• Title, Summary, Keyword: Vector Space Model

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A Prediction Model Based on Relevance Vector Machine and Granularity Analysis

  • Cho, Young Im
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.16 no.3
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    • pp.157-162
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    • 2016
  • In this paper, a yield prediction model based on relevance vector machine (RVM) and a granular computing model (quotient space theory) is presented. With a granular computing model, massive and complex meteorological data can be analyzed at different layers of different grain sizes, and new meteorological feature data sets can be formed in this way. In order to forecast the crop yield, a grey model is introduced to label the training sample data sets, which also can be used for computing the tendency yield. An RVM algorithm is introduced as the classification model for meteorological data mining. Experiments on data sets from the real world using this model show an advantage in terms of yield prediction compared with other models.

Deadbeat and Hierarchical Predictive Control with Space-Vector Modulation for Three-Phase Five-Level Nested Neutral Point Piloted Converters

  • Li, Junjie;Chang, Xiangyu;Yang, Dirui;Liu, Yunlong;Jiang, Jianguo
    • Journal of Power Electronics
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    • v.18 no.6
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    • pp.1791-1804
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    • 2018
  • To achieve a fast dynamic response and to solve the multi-objective control problems of the output currents, capacitor voltages and system constraints, this paper proposes a deadbeat and hierarchical predictive control with space-vector modulation (DB-HPC-SVM) for five-level nested neutral point piloted (NNPP) converters. First, deadbeat control (DBC) is adopted to track the reference currents by calculating the deadbeat reference voltage vector (DB-RVV). After that, all of the candidate switching sequences that synthesize the DB-RVV are obtained by using the fast SVM principle. Furthermore, according to the redundancies of the switch combination and switching sequence, a hierarchical model predictive control (MPC) is presented to select the optimal switch combination (OSC) and optimal switching sequence (OSS). The proposed DB-HPC-SVM maintains the advantages of DBC and SVM, such as fast dynamic response, zero steady-state error and fixed switching frequency, and combines the characteristics of MPC, such as multi-objective control and simple inclusion of constraints. Finally, comparative simulation and experimental results of a five-level NNPP converter verify the correctness of the proposed DB-HPC-SVM.

Modeling and Analysis of the Micro-Grid with SVPWM Micro-Sources (SVPWM 방식 마이크로소스로 구성된 마이크로그리드 모델링 및 해석)

  • Son, Kwang-Myung;Lee, Kye-Byung;Kim, Young-Seob
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.20 no.3
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    • pp.12-19
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    • 2006
  • Micro-source units having power ratings in thousands of watts can provide power quality with higher reliability and efficiency than the conventional large scale units. This paper develops switching level model of micro-source and studies the characteristics of the micro-grid consisting of multiple micro-sources and interfaced with electric power system. The developed model adopts the space vector PWM to fully utilize the capacity of inverter. The interaction of the grid connected micro-sources and the characteristics of the control system parameters are investigated. Micro-sources and micro-grid are implemented using PSCAD/EMTDC. Simulation results show that the proposed model is efficient for studying micro-grid system.

dSPACE를 이용한 유도전동기 벡터제어 시스템의 실시간 시뮬레이션

  • Park, Sang-Eun;Lee, Byung-Ha
    • Proceedings of the KIEE Conference
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    • pp.368-371
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    • 2003
  • In this paper, we present a way that can implement the vector control Algorithm of induction motor and PWM signal generation on the condition Matlab/Simulink. The overall system model is designed by Simulink toolbox for vector control in induction motor. and then implement experiment with the DS1103 board of dSPACE. Although we are not coding the system, it is capable of doing simulation and experiment simultaneously. That is why Matlab and dSPACE board compiler can generate the "*.c" and "*.obj" files on the designed system automatically. After considering about hardware structure and driving system in DS1103 board. we verify the availability of proposed method through making a comparison/analysis between simulation and experiment.

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Double-Objective Finite Control Set Model-Free Predictive Control with DSVM for PMSM Drives

  • Zhao, Beishi;Li, Hongmei;Mao, Jingkui
    • Journal of Power Electronics
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    • v.19 no.1
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    • pp.168-178
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    • 2019
  • Discrete space vector modulation (DSVM) is an effective method to improve the steady-state performance of the finite control set predictive control for permanent magnet synchronous motor drive systems. However, it requires complex computations due to the presence of numerous virtual voltage vectors. This paper proposes an improved finite control set model-free predictive control using DSVM to reduce the computational burden. First, model-free deadbeat current control is used to generate the reference voltage vector. Then, based on the principle that the voltage vector closest to the reference voltage vector minimizes the cost function, the optimal voltage vector is obtained in an effective way which avoids evaluation of the cost function. Additionally, in order to implement double-objective control, a two-level decisional cost function is designed to sequentially reduce the stator currents tracking error and the inverter switching frequency. The effectiveness of the proposed control is validated based on experimental tests.

