• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.10a
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• pp.115-124
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• 2000

An efficient multi-level (EML) optimization algorithm using discrete variables of framed structures is proposed in this paper. For the efficiency of the proposed algorithm multi-level optimization techniques using a decomposition method that separates both system-level and element-level are incorporated in the algorithm In the system-level, to save the numerical efforts an efficient reanalysis technique through approximated structural responses such as moments and frequencies with respect to intermediate variables is proposed in the paper. Sensitivity analysis of dynamic structural response is executed by automatic differentiation (AD) that is a powerful technique for computing complex or implicit derivatives accurately and efficiently with minimal human effort. In the element-level, to use AISC W-sections a section search algorithm is introduced. The efficiency and robustness of the EML algorithm, compared with a conventional multi-level (CML) algorithm and single-level genetic algorithm is successfully demonstrated in the numerical examples.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.4
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• pp.26-34
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• 2003

In this paper, a novel technique is proposed to arbitrarily activate the nodal points in finite element model through the meshless approximation methods such as MLS(moving least squares method), and theoretical investigations are carried out including the consistency and boundeness of numerical solution to prove the validity of the proposed method. By using the proposed node activation technique, one can activate and handle only the concerned nodes as unknown variables among the large number of nodal points in the finite element model. Therefore, the proposed technique has a great potential in design and reanalysis procedure.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.599-604
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• 2002

Substructures position is considered as design parameter to obtain optimal structural changes to raise its dynamic characteristics. In conventional SDM (structural dynamics modification) method, the layout of modifying substructures position is first fixed and at that condition the structural optimization is performed by using the substructures size and/or material property as design parameters. But in this paper as a design variable substructures global translational and rotational position is treated. For effective structural modification the eigenvalue sensitivity with respect to that design parameter is derived based on measured frequency response function. The optimal structural modification is calculated by combining eigenvalue sensitivities and eigenvalue reanalysis technique iteratively. Numerical examples are presented to the case of beam stiffener optimization to raise the natural frequency of plate.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.255-262
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• 2002

In this paper, an improved multi-objective optimmum design method is proposed. And it is applied to steel frames under seismic loads. The multi-objective optimization problem is formulated with three optimality criteria, namely, minimum structural weight and maximum strain energy and stability. The Pareto curve can be obtained by performing the multi-objective optimization for multistory shear buildings. In order to efficiently solve the multi-objective optimization problem the decomposition method that separates both system-level and element-level is used. In addition, various techniques such as effective reanalysis technique with respect to intermediate variables and sensitivity analysis using an automatic differentiation (AD) we incorporated. Moreover, the relationship function among section properties induced from the profile is used in order to link system-level and element level. From the results of numerical investigation, it may be stated that the proposed method will lead to the more rational design compared with the conventional one.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.666-669
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• 2004

Substructures position is considered as design parameter to obtain optimal structural changes to raise its dynamic characteristics. In conventional SDM (structural dynamics modification) method, the layout of modifying substructures position is first fixed and at that condition the structural optimization is performed by using the substructures size and/or material property as design parameters. But in this paper as a design variable substructures global translational and rotational position is treated. For effective structural modification the eigenvalue sensitivity with respect to that design parameter is derived based on measured frequency response function. The optimal structural modification is calculated by combining eigenvalue sensitivities and eigenvalue reanalysis technique iteratively. Numerical examples are presented to the case of beam stiffener optimization to raise the natural frequency of plate.

• Journal of Korean Society of Steel Construction
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• v.12 no.5 s.48
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• pp.569-579
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• 2000

An improved multi-level (IML) optimization algorithm using automatic differentiation (AD) of framed structures is proposed in this paper. For the efficiency of the proposed algorithm, multi-level optimization techniques using a decomposition method that separates both system-level and element-level optimizations, that utilizes and an artificial constraint deletion technique, are incorporated in the algorithm. And also to save the numerical efforts, an efficient reanalysis technique through approximated structural responses such as moments and frequencies with respect to intermediate variables is proposed in the paper. Sensitivity analysis of dynamic structural response is executed by AD that is a powerful technique for computing complex or implicit derivatives accurately and efficiently with minimal human effort. The efficiency and robustness of the IML algorithm, compared with a plain multi-level (PML) algorithm, is successfully demonstrated in the numerical examples.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.57-81
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• 2019

