• Korean Chemical Engineering Research
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• 제58권2호
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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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• 제36권5_3호
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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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• 제64권4호
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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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• 제21권1호
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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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• 제25권3호
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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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• 제2권1호
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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.

• Journal of Korean Society for Geospatial Information Science
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• 제21권4호
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• pp.95-100
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• 2013

Several observation equipments are being used for determination of the water vapor content and precipitable water vapor (PWV) because the water vapor is highly variable temporally and spatially. In this study, we used GNSS systems such as GPS and GLONASS in standalone and combined modes to compute PWV and validated their accuracy with respect to the results of other water-vapor monitoring systems. The other systems used were radiosonde and microwave radiometer, and the comparisons were convenient because all three systems were collocated at the test site. The differences of PWW were in the range of 0.6-3.4 mm in the mean sense, and their standard deviations were 1.0-3.8 mm. The relatively large difference of GNSS compared with the other two systems were believed to be caused by the fact that the GNSS antenna used in this study was the kind for which the international standard of phase center variations (PCV) calibration is not available. We expect better accuracy of PWV determination and improved availability of it through integrated data processing of GPS/GLONASS when an appropriate antenna with PCV correction model is used.

• Journal of the Korea Society of Computer and Information
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• 제25권9호
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• pp.45-52
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• 2020

In this paper, we propose an intention to study the purchase of smart music by Chinese consumers. To study the influence of the originality and usefulness of intelligent music products on the purchase intention of Chinese consumers, and to explore how the originality and usefulness of intelligent music products affect the purchase intention. To achieve this goal, 372 questionnaires were collected through the Internet for frequency analysis, factor analysis, confidence analysis and structural equation analysis of data collection, and were carried out by SPSSV22.0 and AMOSV22.0 methods. Research the validation of assumptions in the model to reveal the psychological and behavioral responses of consumers to smart music products. The results show that the originality and usefulness of new products not only directly affect the purchase intention of Chinese consumers, but also indirectly affect their purchase intention by enhancing their attractiveness. The conclusion of this study is of guiding significance for the development of intelligent music product development and marketing strategy.

• Journal of the Korean Geotechnical Society
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• 제36권11호
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• pp.61-70
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• 2020

In dynamic centrifuge tests, equivalent shear beam (ESB) container minimizes the boundary effect between the soil model and the wall of the container so as to effectively simulate the boundary conditions of real field state. The ESB container at KAIST was evaluated to be performing properly by Lee et al. (2013). However, it is necessary to re-evaluate the performance of ESB container since the ESB container may have deteriorated over time. Thus, the performance of eight-year-old ESB container was re-evaluated through dynamic centrifuge tests. Firstly, the natural period of the empty ESB container was compared with the results of Lee et al. (2013). Then the boundary effect of sand-filled ESB container was evaluated. Results show that the dynamic behavior of the sand-filled ESB container was similar to that of the ground, despite a decrease in the natural period of the empty ESB container over time. In addition, the dynamic response of the ground built in the ESB container and the same ground simulated through numerical analysis with free-field boundary conditions were similar. Therefore, it was found that the boundary effect of the ESB container due to the decrease in the natural period was not significant.

• The korean journal of orthodontics
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• 제37권1호통권120호
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• pp.16-28
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• 2007

Objective: Evaluation of individual growth is important in orthodontics. The aim of this study was to develop a convenient software that can evaluate current growth status and predict further growth. Methods: Stature data of 2 to 20 year-old Koreans (4893 boys and 4987 girls) were extracted from a nationwide data. Age-sex-specific continuous functions describing percentile growth curves were constructed using natural cubic spline function (NCSF). Then, final stature prediction algorithm was developed and its validity was tested using longitudinal series of stature measurements on randomly selected 200 samples. Various accuracy measurements and analyses of errors between observed and predicted stature using NCSF growth curves were performed. Results: NCSF growth curves were shown to be excellent models in describing reference percentile stature growth curie over age. The prediction accuracy compared favorably with previous prediction models, even more accurate. The current prediction models gave more accurate results in girls than boys. Although the prediction accuracy was high, the error pattern of the validation data showed that in most cases, there were a lot of residuals with the same sign, suggestive of autocorrelation among them. Conclusion: More sophisticated growth prediction algorithm is warranted to enhance a more appropriate goodness of model fit for individual growth.