• Journal of Distribution Research
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• v.17 no.5
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• pp.85-111
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• 2012

Introduction : The purpose of this study was to examine the relationship between perceptual characteristics of consumers and intent of relational continuance for PB(Private Brand) products in discount stores. This study was conducted as an empirical study based on survey. For the empirical study, factors of PB products as characteristics perceived by consumers such as perceived quality, store image, brand image and perceived value were deduced from preceding studies. The effect of such factors on intent of relational continuance mediated by brand trust and brand equity of PB products was structurally examined. Research Model : Based on theory analysis and hypotheses, constructed a Structural Equation Model(SEM). The research model is shown in Figure 1. Research Method : This paper is based on s qualitative study of selected literature and empirical data. The survey for empirical study was carried out on consumers in Gyeonggi and Busan between January 2012 and May 2012. 300 surveys were distributed and 253 (84.3%) of them were returned. After excluding omissions and insincere responses, 245 surveys (81.6%) were used for final analysis as effective samples. Result : First of all, the Reliability was carried out for instrument used. The lower limit of 0.7 for Cronbach's Alpha as suggested by Hair et al. (1998). And Construct validity was established by carrying out exploratory factor analysis by Varimax rotation for all. Four factor result for the consumer's perceptual characteristics of PB Products, two mediating factors and one dependent factor. All constructs included in research framework have acceptable validity and reliability. Table 1 shows the factor loading, eigen value, explained variance and Cronbach's alpha for each factor. In order to assure validity of constructs, I implemented Confirmatory Factor Analysis (CFA), using AMOS 20.0. In confirmatory factor analysis, researcher can take control over the specification of indicators for each factor by hypothesizing that a specific factor is loaded with the relevant indicators. Moreover, CFA is particularly useful in the validation of scale for the measurement of specific construct. CFA result summarized Table 2 shows that the fit measures of all constructs fulfill the recommended level and loadings are significant. To test causal relationship between constructs in the research model, used AMOS 20.0 that provides a graphic module as method for analysing Structural Equation Modeling. The result of hypothesis test is shown in Table 3. As a result of empirical study, perceived quality, brand image and perceived value as selected attributes for PB products showed significantly positive (+) effect on brand trust and brand equity. Furthermore, brand trust and brand equity showed significantly positive (+) effect on intent of relational continuance. However, store image of discount stores selling the PB products was analyzed to have positive (+) effect on brand trust and no significant effect on brand equity. Discussion : Based on the results of this study, the relationship between overall quality, store image, brand image and value perceived by consumers about PB products and intent of relational continuance was structurally verified as being mediated by brand trust and brand equity. Looking at the results, a strategic approach that maximizes brand trust and equity value for PB products by large discount stores is required on top of basic efforts to improve quality, brand image and value of PB products in order to maximize consumer's intent of relational continuance and to continuously attract repeated purchase of products.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.5
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• pp.779-789
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• 2016

The effective rainfall is calculated considering the soil moisture. It utilizes observed data directly in order to incorporate the soil moisture into the rainfall-runoff model, or it calculates indirectly within the model. The rainfall-runoff model, IHACRES, used in this study computes the catchment wetness index (CWI) first varying with temperature and utilize it for estimating precipitation loss. The nonlinear relationship between the CWI and the effective rainfall in the Hapcheondam watershed was derived and utilized for the long-term runoff calculation. The effects of variable and constant CWI during calibration and validation were suggested by flow regime. The results show the variable CWI is generally more effective than the constant CWI. The $R^2$ during high flow period shows relatively higher than the ones during normal or low flow period, but the difference between cases of the variable and constant CWI was insignificant. The results indicates that the high flow is relatively less sensitive to the evaporation and soil moisture associated with temperature. On the other hand, the variable CWI gives more desirable results during normal and low flow periods which means that it is crucial to incorporate evaporation and soil moisture depending on temperature into long-term continuous runoff simulation. The NSE tends to decrease during high flow period with high variability which could be natural because NSE index is largely influenced by outliers of underlying variable. Nevertheless overall NSE shows satisfactory range higher than 0.9. The utilization of variable CWI during normal and low flow period would improve the computation of long-term rainfall-runoff simulation.

