• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2200-2203
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• 2003

In an active noise control(ANC) system using the Filtered-X least mean square(LMS) algorithm, the online secondary path modeling method by exploiting a random noise generator is applied. This method is suitable for secondary path modeling. However, it is increased the residual error of the ANC system. In this paper, we presents an ANC system improved online secondary path modeling method which is modified Kuo and Zhang model that is the secondary path estimation by the additive noise. In addition, our proposed model is used that additive noise is transformed into the signal multiplied reference signal by gain control parameter and delayed.

• Journal of Power Electronics
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• v.11 no.5
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• pp.713-718
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• 2011

This paper presents an online voltage disturbance estimator to achieve precise torque control of IPMSMs over a high speed operating region. The proposed design has a type of state-filter based on a Luenburger-style closed loop stator current vector observer. Utilizing the frequency response plot (FRF) approach, the estimation accuracy and the parameter sensitivities are analyzed. Accurate torque control and improved efficiency are provided with the decoupling of the effect of the parameter variations. The feasibility of the presented idea is verified by laboratory experiments.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.287-297
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• 2018

To improve the performance of sensorless induction motor (IM) drives, a novel speed estimation method based on the real-time adaptive extended Kalman filter (RAEKF) is proposed in this paper. In this algorithm, the fuzzy factor is introduced to tune the measurement covariance matrix online by the degree of mismatch between the actual innovation and the theoretical. Simultaneously, the fuzzy factor can be continuously self-tuned tuned by the fuzzy logic reasoning system based on Takagi-Sugeno (T-S) model. Therefore, the proposed method improves the model adaptability to the actual systems and the environmental variations, and reduces the speed estimation error. Furthermore, a simple exponential function based on the fuzzy theory is used to reduce the computational burden, and the real-time performance of the system is improved. The correctness and the effectiveness of the proposed method are verified by the simulation and experimental results.

• Journal of Power Electronics
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• v.6 no.3
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• pp.235-244
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• 2006

Recently, speed sensorless vector control for synchronous reluctance motors (SYRMs) has deserved attention because of its advantages. Although rotor angle calculation using flux estimation is a straightforward approach, the DC offset can cause an increasing pure integrator error in this estimator. In addition, this method is affected by parameter fluctuation. In this paper, to control the motor at the low speed region, a modified programmable cascaded low pass filter (MPCPLF) with sensorless online parameter identification based on a block pulse function is proposed. The use of the MPCLPF is suggested because in programmable, cascade low pass filters (PCLPF), which previously have been applied to induction motors, the drift increases vastly wl)en motor speed decreases. Parameter identification is also used because it does not depend on estimation accuracy and can solve parameter fluctuation effects. Thus, sensorless speed control in the low speed region is possible. The experimental system includes a PC-based control with real time Linux and an ALTERA Complex Programmable Logic Device (CPLD), to acquire data from sensors and to send commands to the system. The experimental results show the proposed method performs well, speed and angle estimation are correct. Also, parameter identification and sensorless vector control are achieved at low speed, as well as, as at high speed.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.535-541
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• 2022

Zoom has been most popularly used as a non-face-to-face online class tool since COVID19, but due to the recent spread of the metaverse, the use of the metaverse platform is increasing. In particular, since a metaverse platform 'Spatial' provides online classroom creation and various learning functions, and various interactions between instructors and learners or learners and learners are possible, it is highly likely to be used in university classes. Since Zoom and Spatial each have their own strengths and weaknesses for the purpose of class use, it is necessary to find out the strengths and weaknesses of each by comparing and analyzing the learner's experience in class use. In this study, a quantitative analysis of usability, immersion, and satisfaction and a qualitative analysis of individual opinions were performed in order to compare and analyze the learner's experience. SUS (System Usability Scale) was used for usability evaluation, and Magnitude Estimation method was used for immersion and satisfaction evaluation. Thirty-five people who had participated in classes using Zoom and Spatial participated as subjects in this study. Zoom was higher than Spatial at the significance level of 0.05 in usability and satisfaction. On the other hand, the immersion in class was higher in Spatial than in Zoom. Since Spatial provides online classroom creation and various learning functions, and provides various interactions and fun elements between instructors and learners or learners and learners, the immersion in classes was high. If the user interface and interaction of Spatial are improved in the future, it is judged that it can be used as an effective online teaching tool that can replace zoom in university classes.

• Proceedings of the KIPE Conference
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• 2011.07a
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• pp.567-568
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• 2011

영구자석 동기 전동기의 센서리스 구동에 있어서 위치와 속도를 정확히 추정하는 것이 중요하다. 정확한 위치와 속도의 추정을 위해서는 정확한 모터 파라미터가 필요한데, 특히나 magnetic saturation에 의한 q-축 인덕턴스의 영향이 가장 크기 때문에 이 논문에서는 매입형 영구자석 동기 전동기(IPMSM)의 센서리스 구동을 위한 q축 인덕턴스 추정에 대한 시뮬레이션 결과와 실험 결과를 나타내었다.

• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.199-202
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• 2009

Adaptive memory management is a serious issue in data stream management. Data stream differ from the traditional stored relational model in several aspect such as the stream arrives online, high volume in size, skewed data distributions. Data skew is a common property of massive data streams. We propose the predicted allocation strategy, which uses predictive processing to cope with time varying data skew. This processing includes memory usage estimation and indexing with timestamp. Our experimental study shows that the predictive strategy reduces both required memory space and latency time for skewed data over varying time.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.90-94
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• 1986

This paper describes expansion of the state space pole assignment self-tuning control of SISO systems with system noise and abservation noise to that of MIMO systems. Resursive Prediction Error method is used for both parameter and state estimation in the block controllable canonical form. This simplifies the state feedback law by eliminating the online computation of transformation matrix.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2149-2151
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• 2002

In this paper, a novel speed estimation method of an induction motor using neural networks(NNs) is presented. The NN speed estimator is trained online by using the error backpropagation algorithm, and the training starts simultaneously with the induction motor working. The neural network based vector controller has the advantage of robustness against machine parameter variation. The simulation results using Matlab/Simulink verify the useful of the proposed method.

• Proceedings of the KIPE Conference
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• 2015.11a
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• pp.13-14
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• 2015

본 논문은 추정 정확도를 높이기 위해 비례적분 관측기를 이용한 실시간 잔존용량 추정 알고리즘을 제안한다. 시뮬레이션을 통해 제안하는 알고리즘의 타당성을 검증하였고, 초기 잔존 용량이 불명확하거나 배터리 모델 파라미터 값이 실제와 일치하지 않더라도 평균 추정오차는 0.3% 미만으로 확인되었다.