• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.2
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• pp.174-189
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• 2015

The objective of this study was to reconstruct spatial information using the results of the investigation and evaluation of the health of the living organisms, habitat, and water quality at the investigation points for the aquatic ecosystem health of the Nakdong River basin, to support the rational decision making of the aquatic ecosystem preservation and restoration policies of the Nakdong River basin using spatial analysis techniques, and to present efficient management methods. To analyze the aquatic ecosystem health of the Nakdong River basin, punctiform data were constructed based on the position information of each point with the aquatic ecosystem health investigation and evaluation results of 250 investigation sections. To apply the spatial analysis technique, the data need to be reconstructed into areal data. For this purpose, spatial influence and trends were analyzed using the Kriging interpolation(ArcGIS 10.1, Geostatistical Analysis), and were reconstructed into areal data. To analyze the spatial distribution characteristics of the Nakdong River basin health based on these analytical results, hotspot(Getis-Ord Gi, $G^*_i$), LISA(Local Indicator of Spatial Association), and standard deviational ellipse analyses were used. The hotspot analysis results showed that the hotspot basins of the biotic indices(TDI, BMI, FAI) were the Andong Dam upstream, Wangpicheon, and the Imha Dam basin, and that the health grades of their biotic indices were good. The coldspot basins were Nakdong River Namhae, the Nakdong River mouth, and the Suyeong River basin. The LISA analysis results showed that the exceptional areas were Gahwacheon, the Hapcheon Dam, and the Yeong River upstream basin. These areas had high bio-health indices, but their surrounding basins were low and required management for aquatic ecosystem health. The hotspot basins of the physicochemical factor(BOD) were the Nakdong River downstream basin, Suyeong River, Hoeya River, and the Nakdong River Namhae basin, whereas the coldspot basins were the upstream basins of the Nakdong River tributaries, including Andong Dam, Imha Dam, and Yeong River. The hotspots of the habitat and riverside environment factor(HRI) were different from the hotspots and coldspots of each factor in the LISA analysis results. In general, the habitat and riverside environment of the Nakdong River mainstream and tributaries, including the Nakdong river upstream, Andong Dam, Imha Dam, and the Hapcheon Dam basin, had good health. The coldspot basins of the habitat and riverside environment also showed low health indices of the biotic indices and physicochemical factors, thus requiring management of the habitat and riverside environment. As a result of the time-series analysis with a standard deviation ellipsoid, the areas with good aquatic ecosystem health of the organisms, habitat, and riverside environment showed a tendency to move northward, and the BOD results showed different directions and concentrations by the year of investigation. These aquatic ecosystem health analysis results can provide not only the health management information for each investigation spot but also information for managing the aquatic ecosystem in the catchment unit for the working research staff as well as for the water environment researchers in the future, based on spatial information.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.6
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• pp.653-663
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• 2012

The study considers the high maneuverability flight and path optimization is conducted to investigate the appropriate generation of the lift and thrust considering the angle of the stroke plane. The path optimization problem is defined according to the various purposes of the high maneuverability flight. The flying purposes are to maximize thrust force, lift force and both lift and thrust forces. The flapping motion of the airfoil is made by a combined sinusoidal plunging and pitching motion in each problem. The optimization process is carried out by using well-defined surrogate models. The surrogate model is determined by the results of two-dimensional computational fluid dynamics analysis. The Kriging method is used to make the surrogate model and a genetic algorithm is utilized to optimize the surrogate model. The optimization results show the flapping motions for the high maneuverable flight. The effects on the generation of lift and thrust forces are confirmed by analyzing the vortex.

