• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.56-65
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• 2019

The purpose of this study was to identify factors associated with character of nursing students using ecological theory. A descriptive cross-sectional study was conducted with 296 nursing students. Collected data from self report questionnaires were analyzed using descriptive statistics, t-test, ANOVA and multiple regression with SPSS WIN 21.0. The Results of this study were as follows. A total of 2 models were examined according to individual, microsystem in ecological system theory. In the first model including individual factors, positive emotion, communication ability were significant factors explain character of nursing students. In the second model adding micro system factors family strength and major satisfaction found to be significant factors. The prediction factors of nursing student' character were communication ability (${\beta}=.431$, p<.001), major satisfaction (${\beta}=.310$, p<.001) and family strength (${\beta}=.176$, p<.001). The explanation power was 55.6%. These results showed that factors affecting character of nursing students are communication ability, major satisfaction, and family strength. Therefore, we suggest to develop various character education programs considering these factors.

• Journal of Ocean Engineering and Technology
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• v.33 no.1
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• pp.1-9
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• 2019

This paper considers a numerical assessment of the self-propulsion performance of a damaged ferry carrying cars in irregular waves. Computational fluid dynamics(CFD) simulations were performed to see whether the ferry complied with the Safe Return to Port (SRtP) regulations of Lloyd's register, which require that damaged passenger ships should be able to return to port with a speed of 6 knots (3.09 m/s) in Beaufort 8 sea conditions. Two situations were considered for the damaged conditions, i.e., 1) the portside propeller was blocked but the engine room was not flooded and 2) the portside propeller was blocked and one engine room was flooded. The self-propulsion results for the car ferry in intact condition and in the damaged conditions were assessed as follows. First, we validated that the portside propeller was blocked in calm water based on the available experimental results provided by KRISO. The active thrust of starboard propeller with the portside propeller blocked was calculated in Beaufort 8 sea conditions, and the results were compared with the experimental results provided by MARIN, and there was reasonable agreement. The thrust provided by the propeller and the brake horsepower (BHP) with one engine room flooded were compared with the values when the engine room was not flooded. The numerical results were compared with the maximum thrust of the propeller and the maximum brake horse power of the engine to determine whether the damaged car ferry could attain a speed of 6 knots(3.09 m/s).

• Analytical Science and Technology
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• v.34 no.3
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• pp.134-141
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• 2021

In this study, the fatty acid (FA) composition of commercial sesame oils (n = 62) was investigated using gas chromatography with flame ionization detector (GC-FID). Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA), were applied to the chromatographic data of the FAs to discriminate the geographical origin of sesame oils. A statistically significant difference was observed in the content of C16:0, C18:0, C18:1, and C18:2 between domestic and imported sesame oils. A satisfactory recovery rate of 82.8-100.2 % was achieved for C16:0, C18:0, C18:1, C18:2, and C18:3. The correlation of C16:0, C18:1, and C18:2 in domestic sesame oils showed opposite trends compared to imported oils. The PCA plot demonstrated that sesame oils were clustered in distinct groups according to their origin. LDA was used to predict sesame oil samples in one of the two groups. C16:0 (Wilks λ = 0.361) and C18:1 (Wilks λ = 0.637) demonstrated the highest discriminant power for classifying the origin of the samples. The correct prediction rates were 88.9 % and 100 % for the domestic and imported samples, respectively. Further, 60 of the 62 sesame oil samples (96.8 %) were correctly classified, indicating that this approach can be used as a valuable tool to predict and classify the geographical origin of sesame oils.

• Ecology and Resilient Infrastructure
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• v.10 no.1
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• pp.11-19
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• 2023

Alien species are known to threaten regional biodiversity globally, which has increased global interest regarding introduction of alien species. The Ministry of Environment of Korea designated species that have not yet been introduced into the country with potential threat as alert alien species to prevent damage to the ecosystem. In this study, potential habitats of Esox lucius and Maccullochella peelii, which are predatory and designated as alert alien fish, were predicted on a national basis. Habitat suitability was evaluated using EHSM (Ecological Habitat Suitability Model), and water temperature data were input to calculate Physiological Habitat Suitability (PHS). The prediction results have shown that PHS of the two fishes were mainly controlled by heat or cold stress, which resulted in biased habitat distribution. E. lucius was predicted to prefer the basins at high latitudes (Han and Geum River), while M. peelii preferred metropolitan areas. Through these differences, it was expected that the invasion pattern of each alien fish can be different due to thermal preference. Further studies are required to enhance the model's predictive power, and future predictions under climate change scenarios are required to aid establishing sustainable management plans.

• Economic and Environmental Geology
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• v.55 no.4
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• pp.389-398
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• 2022

Many studies have been conducted to accurately predict the correlations between As and heavy metals content in contaminated soil and cultivated crops; however, due to the low correlation between the two, few clear results were obtained to date. This study aimed to create statistical models that predict the As content transferred from soil to polished rice, considering the physicochemical properties of the soil, as well as the total content and the single-extracted content of As in the soil. Predictive models were derived through regression analysis while sequentially classifying soil samples according to pH, soluble As content by single extraction, and organic matter content of the soil. The correlation coefficients between the As content in 80 polished rice and total As content and Mehlich soluble As content in the soil were low, 0.533 and 0.493, respectively. However, the models derived after sequential classification of the soil by pH, a ratio of total As content to Mehlich soluble As content, and organic matter content greatly increased the predictive power; ① 0.963 for 13 soils with a pH higher than 6.5, ② 0.849 for 15 soils with pH lower than 6.5 and a high ratio of As_Tot/As_Mehlich, ③ 0.935 for 30 soils with pH lower than 6.5, a high ratio of As_Tot/As_Mehlich, and organic matter content lower than 8.5%. The suggested prediction model of As transfer from soil to polished rice derived by soil classification may serve as a statistically significant methodology in establishing a rice cultivation standard for arsenic-contaminated soil.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.159-165
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• 2023

