• The Journal of Engineering Geology
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• v.25 no.1
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• pp.67-81
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• 2015

A FORTRAN program was developed to determine the optimal locations of multiple recharge wells in an aquifer with different arrangements of pumping wells. The simulated annealing algorithm was used to find optimal locations of two recharge wells which satisfied three objective functions. The model results show that locating two injection wells inside the cluster of pumping wells is efficient if the recovery rate only was taken into account. In contrast, placing injection wells to the side of the cluster is desirable if the simulation considers aggregate objective function. Therefore, installing an injection well on each side of the cluster seems to yield the maximum recovery rates for the existing pumping wells, and it yields similar increases in pumping rate for all wells in the cluster. The locations of recharge wells can be arranged in numerous configurations, because there are multiple near-optimal local minima or maxima. These results indicate that the simulated annealing can yield effective evaluations of the optimal locations of multiple recharge wells. In addition, the suggested aggregate objective function can be utilized as an appropriate multi-objective optimization.

• Journal of Soil and Groundwater Environment
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• v.19 no.3
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• pp.66-81
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• 2014

A Fortran program was developed to determine the optimal locations of an artificial recharge well. Three objective functions were considered: (1) maximizing the recovery rates, (2) maximizing the injection rates, and (3) minimizing the coefficient of variation of the increased pumping rates. We also suggested a new aggregate objective function which combined the first and the third objective functions. The model results showed that locating the injection well inside the cluster of pumping wells was desirable if either the recovery or the injection rate was taken into account. However, the injection well located outside the cluster evenly increased the pumping rates in existing pumping wells. Therefore, for clustered pumping wells, installing an injection well at the center or the upstream of the pumping wells seems beneficial. For linear arrangement of pumping wells parallel to the constant head boundary, locating the injection well in the upstream was recommended. On the contrary, in case of the linear arrangement perpendicular to the constant head boundary, the injection well installed on both sides of the central part of the pumping wells was preferable.

• International Journal of Highway Engineering
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• v.6 no.3 s.21
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• pp.79-90
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• 2004

Rutting on asphalt pavement surface is an important damage in most roadways in the world. Most of researches have developed prediction model for rutting on asphalt pavement as a function of physical properties of asphalt binder. But this study was devised to estimate rutting based on fundamental properties of asphalt mixture, not binder. Therefore this study objective is to estimate rutting based on volumetric properties, that is Air void, Void in mineral aggregate(VMA) and Void filled with asphalt(VFA), of asphalt mixture with various asphalt binders, aggregates and aggregate gradation. Results showed that it was possible to estimate rutting depth based on volumetric variables of asphalt mixture. In addition, VMA, the variable which is nor used In mix design in Korea, showed a significant correlation with rutting, It is recommended that VMA is adapted as a variable in domestic mix design. Also, It showed that VFA in the specification should be lowered at least 5% point since VFA was somewhat higher than optimum.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.339-349
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• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.824-826
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• 1999

In this paper, we design an Fuzzy-Neural Networks(FNN) by means of divisions of fuzzy input space with multi-input variables. Fuzzy input space of Yamakawa's FNN is divided by each separated input variable, but that of the proposed FNN is divided by mutually combined input variables. The membership functions of the proposed FNN use both triangular and gaussian membership types. The parameters such as apexes of membership functions, learning rates, momentum coefficients, weighting value, and slope are adjusted using genetic algorithms. Also, an aggregate objective function(performance index) with weighting value is utilized to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the data of sewage treatment process.

• Korean Journal of Mathematics
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• v.29 no.1
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• pp.137-167
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• 2021

Spherical fuzzy set (SFS) is also one of the fundamental concepts for address more uncertainties in decision problems than the existing structures of fuzzy sets, and thus its implementation was more substantial. The well-known sine trigonometric function maintains the periodicity and symmetry of the origin in nature and thus satisfies the expectations of the experts over the multi parameters. Taking this feature and the significance of the SFSs into the consideration, the main objective of the article is to describe some reliable sine trigonometric laws (ST L) for SFSs. Associated with these laws, we develop new average and geometric aggregation operators to aggregate the Spherical fuzzy numbers (SFNs). Then, we presented a group decision- making (DM) strategy to address the multi-attribute group decision making (MAGDM) problem using the developed aggregation operators. In order to verify the value of the defined operators, a MAGDM strategy is provided along with an application for the selection of laptop. Moreover, a comparative study is also performed to present the effectiveness of the developed approach.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.40-49
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• 2004

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional SOPNN. In order to generate the structurally optimized SOPNN, GA-based design procedure at each stage (layer) of SOPNN leads to the selection of preferred nodes (or PNs) with optimal parameters- such as the number of input variables, input variables, and the order of the polynomial-available within SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based SOPNN, the model is experimented with using two time series data (gas furnace and NOx emission process data of gas turbine power plant). A comparative analysis shows that the proposed GA-based SOPNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.423-434
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• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.

• International Journal of Highway Engineering
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• v.16 no.6
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• pp.121-128
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• 2014

PURPOSES : The objective of this study is to evaluate the tack-coating material's properties using the bitumen bond strength(BBS) test and damping test as function of changed curing times. In this study, bonding strength tests were performed according to the curing time of tack coating materials. METHODS : In order to investigate bonding characteristic of tack coating materials, the Pneumatic Adhesion tensile Testing Instrument(PATTI) device is used to measure the bond strength between the tack coating materials and aggregate substrate based on the AASHTO TP-91. Also, damping test as in situ test was used to determine an appropriate traffic openting time for construction vehicle. Four different tack-coating materials were used in this study. The BBS tests were performed a one hour curing and testing temperatures of $5^{\circ}C$, $15^{\circ}C$, and $25^{\circ}C$. Damping test was conducted at 30min, 60min, 90min, and 120 min of curing times with temperatures of $20^{\circ}C$ and $30^{\circ}C$. RESULTS and CONCLUSIONS : The BBS test results show various bond strength as function of tack coat materials. At the same testing condition, A tack coat material shows almost two times higher than D tack coat materials although both materials are satisfied the criteria of material's physical properties. Also, Dampting test results shows similar trend with BBS test result. The damping test result was significantly changed as function of tack coat materials. Based on this study, the tack coating material's curing time is very important. Therefore, both curing time and the bond strength's characteristic has to be considered in standard specification.

• Journal of the Korean Institute of Landscape Architecture
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• v.16 no.3
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• pp.1-6
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• 1989

The current linear programming model as for city park planning has the following intrinsic constraints. First of all, it cannot explicity consider choice behaviors of people. Secondly, the objective function of linear programming model cannot sufficiently intergrate satisfactions of people. In order to overcome these weak points of linear programming model, the following extensions have been made in this paper. First of all, bionominal and multinominal logit models based upon logit models, utility maximization of people have been constructed, Secondly, based upon logit models, social welfare function has been constructed in order to aggregate satisfactions of people. By doing this, intrinsic oonstraints of linear programming model have been successfully overcome. In the future research, empirical study of the model developed in this paper will be necessary. By doing this, the construction of optimal investment plan for city parks will be possible.