• Journal of Korean Society on Water Environment
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• v.31 no.5
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• pp.507-522
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• 2015

In this study, some of the model verification results of STREAM (Spatio-Temporal River-basin Ecohydrology Analysis Model), a newly-developed hybrid watershed model, are presented for the runoff processes of nonpoint source pollution. For verification study of STREAM, the model was applied to a test watershed and a sensitivity analysis was also carried out for selected parameters. STREAM was applied to the Mankyung River Watershed to review the applicability of the model in the course of model calibration and validation against the stream flow discharge, suspended sediment discharge and some water quality items (TOC, TN, TP) measured at the watershed outlet. The model setup, simulation and data I/O modules worked as designed and both of the calibration and validation results showed good agreement between the simulated and the measured data sets: NSE over 0.7 and $R^2$ greater than 0.8. The simulation results also include the spatial distribution of runoff processes and watershed mass balance at the watershed scale. Additionally, the irrigation process of the model was examined in detail at reservoirs and paddy fields.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.6
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• pp.507-516
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• 2021

In this study, the machine learning which has been widely used in prediction algorithms recently was used. the research point was the CD(chudong) point which was a representative point of Daecheong Lake. Chlorophyll-a(Chl-a) concentration was used as a target variable for algae prediction. to predict the Chl-a concentration, a data set of water quality and quantity factors was consisted. we performed algorithms about random forest and gradient boosting with Python. to perform the algorithms, at first the correlation analysis between Chl-a and water quality and quantity data was studied. we extracted ten factors of high importance for water quality and quantity data. as a result of the algorithm performance index, the gradient boosting showed that RMSE was 2.72 mg/m³ and MSE was 7.40 mg/m³ and R² was 0.66. as a result of the residual analysis, the analysis result of gradient boosting was excellent. as a result of the algorithm execution, the gradient boosting algorithm was excellent. the gradient boosting algorithm was also excellent with 2.44 mg/m³ of RMSE in the machine learning hyperparameter adjustment result.

• Geomechanics and Engineering
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• v.38 no.5
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• pp.507-515
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• 2024

An accurate estimation of the geotechnical parameters in front of tunnel faces is crucial for the safe construction of underground infrastructure using tunnel boring machines (TBMs). This study was aimed at developing a data-driven model for predicting the rock quality designation (RQD) of the ground formation ahead of tunnel faces. The dataset used for the machine learning (ML) model comprises seven geological and mechanical features and 564 RQD values, obtained from an earth pressure balance (EPB) shield TBM tunneling project beneath the Han River in the Republic of Korea. Four ML algorithms were employed in developing the RQD prediction model: k-nearest neighbor (KNN), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGB). The grid search and five-fold cross-validation techniques were applied to optimize the prediction performance of the developed model by identifying the optimal hyperparameter combinations. The prediction results revealed that the RF algorithm-based model exhibited superior performance, achieving a root mean square error of 7.38% and coefficient of determination of 0.81. In addition, the Shapley additive explanations (SHAP) approach was adopted to determine the most relevant features, thereby enhancing the interpretability and reliability of the developed model with the RF algorithm. It was concluded that the developed model can successfully predict the RQD of the ground formation ahead of tunnel faces, contributing to safe and efficient tunnel excavation.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.309-316
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• 2021

The purpose of this study is to investigate the level of adaptation to college life and identity the influencing factors on adaptation to college life of college freshmen's. Data were collected from 2088 college freshmen's in B city and analyzed by t-test, ANOVA, Pearson correlation coefficient, and multiple regression using SPSS/WIN 22.0. The degree of adaptation to college life in college freshman was 3.75±0.73. There were significant differences in college life adaptation with respect to gender(t=3.947, p<.001), age(F=3.445, p=.032), major(F=5.539, p=.001), family type(F=6.958, p<.001). There was negative correlation between adaptation to college life and perceived stress(r=-.696, p<.001), depression(r=-.507, p<.001), positive correlation were found between perceived stress and depression(r=.567, p<.001). The factors affecting the adaptation to college life of the study subjects were perceived stress, depression, age, major, family type with an explanatory power of 50.4%. In conclusion, to enhance adaptation to college life of college freshmen's, it is necessary to develop and adopt various program of adaptation to college life.

