• 전자공학회논문지B
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• 제31B권8호
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• pp.219-226
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• 1994

Sinusoidal current tracking inverters have substantial advantages in high performance acdrive systems and various control strategies for the inverter have been proposed by several researchers. This paper develops a sinusoidal current tracking inverter with neural networks. The neural network are trained to follow a set of reference current waveforms by erro back propagation algorithm and the trained neural networks are applied to the current control. We compare neural networks method with conventional current control methods (fixed band and sinusiidal band hystersis methods) and simulation results are presented.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.433-439
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• 1998

In this paper, we present an approach to the structure identification based on the input space partition methods and to the parameter identification by hybrid learning method in neuro-fuzzy system. The structure identification can automatically estimate the number of membership function and fuzzy rule using grid partition, tree partition, scatter partition from numerical input-output data. And then the parameter identification is carried out by the hybrid learning scheme using back-propagation and least squares estimate. Finally, we sill show its usefulness for neuro-fuzzy modeling to truck backer-upper control.

• Nuclear Engineering and Technology
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• 제52권4호
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• pp.681-688
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• 2020

The telegrapher's theory was used to develop a new formulation for the neutron noise equation. Telegrapher's equation is supposed to demonstrate a more realistic approximation for neutron transport phenomena, especially in comparison to the diffusion theory. The physics behind such equation implies that the signal propagation speed is finite, instead of the infinite as in the case of ordinary diffusion. This paper presents the theory and results of the development of a new method for calculation of the neutron noise using the telegrapher's equation as its basis. In order to investigate the differences and strengths of the new method against the diffusion based neutron noise, a comparison was done between the behaviors of two methods. The neutron noise based on SN transport considered as a precision measuring point. The Green's function technique was used to calculate the neutron noise based on telegrapher's and diffusion methods as well as the transport. The amplitude and phase of Green's function associated with the properties of the medium and frequency of the noise source were obtained and their behavior was compared to the results of the transport. It was observed, the differences in some cases might be considerable. The effective speed of propagation for the noise perturbations were evaluated accordingly, resulting in considerable deviations in some cases.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2010년도 춘계 학술발표회
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• pp.198-207
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• 2010

Many methods and techniques have been developed to obtain the accurate design parameters in soft soils. In particular, several researchers suggest the techniques to get the void ratio for understanding the soil behavior. The objective of this paper verifies the accuracy of the proposed analytical solution for determining the void ratio based on the elastic wave velocities. The paper covers the theories of Wood, Biot, Gassmann and Foti proposed chronological order. The total theory represents the wave propagation in fully saturated medium. To verify the proposed analytical solution, the laboratory and field tests are carried out. After measuring the elastic wave, the void ratios are assessed using proposed equation. The volume based void ratios are also obtained for comparing with the estimated value by several equations. The values estimated by volume, Gassmann and Biot are show good similarity. However, the void ratios based on Wood and Foti methods have a slightly different trend. This study suggests that the theories of Biot and Gassmann may be a useful equation for assessing the void ratio using elastic wave velocities in the field.

• 전기전자학회논문지
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• 제10권1호
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• pp.69-74
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• 2006

이 논문은 Trellis Coded Modulation 시스템에서 궤한 채널 등화기의 성능 향상을 위한 효율적인 심볼 판정 알고리듬을 제안한다. 제안된 심벌 판정기는 Trellis 코드의 구조를 이용하여 심벌 에러율 을 향상시킨다. 예컨대 8-PAM시그널의 경우 20dB SNR에서 기존 강제 심볼 판정기의 에러율 $2.5{\times}10^{-2}$에서 $2{\times}10^{-5}$으로 향상되었다. 이런 판정기의 심벌 에러율 의 개선은 다중경로채널에서의 DFE의 심벌 에러율을 성능을 향상시키는데 본 논문의 시뮬레이션 결과 심벌 에러율 0.26에서 0.01 과 0.005 으로 개선되었음을 확인하였다.

