• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.2
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• pp.7-15
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• 2017

Recently, an electricity sensor system has been installed and operated to prevent failures and accidents by identifying whether a transformer is normal in advance of failure. This electricity sensor system is able to both measure and monitor the transformer's power and voltage remotely and send information to a manager when unusual operation is discovered. However, a battery is required to operate power detection devices, and battery systems need ongoing management such as regular replacement. In addition, at a maintenance cost, occasional human resources and worker safety problems arise. Accordingly, we apply a linear electromagnetic generator using vibration energy from a transformer for an electric sensor system's drive in this research and we conduct optimal design to maximize the linear electromagnetic generator's power. We consider design variables using the provided design method from Process Integration, Automation, and Optimization (PIAnO), which is common tool from process integration and design optimization (PIDO). In addition, we analyze the experiment point from the design of the experiments using "MAXWELL," which is a common electromagnet analysis program. We then create an approximate model and conduct accuracy verification. Finally, we determine the optimal model that generates the maximum power using the proven approximate kriging model and evolutionary optimization algorithm, which we then confirm via simulation.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.345-356
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• 2010

In this paper, we introduce the linear regression model taking the parametric spatial association structure into account and employ it to five-year averaged wind speed data measured at 460 meteorological monitoring stations in South Korea. From the prediction map obtained by the model with spatial association parameters, we can see that inland area has smaller wind speed than coastal regions. When comparing the spatial linear regression model with classical one by using one-leave-out cross-validation, the former outperforms the latter in terms of similarity between the observations and the corresponding predictions and coverage rate of 95% prediction intervals.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.3
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• pp.285-295
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• 2016

A contact strip shape of a high speed train pantograph system was optimized with CFD to increase the aerodynamic performance and stability of contact force, and the results were validated by a wind tunnel test. For design of the optimal contact strip shape, a Kriging model and genetic algorithm were used to ensure the global search of the optimal point and reduce the computational cost. To enhance the performance and robustness of the contact strip for high speed pantograph, the drag coefficient and the fluctuation of the lift coefficient along the angle of attack were selected as design objectives. Aerodynamic forces were measured by a load cell and HWA (Hot Wire Anemometer) was used to measure the Strouhal number of wake flow. PIV (Particle Image Velocimetry) was adopted to visualize the flow fields. The optimized contact strip shape was shown a lower drag with smaller fluctuation of vertical lift force than the general shaped contact strip. And the acoustic noise source strength of the optimized contact strip was also reduced. Finally, the reduction amount of drag and noise was assessed when the optimized contact strip was applied to three dimensional pantograph system.

• Journal of Urban Science
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• v.9 no.2
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• pp.31-44
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• 2020

This study aims to analyze the relations among greenbelt, air pollution empirically in order to assess the environmental effects of the greenbelt in the Seoul metropolitan area, objectively. For this purpose, this study conducts an empirical analysis of impacts of greenbelt on urban air pollution using a multiple-regression model. The major findings are summarized as follows. As a result of an empirical analysis of the impacts of greenbelt on air pollution, it is found that the characteristics of the city have impacts on air pollution concentration. It is found that the population and employment are the causes of increases in CO and NO2 concentrations, and the number of employees in the manufacturers has impacts on increases of O3 and SO2, while power plants have impacts on PM10, CO and NO2. Intersections have impacts on O3 and SO2, while the areas of the roads have impacts on CO and NO2. In addition, as for the spatial distribution of air pollutants, it is found that CO and NO2 concentrations are relatively higher in the center of the Seoul metropolitan area, while PM10, O3 and SO2 concentrations are relatively higher in the suburbs. It is found that air pollution concentration is low in greenbelt zone. In the greenbelt zone, PM10, CO and SO2 concentrations are low.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.445-454
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• 2017

The performance of spatial downscaling models depends on the quality of input coarse scale products. Thus, the impact of intrinsic errors contained in coarse scale satellite products on predictive performance should be properly assessed in parallel with the development of advanced downscaling models. Such an assessment is the main objective of this paper. Based on a synthetic satellite precipitation product at a coarse scale generated from rain gauge data, two synthetic precipitation products with different amounts of error were generated and used as inputs for spatial downscaling. Geographically weighted regression, which typically has very high explanatory power, was selected as the trend component estimation model, and area-to-point kriging was applied for residual correction in the spatial downscaling experiment. When errors in the coarse scale product were greater, the trend component estimates were much more susceptible to errors. But residual correction could reduce the impact of the erroneous trend component estimates, which improved the predictive performance. However, residual correction could not improve predictive performance significantly when substantial errors were contained in the input coarse scale data. Therefore, the development of advanced spatial downscaling models should be focused on correction of intrinsic errors in the coarse scale satellite product if a priori error information could be available, rather than on the application of advanced regression models with high explanatory power.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.1093-1107
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• 2008

