• Korean Journal of Remote Sensing
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• v.29 no.3
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• pp.275-291
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• 2013

Monitoring the global Gross Primary Pproduction (GPP) is relevant to understanding the global carbon cycle and evaluating the effects of interannual climate variation on food and fiber production. GPP, the flux of carbon into ecosystems via photosynthetic assimilation, is an important variable in the global carbon cycle and a key process in land surface-atmosphere interactions. The Moderate-resolution Imaging Spectroradiometer (MODIS) is one of the primary global monitoring sensors. MODIS GPP has some of the problems that have been proven in several studies. Therefore this study was to solve the regional mismatch that occurs when using the MODIS GPP global product over Korea. To solve this problem, we estimated each of the GPP component variables separately to improve the GPP estimates. We compared our GPP estimates with validation GPP data to assess their accuracy. For all sites, the correlation was close with high significance ($R^2=0.8164$, $RMSE=0.6126g{\cdot}C{\cdot}m^{-2}{\cdot}d^{-1}$, $bias=-0.0271g{\cdot}C{\cdot}m^{-2}{\cdot}d^{-1}$). We also compared our results to those of other models. The component variables tended to be either over- or under-estimated when compared to those in other studies over the Korean peninsula, although the estimated GPP was better. The results of this study will likely improve carbon cycle modeling by capturing finer patterns with an integrated method of remote sensing.

• Transactions on Electrical and Electronic Materials
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• v.13 no.6
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• pp.273-277
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• 2012

The vacuum drying process for drying of paper in current transformers was modeled with an aim to develop an understanding of the drying mechanism involved and also to predict the water collection rates. A molecular as well as macroscopic approach was adopted for the prediction of drying rate. Ficks law of diffusion was adopted for the prediction of drying rates at macroscopic levels. A steady state and dynamic mass transfer simulation was performed. The bulk diffusion coefficient was calculated using weight loss experiments. The accuracy of the solution was a strong function of the relation developed to determine the equilibrium moisture content. The actually observed diffusion constant was also important to predict the plant water removal rate. Thermo gravimetric studies helped in calculating the diffusion constant. In addition, simulation studies revealed the formation of perpetual moisture traps (loops) inside the CT. These loops can only be broken by changing the temperature or pressure of the system. The change in temperature or pressure changes the kinetic or potential energy of the effusing vapor resulting in breaking of the loop. The cycle was developed based on this mechanism. Additionally, simulation studies also revealed that the actual mechanism of moisture diffusion in CT's is by surface jumps initiated by surface diffusion balanced against the surrounding pressure. Every subsequent step in the cycle was to break such loops. The effect of change in drying time on the electrical properties of the insulation was also assessed. The measurement of capacitance at the rated voltage and one third of the rated voltage demonstrated that the capacitance change is within the acceptance limit. Hence, the new cycle does not affect the electrical performance of the CT.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.6
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• pp.35-41
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• 2022

Heavy snow damage can be prevented in advance with an appropriate security system. To develop the security system, we developed a model that predicts snow depth after a few hours when the snow depth is observed, and utilized it to calculate a failure probability with various types of greenhouses and observed snow depth data. We compared the Markov chain model and Bayesian long short-term memory models with varying input data. Markov chain model showed the worst performance, and the models that used only past snow depth data outperformed the models that used other weather data with snow depth (temperature, humidity, wind speed). Also, the models that utilized 1-hour past data outperformed the models that utilized 3-hour data and 6-hour data. Finally, the Bayesian LSTM model that uses 1-hour snow depth data was selected to predict snow depth. We compared the selected model and the shifting method, which uses present data as future data without prediction, and the model outperformed the shifting method when predicting data after 11-24 hours.