• 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.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.4
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• pp.46-51
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• 2012

Injection pressure, an important factor in filling process, should be minimized to enhance injection molding quality. Injection pressure can be controlled by valve gate open timing. In this work, we decided the valve gate open timing to minimize the injection pressure. To solve this design problem, we integrated MAPS-3D (Mold Analysis and Plastic Solution-3Dimension), a commercial injection molding CAE tool, to PIAnO (Process Integration, Automation and Optimization), a commercial PIDO (Process Integration, and Design Optimization) tool using the file parsing method. In order to reduce computational cost, we performed an approximate optimization using meta-models that replaced expensive computer simulations. At first, we carried out DOE (Design of Experiments) using OLHD (Optimal Latin Hypercube Design) available in PIAnO. Then, we built Kriging models using the simulation results at the sampling points. Finally, we used micro GA (Genetic Algorithm) available in PIAnO. Using the proposed design approach, the injection pressure has been reduced by 13.7% compared to the initial one. This design result clearly shows the validity of the proposed design approach.

• Journal of Biosystems Engineering
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• v.39 no.4
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• pp.377-388
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• 2014

Purpose: Determining the spatial structure of data is important in understanding within-field variability for site-specific crop management. An understanding of the spatial structures present in the data may help illuminate interrelationships that are important in subsequent explanatory analyses, especially when site variables are correlated or are a combined response to multiple causative factors. Methods: In this study, correlation, principal component analysis, and single and nested variogram models were applied to soil electrical conductivity and chemical property data of two fields in central Missouri, USA. Results: Some variables that were highly correlated, or were strongly expressed in the same principal component, exhibited similar spatial ranges when fitted with a single variogram model. However, single variogram results were dependent on the active lag distance used, with short distances (30 m) required to fit short-range variability. Longer active lag distances only revealed long-range spatial components. Nested models generally yielded a better fit than single models for sensor-based conductivity data, where multiple scales of spatial structure were apparent. Gaussian-spherical nested models fit well to the data at both short (30 m) and long (300 m) active lag distances, generally capturing both short-range and long-range spatial components. As soil conductivity relates strongly to profile texture, we hypothesize that the short-range components may relate to the scale of erosion processes, while the long-range components are indicative of the scale of landscape morphology. Conclusion: In this study, we investigated the effect of changing active lag distance on the calculation of the range parameter. Future work investigating scale effects on other variogram parameters, including nugget and sill variances, may lead to better model selection and interpretation. Once this is achieved, separation of nested spatial components by factorial kriging may help to better define the correlations existing between spatial datasets.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• The KSFM Journal of Fluid Machinery
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• v.17 no.6
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• pp.52-58
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• 2014

In the present work, design optimization of film-cooling hole array on the pressure side of high pressure turbine nozzle was conducted. There are four rows of fan-shaped film cooling holes on the nozzle pressure side surface and each row has a straight array of holes in the spanwise direction for baseline model. For design optimization, hole distributions in streamwise and spanwise directions for three rows of holes except first row are parameterized as a 2nd-order shape function. Three-dimensional compressible RANS equations are used for flow and thermal analysis around the nozzle surface and optimization technique using Design of Experiment, Kriging surrogate model and Genetic Algorithm is used. The results shows that averaged adiabatic wall temperature at the whole nozzle surface decreases about 2.7% and averaged film cooling effectiveness at the pressure side of nozzle increased about 8.2%.

• Korean Journal of Ecology and Environment
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• v.53 no.2
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• pp.195-207
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• 2020

In Korea, specific thermal elements such as annual mean temperature (AMT) 13℃, 14℃, and Kira's coldness index (CI) -10℃, have been suggested about the northernmost distribution of the warm-temperate evergreen broad-leaved forest zone. We reviewed the relationship between three thermal elements and the actual distribution of evergreen broad-leaved woody plants or its communities. Thiessen and Kriging method using point-data calibrated by seasonal lapse rate according to altitude were utilized for the spatial distribution pattern analysis. Several phytoclimatic maps were also produced in order to compare different thermal values. We identified that the AMT 13℃ was the best thermal element to demarcate the northern limit of the warm-temperate forest zone. Its area was estimated ca. 20,334 ㎢ and larger than those of other thermal elements. We concluded that an indirectly fabricated index i.e. CI -10℃ is useless and it was enough for a direct value of AMT 13℃ to represent the northern-limit distribution of warm-temperate forest zone, at least in Korea. Further researches on the reciprocity between floristic regions and phytoclimate zones are raised.

