• Journal of Distribution Research
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• v.10 no.3
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• pp.1-14
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• 2005

It is important to control performance and a Sustainable Collaboration (SC) for the successful Supply Chain Management (SCM). This research developed a control model which analyzed SCM performances based on a Balanced Scorecard (ESC) and an SC using Support Vector Machine (SVM). 108 specialists of an SCM completed the questionnaires. We analyzed experimental data set using SVM. This research compared the forecasting accuracy of an SCMSC through four types of SVM kernels: (1) linear, (2) polynomial (3) Radial Basis Function (REF), and (4) sigmoid kernel (linear > RBF > Sigmoid > Polynomial). Then, this study compares the prediction performance of SVM linear kernel with Artificial Neural Network. (ANN). The research findings show that using SVM linear kernel to forecast an SCMSC is the most outstanding. Thus SVM linear kernel provides a promising alternative to an SC control level. A company which pursues an SCM can use the information of an SC in the SVM model.

• Journal of Electrical Engineering and Technology
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• v.8 no.2
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• pp.304-315
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• 2013

In this paper, analysis of cascaded H-bridge multilevel inverter in DTC-SVM (Direct Torque Control-Space Vector Modulation) based induction motor drive for FCEV (Fuel Cell Electric Vehicle) is presented. Cascaded H-bridge multilevel inverter uses multiple series units of H-bridge power cells to achieve medium-voltage operation and low harmonic distortion. In FCEV, a fuel cell stack is used as the major source of electric power moreover the battery and/or ultra-capacitor is used to assist the fuel cell. These sources are suitable for utilizing in cascaded H-bridge multilevel inverter. The drive control strategy is based on DTC-SVM technique. In this scheme, first, stator voltage vector is calculated and then realized by SVM method. Contribution of multilevel inverter to the DTC-SVM scheme is led to achieve high performance motor drive. Simulations are carried out in Matlab-Simulink. Five-level and nine-level inverters are applied in 3hp FCEV induction motor drive for analysis the multilevel inverter. Each H-bridge is implemented using one fuel cell and battery. Good dynamic control and low ripple in the torque and the flux as well as distortion decrease in voltage and current profiles, demonstrate the great performance of multilevel inverter in DTC-SVM induction motor drive for vehicle application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.2
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• pp.276-281
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• 2008

This paper proposes a new sliding surface which can have the same dynamics of nominal system based on SVM(Support Vector Machines). The conventional sliding mode control can not have the properties of $H_{\infty}$ controller because its sliding surface has lower order dynamics than the original system. The additional states must be used to solve this problem. However, The sliding surface of this paper can have the dynamics of $H_{\infty}$ control system by using support vector machines without defining any additional dynamic state. By using SVM, the property of $H_{\infty}$ control system can be estimated as a relationship between the states. With this relationship, a new sliding surface can be designed and have $H_{\infty}$ control system properties. As a result, in spite of the parameter uncertainty, the proposed controller can have the same dynamic of nominal system controlled by $H_{\infty}$ controller.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.6
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• pp.540-545
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• 2012

In this paper, we propose a fault diagnosis method using artificial neural network and SVM (Support Vector Machine) to detect and isolate faults in the nonlinear systems. The proposed algorithm consists of two main parts: fault detection through threshold testing using a artificial neural network and fault isolation by SVM fault classifier. In the proposed method a fault is detected when the errors between the actual system output and the artificial neural network nominal system output cross a predetermined threshold. Once a fault in the nonlinear system is detected the SVM fault classifier isolates the fault. The computer simulation results demonstrate the effectiveness of the proposed SVM and artificial neural network based fault diagnosis method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1513-1516
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• 2003

Genetic algorithm is well known as a stochastic searching method. In this paper, a modified genetic algorithm using 'Suppor Vector Machines (SVM)' is proposed. SVM is used to reduce the number of calling the objective function which in turn accelerate the searching speed compared to the conventional GA.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1413-1415
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• 2004