• Korea Trade Review
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• v.42 no.2
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• pp.77-96
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• 2017

This paper attempts to analyze the influences of Korean global retailer's service marketing mix on local customers' satisfaction and their loyalty behaviors. Based on a literature review, three hypotheses are putting forward. The data from 139 customers in Weihai, China were used to test these hypotheses. This paper used Structural Equation Modeling to identify the relationship among the service marketing mix, the customer satisfaction and the customer loyalty behaviors. According to the empirical analysis, this study showed satisfactory data-fit of the proposed model and supported two of the three hypotheses. The empirical results indicated that the service marketing mix factors except the promotion factor take significant effect on the local customer satisfaction, and this in turn have influence on the customer loyalty behaviors. The result shows that Korean global retailers will need to leverage service marketing mix strategically when entering China. Practical implications of these findings needs to be considered for the global retailer to establish an effective marketing strategy.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.289-300
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• 2003

In this paper, we introduce an identification method in Fuzzy Relation-based Fuzzy Neural Networks (FRFNN) through a hybrid identification algorithm. The proposed FRFNN modeling implement system structure and parameter identification in the efficient form of "If...., then... " statements, and exploit the theory of system optimization and fuzzy rules. The FRFNN modeling and identification environment realizes parameter identification through a synergistic usage of genetic optimization and complex search method. The hybrid identification algorithm is carried out by combining both genetic optimization and the improved complex method in order to guarantee both global optimization and local convergence. An aggregate objective function with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. The proposed model is experimented with using two nonlinear data. The obtained experimental results reveal that the proposed networks exhibit high accuracy and generalization capabilities in comparison to other models.er models.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.47-48
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• 2008

UML(Unified Modeling Language) is the most frequently used modeling language in the process of analysis, design, implementation and etc. The main reason of using UML is not only to help users to work visually but also to draw better communication among developers. In addition, UML is one of the global standards and supports MDA. In this paper embedded software development method for USN using UML is discussed To show how the development method is used, IEEE 802.15.4 radio module are programmed using UML software tool.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.317-324
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• 2004

Shape design optimization of shell structure is implemented on a basis of integrated framework of geometric modeling and finite element analysis which is constructed on the geometrically exact shell theory. This shell theory enables more accurate and robust analysis for complicated shell structures, and it fits for the nature of B-spline function which Is popular modeling scheme in CAD field. Shape of laminated composite shells is optimized through genetic algorithm and sequential linear programming, because there ire numerous optima for various configurations, constraints, and searching paths. Sequential adaptation of global and local optimization makes the process more efficient. Two different optimized results of laminated composite shell structures to minimize strain energy are shown for different layup sequence.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.157-160
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• 2000

This paper deals with the fuzzy modeling for the complex and uncertain system in which conventional and mathematical models may fail to give satisfactory results. mGA(messy Genetic Algorithm) has more effective and adaptive structure than sGA with respect to using changeable-length string and VEGA(Virus Evolution Genetic) Algorithm) can search the global and local optimal solution simultaneously with reverse transcription operator and transduction operator. Therefore in this paper, the optimal fuzzy model is obtained using Virus-messy Genetic Algorithm(Virus-mGA). In this method local information is exchanged in population so that population may sustain genetic divergence. To prove the surperioty of the proposed approach, we provide the numerical example.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.4
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• pp.499-506
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• 2012

In this paper, a dynamic modeling for the velocity and position information of a single frequency stand-alone GPS(Global Positioning System) receiver is described. In static condition, the position error dynamic model is identified as a first/second order transfer function, and the velocity error model is identified as a band-limited Gaussian white noise via non-parametric method of a PSD(Power Spectrum Density) estimation in continuous time domain. A Kalman filter is proposed considering both correlated/white measurements noise based on identified GPS error model. The performance of the proposed Kalman filtering method is verified via numerical simulation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.301-304
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• 2002

Powertrain simulation is important fur the analysis of a vehicle performance. Automotive powertrain has been considered as the unified system and should be remodeled, whenever a powertrain system is changed. In this study, a new method is proposed for the synthetic modeling for the automotive powertrain. Components are separated from the powertrain system and constructed the matrix through dynamic relationships. The dynamic equation of the total powertrain system can be driven from the combination of each component. In order to combine each component, the superposition method is modified for the powertrain composition.