• Korean Journal of Hydrosciences
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• v.4
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• pp.1-9
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• 1993

It is a major objective for the management and operation of water resources system to forecast streamflows. The applicability of artificial neural network model to hydrologic system is analyzed and the performance is compared by statistical method with observed. Multi-layered perception was used to model rainfall-runoff process at Pyung Chang River Basin in Korea. The neural network model has the function of learning the process which can be trained with the error backpropagation (EBP) algorithm in two phases; (1) learning phase permits to find the best parameters(weight matrix) between input and output. (2) adaptive phase use the EBP algorithm in order to learn from the provided data. The generalization results have been obtained on forecasting the daily and hourly streamflows by assuming them with the structure of ARMA model. The results show validities in applying to hydrologic forecasting system.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.420-423
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• 2001

In this paper, the Multi-FNNs(Fuzzy-Neural Networks) architecture is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNNs architecture uses simplified inference and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNNs according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNNs model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model we use the time series data for gas furnace and the NOx emission process data of gas turbine power plant.

• Computers and Concrete
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• v.9 no.3
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• pp.227-244
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• 2012

A theoretical model for shear strength evaluation of fibrous concrete beams reinforced with stirrups is proposed. The formulation is founded on the theory of plasticity and the stress field concepts, generalizing a known plastic model for calculating the bearing capacity of reinforced concrete beams, to the case of fibrous concrete. The beneficial effect of steel fibres is estimated taking into account the residual tensile strength of fibrous concrete, by modifying an analytical constitutive law which presents a plastic plateau as a post-peak branch. Around fifty results of experimental tests carried out on steel fibrous concrete beams available in the literature were collected, and a comparison of shear strength estimation provided by other semi-empirical models is performed, proving that the numerical values obtained with the proposed model are in very good agreement with the experimental results.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.10
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• pp.44-52
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• 1994

In this paper, we assume that the dynamics of DC motor and nonlinear load are unknown. We propose an inverse dynamic model of DC motor and nonlinear load using the artificial neural network and construck speed control system based on the proposed dynamic model. We also propose another dynamic model with speed prediction scheme using the artificial neural network that removes the undesirable time delay effect caused by the computation time during the real-time control. We suggest a dynamic model which has arbitrary number of speed arguments and is especially effective when the motor and load has large moment of inertia. Next, we suggest a controller that combine the neurocontrol and PID control with constant gain. We show that the proposed neurocontrol systems have capabilities of noise rejection and generalization to have good velocity tracking through computer simulations and experiments.

• The Korean Journal of Ceramics
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• v.4 no.3
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• pp.254-259
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• 1998

A kinetic model predicting the oxidation of carbon fiber reinforced glass matrix composites has been described. The weight loss of composites during oxidation implied that a gasification of carbon fiber takes place and the transport of reactants $(O_2)$ or product (CO or $CO_3$) in the glass matrix was partially the rate controlling step. The kinetic model in this study was based on the work of Sohn and Szekely which may be regarded as a generalization of numerous models in the gas-solid reaction system. A comparison of this model with experimental data is also presented.

• International Journal of Reliability and Applications
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• v.8 no.1
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• pp.53-66
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• 2007

This paper proposes a new non-homogeneous Poisson process software reliability growth model based on the coverage information. The new model incorporates the coverage information in the fault detection process by assuming that only the faults in the covered constructs are detectable. Since the coverage growth behavior depends on the testing strategy, the fault detection process is first modeled for the general testing strategy and then realized for the uniform testing. Finally the model for the uniform testing is empirically evaluated by applying it to real data sets.

• Journal of the Korea Safety Management & Science
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• v.15 no.1
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• pp.177-182
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• 2013

More and more medium and small enterprises do business across the world according to generalization of online shopping mall such as eBay and Amazon. But high export and import transportation cost make weak the price competitive power of medium and small enterprises products. Then this paper deals with the development of new business model which can reduce the transportation cost for global logistics through efficiently overcoming the constraints as length, size, and weight of product. We explain this model with application for company which export the automobile parts. We can expect the transportation cost reduction by 50%.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.3
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• pp.63-80
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• 2007

This study is a generalization of 'stable dynamics' recently suggested by Nesterov and de Palma[29]. Stable dynamics is a new model which describes and provides a stable state of congestion in urban transportation networks. In comparison with user equilibrium model that is common in analyzing transportation networks, stable dynamics requires few parameters and is coincident with intuitions and observations on the congestion. Therefore it is expected to be an useful analysis tool for transportation planners. An equilibrium in stable dynamics needs only maximum flow in each arc and Wardrop[33] Principle. In this study, we generalize the stable dynamics into the model with multiple traffic classes. We classify the traffic into the types of vehicle such as cars, buses and trucks. Driving behaviors classified by age, sex and income-level can also be classes. We develop an equilibrium with multiple traffic classes. We can find the equilibrium by solving the well-known network problem, multicommodity minimum cost network flow problem.

• The Journal of Sustainable Design and Educational Environment Research
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• v.11 no.3
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• pp.46-54
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• 2012

The accuracy of cost estimation at an early stage in school building project is one of the critical factors for successful completion. So various of techniques are developed to predict the construction cost accurately and expeditely. Among the techniques, Support Vector Machine(SVM) has an excellent ability for generalization performance. Therefore, the purpose of this study is to construct the prediction model for construction cost of educational building project using support vector machine technique. And to verify the accuracy of prediction model for construction cost. The performance data used in this study are 217 school building project cost which have been completed from 2004 to 2007 in Gyeonggi-Do, Korea. The result shows that average error rate was 7.48% for SVM prediction model. So using SVM model on predicting construction cost of educational building project will be a considerably effective way at the early project stage.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.1
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• pp.92-96
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• 2005

This paper deals with a batch processor model in which the batch processing times depend on the jobs assigned to the batch. Each job has a distinct processing time which is determined as not the exact value but the range from the lower limit to the upper, which makes it possible to group several jobs into the same batch. In point of this flexibility our model can be referred to as the generalization of the bum-in model in which the upper limit of each job is unbounded. The jobs to be scheduled may be available nonsimultaneously. Therefore they have different ready times. We develop the model to describe the problem situation and the heuristic methods to minimize the total tardiness. And our batching rule is compared with other dispatching ones.