• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.709-720
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• 2018

In order to optimize the evaluation of biomass in crop monitoring, accurate and timely data of the crop-field are required. Evaluating above-ground biomass helps to monitor crop vitality and to predict yield. Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study reports on the development of remote sensing techniques for evaluating the biomass of winter crop. Specific objective was to develop statistical models for estimating the dry weight of barley and wheat using a Excess Green index ($E{\times}G$) based Vegetation Fraction (VF) and a Crop Surface Model (CSM) based Plant Height (PH) value. As a result, the multiple linear regression equations consisting of three independent variables (VF, PH, and $VF{\times}PH$) and above-ground dry weight provided good fits with coefficients of determination ($R^2$) ranging from 0.86 to 0.99 with 5 cultivars. In the case of the barley, the coefficient of determination was 0.91 and the root mean squared error of measurement was $102.09g/m^2$. And for the wheat, the coefficient of determination was 0.90 and the root mean squared error of measurement was $110.87g/m^2$. Therefore, it will be possible to evaluate the biomass of winter crop through the UAV image for the crop growth monitoring.

• Clean Technology
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• v.19 no.3
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• pp.306-312
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• 2013

We investigated the effects of the concentration of carbon dioxide on the char-$CO_2$ gasification reaction under isothermal conditions of $850^{\circ}C$ using the Drayton coal. Potassium carbonate was used to improve the low-temperature gasification reactivity. The enhancement of carbon dioxide concentration increased the gasification rate of char, while gasification rate reached a saturated value at the concentration of 70%. The best $CO_2$ concentration for gasification is determined to be 70%. We compared the shrinking core model (SCM), volumetric reaction model (VRM) and modified volumetric reaction model (MVRM) of the gas-solid reaction models. The correlation coefficient values, by linear regression, of SCM are higher than that of VRM at low concentration. While the correlation coefficients values of VRM are higher than that of SCM at high concentration. The correlation coefficient values of MVRM are the highest than other models at all concentration.

• Journal of the Korea Concrete Institute
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• v.24 no.2
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• pp.195-204
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• 2012

In the construction of high-rise buildings, bent re-bars are manually straightened to connect slabs to core-walls, which are usually cast before floor structures. During cold bending and straightening of re-bars, plastic deformation causing work hardening, Bauschinger effect and aging hardening is unavoidable. Tensile tests of coldly bent and straightened re-bars were conducted with test parameters of grade, diameter, and bend radius of re-bars as well as age between bending and straightening. Test results showed that proportional limits were lower and strain hardening occurred without yield plateaus. Inside and outside of re-bars with compression and tension deformations, respectively, during bending showed lower yield points due to Bauschinger effect and no yield plateaus due to work hardening, respectively. When re-bar grade was higher, yield point became significantly lower where Grade 400 re-bars had yield strengths lower than specified yield strength of 400 MPa. Because the surface of re-bar has higher strength than the core of re-bar, Bauschinger effect was more obvious for higher-grade re-bars. When age between bending and straightening was greater, yield strength increased and elongation decreased (i.e. embrittlement occurs). Using measured data, stress-strain relationship for straightened re-bars was developed based on Ramberg-Osgood model, which can be used to evaluate stiffness of joints when straightened re-bars are applied.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.36-51
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• 2012

The demand for a climatological dataset with a regular spaced grid is increasing in diverse fields such as ecological and hydrological modeling as well as regional climate impact studies. PRISM(Precipitation-Elevation Regressions on Independent Slopes Model) is a useful method to estimate high-altitude precipitation. However, it is not well discussed over the optimization of PRISM parameters and DEM(Digital Elevation Model) resolution in South Korea. This study developed the PRISM and then optimized parameters of the model and DEM resolution for producing a gridded annual average precipitation data of South Korea with 1km spatial resolution during the period 2000-2005. SCE-UA (Shuffled Complex Evolution-University of Arizona) method employed for the optimization. In addition, sensitivity analysis investigates the change in the model output with respect to the parameter and the DEM spatial resolution variations. The study result shows that maximum radius within which station search will be conducted is 67km. Minimum radius within which all stations are included is 31km. Minimum number of stations required for cell precipitation and elevation regression calculation is four. Optimizing DEM resolution is $1{\times}1km$. This study also shows that the PRISM output very sensitive to DEM spatial resolution variations. This study contributes to improving the accuracy of PRISM technique as it applies to South Korea.