• 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 Korean Society of Forest Science
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• v.98 no.6
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• pp.791-798
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• 2009

The role of forest and soil carbon under global climate change is getting important as a carbon sink and it is necessary to research on applicable forest models as well as in the field for a study of these dynamics. On this study, historical annual litter dataset as a major input data for the forest soil carbon model, Yasso was established using a dendrochronological reconstruction method, and the soil carbon dynamics of a Pinus densiflora forest in Gwangneung, Korea was simulated using Yasso. The amount of litter (needle, branch, stem and fine root) production, which was estimated using the dendrochronological method, has increased continuously from 1971 to 2006. Furthermore, there was no significant error between estimated and measured values of litter production (needle and branch) in 2006. The average of simulated soil carbon stock up to 30 cm depth was $46.30{\pm}4.28tCha^{-1}$, which accounted for 53% of carbon stock in trees of the forest, and had no significant difference and error with measured soil carbon stock. Under the climate change trend in Korea according to IPCC A1B scenario, it was estimated that the simulated soil carbon stock in the region would increase continuously from 1971 to 2041 and then decreased until 2100. Compared to the result of the scenario that there is no climate change, the soil carbon stock could be decreased up to 7.58% at 2100. It was inferred the dendrochronological reconstruction method and simulation of Yasso model are useful to estimate soil carbon dynamics of the natural P. densiflora forest. Follow-up researches, such as improvement of the dendrochronological method and Yasso model and their application and validation in various environment, are needed to produce more reliable results.

• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.235-247
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• 2020

Simulation study and validation on 50 L/hr pilot-scale Bunsen process was carried out in order to investigate thermodynamics parameters, suitable reactor type, separator configuration, and the optimal conditions of reactors and separation. Sulfur-Iodine is thermochemical process using iodine and sulfur compounds for producing hydrogen from decomposition of water as net reaction. Understanding in phase separation and reaction of Bunsen Process is crucial since Bunsen Process acts as an intermediate process among three reactions. Electrolyte Non-Random Two-Liquid model is implemented in simulation as thermodynamic model. The simulation results are validated with the thermodynamic parameters and the 50 L/hr pilot-scale experimental data. The SO₂ conversions of PFR and CSTR were compared as varying the temperature and reactor volume in order to investigate suitable type of reactor. Impurities in H₂SO₄ phase and HI_X phase were investigated for 3-phase separator (vapor-liquid-liquid) and two 2-phase separators (vapor-liquid & liquid-liquid) in order to select separation configuration with better performance. The process optimization on reactor and phase separator is carried out to find the operating conditions and feed conditions that can reach the maximum SO₂ conversion and the minimum H₂SO₄ impurities in HI_X phase. For reactor optimization, the maximum 98% SO₂ conversion was obtained with fixed iodine and water inlet flow rate when the diameter and length of PFR reactor are 0.20 m and 7.6m. Inlet water and iodine flow rate is reduced by 17% and 22% to reach the maximum 10% SO₂ conversion with fixed temperature and PFR size (diameter: 3/8", length:3 m). When temperature (121℃) and PFR size (diameter: 0.2, length:7.6 m) are applied to the feed composition optimization, inlet water and iodine flow rate is reduced by 17% and 22% to reach the maximum 10% SO₂ conversion.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1053-1066
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• 2020

Sulfur dioxide (SO₂) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO₂ can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO₂ crucial. In South Korea, SO₂ monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO₂ concentrations. Thus, this research estimated spatially continuous ground-level SO₂ concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO₂ estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO₂ concentrations based on the proposed model agreed with the general seasonality of SO₂ and the temporal patterns of emission sources.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.4
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• pp.353-365
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• 2019

