• Proceedings of the Korean Nuclear Society Conference
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• 1997.05b
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• pp.237-243
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• 1997

The ultimate object of our study is to minimize the release of radioactive material into the environment and to maximize the treatable amount of the generated wastes. In planning the practical operation of the system, however, the operating cost, Process economics and technical flexibility must also be considered. For dealing with these multiple criteria decision making Problems, we used a foal programming which is a kind of multi-objective linear programming. This method requires the decision maker to set goals for each objective that one wishes to attain.

• Coupled systems mechanics
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• v.11 no.1
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• pp.55-78
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• 2022

The main aim of this paper is the identification of the model parameters for the constitutive model of concrete and concrete-like materials capable of representing full set of 3D failure mechanisms under various stress states. Identification procedure is performed taking into account multi-scale character of concrete as a structural material. In that sense, macro-scale model is used as a model on which the identification procedure is based, while multi-scale model which assume strong coupling between coarse and fine scale is used for numerical simulation of experimental results. Since concrete possess a few clearly distinguished phases in process of deformation until failure, macro-scale model contains practically all important ingredients to include both bulk dissipation and surface dissipation. On the other side, multi-scale model consisted of an assembly micro-scale elements perfectly fitted into macro-scale elements domain describes localized failure through the implementation of embedded strong discontinuity. This corresponds to surface dissipation in macro-scale model which is described by practically the same approach. Identification procedure is divided into three completely separate stages to utilize the fact that all material parameters of macro-scale model have clear physical interpretation. In this way, computational cost is significantly reduced as solving three simpler identification steps in a batch form is much more efficient than the dealing with the full-scale problem. Since complexity of identification procedure primarily depends on the choice of either experimental or numerical setup, several numerical examples capable of representing both homogeneous and heterogeneous stress state are performed to illustrate performance of the proposed methodology.

• Earthquakes and Structures
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• v.14 no.1
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• pp.1-10
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• 2018

Seismic retrofitting of existing buildings and design of earth-quake resistant buildings are important issues associated with earthquake-prone zones. Use of metallic-yielding dampers as an energy dissipation system is an acceptable method for controlling damages in structures and improving their seismic performance. In this study, the optimal distribution of dampers for reducing the seismic response of steel frames with multi-degrees freedom is presented utilizing the uniform distribution of deformations. This has been done in a way that, the final configuration of dampers in the frames lead to minimum weight while satisfying the performance criteria. It is shown that such a structure has an optimum seismic performance, in which the maximum structure capacity is used. Then the genetic algorithm which is an evolutionary optimization method is used for optimal arrangement of the steel dampers in the structure. In continuation for specifying the optimal accurate response, the local search algorithm based on the gradient concept has been selected. In this research the introduced optimization methods are used for optimal retrofitting in the moment-resisting frame with inelastic behavior and initial weakness in design. Ultimately the optimal configuration of dampers over the height of building specified and by comparing the results of the uniform deformation method with those of the genetic algorithm, the validity of the uniform deformation method in terms of accuracy, Time Speed Optimization and the simplicity of the theory have been proven.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.395-408
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• 2020

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.1
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• pp.165-172
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• 2015

The appropriateness of railway route design is generally evaluated by the future value corresponding to the travel demand or benefit-cost analysis. These methods may have the limitation for the reasons that all the design alternatives cannot be considered, and the differentiation between the alternatives may not be significant because the alternatives are based on the strict basic scheme such as the design criteria. In addition, the cost varies by the design elements. In this study, all the design alternatives are considered with the automatized tool and the design criteria, and evaluated with the multi-criterion decision making method. The weight for each design element with the analytic hierarchical process may be helpful to derive more efficient railway alignment.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.13 no.10
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• pp.785-790
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• 2003