• Korean journal of applied entomology
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• v.61 no.4
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• pp.577-590
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• 2022

Ostrinia scapulalis is one of important pests in leguminous crops, especially red bean. In order to understand the biological characteristics of the insect, we investigated the effects of temperature on development of each life stage, adult longevity and fecundity of O. scapulalis at eleven constant temperatures of 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, and 36℃. Eggs and larvae successfully developed next life stage at most temperature subjected except 7, 10 and 13℃. The developmental period of egg, larva and pupa decreased as temperature increased. Lower and higher threshold temperature (TL and TH) were calculated by the Lobry-Rosso-Flandrois (LRF) and Sharpe-Schoolfield-Ikemoto (SSI) models. The lower developmental threshold (LDT) and thermal constant (K) from egg hatching to adult emergence of O. scapulalis were estimated by linear regression as 13.5℃ and 384.5DD, respectively. TL and TH from egg hatching to adult emergence using SSI model were 19.4℃ and 39.8℃. Thermal windows, i.e., the range in temperature between the minimum and maximum rate of development, of O. scapulalis was 20.4℃. Adults produced viable eggs at the temperature range between 16℃ and 34℃, and showed a maximum number, ca. 416 offsprings, at 25℃. Adult models including aging rate, age-specific survival rate, age-specific cumulative oviposition, and temperature-dependent fecundity were constructed, using the temperature-dependent adult traits. Temperature-dependent development models and adult oviposition models will be useful components to understand the population dynamics of O. scapulalis and will be expected using a basic data for establishing the strategy of integrated pest management in leguminous crops.

• Spatial Information Research
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• v.22 no.1
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• pp.55-63
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• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of the Korea Concrete Institute
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• v.18 no.5 s.95
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• pp.611-620
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• 2006

In the shear failure mechanism of a beam, beam and arch actions always exist simultaneously. According to the shear span to depth ratio, the proportion between these two actions is varied and the contribution of these actions to shear capacity is changed. Moreover, the current codes provide recommendations based on experimental results of normal strength concrete, so the application range of concrete strength must be extended. Based on this mechanism and new requirement, a simplified analytical equation for shear capacity prediction of reinforced high strength concrete beams without stirrups is proposed. To reflect the change in the contribution between these actions, stress variation in the longitudinal reinforcement along the span is considered by use of the Jenq and Shah Model. Dowel action with horizontal splitting failure and shear friction between cracks are also taken into account. ize effect is included to derive a more precise equation. Regression analysis is performed to determine each variable and simplify the equation. And, the formula derived from theoretical approaches is evaluated by comparison with numerous experimental data, which are in broad range of concrete strength(especially in high strength concrete), shear span to depth ratio, geometrical size and longitudinal steel ratio. It is shown that the proposed equation is more accurate and simpler than other empirical equations, so a wide range of a/d can be considered in one equation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.227-237
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• 2020

This study was designed to examine whether the self-directed learning method could improve self-directed learning readiness and the effects of academic achievement level. Self-directed learning readiness was investigated among 63 first-year Medical Terminology undergraduates in the C area. A repeat measurement variance analysis of the general linear model was conducted to evaluate the effects of improving self-directed learning readiness according to the general characteristics and level of academic achievement, while a regression analysis was performed to identify the factors affecting self-directed learning readiness. Self-directed learning readiness increased from 177.3 to 180.8 for those under 18 years of age, and 192.9 to 196.5 for those over 19 years of age (p<0.05). After the team activity, the overall self-directed learning readiness was improved, and both high- and low-achieving groups showed statistically significant improvements (p<0.05). The environment surrounding learners was confirmed to have a positive effect on improving self-directed learning when given the right degree of self-directed learning and appropriate feedback. The study results are expected to form basic foundation material for professors and class designers who want to draw self-directed learning skills from memorizing subjects.

• The Journal of the Acoustical Society of Korea
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• v.37 no.3
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• pp.147-155
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• 2018

In this paper, we tried to find out sound quality metrics to express discomfort of overload excavator noise and to develop sound quality indexes through multiple regression analysis by using them. For this purpose, the interior noise of cabin under overload condition was recorded for six excavator models with different noise properties and Jury test was carried out by PCM (Paired Comparison Method) and MEM (Magnitude Estimation Method). Jury test result with low consistency was classified into two groups with different preference tendencies by cluster analysis and multiple regression analysis was conducted in order to find out which sound quality metrics have significant effects on discomfort(low preference). As a result, we figured out that the sound quality metrics to express the discomfort were the partial loudness (= $PN_{10Bark}$) between 0 and 10 Bark in case of group1 and the difference between engine noise(= $dB_{EG}$) and hydraulic system noise ($dB_1$) in case of group2. Using the results of preference ranking and tendency analysis of PCM followed by the correlation analysis between PCM and MEM, the more reliable results were adopted by excluding the data with low consistency obtained from Jury test via MEM.