• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제51권3호
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• pp.116-124
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• 2002

Design and analysis of disturbance observer-based deadbeat control fur single-phase inverter applications are comprehensively presented in this paper. Load current can be estimated by disturbance observer, which is basically structured with the first order equation in this case and is regarded as a relatively simple method in comparison with conventional full-order Luenberger observer. Also, an inherent one-step delay problem appeared in the deadbeat control method is overcome by a simple prediction technique proposed. Output voltage dip is reduced by the feedforward control with the change rate of the estimated load current involved in the deadbeat current control loop. The proposed algorithms are verified by the respective simulation and experiment results.

• Nuclear Engineering and Technology
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• 제52권11호
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• pp.2565-2571
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• 2020

Background: Understanding the behaviour of nuclear fuel claddings by conducting burst test on single cladding tube under simulated loss-of-coolant accident conditions and developing theoretical cum empirical predictive computer codes have been the focus of several investigations. The developed burst criterion (a) assumes symmetrical deformation of cladding tube in contrast to experimental observation (b) interpolates the properties of Zircaloy-4 cladding in mixed α+β phase (c) does not account for azimuthal temperature variations. In order to overcome all these drawbacks of burst criterion, it is reasoned that artificial intelligence technique may be a better option to predict the burst parameters. Methods: Artificial neural network models based on feedforward backpropagation algorithm with logsig transfer function are developed. Results: Neural network architecture of 2-4-4-3, that is model with two hidden layers having four nodes in each layer is found to be the most suitable. The mean, maximum, and minimum prediction errors for this optimised model are 0.82%, 19.62%, and 0.004%, respectively. Conclusion: The burst stress, burst temperature, and burst strain obtained from burst criterion have average deviation of 19%, 12%, and 53% respectively whereas the developed neural network model predicted these parameters with average deviation of 6%, 2%, and 8%, respectively.

• Structural Engineering and Mechanics
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• 제12권5호
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• pp.527-540
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• 2001

A new sort of learning algorithm named whole learning algorithm is proposed to simulate the nonlinear and dynamic behavior of RC members for the estimation of structural integrity. A mathematical technique to solve the multi-objective optimization problem is applied for the learning of the feedforward neural network, which is formulated so as to minimize the Euclidean norm of the error vector defined as the difference between the outputs and the target values for all the learning data sets. The change of the outputs is approximated in the first-order with respect to the amount of weight modification of the network. The governing equation for weight modification to make the error vector null is constituted with the consideration of the approximated outputs for all the learning data sets. The solution is neatly determined by means of the Moore-Penrose generalized inverse after summarization of the governing equation into the linear simultaneous equations with a rectangular matrix of coefficients. The learning efficiency of the proposed algorithm from the viewpoint of computational cost is verified in three types of problems to learn the truth table for exclusive or, the stress-strain relationship described by the Ramberg-Osgood model and the nonlinear and dynamic behavior of RC members observed under an earthquake.

• 조명전기설비학회논문지
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• 제20권6호
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• pp.96-103
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• 2006

서보 드라이브의 성능 향상을 위해 AC 서보 전동기의 MRAC (Model Reference Adaptive Control) 기반 적응 전류 제어 기법이 제시된다. 인버터 구동 전류 제어 기법 중 예측형 전류 제어 기법은 이상적인 과도 응답 및 정상 상태 응답을 주지만, 전동기 파라미터 변화 시 정상상태 응답 성능이 저하된다. 이러한 제한 점을 극복하기 위해 파라미터 변화에 의한 외란이 MRAC 기법에 의해 추정되어 전향 제어에 의해 보상된다. 제안된 방식은 기존의 외란 추정 방식과 달리 관측기 구성을 위한 인버터의 상전압 측정을 필요로 하지 않는다. 제안된 적응 제어 방식의 점근안정성과 효과적으로 서보 드라이브에 적용될 수 있음이 입증된다. 제안된 방식이 DSP TMS320C31을 이용하여 구현되고 유용성이 시뮬레이션과 실험을 통해 입증된다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2019년도 전력전자학술대회
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• pp.207-209
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• 2019

This paper proposes a grid-tied photovoltaic (PV) system, consisting of Voltage-fed dual-active-bridge (DAB) dc-dc converter with single phase inverter. The proposed converter allows a small dc-link capacitor, so that system reliability can be improved by replacing electrolytic capacitors with film capacitors. The double line frequency free maximum power point tracking (MPPT) is also realized in the proposed converter by using Ripple Correlation method. First of all, to eliminate the double line frequency ripple which influence the reduction of DC source capacitance, control is developed. Then, a designing of Current control in DQ frame is analyzed and to fulfill the international harmonics standards such as IEEE 519 and P1547, $3^{rd}$ harmonic in the grid is directly compensated by the feedforward terms generated by the PR controller with the grid current in stationary frame to achieve desire Total Harmonic Distortion (THD). 5-kW PV converter and inverter module with a small dc-link film capacitor was built in the laboratory with the proposed control and MPPT algorithm. Experimental results are given to validate the converter performance.

