• Journal of People, Plants, and Environment
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• v.24 no.6
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• pp.629-638
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• 2021

Background and objective: This study was conducted to develop diameter growth models for thinned Quercus glauca Thunb. (QGT) stands to inform production goals for treatment and provide the information necessary for the systematic management of this stands. Methods: This study was conducted on QGT stands, of which initial thinning was completed in 2013 to develop a treatment system. To analyze the tree growth and trait response for each thinning treatment, forestry surveys were conducted in 2014 and 2021, and a one-way analysis of variance (ANOVA) was executed. In addition, non-linear least squares regression of the PROC NLIN procedure was used to develop an optimal diameter growth model. Results: Based on growth and trait analyses, the height and height-to-diameter (H/D) ratio were not different according to treatment plot (p > .05). For the diameter of basal height (DBH), the heavy thinning (HT) treatment plot was significantly larger than the control plot (p < .05). As a result of the development of diameter growth models by treatment plot, the mean squared error (MSE) of the Gompertz polymorphic equation (control: 2.2381, light thinning: 0.8478, and heavy thinning: 0.8679) was the lowest in all treatment plots, and the Shapiro-Wilk statistic was found to follow a normal distribution (p > .95), so it was selected as an equation fit for the diameter growth model. Conclusion: The findings of this study provide basic data for the systematic management of Quercus glauca Thunb. stands. It is necessary to construct permanent sample plots (PSP) that consider stand status, location conditions, and climatic environments.

• Membrane Journal
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• v.33 no.1
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• pp.34-45
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• 2023

Two computational intelligence techniques namely artificial neural networks (ANN) and support vector machine (SVM) are employed to model the permeate flux based on seven input variables including time, transmembrane pressure, rotating velocity, the pore diameter of the membrane, dynamic viscosity, concentration and density of the feed fluid. The best-fit model was selected through the trial-error method and the two statistical parameters including the coefficient of determination (R²) and the average absolute relative deviation (AARD) between the experimental and predicted data. The obtained results reveal that the optimized ANN model can predict the permeate flux with R² = 0.999 and AARD% = 2.245 versus the SVM model with R² = 0.996 and AARD% = 4.09. Thus, the ANN model is found to predict the permeate flux with high accuracy in comparison to the SVM approach.

• Smart Media Journal
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• v.13 no.4
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• pp.57-64
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• 2024

Attributes such as material, color, and fit in fashion images are important factors for consumers to purchase clothing. However, the process of classifying clothing attributes requires a large amount of manpower and is inconsistent because it relies on the subjective judgment of human operators. To alleviate this problem, there is a need for research that utilizes artificial intelligence to classify clothing attributes in fashion images. Previous studies have mainly focused on classifying clothing attributes for either tops or bottoms, so there is a limitation that the attributes of both tops and bottoms cannot be identified simultaneously in the case of full-body fashion images. In this study, we propose a deep learning model that can distinguish between tops and bottoms in fashion images and classify the category of each item and the attributes of the clothing material. The deep learning models ResNet and EfficientNet were used in this study, and the dataset used for training was 1,002,718 fashion images and 125 labels including clothing categories and material properties. Based on the weighted F1-Score, ResNet is 0.800 and EfficientNet is 0.781, with ResNet showing better performance.

• Korean Journal of Mineralogy and Petrology
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• v.36 no.4
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• pp.289-302
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• 2023

