• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1373-1389
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• 2019

KOMPSAT-5 is the first satellite in Korea equipped with X-band Synthetic Aperture Radar (SAR) instrument and has been operated since August 2013. KOMPSAT-5 is used to monitor the global environment according to its observation purpose and the availability of KOMPSAT-5 is also highlighted as the need of high resolution wind data for investigating the coastal region. However, the previous study for the validation of wind derived from KOMPSAT-5 showed that the accuracy is lower than that of other SAR satellites. Therefore, in this study, we developed the correction equation of normalized radar cross section (NRCS or backscattering coefficient) for improvement of wind from the KOMPSAT-5 and validated the effect of the equation using the in-situ measurement of ocean buoys. Theoretical estimated NRCS and observed NRCS from KOMPSAT-5 showed linear relationship with incidence angle. Before applying the correction equation, the accuracy of the estimated wind speed showed the relatively high root-mean-square errors (RMSE) of 2.89 m s^-1 and bias of -0.55 m s^-1. Such high errors were significantly reduced to the RMSE of 1.60 m s^-1 and bias of -0.38 m s^-1 after applying the correction equation. The improvement effect of the correction equation showed dependency relying on the range of incidence angle.

• Korean Chemical Engineering Research
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• v.48 no.6
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• pp.717-724
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• 2010

The flash point is one of the most important physical properties used to determine the potential for fire and explosion hazards of flammable liquids. Despite the needs of the experimental flash point data for the design and construction of chemical plants, there is often a significant gap between the demands for the data and their availability. This study have built and compared two models of partial least squares(PLS) and support vector machine(SVM) to predict the experimental flash points of 893 organic compounds out of DIPPR 801. As the independent variables of the models, 65 functional groups were chosen based on the group contribution method that was oriented from the assumption that each fragment of a molecule contributes a certain amount to the value of its physical property, and the logarithm of molecular weight was added. The prediction errors calculated from cross-validation were employed to determine the optimal parameters of two models. And, an optimization technique should be used to get three parameters of SVM model. This work adopted particle swarm optimization that is one of heuristic optimization methods. As the selection of training data can affect the prediction performance, 100 data sets of randomly selected data were generated and tested. The PLS and SVM results of the average absolute errors for the whole data range from 13.86 K to 14.55 K and 7.44 K to 10.26 K, respectively, indicating that the predictive ability of the SVM is much superior than PLS.

• Journal of Energy Engineering
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• v.29 no.2
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• pp.1-9
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• 2020

In recent days, fuel cell has received attention from the world as an alternative power source to hydrocarbon used in automobile engines. With the industrial advances of fuel cell, There have been a lot of researches actively conducted to find a way of generating hydrogen. Among many hydrogen production methods, Solid Oxide Electrolysis Cell(SOEC) is not only a basic way but also environment-friendly method to produce hydrogen gas. Solid Oxide Electrolysis Cell has lower electrical energy demands and high thermal efficiency since it is possible to operate under high temperature and high pressure conditions. For these reasons, experimental researches as well as studies on numerical modeling for Solid Oxide Electrolysis Cell have been under way. However, studies on numerical modeling are relatively less enough than experimental accomplishments and have limited performance prediction, which mostly is considered as a result from inadequate effects of electrochemical properties by temperature and pressure. In this study, various experimental studies of commercial Membrane Electrode Assembly (MEA) composed of Ni-YSZ (40wt%, Ni-60 wt% YSZ)/8-YSZ (TOSOH, TZ8Y)/LSM (La_0.9Sr_0.1MnO₃) was utilized for improving effectiveness of SOEC model. After numerically analyzing effects of electrochemical properties according to operating temperature, causing the largest deviation between experiments and simulation are that Charge Transfer Coefficient (CTC), exchange current density, diffusion coefficient, electrical conductivity in SOEC. Analyzing temperature effect on parameter used in overpotential model is conducted for modeling of SOEC. cross-validation method is adopted for application of various MEA and evaluating feasibility of model. As a result, the study confirm that the numerical model of SOEC based on structured process of effectiveness evaluation makes performance prediction better.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.4
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• pp.371-376
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• 2017