• Nuclear Engineering and Technology
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• 제52권2호
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• pp.287-295
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• 2020

Group method of data handling (GMDH) is considered one of the earliest deep learning methods. Deep learning gained additional interest in today's applications due to its capability to handle complex and high dimensional problems. In this study, multi-layer GMDH networks are used to perform uncertainty quantification (UQ) and sensitivity analysis (SA) of nuclear reactor simulations. GMDH is utilized as a surrogate/metamodel to replace high fidelity computer models with cheap-to-evaluate surrogate models, which facilitate UQ and SA tasks (e.g. variance decomposition, uncertainty propagation, etc.). GMDH performance is validated through two UQ applications in reactor simulations: (1) low dimensional input space (two-phase flow in a reactor channel), and (2) high dimensional space (8-group homogenized cross-sections). In both applications, GMDH networks show very good performance with small mean absolute and squared errors as well as high accuracy in capturing the target variance. GMDH is utilized afterward to perform UQ tasks such as variance decomposition through Sobol indices, and GMDH-based uncertainty propagation with large number of samples. GMDH performance is also compared to other surrogates including Gaussian processes and polynomial chaos expansions. The comparison shows that GMDH has competitive performance with the other methods for the low dimensional problem, and reliable performance for the high dimensional problem.

• 한국위성정보통신학회논문지
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• 제8권1호
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• pp.77-82
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• 2013

최근 주목을 받고 있는 Particle Filtering은 실제 객체 추적에서 발생하는 비선형, 비 가우시안 분포를 가지는 상태 벡터의 사후확률을 추정하기 위한 Monte Carlo 시뮬레이션에 기반을 둔 추적 방법론이다. 우리는 본 논문에서 Particle Filtering을 이용한 객체 추적성능을 향상시킬 수 있는 두 가지 방법론을 제안한다. 첫 번째는 확률이 가장 낮은 샘플을 이전 프레임의 추정된 상태 벡터로 대치하는 피드백 방법론이고, 두 번째는 객체 확률 분포를 추정된 객체 후보영역에 역투영하여 신뢰구간을 구함으로써 추적 박스의 정확도를 향상시키는 방법이다. 또한, 실험을 통해 구한 추적 샘플의 진화 방정식을 제시하였다. 우리는 다양한 상황이 설정된 실험 데이터 셋을 구성하여 실험을 실시하여 제안한 방법론의 우수성을 입증하였다.

• Journal of Advanced Marine Engineering and Technology
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• 제26권6호
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• pp.695-704
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• 2002

Generally, induction motor controller requires rotor speed sensor for commutation and current control, but it increases cost and size of the motor. So in these days, various researches including speed sensorless vector control have been reported and some of them have been put to practical use. In this paper a new speed estimation method using neural networks is proposed. The optimal neural network structure was tracked down by trial and error, and it was found that the 8-16-1 neural network has given correct results for the instantaneous rotor speed. Supervised learning methods, through which the neural network is trained to learn the input/output pattern presented, are typically used. The back-propagation technique is used to adjust the neural network weights during training. The rotor speed is calculated by weights and eight inputs to the neural network. Also, the proposed method has advantages such as the independency on machine parameters, the insensitivity to the load condition, and the stability in the low speed operation.

• 한국통신학회논문지
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• 제22권8호
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• pp.1833-1841
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• 1997

집적광학에서 가장 기본적이고 필수적인 소자인 구형 유전체 도파로는 근사적인 해석방법에서부터 수치계산방법까지 여러가지 방법으로 연구되어왔다. 본 논문에서는 구형 유전체도파로의 해석을 위해 연속적인 섭동궤환방법을 이용하여 최적 등가도파로모델을 선택하고, 시뮬레이션을 통한 비교, 분석 결과, 섭동궤환방법을 이용해서 구한 전파상수가 다른 방법에 의해 얻어진 결과보다 더욱 정확한 값에 일치함으로써 최적의 근사치를 얻을 수 있음을 확인하였고, 또한 여기서 구한 전파 상수를 이용하여 여러 가지 집적광학 소자의 설계에 유용한 필드 특성을 표현하였다.

• 한국안전학회지
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• 제19권1호
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• pp.23-30
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• 2004

Modern production systems are very complex by request of automation, and failure modes that occur in thisautomatic system are very various and complex. The efficient fault diagnosis for these complex systems is essential for productivity loss prevention and cost saving. Traditional fault diagnostic system which perforns sequential fault diagnosis can cause catastrophic failure during diagnosis when fault propagation is very fast. This paper describes the Real-time Intelligent Multiple Fault Diagnosis System (RIMFDS). RIMFDS assesses current machine condition by using sensor signals. This system deals with multiple fault diagnosis, comprising of two main parts. One is a personal computer for remote signal generation and transmission and the other is a host system for multiple fault diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault diagnosis and graphic representation of the results. RIMFDS diagnoses multiple faults with fast fault propagation and complex physical phenomenon. The new system based on multiprocessing diagnoses by using Hierarchical Artificial Neural Network (HANN).