• Journal of the Korean Applied Science and Technology
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• v.40 no.3
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• pp.507-520
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• 2023

The purpose of this study is to inquire into the effect on the coping with stress by the knowledge of Emerging infectious disease, clinical practice stress. A research was held to the nursing students living in Seoul and Chungbuk from September 10 to October 10, 2022, 259copies of the data were used for the final analysis, Pearson's correlation coefficient and Multiple linear regressions was used. As a result of the study, there was a positive correlation between clinical practice stress due to burden of work, practice education environment stress, and active coping with stress. Clinical practice stress due to interpersonal conflicts, conflicts with patients, burden of work, and undesirable role models was positively correlated with passive coping with stress. Satisfaction of clinical practice, practical educational environment stress and gender accounted for 15.0% of the total variance in the active stress coping, and burden of work accounted for 7% of the total variance in the passive stress coping. Consequently, this study could be suggested as a basis for counseling and developing practical education program for active coping with stress.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.10
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• pp.507-511
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• 2005

We have derived the linear complexity of a binary sequence generated by a Feedback with Carry Shift Regiater(FCSR) under the following condition: q is a power of a prime such that $q=r^e,\;(e{\geq}2)$ and r=2p+1, where both r and p are 2-prime. Also, a summation generator creates sequence from addition with carry of LFSR(Linear Feedback Shift Register) sequences. Similarly, it is possible to generate keystream by bitwise exclusive-oring on two FCSR sequences. In this paper, we described the cryptographic properties of a sequence generated by the FCSRs in view of the linear complexity.

• Journal of the Korean Applied Science and Technology
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• v.27 no.4
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• pp.501-507
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• 2010

In this study, we evaluate the anti-microbiological activity of paraben in eye shadows that are composed of pigments and oil binders using various analytical methods and microbiological tests. Paraben does not show the microbiological activity properly when it was used with Nylon SP$^{(R)}$ 10, Talc RF SSA$^{(R)}$, OMC Talc AS$^{(R)}$ and $BaSO_4$. In the test of fungi, Nylon SP$^{(R)}$ 10 causes the decrease of microbiological activity regardless of the type of oil binders. The pigment of Mango violet also causes the decrease of microbiological activity when ester oil binder was used. Regardless of the type of oil binder, samples containing nylon SP 10, 0.15% of methyl paraben and 0.05% of propyl paraben had not been able to maintain microbiological activity only if the concentration of parabens were increased. Trace amounts of metal ions present in pigments reduced the activity of preservatives by inactivation of hydroxyl group of paraben. It is thought that swollen nylon SP 10 in ester oil increase the absorption or interaction of parabens and swollen nylon powder causes the inactivation of paraben.

• Research in Plant Disease
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• v.16 no.2
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• pp.158-162
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• 2010

Four promising biocontrol agents against Phytophthora capsici were selected from 507 bacterial isolates collected from rhizosphere soils and roots of pepper plants. In vitro experiment, these four biocontrol agents inhibited mycelial growth, germination of cystospores, and formation of zoosporangia and zoospores of Phytophthora capsici. In the pot experiment, the four biocontrol agents showed control efficiency higher than 70%. In greenhouse experiment, the isolates G28-6 gave the control value of 79.4%. These four biocontrol agents successfully colonized in the population density beyond 105 cfu/g on roots of pepper in vitro. The isolates G28-6 was identified as Pseudomonas aurantiaca, based on its cultural, morphological, and biochemical characterization and 16S rRNA gene sequence analysis.

• Applied Biological Chemistry
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• v.50 no.2
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• pp.127-131
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• 2007

The molecular design and holographic (H) quantitative structure-activity relationships (HQSARs) on the binding affinity constants between new 3-benzylidenemyosmine analogues and nicotin acetylcholine receptors (nAChRs) of American cockroach (Periplaneta. americana L.) were studied quantitatively. The optimized HQSAR model (IV-2) for the binding affinity constants was derived from fragment distinction of hydrogene atoms in fragment size, 5${\sim}$8 bin. The statistical results of the HQSAR model (IVI-2) exhibited the best predictability and fitness for the binding affinity constants based on the cross-validated value (q$^2$=0.507) and non cross-validated value (r$^2_{nev.}$=0.944). From the graphical analyses of atomic contribution maps, it was revealed that the binding affinity constants depends upon the anabaseine ring in molecule and the most active compounds were designed by optimized HQSAR model (VI-2).

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.4
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• pp.361-368
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• 2002

The aim of this study was to investigate the effect of preprocessing the transmitted energy spectrum data on development of a robust model to predict the sugar content in intact apples. The spectrum data were measured from 120 Fuji apple samples conveying at the speed of 2 apples per second. Computer algorithms of preprocessing methods such as MSC, SNV, first derivative, OSC and their combinations were developed and applied to the raw spectrum data set. The results indicated that correlation coefficients between the transmitted energy values at each wavelength and sugar contents of apples were significantly improved by the preprocessing of MSC and SNV in particular as compared with those of no-preprocessing. SEPs of the prediction models showed great difference depending on the preprocessing method of the raw spectrum data, the largest of 1.265%brix and the smallest of 0.507% brix. Such a result means that an appropriate preprocessing method corresponding to the characteristics of the spectrum data set should be found or developed for minimizing the prediction errors. It was observed that MSC and SNV are closely related to prediction accuracy, OSC is to number of PLS factors and the first derivative resulted in decrease of the prediction accuracy. A robust calibration model could be d3eveloped by the combined preprocessing of MSC and OSC, which showed that SEP=0.507%brix, bias=0.0327 and R2=0.8823.