This paper presents an approach to optimally design the air-hearing surface (ABS) of the optical flying head for near-field recording technology (NFR) NFR is an optical recording technology using very small beam spot size by overcoming the limit of beam diffraction. One of the most important problems in NFR Is a head disk interface (HDI) issue over the recording band during the operation. A multi-criteria optimization problem is formulated to enhance the flying performances over the entire recording band during the steady state. The optimal solution of the slider, whose target flying height is 50 nm, is automatically obtained. The flying height during the steady state operation becomes closer to the target values than those for the Initial one. The pitch and roll angles are also kept within suitable ranges over the recording band. Especially. all of the all-hearing stiffness are drastically increased by the optimized geometry of the air hearing surface.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3027-3033
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• 2022

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.

• Structural Engineering and Mechanics
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• v.89 no.2
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• pp.213-223
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• 2024

This paper proposes a composite design of fuzzy adaptive control scheme based on TMD RC structural system and the gain of two-dimensional fuzzy control is controlled by parameters. Monitoring and learning in LMI then produces performance indicators with a weighting matrix as a function of cost. It allows to control the trade-off between the two efficiencies by adjusting the appropriate weighting matrix. The two-dimensional Boost control model is equivalent to the LMI-constrained multi-objective optimization problem under dual performance criteria. By using the proposed intelligent control model, the fuzzy nonlinear criterion is satisfied. Therefore, the data connection can be further extended. Evaluation of controller performance the proposed controller is compared with other control techniques. This ensures good performance of the control routines used for position and trajectory control in the presence of model uncertainties and external influences. Quantitative verification of the effectiveness of monitoring and control. The purpose of this article is to ensure access to adequate, safe and affordable housing and basic services. Therefore, it is assumed that this goal will be achieved in the near future through the continuous development of artificial intelligence and control theory.

• Journal of Korea Water Resources Association
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• v.30 no.2
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• pp.107-118
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• 1997

The Upper Fenhe Reservoir System studied by KOWACO to supply water to Taiyuan City, capital of Shanxi Province in China, is a very complicated one. Many reservoirs will be connected serially and it will be operated as a multi-purpose and multi-criteria system because several objectives and appraisal functions are taken into account regarding system operation. For reservoirs in the system, the critical system operation objectives are to minimize water shortage and reservoir sediment. Furthermore the reservoir system will be jointed with a large-scale pumping system, namely Yellow River Diversion Project. The water development cost in the Yellow River Diversion Project is much higher than that of reservoir system, and around the year 2020 the diversion volume will be twice of the surface water available in the Upper Fenhe Basin. In this study, an optimization technique for connecting the system of reservoirs and pumping station was developed to solve a conjunctive low River Diversion Project. The developed scheme includes a suggestion on the combining methodology of real reservoir system and pumping system using imaginary reservoir concept for the Yellow River Diversion Project, and practical examples to the minimization problem of the Yellow River diversion satisfying other reservoir operation objectives.

• Journal of Korea Water Resources Association
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• v.50 no.3
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• pp.191-200
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• 2017

A robust parameter set (ROPS) selection framework for an unsteady flow model was developed by combining Pareto optimums obtained by outcomes of model calibration using multi-site observations with the minimax regret approach (MRA). The multi-site calibration problem which is a multi-objective problem was solved by using an aggregation approach which aggregates the weighted criteria related to different sites into one measure, and then performs a large number of individual optimization runs with different weight combinations to obtain Pareto solutions. Roughness parameter structure which can describe the variation of Manning's n with discharges and sub-reaches was proposed and the related coefficients were optimized as model parameters. By applying the MRA which is a decision criterion, the Pareto solutions were ranked based on the obtained regrets related to each Pareto solution, and the top-rated one due to the lowest aggregated regrets of both calibration and validation was determined as the only ROPS. It was found that the determination of variable roughness and the corresponding standardized RMSEs at the two gauging stations varies considerably depending on the combinations of weights on the two sites. This method can provide the robust parameter set for the multi-site calibration problems in hydrologic and hydraulic models.