APPLICATION OF SUPPORT VECTOR MACHINE TO THE PREDICTION OF GEO-EFFECTIVE HALO CMES

  • Choi, Seong-Hwan;Moon, Yong-Jae;Vien, Ngo Anh;Park, Young-Deuk
    • Journal of The Korean Astronomical Society
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    • v.45 no.2
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    • pp.31-38
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    • 2012
  • In this study we apply Support Vector Machine (SVM) to the prediction of geo-effective halo coronal mass ejections (CMEs). The SVM, which is one of machine learning algorithms, is used for the purpose of classification and regression analysis. We use halo and partial halo CMEs from January 1996 to April 2010 in the SOHO/LASCO CME Catalog for training and prediction. And we also use their associated X-ray flare classes to identify front-side halo CMEs (stronger than B1 class), and the Dst index to determine geo-effective halo CMEs (stronger than -50 nT). The combinations of the speed and the angular width of CMEs, and their associated X-ray classes are used for input features of the SVM. We make an attempt to find the best model by using cross-validation which is processed by changing kernel functions of the SVM and their parameters. As a result we obtain statistical parameters for the best model by using the speed of CME and its associated X-ray flare class as input features of the SVM: Accuracy=0.66, PODy=0.76, PODn=0.49, FAR=0.72, Bias=1.06, CSI=0.59, TSS=0.25. The performance of the statistical parameters by applying the SVM is much better than those from the simple classifications based on constant classifiers.

A partially occluded object recognition technique using a probabilistic analysis in the feature space (특징 공간상에서 의 확률적 해석에 기반한 부분 인식 기법에 관한 연구)

  • 박보건;이경무;이상욱;이진학
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.26 no.11A
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    • pp.1946-1956
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    • 2001
  • In this paper, we propose a novel 2-D partial matching algorithm based on model-based stochastic analysis of feature correspondences in a relation vector space, which is quite robust to shape variations as well as invariant to geometric transformations. We represent an object using the ARG (Attributed Relational Graph) model with features of a set of relation vectors. In addition, we statistically model the partial occlusion or noise as the distortion of the relation vector distribution in the relation vector space. Our partial matching algorithm consists of two-phases. First, a finite number of candidate sets areselected by using logical constraint embedding local and structural consistency Second, the feature loss detection is done iteratively by error detection and voting scheme thorough the error analysis of relation vector space. Experimental results on real images demonstrate that the proposed algorithm is quite robust to noise and localize target objects correctly even inseverely noisy and occluded scenes.

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Torque Density Improvement of Five-Phase PMSM Drive for Electric Vehicles Applications

  • Zhao, Pinzhi;Yang, Guijie
    • Journal of Power Electronics
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    • v.11 no.4
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    • pp.401-407
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    • 2011
  • In order to enhance torque density of five-phase permanent magnetic synchronous motor with third harmonic injection for electric vehicles (EVs) applications, optimum seeking method for injection ratio of third harmonic was proposed adopting theoretical derivation and finite element analysis method, under the constraint of same amplitude for current and air-gap flux. By five-dimension space vector decomposition, the mathematic model in two orthogonal space plane, $d_1-q_1$ and $d_3-q_3$, was deduced. And the corresponding dual-plane vector control method was accomplished to independently control fundamental and third harmonic currents in each vector plane. A five-phase PMSM prototype with quasi-trapezoidal flux pattern and its fivephase voltage source inverter were designed. Also, the dual-plane vector control was digitized in a single XC3S1200E FPGA. Simulation and experimental results prove that using the proposed optimum seeking method, the torque density of five-phase PMSM is enhanced by 20%, without any increase of power converter capacity, machine size and iron core saturation.

Opposition based charged system search for parameter identification problem in a simplified Bouc-Wen model

  • Shirgir, Sina;Azar, Bahman Farahmand;Hadidi, Ali
    • Earthquakes and Structures
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    • v.18 no.4
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    • pp.493-506
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    • 2020
  • In this paper, a new opposition based charged system search (CSS) is proposed to be used as a parameter identification of highly nonlinear semi-active magneto-rheological damper. By replacing the opposition particles with current solutions, the mentioned strategy is used to enhance the search space and to increase the exploration of CSS. To investigate the effectiveness of the proposed method, a nonlinear modified Bouc-Wen model of MR damper is considered to find its parameters, and compare it with those achieved from experimental model of MR damper. Also, by exploiting the sensitivity analysis and using the importance vector, the less importance parameters in the Bouc-Wen model are eliminated which makes the MR damper model simpler. Results demonstrate the new proposed algorithm (OBLCSS) has a high ability to tackle highly nonlinear problems. Based on the results of the α importance vector, a simplified model is proposed and its parameters are identified by using the presented OBLCSS algorithm. The simplified proposed model also has a high capability of estimating damper responses.