In this paper, we propose an algorithm that can recognize the pattern regardless of the sensor position when performing EMG pattern recognition using circular EMG system equipment. Fourteen features were extracted by using the data obtained by measuring the eight channel EMG signals of six motions for 1 second. In addition, 112 features extracted from 8 channels were analyzed to perform principal component analysis, and only the data with high influence was cut out to 8 input signals. All experiments were performed using k-NN classifier and data was verified using 5-fold cross validation. When learning data in machine learning, the results vary greatly depending on what data is learned. EMG Accuracy of 99.3% was confirmed when using the learning data used in the previous studies. However, even if the position of the sensor was changed by only 22.5 degrees, it was clearly dropped to 67.28% accuracy. The accuracy of the proposed method is 98% and the accuracy of the proposed method is about 98% even if the sensor position is changed. Using these results, it is expected that the convenience of the users using the circular EMG system can be greatly increased.

• Journal of Korean Neurosurgical Society
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• v.49 no.3
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• pp.147-152
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• 2011

Objective : The aim of this study was to evaluate the reliability and validity of the Pediatric Functional Assessment of Cancer Therapy Questionnaire Brain Tumor Survivor (version 2.0) Aged 13 years and older (Parent Form) (pedsFACT-BrS parent of adolescent). Methods : The pedsFACT-BrS parent of adolescent was translated and cross-culturally adapted into Korean, following standard Functional Assessment of Chronic Illness Therapy (FACIT) methodology. The psychometric properties of the pedsFACT-BrS parent of adolescent were evaluated in 170 brain tumor patient's mothers (mean age=43.38 years). Pretesting was performed in 30 mothers, and the results indicated good symptom coverage and overall comprehensibility. The participants also completed the Child Health Questionnaire Parent Form 50 (CHQ-PF-50), Neuroticism in Eysenck Personality Questionnaire, and Karnofsky score. Results : In validating the pedsFACT-BrS parent of adolescent, we found high internal consistency, with Cronbach's ${\alpha}$ coefficients ranging from 0.76 to 0.94. The assessment of test-retest reliability using intraclass correlation coefficient revealed satisfactory values with ICCs ranging from 0.84 to 0.93. The pedsFACT-BrS for parent of adolescent also demonstrated good convergent and divergent validities when correlated with the Child Health Questionnaire Parent Form 50 (CHQ-PF-50) and the Neuroticism in Eysenck Personality Questionnaire. The pedsFACT-BrS parent of adolescent showed good clinical validity, and effectively differentiated between clinically distinct patient groups according to the type of treatment, tumor location, shunt, and Karnofsky score of parent proxy report. Conclusion : We confirmed that this reliable and valid instrument can be used to properly evaluate the quality of life of Korean adolescent brain tumor patients by their parents' proxy report.

• Journal of Conservation Science
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• v.26 no.4
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• pp.397-406
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• 2010

The most ideal method to measure the water-soluble $Cl^-$ ion eluted from iron artifacts is conducting the analysis on desalting solution by Ion Chromatography. But most institutes related to cultural heritages use Cl meter by reason of lack of budget and experts. This study evaluated reliability and stability between Cl meter and Ion Chromatography by doing cross-validation with results from two methods to detect $Cl^-$ ion of desalting solution. From D.I water, extremely small quantities of $Cl^-$ ion was detected by the influence of remaining water-soluble $Cl^-$ ion at the electrode of Cl meter and water-soluble $Cl^-$ which remains in Sodium sesquicarbonate, components of reagent was detected as well. The first desalting solution had the most $Cl^-$ ions, $Cl^-$ ion slightly decreased from the second to the fourth desalting solution and tend to decrease again at the stage of dealkalified in D.I water. Each Cl meter has the standard deviation according to the measured numbers and the higher concentration of $Cl^-$ ion the desalting solution has, the wider the deviation is. But when the concentration of $Cl^-$ ion is low, it was stable to use Cl meter to detect the concentration of $Cl^-$ ion from iron artifacts because there is the small deviation, It is thought that conductivity meter method is not suitable for measuring $Cl^-$ ion, because the electrical conductivity of alkaline solution is too high to measure $Cl^-$ ion.