• Journal of Power Electronics
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• 제19권1호
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• pp.108-118
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• 2019

This paper proposes a DC-link voltage balance controller using the fourth-phase of a three-level neutral-point clamped (NPC) PWM converter with medium vector selection (MVS) PWM for common-mode voltage reduction. MVS PWM makes the voltage reference by synthesizing the voltage vectors that cannot generate common-mode voltage. This PWM method is effective for reducing the EMI noise emitted from converter systems. However, the DC-link voltage imbalance problem is caused by the use of limited voltage vectors. Therefore, in this paper, the effect of MVS PWM on the DC-link voltage of a three-level NPC converter is analyzed. Then a proportional-derivative (PD) controller for the DC-link voltage balance is designed from the DC-link modeling. In addition, feedforward compensation of the neutral point current is included in the proposed PD controller. The effectiveness of the proposed controller is verified by experimental results.

• 한국해양학회지:바다
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• 제11권2호
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• pp.92-96
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• 2006

비선형 주성분 분석은 기존에 널리 알려져 있는 주성분 분석기법과 유사한 다변수 데이터 분석을 위한 새로운 접근 방법이다. 비선형 주성분 분석은 AANN(Auto Associative Neural Network)으로 PCA와 마찬가지로 변수들 간에 존재하는 상관관계를 제거함으로써 고차의 다변수 데이터를 정보의 손실을 최소화하면서 최소 차원의 데이터로 변환하는 기법이다. AANN기반 센서 고장 검출 기법을 실제 방재시스템에 적용하여 봄으로써 센서 드리프트 등과 같은 센서 고장의 검출 및 유효한 센서 보정 성능을 확인하였다.

• 한국전자통신학회논문지
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• 제9권12호
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• pp.1337-1344
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• 2014

디지털 가입자회선과 같은 통신에서 반향 제거기의 목적은 수신 경로에서 하이브리드 회로에 의해 누출되는 전송 신호를 보상하는 것이다. 일반적으로 전이중 통신에서 사용되는 반향 제거기는 지엽적인 신호에 의해 동작되는 적응 시스템이며, 최소 평균 제곱 알고리듬으로 구현된 반향 제거기가 많이 사용되어 왔지만 적은 계산 양의 장점을 가지는 반면에 느린 수렴 성능을 보인다. 또한, 반향 제거기의 길이는 성능과 수렴속도에 직접적인 영향을 미치며, 긴 시간동안 변화하는 반향을 제거하기 위해서는 반향 제거기의 계수 개수가 커야 하는데, 이것은 적응 필터의 수렴 속도를 감소시킨다. 본 논문에서는 통신 채널에서의 반향 제거에 대한 새로운 방법을 제안한다. 제안한 방법은 순방향 알고리듬과 역방향 알고리듬의 가중 결합으로 구성된 양방향 최소 평균 제곱 알고리듬을 사용하여 반향 제거기의 최적의 계수를 계산한다. 마지막으로 수학적 해석 및 모의실험을 통해 제안한 반향 제거기가 계산 양의 증가가 없이 거의 동일한 계산 양으로 기존의 반향 제거기보다 수렴 속도가 빠르다는 사실을 확인하였다.

• Nuclear Engineering and Technology
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• 제53권7호
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• pp.2334-2340
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• 2021

Radioactive particle tracking (RPT) is a minimally invasive nuclear technique that tracks a radioactive particle inside a volume of interest by means of a mathematical location algorithm. During the past decades, many algorithms have been developed including ones based on artificial intelligence techniques. In this study, RPT technique is applied in a simulated test section that employs a simplified mixer filled with concrete, six scintillator detectors and a¹³⁷Cs radioactive particle emitting gamma rays of 662 keV. The test section was developed using MCNPX code, which is a mathematical code based on Monte Carlo simulation, and 3516 different radioactive particle positions (x,y,z) were simulated. Novelty of this paper is the use of a location algorithm based on a deep learning model, more specifically a 6-layers deep rectifier neural network (DRNN), in which hyperparameters were defined using a Bayesian optimization method. DRNN is a type of deep feedforward neural network that substitutes the usual sigmoid based activation functions, traditionally used in vanilla Multilayer Perceptron Networks, for rectified activation functions. Results show the great accuracy of the DRNN in a RPT tracking system. Root mean squared error for x, y and coordinates of the radioactive particle is, respectively, 0.03064, 0.02523 and 0.07653.

• Steel and Composite Structures
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• 제51권4호
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• pp.441-456
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• 2024

This paper aims to develop Machine Learning (ML) algorithms to predict the buckling resistance of cold-formed steel (CFS) channels with restrained flanges, widely used in typical CFS sheathed wall panels, and provide practical design tools for engineers. The effects of cross-sectional restraints were first evaluated on the elastic buckling behaviour of CFS channels subjected to pure axial compressive load or bending moment. Feedforward multi-layer Artificial Neural Networks (ANNs) were then trained on different datasets comprising CFS channels with various dimensions and properties, plate thicknesses, and restraining conditions on one or two flanges, while the elastic distortional buckling resistance of the elements were determined according to the Finite Strip Method (FSM). To develop less biased networks and ensure that every observation from the original dataset has the chance of appearing in the training and test set, a K-fold cross-validation technique was implemented. In addition, the hyperparameters of the ANNs were tuned using a grid search technique to provide ANNs with optimum performances. The results demonstrated that the trained ANNs were able to predict the elastic distortional buckling resistance of CFS flange-restrained elements with an average accuracy of 99% in terms of coefficient of determination. The developed models were then used to propose a simple ANN-based design formula for the prediction of the elastic distortional buckling stress of CFS flange-restrained elements. Finally, the proposed formula was further evaluated on a separate set of unseen data to ensure its accuracy for practical applications.