Satellite images can be integrated into a crop model to strengthen the advantages of each technique for crop monitoring and to compensate for weaknesses of each other, which can be systematically applied for monitoring inaccessible croplands. The objective of this study was to outline the productivity of paddy rice based on simulation of the yield of all paddy fields in North Korea, using a grid crop model combined with optical satellite imagery. The grid GRAMI-rice model was used to simulate paddy rice yields for inaccessible North Korea based on the bidirectional reflectance distribution function-adjusted vegetation indices (VIs) and the solar insolation. VIs and solar insolation for the model simulation were obtained from the Geostationary Ocean Color Imager (GOCI) and the Meteorological Imager (MI) sensors of the Communication Ocean and Meteorological Satellite (COMS). Reanalysis data of air temperature were achieved from the Korea Local Analysis and Prediction System (KLAPS). Study results showed that the yields of paddy rice were reproduced with a statistically significant range of accuracy. The regional characteristics of crops for all of the sites in North Korea were successfully defined into four clusters through a spatial analysis using the K-means clustering approach. The current study has demonstrated the potential effectiveness of characterization of crop productivity based on incorporation of a crop model with satellite images, which is a proven consistent technique for monitoring of crop productivity in inaccessible regions.

• Journal of Korean Society of Steel Construction
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• v.14 no.6
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• pp.783-791
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• 2002

An improved formulation for multi-objective optimization was proposed. This formulation was applied to steel seismic loads. The multi-objective optimization problem was formulated with minimum structural weight, maximum strstability. The global criterion method was employed to find a rational solution closest to the ideal solution for the optimization problem using standard steel profile, To efficiently solve the optimization problem, the decomposition meth both system-level and element-level was used. In addition, various techniques including efficient reanalysis technique intermediate variables and sensitivity analysis using an automatic differentiation(AD) were incorporated. Moreover the reamong section properties fitted to the section profile used in order to link the system level and the element level. From numerical investigation, it could be stated that the proposed method will lead to the more rational design compared with one.

• Ocean and Polar Research
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• v.38 no.4
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• pp.259-270
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• 2016

Sea Surface Salinity (SSS) observations from the Aquarius satellite in the East Sea show large systematic biases mainly caused by the surrounding lands and Radio Frequency Interferences (RFI) along the descending orbits on which the satellite travels from the Asian continent to the East Sea. To develop a technique for correcting the systematic biases unique to the East Sea, the least square regression between in situ observations of salinity and the reanalyzed salinities by HYCOM is first performed. Then monthly mean reanalyzed salinities fitted to the in situ salinities are compared with monthly mean Aquarius salinities to calculate mean biases in $1^{\circ}{\times}1^{\circ}$ boxes. Mean biases in winter (December-March) are found to be considerably larger than those in other seasons possibly caused by the inadequate correction of surface roughness in the sea surrounded by the land, and thus the mean bias corrections are performed using two bias tables. Large negative biases are found in the area near the coast of Japan and in the areas with islands. In the northern East Sea, data sets using the ascending orbit only (SCIA) are chosen for correction because of large RFI errors on the descending orbit (SCID). Resulting mean biases between the reanalysis salinities fitted to in situ observations and the bias corrected Aquarius salinities are less than 0.2 psu in all areas. The corrected mean salinity distributions in March and September demonstrate marked improvements when compared with mean salinities from the World Ocean Atlas (WOA [2005-2012]). In September, salinity distributions based on the corrected Aquarius and on the WOA (2005-2012) show similar distributions of Changjiang Diluted Water (CDW) in the East Sea.

• Atmosphere
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• v.26 no.1
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• pp.35-45
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• 2016

Initialization of the global seasonal forecast system is as much important as the quality of the embedded climate model for the climate prediction in sub-seasonal time scale. Recent studies have emphasized the important role of soil moisture initialization, suggesting a significant increase in the prediction skill particularly in the mid-latitude land area where the influence of sea surface temperature in the tropics is less crucial and the potential predictability is supplemented by land-atmosphere interaction. This study developed a new soil moisture initialization method applicable to the KMA operational seasonal forecasting system. The method includes first the long-term integration of the offline land surface model driven by observed atmospheric forcing and precipitation. This soil moisture reanalysis is given for the initial state in the ensemble seasonal forecasts through a simple anomaly initialization technique to avoid the simulation drift caused by the systematic model bias. To evaluate the impact of the soil moisture initialization, two sets of long-term, 10-member ensemble experiment runs have been conducted for 1996~2009. As a result, the soil moisture initialization improves the prediction skill of surface air temperature significantly at the zero to one month forecast lead (up to ~60 days forecast lead), although the skill increase in precipitation is less significant. This study suggests that improvements of the prediction in the sub-seasonal timescale require the improvement in the quality of initial data as well as the adequate treatment of the model systematic bias.