This paper proposes a SVM(Support Vector Machine) training algorithm to control a service robot with voice command. The service robot with a stereo vision system and dual manipulators of four degrees of freedom implements a User-Dependent Voice Control System. The training of SVM algorithm that is one of the statistical learning theories leads to a QP(quadratic programming) problem. In this paper, we present an efficient SVM speech recognition scheme especially based on less learning data comparing with conventional approaches. SVM discriminator decides rejection or acceptance of user's extracted voice features by the MFCC(Mel Frequency Cepstrum Coefficient). Among several SVM kernels, the exponential RBF function gives the best classification and the accurate user recognition. The numerical simulation and the experiment verified the usefulness of the proposed algorithm.

• Advances in Computational Design
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• v.8 no.3
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• pp.237-253
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• 2023

Torque ripple content and variable switching frequency operation of conventional direct torque control (DTC) are reduced by the integration of space vector modulation (SVM) into DTC. Integration of space vector modulation to conventional direct torque control known as SVM-DTC. It had been more frequently used method in renewable energy and machine drive systems. In this paper, SVM-DTC is used to control the rotor side converter (RSC) of a wind driven doubly-fed induction generator (DFIG) because of its advantages such as reduction of torque ripples and constant switching frequency operation. However, flux and torque ripples are still dominant due to distorted current waveforms at different operations of the wind turbine. Therefore, to smoothen the torque profile a Neural Network Controller (NNC) based SVM-DTC has been proposed by replacing the PI controller in the speed control loop of the wind turbine controller. Also, stability analysis and simulation study of DFIG using process reaction curve method (RRCM) are presented. Validation of simulation study in MATLAB/SIMULINK environment of proposed wind driven DFIG system has been performed by laboratory developed prototype model. The proposed NNC based SVM-DTC yields superior torque response and ripple reduction compared to other methods.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.4
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• pp.293-301
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• 2006

This paper proposes a control algorithm for STATic synchronous COMpensator(STATCOM), based on Space Vector Modulation(SVM) and Hysteresis Current Controller(HCC) techniques. STATCOM is used to reactive power compensation on a distribution network. The proposed algorithm utilizes the advantages of the fast dynamic response of the hysteresis current control and the reduced switching number of the SVM scheme. The controller determines a set of space vectors from a region detector and applies a space vector. A set of space vectors including the zero vector, to reduce the number of switching, is determined from output signals of two hysteresis comparators. The presented control system was tested with digital simulation in the Borland C++ program and demonstrate the advantage of the proposed hysteresis current controller.

• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.1
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• pp.18-26
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• 2017

This paper proposes the model predictive control with space vector modulation (SVM) method for current control of voltage-source inverter. Unlike the conventional method using a limited number of voltage vectors by switching states, the proposed method can consider various voltage vectors to identify the optimized voltage vector. The various voltage vectors are obtained by subdividing existing voltage vectors. The optimized voltage vector that minimizes the cost function is selected and applied to the inverter by using the SVM. The various voltage vectors and SVM reduce current ripples in the output AC side of the inverter compared with the conventional method. The effectiveness and performance of the proposed method are verified through simulation and experiment with a three-phase two-level voltage-source grid-connected inverter.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.247-250
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• 2006

Maximizing the productivity in reactive ion etching, early detection of process equipment anomaly became crucial in current high volume semiconductor manufacturing environment. To address the importance of the process fault detection for productivity, support vector machines (SVMs) is employed to assist the decision to determine process faults in real-time. SVMs for eleven steps of etching runs are established with data acquired from baseline runs, and they are further verified with the data from controlled (acceptable) and perturbed (unacceptable) runs. Then, each SVM is further utilized for the fault detection purpose utilizing control limits which is well understood in statistical process control chart. Utilizing SVMs, fault detection of reactive ion etching process is demonstrated with zero false alarm rate of the controlled runs on a run to run basis.