In this study, we considered linear regression model with various spatial dependency structures in order to make more reliable prediction of precipitation in South Korea. The prediction approaches are based on semi-variogram models fitted by least-squares estimation method and restricted maximum likelihood estimation method. We validated some candidate models from the two different estimation methods in terms of cross-validation and comparison between predicted values and observed values measured at different locations.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.243-250
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• 2015

This paper proposes a novel skewed-type iron slot wedge that can improve both the cogging torque and the output power of a permanent magnet synchronous generator (PMSG). Generally the open slot structure is adopted in a PMSG due to its convenient winding work, but the high cogging torque is undesired. Firstly, an iron slot wedge was utilized to reduce the cogging torque of an open slot type PMSG. However, the output power of the machine decreased rapidly with this method. Thus, a proposed skewed type iron slot wedge is presented to improve the output power as well as the cogging torque as compared to the open slot type. Shape optimization of the skewed-type iron slot wedge is performed to simultaneously maximize the output power and reduce the cogging torque. The Kriging model based on the Halton sequence method and a genetic algorithm are used to optimize the design.

• Journal of Ship and Ocean Technology
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• v.8 no.2
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• pp.41-60
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• 2004

Ship design process requires lots of complicated analyses for determining a large number of design variables. Due to its complexity, the process is divided into several tractable designs or analysis problems. The interdependent relationship requires repetitive works. This paper employs collaborative optimization (CO), one of the multidisciplinary design optimization (MDO) techniques, for treating such complex relationship. CO guarantees disciplinary autonomy while maintaining interdisciplinary compatibility due to its bi-level optimization structure. However, the considerably increased computational time and the slow convergence have been reported as its drawbacks. This paper proposes the use of an approximation model in place of the disciplinary optimization in the system-level optimization. Neural network classification is employed as a classifier to determine whether a design point is feasible or not. Kriging is also combined with the classification to make up for the weakness that the classification cannot estimate the degree of infeasibility. For the purpose of enhancing the accuracy of a predicted optimum and reducing the required number of disciplinary optimizations, an approximation management framework is also employed in the system-level optimization.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.2
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• pp.35-41
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• 2009

The impacts of climate change on paddy irrigation requirements for Nakdong river basin in Korea have been analyzed. The HadCM3 model outputs for SRES A2 and B2 scenarios and International Water Management Institute $10'{\times}10'$ pixels observed data were used with kriging method. Maps showing the predicted spatial variations of changes in climate parameters and paddy irrigation requirements have been produced using the GIS. The results showed that the average growing season temperature was projected to increase by $2.2^{\circ}C$ (2050s A2), $0.0^{\circ}C$ (2050s B2), $3.7^{\circ}C$ (2080s A2) and $2.9^{\circ}C$ (2080s B2) from the baseline (1961-1990) value of $21{\circ}C$. The average growing season rainfall was projected to increase by 15.2% (2050s A2), 24.2% (2050s B2), 41.4% (2080s A2) and 16.7% (2080s B2) from the baseline value of 900 mm. Average volumetric irrigation demands were projected to decrease by 3.7% (2050s A2), 7.0% (2050s B2), 10.2% (2080s A2) and 1.4% (2080s B2) from the baseline value of $1.25{\times}10^9\;m^3$. These results can be used for the agricultural water resources development planning in the Nakdong river basin for the future.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.4
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• pp.527-535
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• 2019

Hanger in a rail-launched missile protrudes in general and causes to increase significant drag force. One method to avoid the significant increase of drag force is to apply fairings on the hanger. In this paper, sloping shaped fairing parameters of height, width, and length are optimized to minimize the drag force under subsonic speed region by examining three configurations of fairings : front-fairing only, rear-faring only, and the both front and rear fairing. We use Latin Hypercube Sampling method to determine the experimental points, and computational fluid dynamics with incompressible RANS solver was applied to acquire the data at sampling points. Then, we construct a meta model by kriging method. We find the best choice among three configurations examined : both front and rear fairing reduce the drag force by 63 % without the constraint of fairing mass, and front fairing reduced the drag force by 52 % with the constraint of hanger mass.