The study area was Gangnim-myeon, Hoengseong-gun, Gangwon-do, composed of the Chiaksan gneiss complex, and it was revealed that the concentrations of uranium (U) and thorium (Th) within the groundwater of the study area exceeded their water quality standards. Hence, artificial weathering experiments were conducted to elucidate mineralogically the mechanisms of their leaching using drilling cores obtained from the corresponding groundwater aquifers. First of all, the mineralogical compositions of core samples were observed, and the results indicated that the content of clinochlore, a member of the chlorite group of minerals that can form through low- and intermediate-temperature metamorphisms, was relatively higher. In addition, the Th concentration was measured ten times higher than that of U. The results of artificial weathering experiments suggested that the Th concentrations gradually increased through the dissolution of radioactive-element-bearing minerals up to the first day, and then they tended to decrease. It could be attributed to the fact that Th was leached with the dissolution of thorite, which might be a secondary mineral, and then dissolved Th was re-precipitated as the various forms of salt, such as sulfate. Even though the U content was lower than that of Th in the core samples, the U concentration was one hundred times higher than that of Th after the weathering experiments. It is likely caused by the gradual dissolution and desorption of U included in intensively weathered thorite or adsorbed as a form of UO₂²⁺ on the mineral surface. In addition, the leaching tendency of U and Th was positively correlated with the bicarbonate concentration. However, the concentrations between U and Th in groundwater exhibited a relatively lower correlation, which might result from the fact that they occurred from different sources, as aforementioned. Among various kinetic models, the parabolic diffusion and pseudo-second-order kinetic models were confirmed to best fit the dissolution kinetics of both elements. The period that would be taken for the U concentration to exceed its drinking-water standard was inferred using the regressed parameters of the best-fitted models, and the duration of 29.4 years was predicted in the neutral-pH aquifers with relatively higher concentrations of HCO₃, indicating that U could be relatively quickly leached out into groundwater.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.981-992
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• 2023

Due to recent global climate change, the scale of flood damage is increasing as rainfall is concentrated and its intensity increases. Rain on a scale that has not been observed in the past may fall, and long-term rainy seasons that have not been recorded may occur. These damages are also concentrated in ASEAN countries, and many people in ASEAN countries are affected, along with frequent occurrences of flooding due to typhoons and torrential rains. In particular, the Bandung region which is located in the Upper Chitarum River basin in Indonesia has topographical characteristics in the form of a basin, making it very vulnerable to flooding. Accordingly, through the Official Development Assistance (ODA), a flood forecasting and warning system was established for the Upper Citarium River basin in 2017 and is currently in operation. Nevertheless, the Upper Citarium River basin is still exposed to the risk of human and property damage in the event of a flood, so efforts to reduce damage through fast and accurate flood forecasting are continuously needed. Therefore, in this study an artificial intelligence-based river flood water level forecasting model for Dayeu Kolot as a target station was developed by using 10-minute hydrological data from 4 rainfall stations and 1 water level station. Using 10-minute hydrological observation data from 6 stations from January 2017 to January 2021, learning, verification, and testing were performed for lead time such as 0.5, 1, 2, 3, 4, 5 and 6 hour and LSTM was applied as an artificial intelligence algorithm. As a result of the study, good results were shown in model fit and error for all lead times, and as a result of reviewing the prediction accuracy according to the learning dataset conditions, it is expected to be used to build an efficient artificial intelligence-based model as it secures prediction accuracy similar to that of using all observation stations even when there are few reference stations.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.723-736
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• 2022

In this study, monthly precipitation forecasting models that can predict up to 12 months in advance were constructed for the Geum River basin, and two statistical techniques, multiple linear regression (MLR) and artificial neural network (ANN), were applied to the model construction. As predictor candidates, a total of 47 climate indices were used, including 39 global climate patterns provided by the National Oceanic and Atmospheric Administration (NOAA) and 8 meteorological factors for the basin. Forecast models were constructed by using climate indices with high correlation by analyzing the teleconnection between the monthly precipitation and each climate index for the past 40 years based on the forecast month. In the goodness-of-fit test results for the average value of forecasts of each month for 1991 to 2021, the MLR models showed -3.3 to -0.1% for the percent bias (PBIAS), 0.45 to 0.50 for the Nash-Sutcliffe efficiency (NSE), and 0.69 to 0.70 for the Pearson correlation coefficient (r), whereas, the ANN models showed PBIAS -5.0~+0.5%, NSE 0.35~0.47, and r 0.64~0.70. The mean values predicted by the MLR models were found to be closer to the observation than the ANN models. The probability of including observations within the forecast range for each month was 57.5 to 83.6% (average 72.9%) for the MLR models, and 71.5 to 88.7% (average 81.1%) for the ANN models, indicating that the ANN models showed better results. The tercile probability by month was 25.9 to 41.9% (average 34.6%) for the MLR models, and 30.3 to 39.1% (average 34.7%) for the ANN models. Both models showed long-term predictability of monthly precipitation with an average of 33.3% or more in tercile probability. In conclusion, the difference in predictability between the two models was found to be relatively small. However, when judging from the hit rate for the prediction range or the tercile probability, the monthly deviation for predictability was found to be relatively small for the ANN models.