• The Korean Journal of Pesticide Science
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• v.7 no.2
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• pp.75-82
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• 2003

We discuss that the growth inhibition activities against root and shoot of rice plant (Oryza sativa L.) and barnyard grass (Echinochloa crus-galli) by N-phenyl-3,4,5,6-tetrahydrophthalimide (A) and N-phenyl-3,4-dimethylmaleimide (B) derivatives with changing substituents can be explained and predicted using comparative molecular field analyses (CoMPA) method. And the results show that the cross-validation value, $q^2$ at three components and Pearson correlation coefficient, $r^2$ were rice plant: shoot; $r^2=0.987$, $q^2=0.387$ & root; $r^2=0.923$, $q^2=0.307$ and barnyard grass: shoot; $r^2=0.902$, $q^2=0.535$ & root; $r^2=0.900$, $q^2=0.450$, respectively. In addition, The activities of unknown compounds were predicted by CoMFA method. From the contour map of (A) derivatives, the selective factors to remove barnyard grass takes positive charge on the benzylic carbon atom (C27), negative charged carbon atom (C29) of meta position and steric bulky groups on the cyclic imino ring (C7-C8).

• Journal of Digital Convergence
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• v.18 no.1
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• pp.301-307
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• 2020

In recent years, a recommendation system is introduced in many different fields with the beginning of the 5G era and making a considerably prominent appearance mainly in books, movies, and music. In such a recommendation system, however, the preference degrees of users are subjective and uncertain, which means that it is difficult to provide accurate recommendation service. There should be huge amounts of learning data and more accurate estimation technologies in order to improve the performance of a recommendation system. Trying to solve this problem, this study proposed a movie recommendation system based on Fuzzy-AHP and Word2vec. The proposed system used Fuzzy-AHP to make objective predictions about user preference and Word2vec to classify scraped data. The performance of the system was assessed by measuring the accuracy of Word2vec outcomes based on grid search and comparing movie ratings predicted by the system with those by the audience. The results show that the optimal accuracy of cross validation was 91.4%, which means excellent performance. The differences in move ratings between the system and the audience were compared with the Fuzzy-AHP system, and it was superior at approximately 10%.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.7-12
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• 2012

Recently the medical field to efficiently process the vast amounts of information to decision trees, neural networks, Bayesian Networks, including the application method of various data mining techniques are investigated. In addition, the basic personal information or patient history, family history, in addition to information such as MRI, HRCT images and additional information to collect and leverage in the diagnosis of disease, improved diagnostic accuracy is to promote a common status. But in real world situations that affect the results much because of the variable exists for a particular data mining techniques to obtain information through the enemy can be seen fairly limited. Medical images were taken as well as a minor can not give a positive impact on the diagnosis, but the proportion increased subjective judgments by the automated system is to deal with difficult issues. As a result of a complex reality, the situation is more advantageous to deal with the relative probability of the multivariate model based on Bayesian network, or TAN in the K2 search algorithm improves due to expansion model has been proposed. At this point, depending on the type of search algorithm applied significantly influenced the performance characteristics of the extended Bayesian network, the performance and suitability of each technique for evaluation of the facts is required. In this paper, we extend the Bayesian network for diagnosis of diseases using the same data were carried out, K2, TAN and changes in search algorithms such as classification accuracy was measured. In the 10-fold cross-validation experiment was performed to compare the performance evaluation based on the analysis and the onset of high-risk classification for patients with HRCT images could be possible to identify high-risk data.

• Food Science and Preservation
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• v.24 no.1
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• pp.145-152
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• 2017

In this study, near infrared (NIR) spectroscopy known as a non-destructive analysis technique was applied to investigate peptide cleavage and consequent release of amino acids in soybean lumps as affected by its moisture content and incubation time during fermentation at 25 for 3 weeks. The NIR spectra of the soybean lump semi-dried and soaked in saline water showed that absorption intensity around 1,400 nm originating from hydrogen bonds of water decreased and absorption band shifted to 1,430 nm as moisture content decreased during incubation at 25 for 3 weeks. In addition, absorption around 2,050 nm which was assigned to amino groups increased as incubation time increased. NIR spectra data from 1,000 to 2,250 nm showed higher accuracy in the discriminant analysis between outside and inside parts of fermented soybean lumps than visible spectra result. NIR spectroscopy for the amino acid and moisture contents in traditional fermented soybean lumps showed relatively good accuracy with the multiple correlation coefficient ($R^2$) of 0.91 and 0.81, respectively, and root mean square error of cross validation (RMSECv) of 0.23 and 0.83%, respectively, in partial least square regression (PLSR). These results indicate that NIR spectral observations could be applicable to control the fermentation process for preparation of soybean products.