A near-infrared reflectance spectroscopy (NIRS) prediction model was set to establish a rapid analysis system of wheat germplasm and provide statistical information on the characteristics of protein contents. The variability index value (VIV) of calibration resources was 0.80, the average protein content was 13.2%, and the content range was from 7.0% to 13.2%. After measuring the near-infrared spectra of calibration resources, the NIRS prediction model was developed through a regression analysis between protein content and spectra data, and then optimized by excluding outliers. The standard error of calibration, R², and the slope of the optimized model were 0.132, 0.997, and 1.000 respectively, and those of external validation results were 0.994, 0.191, and 1.013, respectively. Based on these results, a developed NIRS model could be applied to the rapid analysis of protein in wheat. The distribution of NIRS protein content of 6,794 resources were analyzed using a normal distribution analysis. The VIV was 0.79, the average protein was 12.1%, and the content range of resources accounting for 42.1% and 68% of the total accessions were 10-13% and 9.5-14.6%, respectively. The composition of total resources was classified into breeding line (3,128), landrace (2,705), and variety (961). The VIV in breeding line was 0.80, the protein average was 11.8%, and the contents of 68% of total resources ranged from 9.2% to 14.5%. The VIV in landrace was 0.76, the protein average was 12.1%, and the content range of resources of 68% of total accessions was 9.8-14.4%. The VIV in variety was 0.80, the protein average was 12.8%, and the accessions representing 68% of total resources ranged from 10.2% to 15.4%. These results should be helpful to the related experts of wheat breeding.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.129-141
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• 2023

Crop models have been used to predict yield under diverse environmental and cultivation conditions, which can be used to support decisions on the management of forage crop. Cultivar parameters are one of required inputs to crop models in order to represent genetic properties for a given forage cultivar. The objectives of this study were to compare calibration and ensemble approaches in order to minimize the uncertainty of crop yield estimates using the SIMPLE crop model. Cultivar parameters were calibrated using Log-likelihood (LL) and Generic Composite Similarity Measure (GCSM) as an objective function for Metropolis-Hastings (MH) algorithm. In total, 20 sets of cultivar parameters were generated for each method. Two types of ensemble approach. First type of ensemble approach was the average of model outputs (E_em), using individual parameters. The second ensemble approach was model output (E_pm) of cultivar parameter obtained by averaging given 20 sets of parameters. Comparison was done for each cultivar and for each error calculation methods. 'Jowoo' and 'Yeongwoo', which are forage rice cultivars used in Korea, were subject to the parameter calibration. Yield data were obtained from experiment fields at Suwon, Jeonju, Naju and I ksan. Data for 2013, 2014 and 2016 were used for parameter calibration. For validation, yield data reported from 2016 to 2018 at Suwon was used. Initial calibration indicated that genetic coefficients obtained by LL were distributed in a narrower range than coefficients obtained by GCSM. A two-sample t-test was performed to compare between different methods of ensemble approaches and no significant difference was found between them. Uncertainty of GCSM can be neutralized by adjusting the acceptance probability. The other ensemble method (E_pm) indicates that the uncertainty can be reduced with less computation using ensemble approach.

• International Journal of Naval Architecture and Ocean Engineering
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• v.2 no.1
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• pp.1-13
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• 2010

A comparative study between a computation and an experiment has been conducted to predict the performance of a Pod type waterjet for cm amphibious wheeled vehicle. The Pod type waterjet has been chosen on the basis of the required specific speed of more than 2500. As the Pod type waterjet is an extreme type of axial flow type waterjet, theoretical as well as experimental works about Pod type waterjets are very rare. The main purpose of the present study is to validate and compare to the experimental results of the Pod type waterjet with the developed CFD in-house code based on the RANS equations. The developed code has been validated by comparing with the experimental results of the well-known turbine problem. The validation also extended to the flush type waterjet where the pressures along the duct surface and also velocities at nozzle area have been compared with experimental results. The Pod type waterjet has been designed and the performance of the designed waterjet system including duct, impeller and stator was analyzed by the previously mentioned m-house CFD Code. The pressure distributions and limiting streamlines on the blade surfaces were computed to confirm the performance of the designed waterjets. In addition, the torque and momentum were computed to find the entire efficiency and these were compared with the model test results. Measurements were taken of the flow rate at the nozzle exit, static pressure at the various sections along the duct and also the nozzle, revolution of the impeller, torque, thrust and towing forces at various advance speed's for the prediction of performance as well as for comparison with the computations. Based on these measurements, the performance was analyzed according to the ITTC96 standard analysis method. The full-scale effective and the delivered power of the wheeled vehicle were estimated for the prediction of the service speed. This paper emphasizes the confirmation of the ITTC96 analysis method and the developed analysis code for the design and analysis of the Pod type waterjet system.