• 대한교통학회지
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• 제13권4호
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• pp.5-29
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• 1995

Advanced Traveler Inoformation Systems*ATIS) , as a subsystem of ITS influence the travel choices of dreivers by providing them with historical, real-time and predictive information to supprot travel decisions and consequently improves the speed and quality of travel. For thesuccessul accomplishment of ATIS, the time-dependent variations of traffic in a road network and travel times of vehicles during their journey must be predicted . The purpose of this study is to evaluate the past developments in the dynamic route choice models and to apply the instantaneous dynamic user optimal route choice model. recently formulated with flow propagation constraints by Ran, Boyce and LeBlanc, to the real transportation network of Seocho-Ku in Seoul. As input data for this application, the time-dependent travel rates are estimated and the link travel time function is derived. The modelis validated from three view points : the efficiency of model itself the ability to predict traffic volume and travel time on links, and the optimal traffic control.

• Journal of Advanced Marine Engineering and Technology
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• 제34권7호
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• pp.971-980
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• 2010

In this paper, an artificial neural network (ANN) was employed to build a predicting model for refrigerator structure. The predicting model includes three input variables of the plaque depth (D), width (W) and interval distance(S) on the inner wall. Finite element method was utilized to obtain the data, which would be necessary for the ANN training process. Finally, a genetic algorithm (GA) was applied to find the optimal parameters that leaded to the minimum inner case deformation under operating condition. The optimal combination found is the depth(D) of 2.63mm, the width(W) of 19.24mm and the interval distance(S) of 49.38mm which leaded to the smallest deformation of 1.88mm for the given refrigerator model.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.302-305
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• 1996

In this paper, an optimal identification method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzz-neural networks(FNNs) and parameters of membership function are tuned using genetic algorithm(GAs). For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activated sludge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The show that the proposed method can produce the intelligence model w th higher accuracy than other works achieved previously.

• ETRI Journal
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• 제26권6호
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• pp.665-668
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• 2004

The introduction of a mobile virtual network operator (MVNO) system is expected to increase consumer benefits, boost competition in the mobile market, utilize idle bandwidth, and expedite mobile-fixed line convergence and growth in the mobile Internet market. This research endeavors to study the optimal access charge for an MVNO system, which is expected to be introduced to the Korean mobile communications market. We found that the optimal access charge is higher in the interdependent model than in the independent model if demand for mobile phone service is based on a substitution relationship. We also found that the optimal access charge is higher than the marginal cost.

• 전자공학회논문지B
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• 제30B권8호
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• pp.54-63
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• 1993

This paper proposes a method for improving the performances of the neural network for optimization by an optimal estimation of initial states. The optimal initial state that leads to the global minimum is estimated by using the stochastic approximation. And then the update rule of Hopfield model, which is the high speed deterministic algorithm using the steepest descent rule, is applied to speed up the optimization. The proposed method has been applied to the tavelling salesman problems and an optimal task partition problems to evaluate the performances. The simulation results show that the convergence speed of the proposed method is higher than conventinal Hopfield model. Abe's method and Boltzmann machine with random initial neuron output setting, and the convergence rate to the global minimum is guaranteed with probability of 1. The proposed method gives better result as the problem size increases where it is more difficult for the randomized initial setting to give a good convergence.

• ETRI Journal
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• 제42권3호
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• pp.366-375
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• 2020

The network traffic prediction of a smart substation is key in strengthening its system security protection. To improve the performance of its traffic prediction, in this paper, we propose an improved gravitational search algorithm (IGSA), then introduce the IGSA into a wavelet neural network (WNN), iteratively optimize the initial connection weighting, scalability factor, and shift factor, and establish a smart substation network traffic prediction model based on the IGSA-WNN. A comparative analysis of the experimental results shows that the performance of the IGSA-WNN-based prediction model further improves the convergence velocity and prediction accuracy, and that the proposed model solves the deficiency issues of the original WNN, such as slow convergence velocity and ease of falling into a locally optimal solution; thus, it is a better smart substation network traffic prediction model.

• 한국수자원학회논문집
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• 제36권1호
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• pp.45-59
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• 2003

본 연구에서는 상수관의 개량사업을 보다 효율적으로 실시 할 수 있는 방법으로 국내 실정에 적합한 상수관로의 노후도 조사방법을 이용하여 매설된 관의 노후도를 예측 근거한 최적 개량 모형을 제시하였다. 노후도 예측 모형은 확률론적 신경망 이론을 바탕으로 관별 노후도 정도를 5개 등급으로 구분하며 산정된 관별 노후도 등급 및 관경을 바탕으로 최대 잔존수명을 산정하였다. 최적 개량 모형은 관의 유지보수, 갱생, 교체의 시기 및 비용을 산정하는 것으로 최단경로흐름을 응용한 정수계획법을 이용하여 제시하였다. 또한 개발된 최적 개량 모형은 보다 현실적인 모형을 구축하기 위해 예산제약 유무에 따라 2가지 구분하여 모형을 제시하였다. 따라서, 본 연구에서 제안된 모형은 상수관로의 관리를 위해 보다 정량적인 방법으로 활용할 수 있을 것이다.

• Structural Engineering and Mechanics
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• 제26권4호
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• pp.377-392
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• 2007

A discrete sliding mode control (SMC) method based on hybrid model of neural network and nominal model is proposed to reduce the vibration of flexible structures, which is a robust active controller developed by using a sliding manifold approach. Since the thick boundary layer will reduce the virtue of SMC, the multilayer feed-forward neural network is adopted to model the uncertainty part. The neural network is trained by Levenberg-Marquardt backpropagation. The design objective of the sliding mode surface is based on the quadratic optimal cost function. In course of running, the input signal of SMC come from the hybrid model of the nominal model and the neural network. The simulation shows that the proposed control scheme is very effective for large uncertainty systems.

• 스마트미디어저널
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• 제10권2호
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• pp.48-54
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• 2021

Sepsis is one of the leading causes of mortality globally, and it costs billions of dollars annually. However, treating septic patients is currently highly challenging, and more research is needed into a general treatment method for sepsis. Therefore, in this work, we propose a reinforcement learning method for learning the optimal treatment strategies for septic patients. We model the patient physiological time series data as the input for a deep recurrent Q-network that learns reliable treatment policies. We evaluate our model using an off-policy evaluation method, and the experimental results indicate that it outperforms the physicians' policy, reducing patient mortality up to 3.04%. Thus, our model can be used as a tool to reduce patient mortality by supporting clinicians in making dynamic decisions.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 추계종합학술대회 논문집
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• pp.471-474
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• 1998

In this paper, we consider the problem of optimal broadcasting in recursive circulants under multi-port communication model. Recursive circulant G(N, d) that is defined to be a circulant graph with N vertices and jumps of powers of d is a useful interconnection network from the viewpoint of network metrices. Our model assumes that a processor can transmit a message to $\alpha$ neighboring processors simultaneously where $\alpha$ is two or three. For the broadcasting problem, we introduce 3-trees and 4-trees. And then we show that 3-trees and 4-trees are minimum broadcast trees in 2-port model and 3-port model. Using the above results, we show that recursive circulants g(2m, 2) have optimum broadcasting time in 2-port model and 3-port model.