• Journal of Korean Society of Forest Science
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• v.111 no.3
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• pp.428-434
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• 2022

This study was conducted to derive the site index of forest productivity of Robinia pseudoacacia (honey plant) to characterize suitable planting sites and to investigate the effect of the site environmental factors on the site index using the quantification theory I method. The data used in the analysis were growth factors (stand age, dominant height, etc.) of the 6th national forest resources survey and various site environmental factors of a forest soil map (1:5,000). The average site index value of the R. pseudoacacia stand in Korea was 14 (range, 8 to 18). The environmental factors affecting the site index were parent rock, climatic zone, soil texture, local topography, and altitude. The accuracy of the estimation model using quantification theory I was only 33%. However, the correlation between the site index and the site environmental factors was statistically significant at the 1% level. Results of quantification analysis between site index and site environmental factors revealed that metamorphic and igneous rocks received high grades as parent rocks, climate zones received higher grades than central temperate zone, clay loam and silt loam received high grades in soil texture, and hillside received a high grade in local topography. Analysis of the partial correlation between site topographical factors and forest productivity (site index) found that soil class and altitude were partially correlated to x by 0.4129 and 0.4023, respectively, indicating that these factors are the most influential variables.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.1
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• pp.41-49
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• 2023

A convolutional neural network (CNN) is a representative algorithm for implementing artificial neural networks. CNNs have improved on the issues of rapid increase in calculation amount and low object classification rates, which are associated with a conventional multi-layered fully-connected neural network (FNN). However, because of the rapid development of IT devices, the maximum resolution of images captured by current smartphone and tablet cameras has reached 108 million pixels (MP). Specifically, a traditional CNN algorithm requires a significant cost and time to learn and process simple, high-resolution images. Therefore, this study proposes an improved CNN algorithm for implementing an object classification learning model for simple, high-resolution images. The proposed method alters the adjacency matrix value of the pooling layer's max pooling operation for the CNN algorithm to reduce the high-resolution image learning model's creation time. This study implemented a learning model capable of processing 4, 8, and 12 MP high-resolution images for each altered matrix value. The performance evaluation result showed that the creation time of the learning model implemented with the proposed algorithm decreased by 36.26% for 12 MP images. Compared to the conventional model, the proposed learning model's object recognition accuracy and loss rate were less than 1%, which is within the acceptable error range. Practical verification is necessary through future studies by implementing a learning model with more varied image types and a larger amount of image data than those used in this study.

• Composites Research
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• v.36 no.1
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• pp.48-52
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• 2023

Fiber-reinforced composites have anisotropic material properties, so the mechanical properties of composite structures can vary depending on the stacking sequence. Therefore, it is essential to design the proper stacking sequence of composite structures according to the functional requirements. However, depending on the manufacturing condition or the shape of the structure, there are many cases where the designed stacking angle is out of range, which can affect structural performance. Accordingly, it is important to analyze the stacking angle in order to confirm that the composite structure is correctly fabricated as designed. In this study, the stacking angle was predicted from real cross-sectional images of fiber-reinforced composites using convolutional neural network (CNN)-based deep learning. Carbon fiber-reinforced composite specimens with several stacking angles were fabricated and their cross-sections were photographed on a micro-scale using an optical microscope. The training was performed for a CNN-based deep learning model using the cross-sectional image data of the composite specimens. As a result, the stacking angle can be predicted from the actual cross-sectional image of the fiber-reinforced composite with high accuracy.

• Journal of Applied Biological Chemistry
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• v.65 no.4
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• pp.343-348
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• 2022

This study was performed to establish an analytical method for the standardization of Angelicae Dahuricae Radix as a functional ingredient. We established six compounds including oxypeucedanin hydrate (1), byakangelcol (2), oxypeucedanin (3), imperatorin (4), phellopterin (5) and isoimperatorin (6) as marker compounds of Angelicae Dahuricae Radix. An analytical method using Ultra Performance Liquid Chromatography (UPLC) was established and validated for marker compounds of Angelicae Dahuricae Radix. The specificity was confirmed by the chromatogram from UPLC and the value of coefficient determination was also higher than 0.999, indicating high linearity. The relative standard deviation (RSD) and recovery of marker compounds were less than 5% and in the range of 90- 110%, respectively, which means that this method has high accuracy and precision. Therefore, this analytical method could be used as basic data for the development of functional ingredients for health functional food of Angelicae Dahuricae Radix.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.28 no.1
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• pp.41-50
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• 2023

Dissolved oxygen sensors have characteristics in which data drift occurs over time. Therefore, in-situ calibration of the dissolved oxygen sensor is essential to accurately measure the concentration of dissolved oxygen in seawater. In order to provide a method for in-situ calibration, appropriate number of samples for calibration, and laboratory calibration interval of the dissolved oxygen sensor, the dissolved oxygen sensor values were compared with the measured values by titration on a total of 133 samples from three different cruises in the Indian Ocean, Pacific Ocean, and East Sea over a period of about one year. As a result, it is preferable to calibrate the sensor value using the correlation of a straight line obtained by directly comparing the final concentration value given by the sensor and the measured value. For the accurate calibration, at least 30 samples must be used to enable in-situ calibration within an accuracy range of about 1%. In addition, it is recommended that a laboratory calibration should perform within 1 year for the membrane type dissolved oxygen sensor for CTD to achieve a performance of 70% or more.