While the frequency of seismic occurrence has been increasing recently, the domestic seismic response system is weak, the objective of this research is to compare and analyze the seismic vulnerability of buildings using statistical analysis and machine learning techniques. As the result of using statistical technique, the prediction accuracy of the developed model through the optimal scaling method showed about 87%. As the result of using machine learning technique, because the accuracy of Random Forest method is 94% in case of Train Set, 76.7% in case of Test Set, which is the highest accuracy among the 4 analyzed methods, Random Forest method was finally chosen. Therefore, Random Forest method was derived as the final machine learning technique. Accordingly, the statistical analysis technique showed higher accuracy of about 87%, whereas the machine learning technique showed the accuracy of about 76.7%. As the final result, among the 22,296 analyzed building data, the seismic vulnerabilities of 1,627(0.1%) buildings are expected as more dangerous when the statistical analysis technique is used, 10,146(49%) buildings showed the same rate, and the remaining 10,523(50%) buildings are expected as more dangerous when the machine learning technique is used. As the comparison of the results of using advanced machine learning techniques in addition to the existing statistical analysis techniques, in spatial analysis decisions, it is hoped that this research results help to prepare more reliable seismic countermeasures.

• Journal of the Korean Geotechnical Society
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• v.23 no.2
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• pp.61-69
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• 2007

In this study, experimental analysis was performed about lateral load capacity and behavior of laterally loaded-bored piles for soil conditions and pile shape, i.e. cylindrical and taper piles. Also, Calibration chamber load tests were performed for cylindrical and taper piles considering the variations of relative densities and restraint stresses. According to the results of chamber tests, it was found that, while both vertical and horizontal stresses affect load-responses and ultimate lateral load capacity of laterally loaded piles, effect of the horizontal stress was larger than that of the vertical stress. Effect of lateral load capacity and behavior was relatively small compared to relative density and stress state of soils surrounding piles, but showed a little difference for soil conditions. From comparison between predicted and measured lateral load capacity, it was observed that predicted results differ significantly from measured results. This is mainly due to the fact that the effect of horizontal stress is not considered in the conventional prediction methods.

• Journal of the Korean Geotechnical Society
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• v.23 no.7
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• pp.57-64
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• 2007

This study proposes a method to estimate drilling torque during ground boring with an aid of electrical energy required for rotating a boring-auger. Ground boring is commonly used in geotechnical engineering such as preboring precast pile installation, soil-cement grouting, ground exploration and so forth. In order to understand the correlation between required electrical energy to rotate the boring auger and the drilling torque, a small laboratory apparatus was designed and a pilot study was performed. The apparatus rotates common drill bits of $D=5{\sim}25mm$ in CBR specimens. The velocity of a bit is 19 RPM and predefined using a reduction gear which connects a main rotation axis to a 25 Watts AC electrical motor shaft. In the middle of drilling the motor current increments and the drilling torque were measured and the correlation between the current and the torque was obtained through linear square fits. Based on the correlation the acquired motor current during drilling was applied to predict the drilling torque in consequent testing and the prediction results were compared to the measured torque. The comparison leads a conclusion that the motor current during drilling using electrical power may be a good indicator to estimate/determine strength characteristics of the ground.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.93-98
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• 2023

In this paper, an artificial neural network based on simulated annealing was constructed. The mapping relationship between the parameters of micro-scaled flow channels electrochemical machining and the channel shape was established by training the samples. The depth and width of micro-scaled flow channels electrochemical machining on stainless steel surface were predicted, and the flow channels experiment was carried out with pulse power supply in NaNO3 solution to verify the established network model. The results show that the depth and width of the channel predicted by the simulated annealing artificial neural network with "4-7-2" structure are very close to the experimental values, and the error is less than 5.3%. The predicted and experimental data show that the etching degree in the process of channels electrochemical machining is closely related to voltage and current density. When the voltage is less than 5V, a "small island" is formed in the channel; When the voltage is greater than 40V, the lateral etching of the channel is relatively large, and the "dam" between the channels disappears. When the voltage is 25V, the machining morphology of the channel is the best.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.1
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• pp.63-69
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• 2023

'Carbon-neutrality Assessment based on Technology Application Scenario (CATAS)' provides an analysis of greenhouse gas (GHG) reduction effectiveness when applying carbon-neutrality technology to areas such as energy conversion, transportation, and buildings at certain spatial levels. As for the development scope of the model, GHG emission sources were analyzed for direct GHG emissions, and the boundary between direct and indirect emissions are set according to the spatial scope. The technical scope included nine technologies and forest sinks in the transition sector that occupies the largest portion of GHG emissions in the 2050 carbon neutral scenario. The carbon neutrality rate evaluation methodology consists of four steps: ① analysis of GHG emissions, ② prediction of energy production according to technology introduction, ③ calculation of GHG reduction, and ④ calculation of carbon neutrality rate. After the web-based CATAS-BASIC was developed, an analysis was conducted by applying the new and renewable energy distribution goals presented in the ｢2050 Greenhouse Gas Reduction Promotion Plan｣ of the Seoul Metropolitan Government. As a result of applying solar power, hydrogen fuel cell, and hydrothermal, the introduction of technology reduced 0.43 million tCO₂eq of 1.49 million tCO₂eq, which is the amount of emissions from the conversion sector in Seoul, and the carbon neutrality rate in the conversion sector was analyzed to be 28.94 %.