• Journal of Korea Water Resources Association
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• v.33 no.S1
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• pp.327-332
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• 2000

최근 세계 각지에서는 엘리뇨 및 라니냐에 의한 기상변동에 의하여 막대한 인적, 경제적인 직간접적인 피해를 경험하여 왔다. 우리 나라의 기상에도 엘리뇨/라니냐의 영향이 시공간적으로 유의함이 최근 밝혀지고 있으며, 그에 따르는 엘리뇨/라니냐에 의한 영향의 정량적인 고려가 전국적인 수자원 장기 정책 및 관리에 필요함이 인식되어 왔다. 기상 이변 형상은 이미 실존의 현실이며, 이 중 엘리뇨/라니냐의 영향 또한 그러하다고 할 수 있다. 특히, 21세기는 물부족 및 물에 의한 재해의 가능성이 점차 증가될 것이라는 막연한 추측속에서 우리는 살고 있으나 실제적인 대책 및 연구에의 투자는 인색한 형편이다. 막상 닥쳤을 때만의, 당장의 아우성보다는 기초적인 바탕에서부터의 성실하고 결실을 맺을 수 있는 지속적인 연구가 수행되어야 한다. 왜냐하면, 미래는 현재의 연속이기 때문이다. 또한, 이의 연구는 기상, 수문, 수자원, 농업, 경제를 비롯한 여러 분야에서 다각적이고 연계적으로 이루어져야지 일방의 짧은 지식만으로 이루어 질 수는 없다. 본 연구에서는 엘리뇨/라니냐 영향 지수 산정 기법을 개발하고, 이를 이용하여 우리 나라에의 수자원에의 영향을 시공간적으로 정의하는데, 그 목적이 있다. 엘리뇨/라니냐 영향을 정의하기 위한 기법은 물리적인 기법과 통계적인 기법으로 크게 나뉠 수 있으나, 본 연구에서는 간략한 통게적인 기법을 이용하여 지수를 개발하였다. 이 지수는 엘리뇨/라니냐의 발생 강도뿐만 아니라 빈도를 동시에 고려할 수 있도록 하였다. 개발된 엘리뇨/라니냐의 영향 지수를 우선 우리나라 전역의 강수 기상 관측망 자료에 적용하였으며, 산정된 지수들을 공간적으로 Kriging 기법을 사용하여 공간 분포도(영향 지수도)를 작성하여 지역적인 영향 정도를 가시적으로 정의하였다.

• International Journal of Fluid Machinery and Systems
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• v.10 no.3
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• pp.240-253
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• 2017

Latin hypercube sampling is widely used design-of-experiment technique to select design points for simulation which are then used to construct a surrogate model. The exploration/exploitation properties of surrogate models depend on the size and distribution of design points in the chosen design space. The present study aimed at evaluating the performance characteristics of various surrogate models depending on the Latin hypercube sampling (LHS) procedure (sample size and spatial distribution) for a diverse set of optimization problems. The analysis was carried out for two types of problems: (1) thermal-fluid design problems (optimizations of convergent-divergent micromixer coupled with pulsatile flow and boot-shaped ribs), and (2) analytical test functions (six-hump camel back, Branin-Hoo, Hartman 3, and Hartman 6 functions). The three surrogate models, namely, response surface approximation, Kriging, and radial basis neural networks were tested. The important findings are illustrated using Box-plots. The surrogate models were analyzed in terms of global exploration (accuracy over the domain space) and local exploitation (ease of finding the global optimum point). Radial basis neural networks showed the best overall performance in global exploration characteristics as well as tendency to find the approximate optimal solution for the majority of tested problems. To build a surrogate model, it is recommended to use an initial sample size equal to 15 times the number of design variables. The study will provide useful guidelines on the effect of initial sample size and distribution on surrogate construction and subsequent optimization using LHS sampling plan.