• Journal of the Korean Geotechnical Society
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• v.24 no.9
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• pp.41-48
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• 2008

The aim of this study is to present a graphical method in order to evaluate stages in shrinkage cracking. Firstly, the distribution of crack openings is established by sorting the openings of individual cracks in the soil cracking system. Secondly, it is normalized in a range of 0 to 1 to obtain the normalized crack opening distribution. Thirdly, three S-shape curve models introduced by Brooks and Corey(1964), Fredlund and Xing(1994) and van Genuchten(1980) are chosen to fit the normalized crack opening distribution using a curve fitting method. The accuracy of fitting which is described through fitting parameters by the van Genuchten equation is much higher than that by the Brooks and Corey equation and slightly higher than that by the Fredlund and Xing equation; thus the van Genuchten model is used. Finally, the stages of shrinkage cracking are graphically evaluated by drawing three separate straight lines corresponding to three linear parts of the fitted normalized crack opening distribution. The proposed method is tested with different sample thicknesses. The measured data are fitted by the selected model with the fairly high regression coefficient and small root mean square error. The results show graphically that shrinkage cracking comprises three stages; namely, primary, secondary and residual stages. Subsequently, the ranges of evaluated crack opening for each of these stages are presented.

• Journal of Technologic Dentistry
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• v.45 no.2
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• pp.31-38
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• 2023

Purpose: This in vitro study aimed to compare the trueness of 3-unit fixed dental provisional prostheses (FDPs) fabricated by three different additive manufacturing and subtractive manufacturing procedures. Methods: A reference model with a maxillary left second premolar and the second molar prepped and the first molar missing was scanned for the fabrication of 3-unit FDPs. An anatomically shaped 3-unit FDP was designed on computer-aided design software. 10 FDPs were fabricated by subtractive (MI group) and additive manufacturing (stereolithography: SL group, digital light processing: DL group, liquid crystal displays: LC group) methods, respectively (N=40). All FDPs were scanned and exported to the standard triangulated language file. A three-dimensional analysis program measured the discrepancy of the internal, margin, and pontic base area. As for the comparison among manufacturing procedures, the Kruskal-Wallis test and the Mann-Whitney test with Bonferroni correction were evaluated statistically. Results: Regarding the internal area, the root mean square (RMS) value of the 3-unit FDPs was the lowest in the MI group (31.79±6.39 ㎛) and the highest in the SL group (69.34±29.88 ㎛; p=0.001). In the marginal area, those of the 3-unit FDPs were the lowest in the LC group (25.39±4.36 ㎛) and the highest in the SL group (48.94±18.98 ㎛; p=0.001). In the pontic base area, those of the 3-unit FDPs were the lowest in the LC group (8.72±2.74 ㎛) and the highest in the DL group (20.75±2.03 ㎛; p=0.001). Conclusion: A statistically significant difference was observed in the RMS mean values of all the groups. However, in comparison to the subtractive manufacturing method, all measurement areas of 3-unit FDPs fabricated by three different additive manufacturing methods are within a clinically acceptable range.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.331-335
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• 2023

As the viscosity of the blood increases, the blood becomes more sticky and difficult to flow, so the possibility of thrombosis increases and the probability of ischemic cerebral infarction increases. The importance of measuring blood viscosity has recently been emphasized for the prevention of circulatory system diseases, and the need for a viscometer capable of easily and accurately measuring blood viscosity has emerged. In this study, the measured values of a viscosity standard solution and an artificial blood by a parallel-plate viscometer ARS-Medi were compared with the those by Ares-G2 of TA instrument, which is internationally recognized for its accuracy and reliability. The viscosity of N44 standard solution, which is a Newtonian solution, was almost perfectly matched between the two instruments at all shear rates. In the case of an artificial blood, which is a non-Newtonian solution, the measured values between the two instruments showed a difference of about 10% at the lowest shear rate 1 rad/s; however, at a clinically significant shear rate of 10 rad/s or higher, the measured values between them were consistent within the error range. We expect that ARS-Medi, a newly developed parallel-plate viscometer for blood, using disposable plates, will be very useful in clinical practice as it improves the convenience and hygiene of blood viscosity measurement.

• Analytical Science and Technology
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• v.19 no.3
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• pp.239-243
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• 2006

This method is used for the determination of itraconazole in human plasma by liquid-liquid extraction and high performance liquid chromatography. Felodipine was used as an internal standard. After extraction of the plasma with diethyl ether, the centrifuged upper layer was then transferred. The supernantant was evaporated and then reconsituted with mobile phase. The mobile phase was composed of 10 mM ammonium acetate adjusted to pH 7 by phosphoric acid with a flow rate of 0.2 mL/min. A C18 reversed-phase column with a pre-column was used as the analytial column. Linear detection responses were obtained for itraconzole concentration range for 2~1,000 ng/mL. The correlation coefficient of linear regression($R^2$) was 0.9991, limit of quantification (LOQ) was 2 ng/mL, reproducibility was less than 10.8 %, and accuracy was 97.2~108.2%. This method has been successfully applied to the pharmacokinetic study of itraconazole in human plasma.