• Korean journal of applied entomology
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• v.61 no.3
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• pp.423-434
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• 2022

The two previously developed artificial diets (N4 and N6) used for rearing Spodoptera frugiperda (Noctuidae) larvae, were selected as highly-fit ones for rearing Mythimna loreyi larvae. Almost all biological characteristics were not significantly different between the colonies reared on the two diets at 25℃ and 15:9 h (light:dark) photoperiod. The developmental periods were 4.9-5.2 days for eggs, and 22.3-23.2 days for larvae. The pupal period and weight were different between the sexes in each diet colony. The pupal periods in females and males showed 12.6-12.8 days and 14.1-14.5 days, respectively. The pupal weights were ca. 345 mg for females and ca. 380 mg for males. The pupation and emergence rates were ca. 91-94%, and ca. 91-95%, respectively, without significant differences between the two colonies. The pre-oviposition and oviposition periods were 3.4 days and 4.7-4.8 days, respectively. The adult longevity was 8.2 days in females and 10.3-12.4 days in males. Total offsprings produced were found to be 724-847 larvae on an average with ca. 1,400 maximum larvae. In the life table analysis, the intrinsic rates of increases (0.1181 for N4 and 0.1253 for N6) were not significantly different between the two colonies. Individual differences in the larval instar number 5 and 6 were found within a diet colony. The ratios of 5-instar larvae were ca. 22% in N4 colony and ca. 7% in N6 colony. The larval period of 6-instar larvae was longer than that of 5-instar larvae. Width of head capsule in larvae varied from ca. 309 ㎛ for 1st instar to ca. 3,065 ㎛ for 6th instar. Body lengths measured from ca. 2.0 mm for 1st instar to ca. 29.1 mm for 6th instar. Larvae of M. loreyi and M. separata were found at the same time in a maize field during June and July, 2020.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.989-1006
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• 2020

Building change monitoring based on building detection is one of the most important fields in terms of monitoring artificial structures using high-resolution multi-temporal images such as CAS500-1 and 2, which are scheduled to be launched. However, not only the various shapes and sizes of buildings located on the surface of the Earth, but also the shadows or trees around them make it difficult to detect the buildings accurately. Also, a large number of misdetection are caused by relief displacement according to the azimuth and elevation angles of the platform. In this study, object-based building detection was performed using the azimuth angle of the Sun and the corresponding main direction of shadows to improve the results of building change detection. After that, the platform's azimuth and elevation angles were used to detect changed buildings. The object-based segmentation was performed on a high-resolution imagery, and then shadow objects were classified through the shadow intensity, and feature information such as rectangular fit, Gray-Level Co-occurrence Matrix (GLCM) homogeneity and area of each object were calculated for building candidate detection. Then, the final buildings were detected using the direction and distance relationship between the center of building candidate object and its shadow according to the azimuth angle of the Sun. A total of three methods were proposed for the building change detection between building objects detected in each image: simple overlay between objects, comparison of the object sizes according to the elevation angle of the platform, and consideration of direction between objects according to the azimuth angle of the platform. In this study, residential area was selected as study area using high-resolution imagery acquired from KOMPSAT-3 and Unmanned Aerial Vehicle (UAV). Experimental results have shown that F1-scores of building detection results detected using feature information were 0.488 and 0.696 respectively in KOMPSAT-3 image and UAV image, whereas F1-scores of building detection results considering shadows were 0.876 and 0.867, respectively, indicating that the accuracy of building detection method considering shadows is higher. Also among the three proposed building change detection methods, the F1-score of the consideration of direction between objects according to the azimuth angles was the highest at 0.891.

