• 전력전자학회:학술대회논문집
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• 전력전자학회 2005년도 전력전자학술대회 논문집
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• pp.17-19
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• 2005

This paper considers the design and implementation of novel technique of speed estimation and control for IPMSM using learning mechanism-fuzzy neural network(LM-FNN) and artificial neural network (ANN) control. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new hybrid Intelligent control

• 스마트미디어저널
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• 제5권1호
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• pp.55-60
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• 2016

센서노드의 이동성 허용은 센서 네트워크의 효율적인 설계 및 시스템 성능 향상에 기여할 수 있다. 하지만, MAC 프로토콜 및 라우팅 프로토콜에 많은 기능적 변화가 필요하다. 특히, 노드 이동성을 고려한 에너지 효율적인 전송 메커니즘에 대한 연구가 매우 중요하다. 본 논문은 모바일 센서 네트워크 환경에서 센서노드가 싱크노드를 향하여 데이터를 전달할 때 인접 노드들의 거리 정보에 따라 다음 노드를 결정하고, 이를 토대로 거리에 따른 전송 출력을 적절히 제어하는 메커니즘을 제안한다. 시뮬레이션 방법을 통한 성능분석 결과, 본 논문에서 제안한 메커니즘이 평균 에너지 소모량, 네트워크 수명 등의 성능 지표에 대해 기존 최단 홉 라우팅 방식보다 에너지 효율을 향상시킨 것으로 분석되었다.

• 전력전자학회논문지
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• 제10권3호
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• pp.282-289
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• 2005

본 논문에서는 NFC(Neuro-Fuzzy Controller)와 ANN(Artificial Neural network) 제어기를 이용한 IPMSM의 속도 제어 및 추정을 제시한다. PI 제어기에서 나타나는 문제점을 해결하기 위하여 신경회로망과 퍼지제어를 혼합적용한 NFC를 설계한다. 신경회로망의 고도의 적응제어와 퍼지 제어기의 강인성 제어의 장점들을 접목한다. 다음은 ANN을 이용하여 IPMSM 드라이브의 속도 추정기법을 제시한다. 2층 구조를 가진 신경회로망에 BPA(Back Propagation Algorithm)를 적용하여 IPMSM 드라이브의 속도를 추정한다. 추정속도의 타당성을 입증하기 위하여 시스템을 구성하여 제어특성을 분석한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권8호
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• pp.1805-1824
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• 2013

Recently, the use of wireless body area networks (WBAN) has been increasing rapidly in medical healthcare applications. WBANs consist of smart nodes that can be used to sense and transmit vital data such as heart rate, temperature and ECG from a human body to a medical centre. WBANs depend on limited resources such as energy and bandwidth. In order to utilise these resources efficiently, a very well organized medium access control (MAC) protocol must be considered. In this paper, a new, adaptive and energy-efficient MAC protocol, entitled isMAC, is proposed for WBANs. The proposed MAC is based on multi-channel communication and aims to prolong the network lifetime by effectively employing (i) a collision prevention mechanism, (ii) a coordinator node (WCN) selection algorithm and (iii) a transmission power adjustment approach. The isMAC protocol has been developed and modelled, by using OPNET Modeler simulation software. It is based on a networking scenario that requires especially high data rates such as ECG, for performance evaluation purposes. Packet delay, network throughput and energy consumption have been chosen as performance metrics. The comparison between the simulation results of isMAC and classical IEEE 802.15.4 (ZigBee) protocol shows that isMAC significantly outperforms IEEE 802.15.4 in terms of packet delay, throughput and energy consumption.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제55권2호
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• pp.89-97
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_{d}$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using LM-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.566-569
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• 2005

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using artificial intelligent(AI) controller. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using learning mechanism fuzzy neural network(LM-FNN) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also. this paper is proposed the experimental results to verify the effectiveness of AI controller.

• Nuclear Engineering and Technology
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• 제48권1호
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• pp.228-235
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• 2016

Flaws at dissimilar metal welds (DMWs), such as reactor coolant systems components, Control Rod Drive Mechanism (CRDM), Bottom Mounted Instrumentation (BMI) etc., in nuclear power plants have been found. Notably, primary water stress corrosion cracking (PWSCC) in the DMWs could cause significant reliability problems at nuclear power plants. Therefore, phased array ultrasound is widely used for inspecting surface break cracks and stress corrosion cracks in DMWs. However, inspection of DMWs using phased array ultrasound has a relatively low probability of detection of cracks, because the crystalline structure of welds causes distortion and splitting of the ultrasonic beams which propagates anisotropic medium. Therefore, advanced evaluation techniques of phased array ultrasound are needed for improvement in the probability of detection of flaws in DMWs. Thus, in this study, an investigation of focusing and steering phased array ultrasound in DMWs was carried out using a time reversal technique, and an adaptive focusing technique based on finite element method (FEM) simulation. Also, evaluation of focusing performance of three different focusing techniques was performed by comparing amplitude of phased array ultrasonic signals scattered from the targeted flaw with three different time delays.