pp.1456-1470
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    • 2017
  • This paper presents sensorless vector control of induction motor drives with an improved model reference adaptive system observer for rotor speed estimation and parameters identification from measured stator currents, stator voltages and estimated rotor fluxes. The aim of the proposed sensorless control method is to compensate simultaneously stator resistance and rotor time constant variations which are subject of large changes during operation. PI controllers have been used in the model reference adaptive system adaptation mechanism and in the closed loops of speed and currents regulation. The stability of the proposed observer is proved by the Lyapunov's theorem and its feasibility is verified by experimentation. The experimental results are obtained with an 1 kW induction motor using Matlab/Simulink and a dSPACE system with DS1104 controller board showing the effectiveness of the proposed approach in terms of dynamic performance.