• Journal of Soil and Groundwater Environment
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• v.10 no.5
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• pp.52-60
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• 2005

The conceptual model of under-sampled study area will include a great amount of uncertainty. In this study, we investigate the applicability of Markov chain model in a spatial domain as a tool for minimizing the uncertainty arose from the lack of data. A new formulation is developed to generalize the previous two-dimensional coupled Markov chain model, which has more versatility to fit any computational sequence. Furthermore, the computational algorithm is improved to utilize more conditioning information and reduce the artifacts, such as the artificial parcel inclination, caused by sequential computation. A generalized 20 coupled Markov chain (GCMC) is tested through applying a hypothetical soil map to evaluate the appropriateness as a substituting model for conventional geostatistical models. Comparing to sequential indicator model (SIS), the simulation results from GCMC shows lower entropy at the boundaries of indicators which is closer to real soil maps. For under-sampled indicators, however, GCMC under-estimates the presence of the indicators, which is a common aspect of all other geostatistical models. To improve this under-estimation, further study on data fusion (or assimilation) inclusion in the GCMC is required.

• The Journal of Korean Academy of Prosthodontics
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• v.43 no.5
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• pp.611-621
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• 2005

State of problem: Zirconia all-ceramic restoration fabricated with CAM system is on an increasing trend in dentistry. However, evaluation of the marginal and internal fits of zirconia bridge seldomly have been reported. Purpose: This study was to evaluate the at of margin and internal surface in posterior 3-unit zirconia bridge framework fabricated with CAM system(DeguDent, Germany). Material and Method: Preparations of secondary premolar and secondary molar on artificial resin model were performed for fabrication of 3-unit posterior bridge framework. Fits of 5 zirconia bridge framework were compared with 5 precious ceramo-metal alloy framework(V-GnathosPlus, Metalor, Switzerland), and prepared margins were designed to chamfer and shoulder finishing line. Each framework was cemented to epoxy resin model with reinforced glass ionomer(FujiCEM, GC Co., Japan), embedded in acrylic resin and sectioned in two planes, mesio-distal and buccolingual. Samples were divided into six pieces by sectioning and had two pieces of each surface(i.e mesial, distal, buccal and lingual surface) per abutment, so there were eight measuring points in each abutment. External gap was measured at the margin and internal gaps were measured at the margin, axial and occlusal surface. Gaps were observed under the measuring microscope(Compact measuring microscope STM5; Olympus, Japan) at a magnification of $\times100$. T-test were used to determine the statistic significance of the different gaps between zirconia and metal framework. Results and Conclusion: 1. External and internal marginal gaps of zirconia and metal framework were in clinically acceptable range. External marginal gaps were not different significantly between zirconia$(81.9{\mu}m)$ and metal $(81.3{\mu}m)$ framework and internal marginal gaps of zirconia $(44.6{\mu}m)$ were smaller than those of metal framework $(58.6{\mu}m)$. 2. Internal axial gaps of zirconia framework$(96.7{\mu}m)$ were larger than those of metal frame-work$(78.1{\mu}m)$ significantly and adversely, internal occlusal gaps of zirconia frame-work$(89.4{\mu}m)$ were smaller than those of metal framework $(104.9{\mu}m)$ significantly. 3. There were no significant differences in external and internal marginal gaps between chamfer and shoulder finish line when zirconia frameworks were fabricated.