• 한국정보통신학회논문지
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• 제7권6호
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• pp.1103-1108
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• 2003

본 논문에서는 2.4GHz 대역의 근거리 무선 통신에 사용되는 역-F형 내부 안테나의 설계 값에 따른 안테나 특성을 분석하였다. PCB 기판에 인쇄된 형태로 설계하여 안테나의 길이, 단락 스터브의 두께, 피드선과 단락 스터브 사이의 간격, 안테나와 접지면 사이의 간격, 안테나의 두께 및 기판의 두께와 기판의 유전율에 따른 특성 변화를 연구하였다. 설계값에 따른 특성변화 그래프로부터 설계값을 튜닝하여 최적의 안테나를 설계하였다. 설계된 안테나는 VSWR이 1.5이하인 주파수 대역폭이 6.3%, 이득 3dB 정도를 얻었다.

• 제어로봇시스템학회논문지
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• 제16권11호
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• pp.1029-1037
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• 2010

If an internal combustion engine is operated by consolidated control, the minimum fuel consumption is achieved and the demanded objectives are satisfied. For this, it is necessary that the engine is operated on the ideal operating line which satisfies minimum fuel consumption. In this context of view, there are many tries to achieve given object. However, the parameters in the internal combustion engines are variable and depend on the operating points. Therefore, it is necessary to cope with the uncertainties such that the optimal operating may be possible. From this point of view, this paper gives a controller design method and a robust stability condition for engine torque control which satisfies the given control performance and robust stability in the presence of physical parameter perturbation. Exactly, in this paper, we consider the robust stability problem of this 2DOF servosystem with nonlinear type uncertainty in the engine system, and a robust stability condition for the servosystem is shown. This result guarantees that if the plant uncertainty is in the permissible set defined by the given condition, then a gain tuning can be carried out to suppress the influence of the plant uncertainties.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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• pp.65-68
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• 2001

RBFN has some problem that because the basis function isnt orthogonal to each others the number of used basis function goes to big. In this reason, the Wavelet Neural Network which uses the orthogonal basis function in the hidden node appears. In this paper, we propose the composition method of the actual function in hidden layer with the scaling function which can represent the region by which the several wavelet can be represented. In this method, we can decrease the size of the network with the pure several wavelet function. In addition to, when we determine the parameters of the scaling function we can process rough approximation and then the network becomes more stable. The other wavelets can be determined by the global solutions which is suitable for the suggested problem using the genetic algorithm and also, we use the back-propagation algorithm in the learning of the weights. In this step, we approximate the target function with fine tuning level. The complex neural network suggested in this paper is a new structure and important simultaneously in the point of handling the determination problem in the wavelet initialization.

• Journal of Power Electronics
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• 제14권5호
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• pp.1008-1027
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• 2014

An electric scooter with a V-belt continuously variable transmission (CVT) driven by a permanent magnet synchronous motor (PMSM) has a lot of nonlinear and time-varying characteristics, and accurate dynamic models are difficult to establish for linear controller designs. A PMSM servo-drive electric scooter controlled by a novel hybrid modified recurrent Legendre neural network (NN) control system is proposed to solve difficulties of linear controllers under the occurrence of nonlinear load disturbances and parameters variations. Firstly, the system structure of a V-belt CVT driven electric scooter using a PMSM servo drive is established. Secondly, the novel hybrid modified recurrent Legendre NN control system, which consists of an inspector control, a modified recurrent Legendre NN control with an adaptation law, and a recouped control with an estimation law, is proposed to improve its performance. Moreover, the on-line parameter tuning method of the modified recurrent Legendre NN is derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, two optimal learning rates for the modified recurrent Legendre NN are derived to speed up the parameter convergence. Finally, comparative studies are carried out to show the effectiveness of the proposed control scheme through experimental results.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.297-299
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• 2021

치밀오일 미래 생산성 예측은 잔류오일 회수량 및 저류층 거동 분석을 위해 중요한 작업이다. 일반적으로 석유공학적 관점에서 감퇴곡선법을 이용하여 생산성 예측이 이루어지는데, 최근에는 데이터기반의 머신러닝 기법을 이용한 연구도 수행되고 있다. 본 연구에서는 딥러닝 기반 순환신경망과 LSTM, GRU 알고리즘을 이용하여 미래 생산량 예측을 위한 효과적인 모델을 제안하고자 한다. 입력변수로는 치밀오일 생산 시 산출되는 오일, 가스, 물과 이와 더불어 다양한 군집분석을 통해 산출된 표준곡선이 주요 매개변수이고, 출력변수는 월별 오일 생산량이다. 기존의 경험적 모델인 감퇴곡선법과 순환신경망 모델들을 비교하였으며, 모델의 예측성능을 향상시키기 위해 하이퍼파라미터 튜닝을 통해 최적 모델을 도출하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(3)
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• pp.25-28
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• 2001

