• 센서학회지
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• 제13권3호
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• pp.236-243
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• 2004

In this paper, we proposed a brain-computer interface system using EEG signals. It can generate 4 direction command signal from EEG signals captured during imagination of subjects. Bandpass filter used for preprocessing to detect the brain signal, and the power spectrum at a specific frequency domain of the EEG signals for concentration status and non-concentration one is used for feature. In order to generate an adequate signal for controlling the 4 direction movement, we propose a new interface system implemented by using a support vector machine and a time-multiplexing method. Moreover, bio-feed back process and on-line adaptive pattern recognition mechanism are also considered in the proposed system. Computer experimental results show that the proposed method is effective to recognize the non-stational brain wave signal.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 G
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• pp.2296-2298
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• 1998

In this paper, an on-chip learning pulse-mode digital neural network with a massively parallel yet compact and flexible network architecture is suggested. Algebraic neural operations are replaced by stochastic processes using pseudo-random sequences and simple logic gates are used as basic computing elements. Using Back-propagation algorithm both feed-forward and learning phases are efficiently implemented with simple logical gates. RNG architecture using LFSR and barrel shifter are adopted to avoid some correlation between pulse trains. Suggested network is designed in digital circuit and its performance is verified by computer simulation.

• 제어로봇시스템학회논문지
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• 제21권6호
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• pp.518-523
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• 2015

This paper proposes a gait phase classifier using a Recurrent Neural Network (RNN). Walking is a type of dynamic system, and as such it seems that the classifier made by using a general feed forward neural network structure is not appropriate. It is known that an RNN is suitable to model a dynamic system. Because the proposed RNN is simple, we use a back propagation algorithm to train the weights of the network. The input data of the RNN is the lower body's joint angles and angular velocities which are acquired by using the lower limb exoskeleton robot, ROBIN-H1. The classifier categorizes a gait cycle as two phases, swing and stance. In the experiment for performance verification, we compared the proposed method and general feed forward neural network based method and showed that the proposed method is superior.

• 전기학회논문지
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• 제60권9호
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• pp.1700-1707
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• 2011

Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. Since the fuzzy neural network(FNN) is recognized general approximate method to control non-linearities and uncertainties, the development of FNN control systems have also grown rapidly. The FNN controller is compounded of fuzzy and neural network. It has an advantage that is the robustness of fuzzy control and the ability to adapt of neural network. However, the FNN has static problem due to their feed-forward network structure. This paper proposes high performance speed control of IPMSM drive using the recurrent FNN(RFNN) which improved conventional FNN controller. The RFNN has excellent dynamic response characteristics because of it has internally feed-back structure. Also, this paper proposes speed estimation of IPMSM drive using ANN. The proposed method is analyzed and compared to conventional FNN controller in various operating condition such as parameter variation, steady and transient states etc.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.67-69
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• 2007

PMSM (Permanent Magnet Synchronous Motor) current control is a most inner loop of electromechanical driving systems and it plays a foundation role in the hierarchy's control loop of several mechanical machine systems. In this paper, a simple RMRAC control scheme for the PMSM is proposed in the synchronous frame. In the synchronous current model, the input signal is composed of as a calculated voltage by adaptive laws and system disturbances. The gains of feed-forward and feed-back controller are estimated by the proposed e-modification methods respectively, where the disturbances are assumed as filtered current tracking errors. After the estimation of the disturbances from the tracking errors, the corresponding voltage is fed forward to control input to compensate for the disturbances. The proposed method is robust to high frequency disturbances and has a fast dynamic response to time varying reference current trajectory. It also shows a good real-time performance duo to it's simplicity of control structure. Through the simulations considering several cases of external disturbances and experimental results, efficiency of the proposed method is verified

• 한국태양에너지학회 논문집
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• 제29권4호
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• pp.28-33
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• 2009

In 2008, the global photovoltaic(PV) market reached 5.6GW and the cumulative PV power installed totalled almost 15GW compared to 9GW in 2007. Due to a favourable feed-in-tariff, Korea emerged in 2008 as the 4th largest PV market worldwide. PV power installation rose 495.5 percent to 268MW in 2008 compare to 45MW in 2007. Building integrated photovoltaic(BIPV) has the potential to become a major source of renewable energy in the urban environment. BIPV has significant influenced on the reflection by rear materials such as white back sheet and the heat transfer through the building envelope because of the change of the thermal resistance by adding or replacing the building elements. In this study, to use as suitable building materials into environmentally friendly house like green home, characteristic analysis of BIPV module according to rear materials achieved. Electrical output of PV module with white back sheet is high about 10% compared to other pv module because of 83% reflectivity of white back sheet compared to 8.4% reflectivity of other PV modules with different rear materials(black back sheet and glass). In the result of outdoor experiment during a year, electrical output of four different PV module is decreased about 3.72%.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제53권3호
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• pp.188-195
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• 2004

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• Journal of Electrical Engineering and Technology
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• 제4권1호
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• pp.28-34
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• 2009

In a Gas Insulated Substation (GIS), Very Fast Transients (VFTs) are generated mainly due to switching operations. These transients may cause internal faults, i.e., layer-to-layer faults in a capacitively graded bushing as it is one of the most important terminal equipment for GIS. The healthiness of the bushing is generally verified by measuring its leakage current. However, the change in current magnitude/pattern is only marginal for different types of fault conditions. Leakage current monitoring (LCM) systems generate large amounts of data and computer aided interpretation of defects may be of great assistance when analyzing this data. In view of the above, ANN techniques have been used in this study for identification of these minor faults. A single layer perceptron network, a two layer feed-forward back propagation network and cascade correlation (CC) network models are used to identify interlayer faults in the bushing. The effectiveness of the CC network over perceptron and back propagation networks in identification of a fault has been analysed as part of the paper.

• 전기학회논문지
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• 제68권1호
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• pp.77-82
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• 2019

In this paper, a 0.6~6GHz quad-ridge horn antenna which can be used for the antenna measurement of 5.8GHz WiFi system from lowest frequency band of mobile LTE (Long Term Evolution) is designed and implemented. The quad-ridge horn antenna has quadruple ridges of exponential function, a back-short and a cavity. Based on this structure, we design the cavity size, ridge gap and feed gap to have broadband characteristics. For implementation, the plates material of aluminum and copper are used for the horn and four ridges, respectively. And the insulator supports are used to maintain the gap between ridges. By measurement, antenna has the gain of 6.2~13.35dBi with the return loss of less than -6dB (under VSWR 3 : 1) in the entire design band. The results of this study can be widely used to the antenna studies on the mobile communication including low frequency band of LTE, the EMI measurement and the standard calibration measurement.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.2221_2222
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• 2009

본 논문에서는 BLDC 운전 중 각 상의 결상 사고에 따른 토크 및 속도의 최적화에 대해 연구하였다. BLDC 모터의 구동을 위하여 ATmega16이 사용되었으며 제어기법으로는 PWM(Pulse Width Modulation) 기법이 사용된다. 속도 제어는 Hall Sensor의 검출 속도에 따라 Duty비를 제어하여 이루어지며 회전자 위치는 Hall Sensor 검출 방식을 통하여 이루어진다. 이러한 BLDC 모터를 이용하여 예기치 못한 결상 상황의 발생 시 토크의 감소로 인한 급제동에 대비하여 부하에 상응하는 토크를 최대한 낼 수 있도록 알고리즘을 구현하였다.