RBFN has some problem that because the basis function isn't orthogonal to each others the number of used basis function goes to big. In this reason, the Wavelet Neural Network which uses the orthogonal basis function in the hidden node appears. In this paper, we propose the composition method of the actual function in hidden layer with the scaling function which can represent the region by which the several wavelet can be represented. In this method, we can decrease the size of the network with the pure several wavelet function. In addition to, when we determine the parameters of the scaling function we can process rough approximation and then the network becomes more stable. The other wavelets can be determined by the global solutions which is suitable for the suggested problem using the genetic algorithm and also, we use the back-propagation algorithm in the learning of the weights. In this step, we approximate the target function with fine tuning level. The complex neural network suggested In this paper is a new structure and important simultaneously in the point of handling the determination problem in the wavelet initialization.

• Journal of Electrical Engineering and Technology
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• 제12권5호
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• pp.1821-1834
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• 2017

Medium voltage control applications due to obtain better output voltage and reduced electro-magnetic interference multi level inverter is used. In closed loop control with inverter, the PI controller does not operate satisfactorily when the operating point changes. This paper presents the performance of Co-Efficient diagram PI controller based indirect vector controlled induction motor drive fed from three-level inverter under different operating conditions (dynamic and steady state). The proposed Co-Efficient diagram PI controller based three level inverter significantly reduces the torque ripple compared to that of conventional PI controller. The performance of the indirect vector controlled induction motor drive has been simulated at different operating conditions. For three-level inverter control, a simplified space vector modulation technique is implemented, which reduces the coordinate transformations complications in the algorithms. The performance parameters, torque ripple contents and THD of induction motor drive with three-level inverter is compared under different operating conditions using CDM-PI and conventional PI controllers.

• Communications for Statistical Applications and Methods
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• 제29권3호
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• pp.287-299
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• 2022

In radiation epidemiology, the excess relative risk (ERR) model is used to determine the dose-response relationship. In general, the dose-response relationship for the ERR model is assumed to be linear, linear-quadratic, linear-threshold, quadratic, and so on. However, since none of these functions dominate other functions for expressing the dose-response relationship, a Bayesian semiparametric method using splines has recently been proposed. Thus, we improve the Bayesian semiparametric method for the selection of the tuning parameters for splines as the number and location of knots using a Bayesian knot selection method. Equally spaced knots cannot capture the characteristic of radiation exposed dose distribution which is highly skewed in general. Therefore, we propose a nonparametric Bayesian knot selection method based on a Dirichlet process mixture model. Inference of the spline coefficients after obtaining the number and location of knots is performed in the Bayesian framework. We apply this approach to the life span study cohort data from the radiation effects research foundation in Japan, and the results illustrate that the proposed method provides competitive curve estimates for the dose-response curve and relatively stable credible intervals for the curve.

• Smart Structures and Systems
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• 제29권5호
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• pp.677-691
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• 2022

Water cycle algorithm (WCA) has been a very effective optimization technique for complex engineering problems. This study employs the WCA for simultaneous prediction of heating load (L_H) and cooling load (L_C) in residential buildings. This algorithm is responsible for optimally tuning a neural network (NN). Utilizing 614 records, the behavior of the L_H and L_C is explored and the captured knowledge is then used to predict for 154 unanalyzed building conditions. Since the WCA is a population-based algorithm, different numbers of the searching agents were tested to find the most optimum configuration. It was observed that the best solution is discovered by 500 agents. A comparison with five newly-developed benchmark optimizers, namely equilibrium optimizer (EO), multi-tracker optimization algorithm (MTOA), slime mould algorithm (SMA), multi-verse optimizer (MVO), and electromagnetic field optimization (EFO) revealed that the WCANN predicts the desired parameters with considerably larger accuracy. Obtained root mean square errors (1.4866, 2.1296, 2.8279, 2.5727, 2.5337, and 2.3029 for the L_H and 2.1767, 2.6459, 3.1821, 2.9732, 2.9616, and 2.6890 for the L_C) indicated that the most reliable prediction was presented by the proposed model. The EFONN, however, provided a more time-effective solution. Lastly, an explicit predictive formula was elicited from the WCANN.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.511-514
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• 2002

In may automated systems such as manufacturing systems and process plants, an industrial network or fieldbus is a very important component for the exchange of various and sometimes crucial information. Some of the information has a tendency to rapidly lose its value as time elapses after its creation. Such information or data is called real-time data that includes sensor values and control commands. In order to deliver these data in time, the fieldbus network should be tailored to have short delay with respect to the individual time limit of various data. Fine-tuning the network for a given traffic requires the knowledge on the relationship between the protocol parameters such as timer values and the performance measure such as network delay. This paper presents a mathematical performance model to calculate communication delays of the Profibus FMS network when the timer value TTR and the traffic characteristics are given. The results of this model is compared to those from experiments to assess the model's validity.